MASARYK UNIVERSITY FACULTY OF SCIENCE

Molecular interactions in low-grade breast cancer metastasis

Ph.D. Dissertation

JOSEF MARYÁŠ

Supervisor: Doc. Mgr. Pavel Bouchal, Ph.D.

DEPARTMENT OF BIOCHEMISTRY

Brno 2020

Bibliografický záznam

Autor: Mgr. Josef Maryáš Přírodovědecká fakulta, Masarykova univerzita Ústav Biochemie Název práce: Molekulární interakce v metastazování low-grade nádorů prsu

Studijní program: Biochemie

Specializace: Biochemie

Vedoucí práce: Doc. Mgr. Pavel Bouchal, Ph.D.

Akademický rok: 2019/2020

Počet stran: 108+109

Klíčová slova: PDLIM2, TRAF3IP2, RNF25, epiteliálně-mezenchymální tranzice, metastazování, karcinom prsu

Bibliographic Entry

Author: Mgr. Josef Maryáš Faculty of Science, Masaryk University Department of Biochemistry Title of Thesis: Molecular interactions in low-grade breast cancer metastasis

Degree programme: Biochemistry

Specialization: Biochemistry

Supervisor: Doc. Mgr. Pavel Bouchal, Ph.D.

Academic Year: 2019/2020

Number of Pages: 108+109

Keywords: PDLIM2, TRAF3IP2, RNF25, epithelial-mesenchymal transition, metastasis, breast carcinoma

Abstrakt

Nádory prsu (BrCa) představují heterogenní onemocnění, které je klasifikováno na základě histologických, klinických a/nebo imunohistochemických (IHC) markerů, které rovněž slouží jako prognostické a prediktivní faktory. Hlavní příčinou úmrtí pacientů s BrCa jsou však metastázy, které vznikají v procesu metastatické kaskády, multifaktoriálního a vícestupňového procesu zahrnujícího tzv. epiteliálně-mesenchymální tranzici (EMT). Je zřejmé, že metastatická kaskáda včetně EMT zahrnuje velké množství proteinů, na které lze cílit a modulovat tím proces vzniku metastáz. Disertační práce sumarizuje poznatky o proteinech PDZ and LIM domain protein 2 (PDLIM2), ring finger protein 25 (RNF25) a TRAF3 interacting protein 2 (TRAF3IP2), které byly identifikovány jako proteiny související s metastazováním do lymfatických uzlin u luminálních-A nádorů prsu pomocí kombinace proteomických a transkriptomických přístupů. Za účelem pochopení molekulární podstaty tohoto vztahu jsme se nejprve zaměřili na protein-proteinový interaktom a srovnali konvenční pull-down assay se surface plasmon resonance (SPR) čipy v kombinaci se “sequential window acquisition of all theoretical fragment ion spectra mass spectrometry” (SWATH-MS) pro identifikaci interakčních partnerů proteinu PDLIM2. Naše výsledky ukazují, že afinitní matrice a mechanismus promytí a eluce jsou kritickými kroky assaye, a ukazují SPR v kombinaci se SWATH-MS jako relevantní nástroj pro detekci protein-protein interakčních partnerů. Zadruhé, funkční potvrdily vztah proteinu PDLIM2 k EMT, hypoxii, proliferaci a adhezi nádorové buněčné linie MCF7, modelu odvozeném od luminálních-A nádorů prsu. Paralelní experimenty na imortalizovaných buňkách odvozených od normálního prsního epitelu MCF10A však odhalily jeho spíše tumor supresivní funkci, což vede k hypotéze o duální a na biologickém kontextu závislé roli PDLIM2 v procesu vzniku a vývoje nádorů prsu. Zatřetí, širokorozsahová celoproteomová analýza spojená s analýzou protein-proteinové interakční sítě, “gene set enrichment analysis” (GSEA) a analýzou migrace a invaze buněčné linie MCF7 prokázala souvislost zvýšených hladin proteinů PDLIM2, RNF25 a TRAF3IP2 s cytoskeletárními změnami, zvýšenou proliferací, migrací, invazí a s nádorovou progresí. Práce jako celek přispívá k pochopení molekulární sítě zapojené do metastazování luminálních-A nádorů prsu a jejího vlivu na fenotyp nádorových buněk.

Abstract

Breast cancer (BrCa) is a heterogeneous disease currently classified based on histological, clinical and/or immunohistochemical (IHC) markers that also serve as prognostic and predictive factors. However, the most common cause of death of BrCa patients are metastases, which are formed during metastatic cascade, the multistep and multifactorial process involving epithelial-to-mesenchymal transition (EMT). It is evident that metastatic cascade and EMT itself involve a large number of potentially targetable proteins that might modulate the process of metastasis formation. The dissertation thesis focuses on PDZ and LIM domain protein 2 (PDLIM2), ring finger protein 25 (RNF25) and TRAF3 interacting protein 2 (TRAF3IP2) which have been previously identified as proteins related to lymph node metastasis in luminal-A breast tumors using a combination of proteomic and transcriptomic approaches. To understand the molecular basis of this phenomenon, we first focused on protein-protein interactome and compared conventional pull-down assays on streptavidin agarose beads with surface plasmon resonance chips (SPR) in combination with sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) to identify PDLIM2 interactors. Our results show that solid support and mechanics of wash and elution are the critical aspects of the assay, and highlight SPR with SWATH-MS as a relevant tool for the detection of PDLIM2 protein-protein interactions. Second, a series of hypothesis-driven functional experiments confirmed association of PDLIM2 with EMT, hypoxia, proliferation and adhesion of MCF7 breast cancer cell line, a model of luminal-A breast cancer. However, the parallel experiments in immortalized normal epithelial breast cells MCF10A revealed its rather tumor suppressive role, leading to the hypothesis on dual and context dependent role in breast cancer development. Third, large- scale total proteome analysis together with the analysis of protein-protein interaction network, gene set enrichment analysis (GSEA) and functional analysis of migration and invasion of MCF7 breast cancer cell line demonstrated the association of elevated PDLIM2, RNF25 and TRAF3IP2 protein levels with cytoskeletal changes, increased proliferation, migration, invasion, and tumor progression. The work contributes to understanding the molecular network involved in luminal-A breast cancer metastasis and its impact on cancer cell phenotype.

Acknowledgements

I would like to thank my thesis supervisor, Pavel Bouchal for his time, guidance and continuous support. I would like to express my gratitude to him for providing suggestions and commentaries at every step during my PhD studies, while also giving me the freedom to design my own experiments and realize my own ideas.

My work could not have been possible without the help and support of former members of Regional Centre for Applied Molecular Oncology. Especially, I would like to thank Monika D. and Iva P., for all discussions we had. I am also thankful to Jakub F., for his valuable technical support with MS experiments.

My endless thanks belong to my family and friends. They always stand on my side, helped me, supported me and inspired me every day. Without them I would not become the person that I am today.

Pledge

I hereby declare that I have worked on this thesis independently, using primary and secondary sources listed in the bibliography, under the supervision of Dr. Pavel Bouchal.

In Brno ......

Josef Maryáš

List of selected original publications with dissertant’s contributions

The dissertation thesis is based on one peer-reviewed review paper (Review paper 1), two original peer-reviewed papers in journals with impact factor (Research papers 1 - 2), one manuscript in peer-review process published on preprint server (Research paper 3) and one manuscript in preparation (Research paper 4). The dissertant‟s contribution to each paper is described in commentaries below. Publications are listed in Appendices 1 to 5.

Review paper 1 (Appendix1): Maryas J, Faktor J, Dvorakova M, Struharova I, Grell P, Bouchal P. Proteomics in investigation of cancer metastasis: functional and clinical consequences and methodological challenges. Proteomics. 2014;14(4-5):426-40. (IF2014 = 3.807, Q1 in Biochemical Research Methods, Q1 in Biochemistry and Molecular Biology). https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.201300264

Estimated personal contribution: 30 % JM contributed to this work by summarizing the biological basis of cancer metastasis and functional proteomics studies in pro-metastatic mechanisms.

Research paper 1 (Appendix 2): Bouchal P, Dvorakova M, Roumeliotis T, Bortlicek Z, Ihnatova I, Prochazkova I, Ho J.T.C, Maryas J, Imrichova H, Budinska E, et al. Combined Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor κB (NF- κB) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer.

Mol. Cell. Proteomics MCP. 2015;14:1814-30. (IF2015 = 5.912, Q1/92.857 percentile in Biochemical Research Methods). https://www.mcponline.org/content/mcprot/14/7/1814.full.pdf

Estimated personal contribution: 10 % JM‟s contribution to this work consisted of antibodies specificity validation and dilutions optimization using western blot, and preparation of materials for publication.

Research paper 2 (Appendix 3): Maryas J, Faktor J, Skladal P, Bouchal P. Pull-down assay on streptavidin beads and surface plasmon resonance chips for SWATH mass spectrometry based interactomics. Cancer Genomics and Proteomics. 2018;15(5):395-404. (IF2018 = 3.147, Q2 in Oncology, Q2 in Genetics and Heredity). http://cgp.iiarjournals.org/content/15/5/395.full.pdf+html

Estimated personal contribution: 70 % JM designed and performed majority of experiments (cell cultivation, transfection, cell lysis and sample preparation for Methods 1-4 and pull-down for Methods 1-3), analyzed potential interactors and drafted the manuscript.

Research paper 3 (Appendix 4): Maryas J, Pribyl J, Bouchalova P, Skladal P, Bouchal P. PDZ and LIM domain protein 2 plays dual and context dependent roles in breast cancer development. BioRxiv. 2020; doi: 10.1101/2020.01.27.920199 (BMC Cancer, under review). https://www.biorxiv.org/content/10.1101/2020.01.27.920199v1

Estimated personal contribution: 70 % JM designed, performed and evaluated majority of in vitro experiments (cell cultivation, cell transfection and treatment, plasmids construction, stable cell line generation, SDS PAGE, western blot, cell migration, invasion, proliferation and cell adhesion assays) and drafted the manuscript.

Research paper 4 (Appendix 5): Maryas J, Faktor J, Muller P, Capkova L, Bouchal P. RNF25, TRAF3IP2 and PDLIM2 are promising NF-κB modulators associated with metastasis of luminal A breast cancer (in preparation)

Estimated personal contribution: 70 % JM‟s contribution to this work consisted of cell cultivation and transfection, plasmids construction, and performance of SDS PAGE, western blot and migration and invasion assays. JM also prepared samples for pull-down assay and total proteome analysis, contributed to data analysis and drafted the manuscript.

List of abbreviations

2D-LC-MS/MS – liquid chromatography-tandem mass spectrometry with two-dimensional separation 2D-DIGE – 2D fluorescence difference gel electrophoresis 2DE – two-dimensional gel electrophoresis 5-aza-dC – 5-aza-2′-deoxycytidine ACTN4 – alpha-actinin-4 AFM – atomic force microscopy AJCC – American joint committee on cancer ALP – actinin-associated LIM protein ARP3 – actin-related protein 3 ASCO – American society of clinical oncology ATL –adult T-cell leukemia BAFF – B-cell activation factor BM – basement membrane CCD – charge-coupled device CDC42 – cell division control protein 42 CDK – cyclin dependent kinase CPB1 – carboxypeptidase B1 CRPC – castration resistant prostate cancer CSC – cancer stem cell CSF-1 – colony stimulating factor 1 CXCR4 – chemokine receptor 4 DNMT – DNA methyltransferase ECM – extracellular matrix EGF – epidermal growth factor EGFR – epidermal growth factor receptor ELISA – enzyme-linked immunosorbent assay EMT – epithelial-to-mesenchymal transition ER –estrogen receptor ERKs – extracellular signal regulating kinases EREG – epiregulin ESCC – oesophageal squamous cell carcinoma FAK – focal adhesion kinase FDA – US Food and Drug Administration FGF – fibroblast growth factor FISH – fluorescence in situ hybridization GFP – green fluorescent protein GSEA – gene set enrichment analysis HBEGF – EGF-like growth factor HER2 – human epidermal growth receptor 2 HGF – hepatocyte growth factor HIF – hypoxia inducible factors HTLV – human T-cell leukemia virus IDC – invasive ductal carcinoma IGF – insulin growth factor IHC – immunohistochemistry IKK – IκB kinase iTRAQ – isobaric tag for relative and absolute quantification

JNK – c-Jun N-terminal kinases LCIS – lobular carcinoma in situ LCM – laser capture microdissection LC-MS/MS – liquid chromatography-tandem mass spectrometry LFQ – label-free quantification MAPK – mitogen activated protein kinases MET – mesenchymal-to-epithelial transition MMP – matrix metalloproteinase MS – mass spectrometry NES – nuclear export signal NF- κB – nuclear factor κB NGS - Nottingham grading system NK – natural killer NO – nitric oxide NPI - Nottingham prognostic index NSCLC – non-small cell lung cancer NST – no specific type PAGE – polyacrylamide gel electrophoresis PCNA – proliferating cell nuclear antigen PDLIM2 – PDZ and LIM domain protein 2 PEI – polyethylene imine PI3K – phosphatidylinositol-3-kinase PKC – protein kinase C PML – promyelocytic leukemia protein PPI – protein-protein interaction PR – progesterone receptor qRT-PCR – quantitative reverse transcription polymerase chain reaction RIP – receptor interacting protein RING – really interesting new gene RNF25 – ring finger protein 25 ROS – reactive oxygen species RTK – receptor tyrosine kinase SAPK – stress-activated protein kinase SBP – streptavidin binding peptide SDS – sodium dodecyl sulphate shRNA – small hairpin ribonucleic acid SPR – surface plasmon resonance SRM – selected reaction monitoring SWATH – sequential window acquisition of all theoretical fragment ion spectra TAM – tumor associated macrophage TAD – transactivation domain TGF-β – transforming growth factor β TMT – tandem mass tags TNF – tumor necrosis factor TNBC – triple negative breast cancers TNFR – tumor necrosis factor receptor TRAF – TNF receptor-associated factor TRAF3IP2 – TRAF3 interaction protein 2 VEGF – vascular endothelial growth factor WB – western blot

Content

1 Foreword ...... 13 2 Theoretical background ...... 15 2.1 Breast cancer ...... 15 2.1.1 Classification of breast cancer ...... 17 2.1.1.1 Traditional histological, clinical and IHC markers ...... 18 2.1.1.2 Molecular subtypes of breast cancer ...... 22 2.1.1.3 Approved molecular/genetic tests ...... 24 2.2 Cancer metastasis ...... 24 2.2.1 Local invasion ...... 26 2.2.2 Intravasation and survival in circulation ...... 27 2.2.3 Homing and extravasation ...... 29 2.2.4 Survival in secondari loci and metastatic colonization ...... 31 2.2.5 The inefficiency of the metastatic cascade ...... 32 2.3 Epithelial-to-mesenchymal transition ...... 32 2.3.1 EMT in tumorigenesis and metastasis formation ...... 34 2.3.2 EMT regulation ...... 36 2.4 PDZ and LIM domain protein 2 (PDLIM2) ...... 40 2.4.1 PDLIM2 structure and localization ...... 40 2.4.2 Function of PDLIM2 ...... 41 2.4.3 PDLIM2 and cancer ...... 42 2.5 TRAF3 interacting protein 2 (TRAF3IP2) ...... 44 2.5.1 TRAF3IP2 structure and localization ...... 44 2.5.2 Function of TRAF3IP2 ...... 45 2.5.3 TRAF3IP2 and cancer ...... 47 2.6 Ring finger protein 25 (RNF25) ...... 48 2.6.1 RNF25 structure and localization ...... 48 2.6.2 Function of RNF25 ...... 49 2.6.3 RNF25 and cancer ...... 50 3 Hypothesis ...... 51 4 Aims of this work ...... 51 5 Materials and methods ...... 52 5.1 Antibodies, commercial plasmids and siRNAs ...... 52 5.2 Cell lines and cell counting ...... 52 5.3 In house plasmids construction ...... 52 5.3.1 Total RNA isolation and One step RT PCR ...... 52 5.3.2 Specific plasmids construction ...... 52

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5.4 Construction of lentiviral plasmids, lentiviruses and stably transfected cell lines ...... 53 5.5 Cell transfection ...... 53 5.6 TGFβ1 treatment and hypoxia ...... 53 5.7 Monitoring of cell migration and invasion ...... 54 5.8 Monitoring of cell proliferation and adhesion ...... 54 5.9 Young’s modulus mapping by Atomic Force Microscopy ...... 54 5.10 SDS PAGE and Western blot ...... 54 5.11 Pull down assay with LC-SWATH-MS/MS ...... 54 5.12 Total proteome analysis of MCF7 cell lysates ...... 55 5.13 Gene set enrichment analysis of total proteome data ...... 55 5.14 Statistical analysis ...... 56 6 Results ...... 57 6.1 Proteomics in cancer metastasis (review paper 1) ...... 57 6.2 Identification of PDLIM2, RNF25 and TRAF3IP2 as proteins associated with lymph node metastasis of grade 1 luminal A breast cancer (research paper 1) ...... 62 6.3 Analysis of PDLIM2 interactome using pull-down assay on streptavidin beads and surface plasmon resonance chips (research paper 2) ...... 66 6.4 Functional analysis of PDLIM2 in breast cancer (research paper 3) ...... 69 6.5 Analysis of functional and interaction network of RNF25, TRAF3IP2 and PDLIM2 (research paper 4) ...... 75 7 Discussion ...... 80 7.1 The roles of PDLIM2, RNF25 and TRAF3IP2 proteins at cellular level ...... 80 7.2 The roles of PDLIM2, RNF25 and TRAF3IP2 proteins at molecular level ...... 81 8 Conclusions ...... 87 9 References ...... 88 10 List of all publications related to the topic of dissertation thesis .... 106 11 List of contributions at conferences and symposia ...... 108 12 Appendices ...... 109

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1 Foreword

Development of tumors is a multistage process, characterized by the accumulation of genetic and epigenetic changes that leads to neoplastic transformation of the cell of the respective tissue. Formation of tumors depends also on the characteristics of the cancer cells and their interaction with the tumor microenvironment. The origin and development of the primary tumors are nowadays relatively well described and many primary tumors are curable. However, the crucial issue for achieving success of the therapy lies in avoiding the development of metastases which are responsible for most of the cases of death in patients with solid tumors. Metastases are formed in a complex, multistep and multifactorial process called metastatic cascade during which the cancer cells leave the primary tumour site and colonise other organs via body fluids circulation. Despite its clinical importance, the metastatic cascade, however, remains poorly understood at molecular, cellular and tissue levels (chapter 6.1 “Review paper 1”).

The most frequently diagnosed malignancy and the second leading cause of cancer death among women is breast cancer. Clinically, breast cancer is a remarkably heterogeneous disease, which results in different prognosis as well as the therapeutic approach to individual subtypes of these tumors. In clinical practice, both traditional and molecular prognostic markers have been used for risk-group discrimination and determination of metastatic potential of breast tumors (chapter 6.2 “Research paper 1”). However, currently available markers are not sufficient for precise risk-group assessment especially in low grade luminal-A breast tumors, whose general prognosis is very favourable. Nevertheless, about thirty percentages of these tumors develop early lymph node metastases, which is in disagreement with the initial prognosis. The molecular mechanism responsible for this lapse is unknown and current clinical practice lacks the means for predicting its occurrence (chapter 6.2 “Research paper 1”).

Our effort was to uncover potential biomarkers for the selection of luminal-A breast cancer patients with the higher risk of metastasis formation (chapter 6.2 “Research paper 1”), and further studied the role of selected potential markers in cancer development by series of in vitro experiments at molecular and cellular level (chapters 6.3, 6.4 and 6.5 “Research papers 2, 3 and 4”). PDZ and LIM domain protein 2 (PDLIM2) a member of the actinin-associated LIM protein (ALP) family and E3 ubiquitin ligase, plays a dual and context dependent roles in breast cancer development (chapters 6.4 and 6.5 “Research papers 3 and 4”). On the other

13 hand, ring finger protein 25 (RNF25), a predominantly nuclear localized regulator of nuclear factor κB (NF-κB) pathway, together with TRAF3 interaction protein 2 (TRAF3IP2), a regulator of interleukin mediated inflammatory response, both act as oncoproteins and changes in their abundances influence migration and invasion of MCF7 cells, in vitro model of luminal-A tumors (chapter 6.5 “Research paper 4”).

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2 Theoretical background

2.1 Breast cancer Breast cancer is a heterogeneous disease encompassing a variety of morphologically and clinically distinct entities (1). For the characterization of human breast tumours, an understanding of the normal mammary epithelial cell hierarchy is crucial (2). The female breast is composed of mammary gland – a series of branched epithelial ducts; and adipose tissue – a fibrous and fatty tissue with, blood and lymph vessels, lymph nodes and network of Cooper‟s ligaments that help maintain structural integrity of the breast (2) (3). The mammary gland is a very dynamic organ that undergoes extensive morphological changes through development, puberty, pregnancy, lactation and involution (physiological reduction of the number of mammary gland cells) (4) (5). Each mammary gland consists of 5–20 lobes containing 20–40 smaller lobules that are comprised of 10-100 acini, which are connected to lactiferous duct (6). The mammary gland epithelium is a bi-layer epithelium consisting of basally-positioned myoepithelial cells that form the basal outer layer and polarized cuboidal luminal cells that form the inner layer and surround a central lumen (2) (7) (8). Myoepithelial cells ensure the maintenance of the adjacent luminal epithelial cell polarity and the synthesis of a laminin‐rich basement membrane (BM) that forms a structural barrier separating the glandular epithelium from the interstitial stroma (9). The luminal cell compartment of the mammary gland, however, is more heterogeneous and can be resolved into estrogen receptor positive cells (ER+), and estrogen receptor negative alveolar progenitor cells (ER-) (2) (7) (10). During pregnancy, ER- progenitor alveolar cells are able to differentiate to milk producing alveolar cells. In case of ER+ cells, two sub-lineages were identified; nonclonogenic ER+ cells with little or no proliferative potential, and a small population of ER+ progenitors, which are able to further differentiate to ER+ ductal cells (2).

Development and maintenance of the structure and organization of normal mammary epithelium is via breast stem cells (2) (7). These cells with intrinsic self-renewal represent only a small fraction of mammary gland cells and differentiate into a common progenitor that gives rise to committed myoepithelial and luminal cells (11) (12) (see Figure 1).

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Figure 1: Hierarchy of normal breast epithelia development: The breast stem cell (BSC) with intrinsic self- renewal potential differentiates into a common progenitor that gives rise to committed myoepithelial and luminal progenitors that ultimately differentiate into myoepithelial, luminal and ductal epithelial cells (modified from (11)).

As normal breast cancer stem cells are long-time tissue residents, it has been proposed that such cells are candidates for accumulating genetic and epigenetic modifications, leading to the development of a cancer stem cells (CSCs) (11) (13) (14) (15). The cancer stem cell (CSC) model and the sporadic clonal evolution model are currently two generally respected hypotheses for the solid tumors development including breast cancer (11) (13) (16) (17) (see Figure 2). CSC hypothesis claims that breast tumors have a common cell of origin (2) (18). The cancer stem cell theory simply states that, within a tumor, a small subset of cells that is responsible for tumor initiation and progression exists, while the remaining bulk of the tumor cells is of low tumorigenic potential (2) (17) (18). It is believed that the CSCs undergo asymmetric division maintaining the stem cells population while at the same time differentiating into committed progenitor(s) cells that give rise to the different breast cancer subtypes (11) (13) (15) (14). The cell of origin for CSCs is still undetermined but two prevailing hypotheses propose that CSCs either originate from normal cells within the stem cell hierarchy or arise from a common normal stem cell (17) (19). On the other hand, the clonal evolution model asserts that different types of breast tumors originate from different types of breast epithelial cells (2). According to this model, any breast epithelial cell can be the target of random mutations. The cells with advantageous genetic and epigenetic alterations are then selected over time to contribute to tumor progression (11) (20). Support for this hypothesis comes from studies in which different populations of human breast epithelial cells,

16 selected from in vitro cultures or purified using flow cytometry, were reverse-engineered into tumours of distinct subtypes (2) (21) (22). Whether breast tumours have a common cell of origin or whether these tumours originate from different types of cells, or whether it is a combination of these two processes is still not clear and needs further investigation (2) (18). However, breast cancer is, without any doubts, a heterogeneous disease comprised of different subtypes with distinct pathological features and clinical implications such as histological grade, tumor size, lymph node status and hormone receptor status (23) (24) (25) (26).

Figure 2: Two general models for the solid tumors development: A) The cancer stem cell (CSC) model and B) the sporadic clonal evolution model are currently two generally respected hypotheses for the solid tumors development including breast cancer (modified from (13)).

2.1.1 Classification of breast cancer Due to abovementioned different biological and histopathological features breast cancer exhibit distinct behaviours that lead to different treatment responses and should be given different therapeutic strategies (26) (27) (28). In order to organize this heterogeneity and standardize the language, breast cancer classification systems have been developed into a tool that is used to aid in treatment and prognosis (17). Management of breast cancer relies on the availability of robust clinical and pathological prognostic and predictive factors to the selection of treatment options (29). In clinical practice traditional clinicopathological variables involving histopathological grade and type, tumor size and lymph node status, clinical factors (e.g. age, heredity) together with immunohistochemistry (IHC) markers such

17 as estrogen receptor (ER), progesterone receptor (PR), human epidermal growth receptor 2 (HER2) and Ki-67 are well established for patient prognosis and management (25) (26) (27). Although, above mentioned clinical, histological and well-described IHC markers (ER, PR, HER2, Ki-67) show strong association with prognosis and outcome, there are evidence that these variables are limited in their ability to capture the diversity of clinical behaviours of breast cancer (29). Advances in cancer research and the advent of high-throughput platforms for gene expression analysis technology (e.g. c-DNA microarrays, RNA-Seq) have provided a deeper understanding of the complexity of breast cancer and indicate that the current clinical model for breast cancer classification benefits also from intrinsic molecular characteristics of tumor cells (25) (26) (27). Molecular stratification based on gene expression profiling revealed that breast cancers could be classified in five so-called intrinsic subtypes (luminal-A, luminal-B, HER2-enriched, basal like, and normal-like) (30), see Figure 3. The intrinsic molecular subtypes of breast cancer emerged as a result of the work by Perou et al. (31) and Sørlie et al. (32) more than a decade ago and several other studies confirmed these subtypes (25) (27). It has also been shown that molecular subtypes differ in their response to treatment and clinical outcome (25) (33). Molecular methods undoubtedly provide new prognostic and predictive information however, it is important to understand their limitations above and beyond the traditional variables in a practical and cost-effective way (29). It has consistently been shown that combination of clinical variables with molecular and functional biomarkers yields a classification system that is more robust and capable than any one system alone (17).

Figure 3: Intrinsic molecular subtypes of BrCa: Five molecular subtypes of breast cancer and their incidence.

2.1.1.1 Traditional histological, clinical and IHC markers Histopathologic analysis is based on microscopic examination of biopsy specimens that is able to reveals heterogeneity at both cellular and architectural structure (17) (30). According

18 to histological patterns breast cancer can be broadly categorized into two histological subtypes; in situ carcinoma and invasive (infiltrating) carcinoma (17) (30). Invasive carcinomas are a heterogeneous group of tumors differentiated into histological subtypes involving infiltrating ductal, invasive lobular, ductal/lobular, mucinous (colloid), tubular, medullary and papillary carcinomas (17). The most frequent histologic invasive subtype (ie, 70–80% of all invasive lesions) is invasive ductal carcinoma (IDC), comprising the group of tumors that do not have sufficient characteristics of any of the special differentiation patterns (no special type, or NST) (17) (30). Resting subtypes of invasive carcinomas are not so common and some of them are extremely rare (<1%) (30) (34). Similar to invasive carcinomas, in situ carcinomas are a heterogeneous group of tumors that can be further sub- divided according to growth patterns and cytological features as ductal (DCIS) or lobular carcinomas in situ (LCIS) (17) (30) (34). LCIS evince low histological variation on the other hand, more common (15% to 20%) DCIS encompass a heterogeneous group of tumors (30) (34). Based on the architectural features of the tumor, DCIS have been further subclassified into five well recognized subtypes (Comedo, Cribiform, Micropapillary, Papillary and Solid) (17) (34). This classification scheme has been a valuable tool for several decades, and although tumor type provides useful prognostic information, the role of histological typing in clinical management decision making is currently limited. The main reason is the fact that the majority (70% to 80%) of breast cancers have no special type of characteristics (NST carcinomas) and those special types that show distinct prognostic significance are relatively uncommon (17) (30) (34).

More than the histological type of tumor another prognostic factor – histopatological grade is more important for clinical management of patients with breast cancer (25) (29) (30). Histological tumor grade is based on the degree of differentiation of the tumor tissue, and in breast cancer refers to the semi-quantitative evaluation of morphological characteristics (29). The most widely used grading system recommended by various professional bodies internationally is the Nottingham (Elston-Ellis) modification of the Scarff-Bloom-Richardson grading system, also known as the Nottingham Grading System (NGS) (29) (30) (35) (36). NGS is based on the assessment of three morphological features: the proportion of tubule or gland formation, the degree of nuclear pleomorphism, and the mitotic count, each of which is given 1 to 3 points (29) (30) (36), for more details see Elston et al. (36). Based on the overall score, breast tumors are divided into corresponding grade (1, 2 or 3) (36). In general, histopathological grade 1 tumours (low grade) are associated with the best prognosis, whereas

19 grade 3 tumours (high grade) are associated with the poorest prognosis. Grade 2 tumours (intermediate grade) comprise a more heterogeneous group with the intermediate prognosis, but some cases may exhibit similarity with grades 1 or 3 (25) (29).

Valuable and well-established prognostic and predictive factor is also TNM system that measures the anatomic extent of the tumor (29) (37). TNM (tumor, node, metastasis) system consist of information on the primary tumour size and whether it has invaded surrounding tissues (T), involvement of the regional lymph node (N) and the presence of distant metastasis (M) (37) (38). T, N and M are assigned by clinical means and by adding surgical findings and pathological information to the clinical information, for more detail see American joint committee on cancer (AJCC) 8th edition (38). Generally, larger tumors tend to show a worst outcome than smaller ones (29) and regional lymph node-positive patients have about a 4–8 times higher mortality than those without nodal involvement (37) (39). Moreover, patients with distant metastases lesions at the time of diagnosis exhibit very poor outcome compared to metastases free patients (37) (38) (40). Since lymph node status and tumor size have strong prognostic value they have been combined with NGS into prognostic indices called Nottingham Prognostic Index (NPI). NPI segregates patients into four groups, and is used to predict five-year survival, therefore, is used as a surrogate marker of aggressiveness of breast tumors (39) (41) (42) (43).

In addition to the traditional tumor size and grade, lymph node status, and presence of metastases, tumor biology is indispensably important in prognosis and response to therapy (38). More than 75% of breast cancers express estrogen and/or progesterone receptors, markers that have both predictive and prognostic value however, the predictive role is much more significant (30) (37) (44). Immunohistochemical examination of hormone receptors predicts the long-term outcome of hormonal therapy (37). It has been shown that selective ER modulators, e.g. tamoxifen and other endocrine therapy, are able to slow or stop ER and PR positive tumors. The response for ER positive and PR negative is lower and even lover response rate was observed for ER negative and PR positive tumors. ER and PR negative tumors are very unlikely to respond to endocrine therapy (38) (45) (46). Approximately 10% to 15% of breast tumors express the receptor tyrosin kinase HER2 (also erbB2 or neu). This protein is involved in the regulation of cellular growth and is regarded as a prognostic and predictive factor (30) (47). HER2 positivity in breast tumors is associated with poor differentiation (high grade) and with high cell proliferation rates, thus in general with worse prognosis. Nonetheless, it has been shown that the development of HER2-targeting agents has

20 dramatically improved the treatment of HER2-positive breast cancer patients (38) (48) (49). Immunohistochemical determination of HER2 expression is thus a valuable tool for predicting treatment response to transtuzumab (anti-HER2 monoclonal antibody), certain endocrine therapies and chemotherapy (37) (50). As a prognostic and predictive factor in breast tumors expression of cellular marker for proliferation Ki-67 is also used. Although there are no universally agreed cut of points for Ki-67 values, and no standardized methodology, it is without doubts that high Ki-67 levels reflect high proliferative subset of patients with breast cancer and predict response to anthracycline therapy (38) (51) (52).

Based on the current level of knowledge, in 2018 AJCC updated the breast cancer staging guideline to make the prognosis as precise as possible and decide on the best treatment options for breast cancer patient (38). Except for previously used TNM system, AJCC add other cancer characteristics to determine breast cancer‟s stage namely: tumor grade, estrogen and progesterone receptore status and HER2 status. Based on these factors five distinct stages were prescribed (from 0 to IV), each of them with different treatment plan and different prognosis. In general, lower stages correspond with less invasive tumors and better prognosis (53) (54) (55). All the possible characteristic of each stage can be seen in AJCC 8th edition (38).

There are other important clinical factors and patient‟s characteristics used for prognosis and prediction such as: age at the time of diagnosis, comorbidity, menopausal status or hereditary predispositions (37) (38). It has been shown that very young breast cancer patient (<35 years) are more likely to have a more negative clinical presentation (larger tumours, affected lymph nodes…), and thus higher risk of death, compared to older patients (37) (56). However, current adjuvant treatment seems to diminish the poor prognostic value of young age (57). Also, concurrent health conditions (comorbidity) at the time of diagnosis, especially cardiovascular disease, previous cancer and diabetes mellitus have a significant impact on early as well as long-term survival of breast cancer patients (57) (58). Moreover, it is currently estimated that approximately 5–10% of all breast cancers have a hereditary background (59). Hereditary breast cancer is associated with inherited germline mutation, especially in BRCA1 and BRCA2 genes, and tends to develop earlier in life time than noninherited cases (60). This type of breast cancer, however, forms a chapter on its own, and it is not a purpose of this work to describe it more specifically

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2.1.1.2 Molecular subtypes of breast cancer

Breast tumors vary tremendously not only in histological appearance, grade, hormone receptor, and HER2 status, but also on a molecular/genetic basis (38) (32) (61). As already mentioned, gene expression profiling and genomic analysis revealed five intrinsic subtypes of breast tumors (see Figure 3) (30) that have widely different gene expressions, natural histories, metastatic patterns and sensitivity to existing therapies (32) (38) (51). Consequently, classifying breast tumors into relevant molecular subtypes is important for therapeutic decision making (26). Although gene expression profiling has become a more commonly used laboratory technique, it is still not broadly available as a validated diagnostic technique. Therefore, to estimate prognosis and guide therapeutics decisions, clinically defined subtypes have been used instead of gene-expression based molecular subtypes (38). Clinically defined subtypes are based on hormonal receptor and HER2 expression with the measurement of additional factors (e.g. Ki-67, grade, mitotic count and others) (38).

The most common subtypes among breast cancer are luminal tumors. These tumors express hormone receptors and other proteins (e.g. cytokeratins 8/18), with expression profiles reminiscent of the luminal epithelial component of the breast (27) (31). At least two subtypes exist within luminal tumors, i.e., luminal-A and luminal-B, with luminal-A being the majority (25) (26) (27) (38). In general, luminal tumors carry a good prognosis, however, luminal-A tumors have significantly better prognosis than luminal-B subtype (27) (62). Luminal-B tumors generally have a higher expression of genes involved in mitosis and cell proliferation and lower expression of ER-related genes than luminal-A cancer (27) (30). Luminal-B tumors tend to be more aggressive and poorly differentiated and thus of higher grade (grade 2 or 3) than luminal-A tumors (predominantly grade 1) as well (25) (26) (27) (38). Moreover, using the IHC detection, luminal-B tumors can be further distinguished as HER2 negative and HER2 positive where the latter one has much worse prognosis (27). Even treatment response differs between luminal subtypes. Luminal-A tumors generally have a poor response to traditional chemotherapy but have an excellent response to endocrine therapies (38). On the other hand, more proliferative luminal-B tumors benefit more from the combined treatment of chemotherapy and hormonal therapy (27).

The HER2 enriched (or HER2 like) tumors refer to those identified using gene expression array, which is similar to the ER, PR negative and HER2 positive subgroup by IHC or fluorescence in situ hybridization (FISH) (27) (63). These tumors are sensitive to

22 anthracycline and taxane-based neoadjuvant chemotherapy and are likely to be of grade 3 (27) (64). Prior to the introduction of anti-HER2 therapy, HER2 enriched tumors were the most aggressive subtype with the worst prognosis (highest mortality rate and shortest survival) (38). However, in current clinical practice, when appropriately managed with anti-HER2 therapy (e.g. transtuzumab), patients with these tumors have a much better prognosis. Nonetheless, not all these tumors respond to anti-HER2 theraby some of them are transtuzumab resistant (38).

Basal like tumors share the fewest similarities with the other breast cancer subtypes and have the greatest intrinsic diversity (30) (65). Expression profiles of these tumors mimic those of the basal epithelial cells and normal breast myoepithelial cells (27) (31). Such expression patterns include lacking or low expression of hormone receptors and HER2, and high expression of basal markers (such as keratins 5, 6, 14, 17, EGFR) and proliferation related genes (31) (66). The size of basal tumors is, in general, larger than the other subtypes, and tumors of this class tend to show rapid growth (27). Moreover, basal like patients have the highest mortality, highest risk of local and regional relapse within 5 years after diagnosis, and are most difficult to treat with adjuvant therapy (38). Given the triple negative receptor status, basal tumors are not amenable to conventional targeted breast cancer therapies, leaving chemotherapy the only option in the therapeutic armamentarium (27) (31).

Normal like breast tumors has gene expression similar to patterns found in normal breast tissue samples (30). These tumors resemble luminal-A subgroup but their prognosis is slightly worse. Nevertheless, the clinical significance of these tumors remains to be determined because of the lack of studies on this subtype (67). The characteristics of each subtype are shown in Table 1.

Table 1: Overview of the breast tumor molecular subtypes characteristics, modified from Dai et al (27)

Intrinsic molecular subtype IHC status Grade Clinical outcome Luminal-A ER+, PR+, HER2-, Ki-67- 1/2 Good Luminal-B ER+, PR+, HER2-, Ki-67+ 2/3 Intermediate ER+, PR+, HER2+, Ki-67+ Poor HER2 enriched ER-, PR-, HER2+ 2/3 Poor Basal like (Triple-negative) ER-, PR-, HER2-, basal marker+ 3 Poor Normal like ER+, PR+, HER2-, Ki-67- 1/2/3 Intermediate + positivity; - negativity

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2.1.1.3 Approved molecular/genetic tests

Although the first definition of the intrinsic subtypes occurred almost two decades ago, only relatively recently (in 2013) the US Food and Drug Administration (FDA) approved and introduced an assay suited for diagnostic use. This assay called Prosigna is genetic test based on the modified version of the original intrinsic subtype definition, the PAM50, and it assigns each sample to the luminal-A, luminal-B, HER2-enriched, or basal-like subtype (30) (68). Earlier, FDA approved two other prognostic molecular (genetic) tests for breast cancer patients: Oncotype DX and MammaPrint that both calculate a recurrence score (29). MammaPrint test analyze the activity of 70 genes in early-stage breast cancers (stage I or II) and then calculates a recurrence score. Oncotype DX is a genomic test based on the assessment of 21 genes that is used to predict the risk of recurrence of early stage, hormone receptor positive breast cancer (38).

Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications (27). Consequently, a number of classification schemes have been devised to stratify breast tumours, attempting to comprehend the intricate biological mechanisms driving these tumours and to allow more effective prognosis and decision making in clinical treatments (69). There has been much progress in our understanding of the pathology and molecular biology of breast cancer in the last few years (34) and is increasingly clear that intrinsic molecular properties of the disease are associated with clinical outcomes. Despite this, we have not yet arrived at an exact molecularly defined classification of tumors of the breast, and thus an additional effort in this area is strongly needed (69). In current clinical practice, both “old-fashioned” traditional and “modern” gene expression-based prognosticators give additional information over one another (70) and together yields a classification system that is more robust and capable than any one system alone (17).

2.2 Cancer metastasis Breast cancer is the second common cancer worldwide and includes 1.7 million new cases per year and 25% of all types of cancers (71) (72). Its incidence has been increasing but mortality has been stable or has even decreased. This can be explained largely due to the therapeutic improvements and introduction of mass mammographic screening programmes that resulted in earlier detection and diagnosis of small and less aggressive tumours (37) (73). Despite evident progress in breast cancer treatment, the crucial problem for achieving therapeutic success and complete remission lies in the development of secondary tumors –

24 metastasis (74). Statistically, more than 90% of mortality from cancer is attributable to metastases and not the primary tumors from which these malignant lesions arise (75) (76). Because of high therapeutic resistance and heterogeneity of tumors, metastatic disease still remains largely incurable and thus the main cause of death of the oncological patients (75).

Metastases arise following the spread of cancer cells from a primary site and the formation of new tumours in distant organs (77). In general, the metastatic spreading usually occurs in the late stages of the disease, however, in some patients, metastases developed even before their primary tumor had been diagnosed (78). For that reason, great endeavour is given to early diagnostics of cancer disease, before the start of metastatic development (78) (79). However, even when detected early, the risk of further dissemination remains high, for example, in the breast cancer up to 30% of the early diagnosed patients will develop metastases later in their life (80). Due to the clinical importance of metastasis, understanding of the molecular processes and factors that influence metastatic formation is strongly needed (77).

It was found that metastases are formed during multistep and multifactorial cell-biological events termed the metastatic cascade (see Figure 4) (76) (77). This multistep process involves dissemination of cancer cells to anatomically distant organ sites and their subsequent adaptation to foreign tissue microenvironments (76). Each of these events is regulated by the accumulation of genetic and/or epigenetic alterations in tumor cells, as well as by the co- option of non-neoplastic stromal cells, which together bestow initial metastatic cells with characteristics needed to generate macroscopic, life threatening metastases (76). During metastatic cascade tumor cells have to overcome many barriers/steps to successfully develop distant macroscopic metastases, which makes this process extraordinarily inefficient. In fact, some have estimated that only less than 0.01% of metastatic cells eventually develop into macroscopic metastases (77) (81). First of all, cancer cells have to escape from primary tumor and locally invade into surrounding tissues, than locally invasive cancer cells intravasate into the lumina of lymphatic or blood vessels, and survive in the vasculature, which is followed by their arrest at distant organ sites, extravasation into the parenchyma of distant tissues, adaptation and survival in the new microenvironment and formation of micrometastasis, finally resulting in macrometastases formation (see Figure 4) (76) (82). Each of these steps is in more detail described below.

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Figure 4: Metastatic cascade: General description of main steps of complex and multifactorial process of metastasis formation.

2.2.1 Local invasion The first step in metastatic cascade is the successful escape of cancer cells from their primary tumor into the surrounding tumor associated stroma and afterwards into the adjacent normal tissue parenchyma (76) (83). In order to invade the surrounding stroma, cancer cells have to pass through the basal membrane - a specialized extracellular matrix (ECM) that forms a barrier between epithelial and stromal cells, and also plays a crucial role in signal transduction events within carcinoma cells (76) (85). To spread within the tissue, tumor cells modify their motility, which requires altering both, the tissue of origin and the cancer cell (84). It was shown that coordinated transcription programmes promoting expression of motility genes together with extrinsic factors in the tumor microenvironment can promote the motility of cancer cells (85). In vitro and in vivo observation revealed diverse patterns of mechanisms of tumor cells migration and invasion (motility through some sort of physical

26 barrier) (85) (86). Tumor cells can disseminate from the primary tumour either as individual cells, using amoeboid or mesenchymal type of movement, or expand in solid cell sheets, strands and clusters using collective motility (85) (86). The most common mechanism for invasion and metastasis by epithelial tumors is a collective cell movement, in which a heterogeneous multicellular mass of cancer cells in a physical contact move as one functional unit (86) (87). This type of cell movement was observed in breast, colon and other types of carcinomas and generally predominates in highly differentiated tumors (85) (86) (88). Alternatively, tumor cells can completely lose their cell contacts, detach and move through the adjacent connective tissue as individual cells (86) (89). Individual mesenchymal motility was observed in cells from the connective tissue tumours (fibrosarcomas, gliomas) and in epithelial cancers following progressive dedifferentiation (90) (91) (92). These cells have a fibroblast-like spindle-shaped morphology and their movement is dependent on the features of ECM and interactions between the ECM and tumor cells (76) (86) (91). Many tumor cell lines, however, use a less adhesive amoeboid type of single cell movement instead of mesenchymal motility (93). Due to the lack of focal contacts, these cells are highly deformable and their motility is independent of the interaction with the ECM (86) (94). This type of movement is typical for small cell lung or lymphoma cancer cells (95). However, it was shown that tumor cells can convert between aforementioned types of cell movements in response to changing microenvironmental conditions (86). This implies that the inhibition of only one type of motility mode is ineffective and the major challenge for blocking the spread of tumor cells is the inhibition of all modes of motility or identification of their common regulators (85) (86). During local invasion, epithelial cancer cells usually undergo a phenotypic conversion called epithelial-to-mesenchymal transition (EMT), whereby tumor cells lose polarity, undergo dramatic remodelling of the cytoskeleton and increase invasive and migratory properties, which help them invade through BM and surrounding tissue (96) (97) (98) (99) (100). This key event in the tumor progression is described in more details below (see chapter 2.3). Once invading tumor cells overcome BM they are confronted with variety of tumor associated stromal cells. Stromal cells are capable of further enhancing the aggressive behaviours of carcinoma cells, and provide abundant opportunities for these cells to enter the systemic circulation and thereby disseminate to distant sites (76) (101).

2.2.2 Intravasation and survival in circulation Once a cancer cells have detached from the primary tumor, they intravasate into blood vessels or lymphatics, both depending on type of tumor, physical restrictions imposed on

27 invasive tumors and ability to induce angiogenesis (75) (102). Intravasation is regulated by different molecular changes and signalling pathways that promote the ability of tumor cells to cross the pericyte and endothelial cell barriers that form the walls of microvessels (76) (103). For example, the transforming growth factor-β (TGF-β) acts as a positive regulator and augmenting invasiveness of tumor cells. The overexpression of TGF-β enhanced mammary carcinoma intravasation by increasing carcinoma cell penetration of microvessel (104), on the contrary, its inhibition decreased the number of circulating tumor cells (CTCs) in breast carcinoma (105). Additionally, the intravasation of breast carcinoma cells can be enhanced by perivascular tumor associated macrophages (TAMs) via a positive-feedback loop comprised of the reciprocal secretion of epidermal growth factor (EGF) and colony stimulating factor-1 (CSF-1) by TAMs and carcinoma cells, respectively (76) (106). Other molecules regulating intrvasation are CXCL12 that regulates vascular permeability (107), and matrix metalloproteinases (MMPs) such as MMP-1, MMP-2 or MMP-9 that are associated with pathological, metastasis-prone vasculature (108). Generally, the mechanics of intravasation is strongly influenced by the structure of tumor-associated vasculature. Cancer cells and activated stromal fibroblasts secrete elevated levels of vascular endotelial growth factors (VEGFs) that stimulate the formation of new blood vessels via the process termed neoangiogenesis (76) (84). In contrast to normal tissues vasculature, the neovasculature generated by carcinoma cells is in a state of continuous reconfiguration and prone to leakiness, which facilitates the intravasation (109).

Once tumor cells have successfully reached into the lumina of vessels, they can disseminate into distant organs (76). The cells that are en route between primary tumors and sites of disseminations are called circulating tumor cells (CTCs), and information provided by the enumeration of CTCs is currently used to predicting clinical outcome of cancer patients (110). However, quite a lot of CTCs that intravasate die in circulation because of a vastly different mechanical and biochemical environment of the circulation compared to native epithelial tissues from which these cells originate (84). CTCs in circulation have to survive variety of stresses (76). One of them is anoikis – a form of apoptosis triggered by loss of attachment to the ECM and neighbouring cells and controlled by integrin signalling pathways. The absence of extracellular signals enhances integrin disengagement and integrin-dependent signalling, which is followed by ERK signalling inhibition and subsequent decrease of anti- apoptotic protein levels and activation of pro-apoptotic proteins. (111). To survive in circulation, CTCs develop resistance mechanisms to anoikis including modified integrin

28 expression program, overexpression of anti-apoptotic effectors (e.g. Bcl2, Bcl-XL), down- regulation of pro-apoptotic effectors (e.g Bax, caspases) or modulating their energy metabolism, for example using autophagy as a survival mechanism (84) (112) (113). Also, it is not known how long cancer cells survive in the circulation (few minutes up to several hours are supposed) and therefore it is possible that many tumor cells escape the circulation before anoikis activation (114). In addition to anoikis, tumor cells in the circulation must overcome the damage caused by fluid shear stress (mechanical stress generated by changes in blood flow) and predation by cells of the innate immune system – specifically natural killer (NK) cells (76). To evade both of these threats CTCs developed a single mechanism that hijack some features of normal blood coagulation (76). Specifically, CTCs via interaction with blood platelets, mediated by Tissue factor and/or L- and P-selectins, form relatively large emboli that are better able to persist within the circulation (115) and were observed in different cancer types including breast tumors (116). Moreover, the likelihood that these platelet-coated tumor cells will be captured at distant tissues site is increased, due to their large effective diameter (76) (115).

2.2.3 Homing and extravasation Up to now, two seemingly contradictory views of how CTCs are captured at distant sites to form metastasis were proposed. In 1899 Stephen Paget described so called “seed and soil” hypothesis (117), to explain the non-random pattern of breast cancer metastasis. According to this hypothesis, the organ-preference patterns of tumor metastasis are the product of favourable interactions between metastatic tumor cells (the “seed”) and the distant organ microenvironment (the “soil”) (118). One of possible explanations of Paget‟s hypothesis might be specific ligand-receptor interactions, for example between particular chemokines ligands, over-secreted by distant organ, and activated appropriate chemokine receptors expressed on the surface of the CTCs. Clinically well described example of this theory, is the expression of chemokine receptor 4 (CXCR4) on the surface of breast cancer cells. It was described that common sites of metastasis for breast cancer (lungs, liver, bone marrow, and brain) express high levels of CXCL12, a ligand that interact with CXCR4 receptor (119) (120). However, in 1920s James Ewing postulated the second hypothesis about the capture of CTCs proposing that this process is primarily physical and passive (121). According to this theory, capture of CTCs is influenced by mechanical factors. The vascular architecture and blood-flow pattern from the primary tumor determine which organ the cells travel to first. The CTCs are then captured according to physical factors, such as the relative sizes of the cells

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(>20 μm in diameter) and the capillaries (7 μm in diameter), the blood pressure in the organ and the deformability of the cells (76) (77). Currently, experimental data show that both mechanical factors and seed-soil compatibility factors complement each other and contribute to the ability of specific types of cancer cells to spread to various target organs (77) (122) (123). Moreover, it was shown recently that CTCs metabolism adaptations are not only important for survival in circulation, as was described above, but also increase their metastatic potential and affect the selection of specific organ to colonize. For example it was desribed that increased proline catabolism generaly enhances the formation of lung matastasis (124) (125).

Arrested CTCs can proliferate within blood capillaries and form microcolony that ruptures the walls of surrounding vessels, which enables direct contact between tumor cells and tissue parenchyma (126) (127). Alternatively, CTCs can transmigrate the endothelium and pericyte layer as single cells and then invade the tissue in a process called extravasation (128). Predominantly tumor cells invade between two endothelial cells in a process called “paracellular migration” that requires rearrangement of epthelial and pericital layer and disruption of inter-endothelial cell to cell junctions (126) (127) (129). Rarely, tumor cells can pass the endothelium in proces termed “transcellular migration” by penetrating individual bodies of enothelial cells (129) (130). Moreover, to overcome physical bariers and facilitate extravasation, primary tumors are capable of secreting factors (e.g cyclooxygenase-2, MMP- 1/2, angiopoietin-like-4) that breach barriers in these distant microenvironments and induce vascular hyper permeability (75) (76). Numerous in vitro studies have shown that besides physical entrapment of tumor cells within small capillaries a wide range of ligands and receptors (integrins, selectins, cadherins, CD44, immunoglobulin superfamily receptors) contribute to transendothelial migration and thus affect extravasation (77) (129) (131). CTCs can also directly promote their extravasation by secretion of different factors, for example expression of epiregulin (EREG) or heparin-binding EGF-like growth factor (HBEGF) promotes extravasation of breast cancer cells (132). Efficient extravasation requires also the involvement of metastasis-promoting cells of the immune system (133). Recruitment of myeloid cells and plateletes to the vicinity of trapped tumor cells supports their extravasation (134) (135). Except for the above mentioned mechanisms of extravasation, new mechanistic model was proposed. It was shown that CTCs facilitate extravasation by inducing nocroptosis (programmed necrosis) in endothelial cells. Necropotitc endothelial cells release damage-

30 associated molecular patterns (e.g. ATP, amphoterin) that induce loosening of endothelial barrier and promote extravasation (136) (137).

2.2.4 Survival in secondari loci and metastatic colonization For the successful metastasis formation, tumor cells must survive in the distant organ microenvironment that is usually greatly different from that in the site of the primary tumor (76). To address this problem the establishment of a “pre-metastatic niche” allows adaptation of spreading cells in hostile conditions (138). Primary tumors release systemic signals (e.g lysyl oxidase) that induce activation of hematopoietic progenitor cells, which results in secreting of MMP-9 in distant loci that modifies these loci into more hospitable sites prior to the arrival of disseminated tumor cells (138) (139). In order to adapt the distant tissue microenvironment, disseminated tumor cells utilize cell-autonomous programs such as activation of Src tyrosine kinase signalling. In general, disseminated tumor cells use complex mechanisms to adapt the foreign microenvironments in order to survive at these ectopic locations (76) (140).

Survival at the microenvironment of a distant organ still does not warrant that tumor cells will form macroscopic metastases. Instead, the vast majority of disseminated cells remain quiescent in a state of long-termed dormancy as micrometastasis that persists in one of two ways (76) (77). Due to incompatibilities in foreign microenvironment proliferation of disseminated tumor cells is greatly impaired and these cells stay largely quiescent. This quiescence has been attributed to an inability to activate and engage the Src pathways focal adhesion kinase (FAK) and integrin β1 within distant tissues (76) (141) (142). In the second way of micrometastases persistence cancer cells continuously proliferate, however, a no increase in their overall number occurs because of the simultaneous high apoptotic rate (76) (77). To overcome dormancy, tumor cells must revert back to the epithelial phenotype and restore their adhesive and proliferative properties in process termed mesenchymal-to- epithelial transition (MET) (143). Moreover, tumor expansion and macrometastasis formation requires induction of angiogenesis in the secondary site, activation of pro-survival pathways (e.g. Akt, Src…) or recruiting macrophages and other cellular types towards metastatic foci (139) (140) (144) (145). Only those foci that have completed metastatic colonization should be referred as metastases (76).

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2.2.5 The inefficiency of the metastatic cascade Due to the series of obstacles, metastasis formation is highly inefficient process. It was estimated that less than 0.01 % of tumor cells which enter the circulation are capable to develop macroscopic metastases (76) (77). The question is which step or steps of the metastatic cascade are inefficient? Former studies revealed that early steps of the metastatic cascade are fairly efficient; however, one or more of the latter steps are successfully completed only very seldom (146) (147) (148). More specifically, from the time that tumor cells enter the systemic circulation until they extravasate into distant organs the most inefficient steps is survival in the circulation, however, more than 80 % of tumor cells succeed and survive. In contrast, once tumor cells enter the parenchyma of distant tissues, enormous reduction of their numbers is observed and only a small subset of surviving cells forms macrometastases (76) (77) (146). Taken together, the rate-limiting step of the metastatic cascade is the metastatic colonization according to the clinical observations (76) (77).

Despite inefficiency of the metastatic cascade, macrometastases still remain the main source of death in cancer patients. This point draws attention to biomarker discovery for early detection, prognosis and also selection of optimal therapeutic strategies for metastatic disease (76). As an example, American Society of Clinical Oncology (ASCO) has recommended plasminogen activator inhibitor (PAI-1) and urokinase plasminogen activator (uPA) as markers associated with angiogenesis, invasion, and metastasis of breast cancer, and cancer antigen 19–9 (CA 19–9) as a recommended serum marker for pancreatic cancer with metastatic disease. However, in spite of the fact that ASCO has been recommended plenty of others cancer markers, up to now none of the clinically used ones are specifically associated with metastatic disease (149) (150). The better understanding of the metastatic process together with the detection of new metastasis-associated markers and their putting into the clinical practice is thus in an urgent need. That is also the main aim of this thesis, which is focused on the understanding the roles and interactions of novel promising putative biomarkers of metastasis in low grade (luminal-A) breast tumors.

2.3 Epithelial-to-mesenchymal transition Epithelial-to-mesenchymal transition (EMT) is a vital process for large-scale cell movement that plays crucial roles in the formation of the body plan at the time of embryonic development. Tumor cells usurp this developmental program, and EMT plays a fundamental role in the multi-step process of cancer development, tumor invasion and metastasis formation (96) (97).

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The EMT program involves functional and phenotypic changes in cell polarity and differentiation status (143) (see Figure 5). Through this process epithelial cell layers lose lateral cell-cell contacts lose adhesions to the basement membrane and then undergo a dramatic remodelling of the cytoskeleton, which lead to a loss of their apical-basal polarity. On the other hand, cells undergoing EMT gain mesenchymal properties, including fibroblastoid morphology and characteristic gene-expression changes (down-regulation of epithelial markers, e.g. E-cadherin, and up-regulation of mesenchymal markers, e.g. vimentin). The cells also increase motility and invasiveness, gain a resistance to apoptosis and increase secretion of degradative enzymes enabling them to break through the basal membrane and migrate over a long distance (96) (97) (143) (151) (152) (153).

Figure 5: EMT-associated changes: Functional, morphological and gene expression changes during EMT (modified from (154)).

Based on the biological context under which it occurs, three different types of EMT were described so far (152) (153) (155). Type I EMT is involved in the developmental processes during implantation and embryo formation on the other hand Type II is associated with wound healing, tissue injury, regeneration and fibrosis (97) (155). Type III EMT, considered as EMT-like process is a key event in tumorigenesis, including tumor metastasis, acquisition of CSCs phenotype and properties and therapy resistance. It occurs in epithelial cancer cells

33 that exploit this type of EMT to gain migratory and invasive properties in order to invade and metastasize through circulation and generate metastases at distant tissues or organs (89) (97) (155) (156). Nonetheless, for successful generation of macrometastases at distant tissues or organs, Type III EMT has to be reversed by counterpart process-MET, during which epithelial features of the cells are reactivated (157). In spite of the fact that Type III EMT bears many parallels to classical developmental EMT (Type I), the transition is often heterogeneous and/or incomplete leading to hybrid/partial epithelial-mesenchymal phenotype (E/M). Considering the heterogeneity and unstable genetic background of tumor cells, it is perhaps not surprising that Type III EMT is not an on/off binary switch, but rather a graded series of interrelated and overlapping quite variable events in which cancer cells maintain both epithelial and mesenchymal features (143) (152) (158) (159).

2.3.1 EMT in tumorigenesis and metastasis formation During the past few decades, an increasing number of studies have shown that EMT is associated with cancer progression and metastasis (96). For example, the role of EMT in breast cancer has been demonstrated via numerous in vitro studies and approved by using mouse models of mammary cancers (97) (160) (161).

The EMT process involves number of genetic and epigenetic changes leading to the induction of prosurvival mechanisms, augmentation of cell motility, lowering of proliferation rate and up-regulation and activation of many enzymes including matrix metalloproteinases (MMP2, MMP9, MMP3) responsible for extracellular matrix degradation. EMT also involves changes in the levels and localization of many structural protein components responsible for cytoskeleton regulation, cell adhesions and intercellular contacts such as tight, gap or adherens junctions, desmosomes, and hemidesmosomes (143) (162) (163). A hallmark of EMT is losing expression of E-cadherin, the caretaker of epithelial phenotype and key cell- cell adhesion molecule, which is balanced by up-regulation of N-cadherin within the mechanism called cadherin switch (97) (164). Besides E-cadherin, many other markers are used to distinguish between epithelial and mesenchymal-like phenotypes, such as down- regulation of plakoglobin, occludis, α-catenin, claudins 3/4/7, cytokeratins and up-regulation of vimentin, fibronectin, MMPs or β1 and β3 integrins (143) (165) (166).

Epithelial-to-mesenchymal transition has often been considered as the first and essential step in the complex process of metastasis formation (165) (166). However, the role of EMT in metastasis has been more intensively discussed. An overwhelming problem with the EMT

34 concept is that the metastatic nodules examined histologically are not mesenchymal and resemble the primary tumor. Moreover, it is hard to observe EMT in vivo opposing to in vitro conditions owing to the fact that metastatic carcinoma cells evince only a limited number of mesenchymal markers and thus cannot be easily distinguish from surrounding stromal cells (143) (167) (168). To explain this phenomenon, several theories have been put forward. The most popular theory is the existence of mesenchymal-to-epithelial transition (MET), reverse process initiate at distant organs and tissues to generate metastatic nodules. Another explanation might be migration of carcinoma cells in hybrid E/M clusters in which mesenchyme cells that underwent EMT allows the migration and intravasation and cells with predominantly epithelial phenotype could assure metastasis formation at the distant loci (89) (143) (169). Also studies on lymph node metastases bring more scepticism about the role of EMT in cancer progression. Published data suggest that lymphatic metastases formation, especially extravasation, do not require EMT due to the lack of basement membrane and presence of open gaps in the lymphatic capillaries. On the other hand, data of these studies indicate that mesenchymal cells are much more successful at intravasation (143) (170) (171). Take it together, the EMT switch may not be enough to describe all kinds of migratory phenotypes observed within carcinoma cells, but may represent predominant subtype of their invasive behaviour (143).

Epithelial-to-mesenchymal transition may be also linked to the gain of cancer stem cells (CSCs) like properties, which may led to metastatic disease and increased drug resistance (97) (143) (172). CSCs are a small subpopulation of cells within tumors with capabilities of self- renewal, differentiation, and tumorigenicity (173). These cells share similar expression patter of surface markers with normal stem cells (CD44, CD24, CD29, CD90, CD133), however, the expression of CSCs surface markers is tissue type, even tumor subtype-specific. For example for breast CSCs, CD44high/CD24low surface markers were characterized (174) (175) (176). Cancer stem cells also show a plasticity that allows them to transition between epithelial and mesenchymal-like states (143). Published data suggest that the EMT facilitate generation of cells with stem cell signature such as mesenchymal properties needed for dissemination and self-renewal properties necessary for metastasis initiation (177). For example induction of EMT in immortalized human mammary epithelial cells (HMLE) results in an increased ability to form tumorspheres and increased drug resistance, consistent with breast CSCs signatures (CD44high/CD24low) (178). On the other hand, inhibition of the EMT revokes CSCs phenotype and increase sensitivity to chemotherapy (179).

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Many cancer patients ultimately relapse due to the presence of treatment resistant tumor cells (166) (180). Perception of the fact that CSCs are resistant to therapies and perception of the relationship between EMT and CSCs lead to the indirect suggestion that EMT contributes to drug resistance (97). In addition, emerging direct evidence highlight the connection between the EMT phenotype and therapeutic resistance. For instance, in the MCF7 breast cancer cell line, EMT induction has been linked to tamoxifen resistance and increased invasiveness (181). In addition, elevated levels of EMT associated families of transcription factors Twist, and Snail has been connected with therapeutic resistance in breast cancer. The elevated Twist can promote cell survival and resistance to paclitaxel, aberrant expression of SNAI1/2 promotes cells‟ resistance to apoptosis and doxorubicin (182) (183). To find beneficial therapeutic strategies and overcome the problem of therapeutic resistance, elucidation of a molecular mechanism underlying the contribution of CSCs and EMT to drug resistance is crucial (97).

2.3.2 EMT regulation EMT is a complex process that can be influenced by many inducers and regulators including different pathways (143) (184). As such, various EMT-related signalling pathways, including TGF-β, nuclear factor кB (NF-κB), Wnt, Notch, MAPK/ERK and others, so as hypoxia, autocrine growth factors (TGF-β, EGF, HGF, FGF, IGF, VEGF), inflammation and secreted cytokines (TNF-α, IFN-γ, IL6, IL1β) represent the most important inducers of EMT in tumors (96) (143) (185) (see Figure 6). Currently, it is clear that EMT is triggered not only from the program inside the tumor cells but also depends on the signals from the tumor microenvironment, including extracellular matrix, fibroblasts, blood vasculature or inflammatory cells. Within this increasingly complex signalling network, EMT is regulated through signal integration, crosstalk and feedback control (97) (166).

The primary and most well-studied inducer of EMT is TGF-β. A cytokine produced by mesenchymal stromal and inflammatory cells that regulates cell proliferation, differentiation and apoptosis, used both in vitro and in vivo to induce EMT (186). It is now generally accepted that TGF-β plays a dual role in tumorigenesis and metastasis formation. During the early stages of carcinogenesis, TGF-β acts as a tumour suppressor, via inducing apoptosis and arrest growth by up-regulation of cyclin-dependent kinase (CDK) inhibitors (p15 and p21) and shut-down expression of their repressor Myc. At advanced stages of tumor growth, tumor suppressive function of TGF-β is decreased and it promotes metastasis through retained or even increased induction of EMT (166) (187) (188) (189) (190). TGF-β induces tumor EMT,

36 together with its receptors and other receptor tyrosine kinases (RTKs), through a Smad- dependent and -independent TGF-β receptor signalling pathways (see Figure 6). In Smad- dependent pathway, TGF-β binds to type I and type II serine-threonine kinase receptors that form tight complexes leading to phosphorylation and activation of Smad2 and Smad3. Activated Smad2/3 forms heteromeric complexes with Smad4, translocate into the nucleus and control transcription of target genes (see Figure 6) (191) (192) (193) (194). Smad- independent pathway includes the activation of phosphatidylinositol-3-kinase (PI3K), mitogen activated protein kinase (MAPK), Akt, and Rho family GTPases (see Figure 6) (195). Both Smad-dependent and -independent pathways overlap and co-operate to regulate the transcription of various regulators associated with EMT including transcription factors Snail, ZEB and Twist and down-regulate expression of E-cadherin via increased SNAI1/2 and ZEB1/2 levels (196). Moreover, significant cross-talk exists between TGF-β and other signalling pathways to induce EMT and promote invasive phenotype of tumor cells, such as Wnt/β-catenin, Notch, NF-κB and RTKs pathways (197) (198).

Wnt/β-catenin pathway that plays essential roles in cell proliferation and differentiation plays also an important role in EMT induction and oncogenesis (97) (166). β-catenin, one of the downstream signalling molecules of the Wnt signalling pathway has a dual role in the EMT (97) (199). When bound to the cadherin complexes in adherent junction it can act as a bridge enhancing cell-cell adhesion. However, in the presence of Wnt ligands, GSK-3β complex is inactivated, and cytoplasmic β-catenin is stabilised, resulting in translocation into the nucleus (see Figure 6) where it act as a transcription cofactor with DNA-binding proteins of the T cell factor (TCF)/lymphoid enhancer factor (LEF) and regulates the transcription of Wnt target genes, such as SNAI1/2 and Axin2 (166) (199) (200). Axin2 can stabilize the Snail, moreover, Snail may further enhance the activation of Wnt signalling by functional interaction with β-catenin in a positive feedback loop. In addition, Wnt ligands can activate epidermal growth factor receptor (EGFR) signalling pathway while EGFR can reciprocally activate β-catenin via RTK-PI3K/Akt pathway, showing the complexity of EMT regulation mechanisms (201) (202).

Another signalling pathway that plays an important role in the EMT induction is Notch pathway (96) (166). Activation of Notch signalling requires cell-cell contact through ligand- receptor binding; following by proteolytic cleavage and release of Notch intracellular domain that translocates to the nucleus (see Figure 6) and activates targeted genes, e.g. Myc, p21 and SNAI2 (Slug), which is essential for Notch mediated E-cadherin repression (203) (204). Four

37 transmembrane Notch receptors (Notch1 – 4) and five canonical transmembrane ligands (Jagged 1, 2 and Deltalike 1, 3, 4) have been identified in mammals (203). Frequently, the Notch activity needs to be co-ordinated with other signals to induce EMT (205). For example, TGF-β increases Notch activity through Smad3 which subsequently promotes expression of Slug and thereby suppress E-cadherin expression (204). Notch pathway serves also as a critical intermediate in hypoxia-induced EMT. When tumors grow to a certain size there is limited availability of nutrients and oxygen and cancer cells are exposed to hypoxic conditions. Hypoxia thus can be considered as a second factor in the EMT initiation (206). Hypoxia-induced EMT depends on hypoxia-inducible factors (HIF) that induce and stabilize Snail protein via Notch pathway. Hypoxia is also able to induce EMT through activation of TGF-β1 type I receptor kinase and thus TGF-β pathway (207). Moreover, during hypoxia levels of reactive oxygen species (ROS) produced by mitochondria are increased (208). ROS then could directly or by activation of NF-κB pathway facilitate EMT (97) (209) (210).

EMT induction is also dependent on inflammatory components in the malignant microenvironment provided by released factors of tumor associated macrophages (TAMs) and other stromal cells (e.g. IL-1, TNF-α) (211) (212) (213). Tumor necrosis factor-α (TNF-α) is a crucial pro-inflammatory cytokine linked to all steps of tumorigenesis, including invasion and metastasis. TNF-α signalling is mediated via two distinct cell receptors, TNFR1 and TNFR2 (214). Upon contact with ligand, TNF receptors form trimer and their conformational change enables TNFR1-associated death domain protein (TRADD) to bind the death domain and recruits TNF receptor-associated factor (TRAF2) and receptor interacting protein (RIP). TRAF2 in turn recruits IκB kinase (IKK) complex which is further activated by serine- threonine kinase RIP. Activated IKK complex phosphorylates inhibitory protein IκB that normally binds to NF-κB and inhibits its translocation. Phosphorylated IκB proteins are subsequently ubiquitinated and degraded by proteasome, releasing NF-κB that translocate to the nucleus and mediates the transcription of a vast array of proteins involved in tumor progression (see Fig. 6) including EMT associated transcription factors, such as Slug, Snail, Twist, and ZEB1/ZEB2 (97) (215) (216). NF-κB transcription factors were also found to be responsible for the activation of mesenchymal marker vimentin and matrix metalloproteinases (MMPs), such as MMP2 and MMP9 (217) (218). Besides the TNF-α, NF-κB signalling can by activated through different pathways triggered by a variety of cytokines, growth factors or tyrosine kinases. Moreover, TNF-α may also activate MAPK pathways involved in establishment of EMT, and can up-regulate expression of TGF-β and accelerate TGF-β

38 induced EMT (215). Interestingly, TNF-α mediated increase of Snail can up-regulate the expression of proinflammatory mediators (IL-1, IL-6 and IL-8) indicting a positive feedback loop between inflammatory and EMT (96) (219).

In addition to the signalling pathways mentioned above, numerous others have been found to contribute to EMT induction and tumor progression. For example, numerous growth factors, such as hepatocyte growth actor (HGF), epidermal growth factor (EGF) or fibroblast growth factors (FGF) activate receptor tyrosin kinases (RTKs) and their major downstream effector Ras. Constitutive activation of RTKs and their downstream effectors (Ras, MAPK, PI3K) plays a crucial role in promotion of EMT (97) (220) (221) (222). microRNAs (miRNAs) also play an important role in the induction of EMT. For example, miR-200s is the most cited EMT-related miRNA family, but its exact mechanisms and function is still not fully understood (96) (223) (224).

On the transcription level, several families of transcription factors (TFs) have been described to be involved in EMT regulation (see Figure 6), and among them ZEB, Snail and Twist are the most extensively studied. However, other TFs e.g. FOX or GATA play irreplaceable role in EMT regulation as well (225). ZEB family of transcription factors involves two members, ZEB1 and ZEB2 (226). ZEB transcription factors are characterized by the presence of zinc finger clusters through which they bind to the specific sequences of DNA called E-boxes. It is known that ZEB1 repress transcription of CDH1 gene coding epithelial E-cadherin and also other genes coding for epithelial proteins. Moreover, both ZEB1 and ZEB2 regulate tumor cell migration, activation of matrix metalloproteinases (e.g. MMP1, 9 or 14) and stabilize mesenchymal stem cell marker CD44, making them a key EMT regulators (227) (228) (229). Snail family of transcription factors consist of five zinc-finger TFs: SNAI1 (Snail), SNAI2 (Slug), SNAI3 (Smuc), SCRATCH1 and SCRATCH2 (230). Nonetheless, only Snail and Slug are involved in EMT regulation. Both, Snail and Slug repress CDH1 gene transcription and are also involved in hypoxia-induced EMT. Snail also targets other specific genes, such as claudins or occluding, crucial components of tight junctions. All those facts give the importance of Snail and Slug in the regulation of EMT process (231) (232) (233) (234). Twist family of transcription factors consist of two members, TWIST1 and TWIST2 (235). TWIST1 plays a role in tumor cell migration, invasion and metastasis formation and is involved in hypoxia-induced EMT regulation via its hypoxia responsive promoter. TWIST2 is important protector against oncogene-induced senescence and induces down-regulation of

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CDH1. Above mentioned data prove an importance of both TWIST1 and TWIST2 in EMT regulation (236) (237) (238) (239).

In summary, EMT is an extremely complex process with variety of factors involved in its induction/regulation, and plays a significant role in tumor metastasis. However, despite recent advances, much remains unknown about the EMT program in tumor progression and metastasis. Therefore, it is a challenge to probe and uncover multimodal nature of oncogenic EMT, which will contribute to better understanding of metastasis formation (97) (166).

Figure 6: EMT regulation: Complex process of EMT regulation influenced by many inducers, regulators, EMT-related signalling pathways and transcription factors (modified from (158) (225) (240)).

2.4 PDZ and LIM domain protein 2 (PDLIM2)

2.4.1 PDLIM2 structure and localization PDZ and LIM domain protein 2 (PDLIM2) also known as Mystique or SLIM is a protein encoded by PDLIM2 gene located on chromosome 8p21.2 (241). Three isoforms were identified at the transcript level (see Figure 7), however, only one isoform - isoform 1, also known as Mystique 2, with molecular weight 35.7 kDa - was detectable at the protein level in cells (241). PDLIM2 was firstly identified in corneal epithelial cells, in cells transformed by over-expression of insulin-like growth factor-1 receptor (IGFR-1R), in T lymphocytes, and in macrophages (242) (243) (241) (244) (245) (246). This protein is localized both in the nucleus and in the cytoplasm and sub-cellular localization significantly regulates its functions (242).

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Moreover, it was found that in non-differentiated cells, PDLIM2 is located in the nucleus, whereas in differentiated cells in the cytoplasm (241). PDLIM2 belongs to the actinin- associated LIM protein (ALP) protein family that contains a single N-terminal PDZ and C- terminal LIM domain (242) (243) (see Figure 7). LIM domains are cysteine-rich zinc finger motives that serve as a protein binding interfaces mediating diverse interactions ranging from non-receptor and receptor kinases to other PDZ or LIM domains (241) (244). Characteristic LIM domain of PDLIM2 is a double zinc finger domain localized between amino acids 284- 344 at the C-terminus, which besides above mentioned things mediates E3 ubiquitin ligase function of PDLIM2 (242) (247). On the other hand, PDZ domains are 80-90 amino acids domains that mediate less diverse protein-protein interactions and the assembly of protein complexes especially associated with actin cytoskeleton (241) (248). PDZ domain of PDLIM2 then represents first 84 amino acids at the N-terminus including a nuclear export signal (NES) at amino acids 71-79, which plays a key role in its translocation between the nucleus and the cytoplasm (242).

Figure 7: PDLIM2 isoforms: Illustration of the PDZ and LIM domains in PDLIM2 isoforms (modified from (241)).

2.4.2 Function of PDLIM2 PDLIM2 acts as an E3 ubiquitin ligase localized both in the nucleus and in the cytoplasm (241). In the nucleus PDLIM2 regulates activity of NF-κB signalling pathway that plays a crucial role in many cellular events such as inflammation, host defense, cell migration, proliferation, apoptosis and its deregulation is associated with many diseases including cancer (249) (250). It was found that PDLIM2 targets nuclear p65, member of NF-κB transcription factors family, to promyelocytic leukemia protein (PML) nuclear bodies for ubiquitination and subsequent degradation and thus acts as an inhibitor of NF-κB signalling (242) (245). Targeting of p65 to the PML bodies is driven by PDZ domain, whereas ubiquitination of p65 is mediated by LIM domain of PDLIM2 (242) (245). Moreover, in the nucleus PDLIM2 can interact with STAT proteins and promotes degradation of phosphorylated STAT1 and STAT4 (242) (245). Degradation of STATs leads to termination of JAK-STAT signalling, which is

41 involved in the regulation of in immune response, cell division, development and/or death, just as in tumor formation (251). NES-mediated export of PDLIM2 into the cytoplasm is associated with increased nuclear p65 expression and activation of NF-κB targeted genes and JAK-STAT signalling (242) (245). Besides that, in the cytoplasm PDLIM2 directly interacts with actin binding proteins (e.g. filamin, α-actinin…) and regulates actin cytoskeleton (243). The actin cytoskeleton plays an essential role in cell motility, migration and maintenance of cell shape and polarity, and thus it is not surprising that PDLIM2 is involved in the regulation of cell adhesion and is essential for the migration of epithelial cells (241) (242). Except for sub-cellular localization, PDLIM2 function is regulated by phosphorylation of serine residues, which mediates PDLIM2 effect on cell adhesion and migration. Moreover, the phosphorylation of PDLIM2 is necessary for activation of NES-mediated export and for its stabilization in the cytoplasm, because phosphorylated PDLIM2 is resistant to proteasome- mediated degradation. It was found that PDLIM2 phosphorylation is promoted by extracellular signal-regulated kinases (ERKs) and protein kinase C (PKC) family of serine/threonine kinases (242). On top of that PDLIM2 expression is driven by vitamin D3, and PDLIM2 is involved in the anti-migration, anti-invasion and pro-adhesion activity of vitamin D3 (252).

2.4.3 PDLIM2 and cancer All members of the ALP family have been previously associated with cancer (253), and the expression of PDLIM2 is connected with both, oncogenesis and tumor suppression (246). It was found that PDLIM2 expression is epigenetically suppressed by promoter hypermethylation in different cancers (246) and its re-expression is able to inhibit tumorigenicity and induce tumor cell death both in vitro and in vivo (254). This mechanism was firstly described in human T-cell leukemia virus type I (HTLV-I)-mediated tumorigenesis, the etiological agent of adult T-cell leukemia (ATL). PDLIM2 repression in ATL cells is associated with PDLIM2 promoter hypermethylation by DNA methyltransferases 1 and 3b (DNMT1 and 3b), and the 5-aza-2′-deoxycytidine (5-aza-dC) hypomethylating agent is able to restored PDLIM2 expression and induced death of these malignant cells (254) (255). The same effect of DNMT1, DNMT3b and 5-aza-dC on PDLIM2 expression was observed also in breast cancer cell lines MCF7 and MDA-MB-231. It was found that treatment of the breast cancer cells with 5-aza-2-dC reverses the methylation of the PDLIM2 promoter, restores PDLIM2 expression, and suppresses tumorigenicity of human breast cancer cells both, in vitro and in vivo (256). Except for 5-aza-2-dC, the biologically active form of vitamin

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D3 (1,25(OH)2D3) induces demethylation of PDLIM2 promoter in breast cancer cells, as well.

It was found that PDLIM2 mediates the ability of 1,25(OH)2D3 to suppress breast cancer cell migration and invasion (252). Epigenetic repression of PDLIM2 was also observed in various human colorectal cancer cell lines. Study shows that PDLIM2 re-expression by 5-aza-dC treatment inhibits NF-κB constitutive activation, and is sufficient to supress in vitro anchorage independent growth, and in vivo tumor formation of these malignant cells (254). Additionally, PDLIM2 is epigenetically repressed by DNA in ovarian cancer cells compared to normal tissues. PDLIM2 inhibition promotes in vitro and in vivo ovarian cancer growth via nitric oxide (NO) signalling, which has been reported to be closely associated with tumorigenesis but its contribution in ovarian cancer pathogenesis remains unknown (257). Aside from this, published data shows that specific PDLIM2 silencing by small hairpin RNA (shRNA) results in increased tumorigenicity of MCF7 and MDA-MB-231 breast cancer cells (252), and the suppression of PDLIM2 by specific small interfering RNA (siRNA) is associated with decreased adhesion and increased NF-κB transcription activity in human monocytic leukemia cell line THP-1 (242). Recent in silico study has also shown that PDLIM2 is one of the most significant down-regulated genes in oesophageal squamous cell carcinoma (ESCC) and might be a novel prognostic predictor in terms of overall survival. Down-regulation and possible tumor suppressive role of PDLIM2 was described in come other tumors, such as gastric cancer, classical Hodgkin and anaplastic large cell lymphoma and Kaposi sarcoma (258) (259) (260).

In comparison, PDLIM2 is highly expressed in invasive cancer cells and it is associated with metastasis formation and tumor progression (261) (262). In line with this, PDLIM2 is robustly expressed in breast and prostate cancer cells that exhibit EMT phenotype and its expression has been linked to highly aggressive ovarian cancer (241) (262) (263) (264). PDLIM2 protein is enriched in more than 60% of invasive triple negative breast cancers (TNBC) (262), and suppression of PDLIM2 in MDA-MB-231 cells (model of TNBC) is sufficient to impair migratory potential, clonogenic growth (261) and impair in vivo spread of these cells (262). These results suggest that high PDLIM2 may facilitate breast cancer progression in TNBC tumors (262). On the other hand, in the non-tumorigenic MCF10A breast epithelial cell line, PDLIM2 is required for maintaining cell polarization and 3D acinar formation (246), indicating different role of PDLIM2 in TNBC and normal cells (262). High levels of PDLIM2 were also found in androgen-insensitive human castration resistant prostate cancer (CRPC)-like cells (PC3, DU145). PDLIM2 suppression significantly reduced

43 oncogenic phenotypes of these cells such as invasiveness or clonogenicity. Inhibition of PDLIM2 reduces cell viability and proliferation by promoting apoptotic cell death and inducing cell cycle disturbance (the G2/M arrest) and significantly reduces tumor growth in human CRPC xenograft models both in vitro and in vivo. Additionally, a potential mechanistic connection was found between the oncogenic action of PDLIM2 and the MAPK/ERK pathway in human CRPC-like cells, which may also affect cell viability, proliferation, and mesenchymal change of cell fate. All these data indicate that PDLIM2 may be considered a novel therapeutic target gene for treating human CRPC (265). Moreover, suppression of PDLIM2 in invasive prostate and breast cancer cell lines (DU145, MDA-MB- 231) causes increased cell–cell contact, loss of directional migration, reversal of EMT (increased E-cadherin, decreased Vimentin and N-cadherin) and altered expression, protein stability and activity of many transcription factors associated with tumorigenesis (263). Possible explanation of altered protein stability and activity in these cells might be previously described physical interaction between PDLIM2 and COP9 signalosome, which is a protein complex regulating the activity of Cullin-E3 ligase complexes and protein degradation (261). Aside above mentioned, chromosome region 8p21, where PDLIM2 gene is localized, is frequently disrupted in various cancers and associated with metastasis (241) (246). For example it was found that PDLIM2 up-regulation in Merlin-deficient Meningioma and Schwannoma is associated with increased proliferation of tumour cells (266).

Taken together, inconsistent and contradictory data mentioned above suggest that PDLIM2 may have a context-dependent role in various human cancer malignancies and may play distinct roles in tumor suppression and oncogenesis (262) (265).

2.5 TRAF3 interacting protein 2 (TRAF3IP2)

2.5.1 TRAF3IP2 structure and localization TRAF3IP2 also called adapter protein CIKS or nuclear factor NF-κB activator 1 (Act1) is 64 kDa large, widely expressed cytoplasmic protein encoded by TRAF3IP2 gene localized on the long arm of chromosome 6, specifically at the 6q21 sub-region (267) (268). TRAF3IP2 contains several structural domains, which determine its function and behaviour in cells. N- terminal part of TRAF3IP2 contains a putative TRAF binding site (TB1) and helix-loop-helix (HLH) region, in the central region Ufd2-box (U-box) domain is with TB2 localized, and C- terminal part of TRAF3IP2 contains a SEFIR domain (269) (270) (271) (272) (see Figure 8). HLHs are 50-60 amino acids long domains consisting of short basic region, these domains are

44 common in eukaryotic transcription factors (273) and are involved in protein interactions (268). U-box domain is structurally similar to really interesting new gene (RING) finger domain, however, is not stabilized by cysteine residues chelating zinc ions, but by hydrogen bonds and salt bridges. This domain mediates ubiquitin transfer from E2 ubiquitin ligases to their target proteins and is essential for E3 ubiquitin ligase function of TRAF3IP2 (270) (274). Both, HLH and U-box domains have relatively recent evolutionary origins in higher vertebrates and are highly conserved across vertebrae species (261). The SEFIR domain at residues 409-550 is predicted to be structurally similar to toll interacting region (TIR), and together they constitute the STIR-domain superfamily (269) (275). SEFIR core consists of five-stranded parallel β-sheets sourrounded by five α-helices, and this domain is crucial for mammalian adaptive imunity (275) (276).

Figure 8: The structure of TRAF3IP2: Localization of TRAF binding site (TB1) helix-loop-helix (HLH) region, Ufd2-box (U-box) domain with TB2 site, and SEFIR domain of TRAF3IP2 (modified from (277)).

2.5.2 Function of TRAF3IP2 TRAF3IP2 acts as an adaptor protein and E3 ubiquitin ligase, and plays an important role in several cellular processes (268) (270) (274). This redox sensitive membrane-proximal adaptor is a key signalling molecule downstream of the interleukin 17 receptor (IL-17R) that triggers an induction of inflammatory genes (278) (279) (280). The binding of interleukin 17

(IL-17), key signature cytokine of TH17 cells with essential role in vertebrate immunity (281), to heteromeric IL-17R leads to recruitment of TRAF3IP2 through SEFIR domains present within TRAF3IP2 and IL-17R cytoplasmic region (280) (281). TRAF3IP2 in turn engages members of the TRAF family, activating NF-κB, C/EBP, JAK-STAT, MAPK, JNK/AP-1, and NOTCH1 pathways (267) (262) (271) (282) (283). Following the recruitment of TRAF3IP2 to IL-17R cytoplasmic region, TAK1 kinase and E3 ubiquitin ligase TRAF6 are recruited to the receptor and facilitate activation of the IKK complex via interaction with its regulatory subunit IKKγ (NEMO). Activated IKK leads to phosphorylation and subsequent degradation of IκB proteins and following activation of NF-κB, as was described above (see chapter 2.3.2) (215) (216) (280) (282). It was found that IL-17 mediated activation of NF-κB

45 relies on TRAF6 binding to the N-terminal evolutionarily conserved PVEVDE motif of TRAF3IP2 (270). Physical interaction between TRAF3IP2 and TRAF6 was also found to be involved in ubiquitination and subsequent proteasomal degradation of STAT3 protein followed by inhibition of JAK-STAT signalling pathway, which shows that TRAF3IP2- TRAF6 complex is a regulator of this pathway (284). Moreover, heterotypic interaction between TARF3IP2-TRAF6 and IL-17R SEFIR domain is involved in activation of MAPK, specifically p38, ERK1/2 and JNK kinases (278) (280) (281). The MAP kinase c-Jun N- terminal kinases/stress-activated protein kinase (JNK/SAPK) activates AP-1 transcription factor, which subsequently induces expression of targeted genes with pro-migratory and pro- mitogenic effects (268) (279) (281). Interestingly, it was shown that not only the interaction with TRAF6 but in general interactions with TRAF proteins determine downstream specificity of IL-17R–induced TRAF3IP2 mediated signalling (285) (286). TRAF3IP2 is involved in IL-17 induced NOTCH1 activation as well (283). IL-17R mechanistic interaction with NOTCH1 facilitates the cleavage of NOTCH1 intracellular domain (NICD1), which interacts with TRAF3IP2 followed by the translocation of the TRAF3IP2–NICD1 complex into the nucleus and activation of NOTCH1 target genes involved in variety of developmental processes in cells (283). TRAF3IP2 is also an important adaptor and downstream signalling molecule in IL-18 induced cardiac fibroblast migration and proliferation, via TRAF3IP2- TRAF6 mediated activation of JNK/AP-1 and NF-κB signalling (279) (287) (288). Beside this, TRAF3IP2 is an adaptor protein and negative regulator of CD40 and B-cell activation factor of the TNF family (BAFF)-receptor induced signalling (289) (290). Both, CD40 and BAFF-signalling play an essential role in adaptive immunity. CD40 signalling results in B- cell differentiation, immunoglobulin (Ig) class switching and generation of memory B-cell responses. BAFF signalling is essential for B-cells maturation and survival. It was found that upon binging of CD40 ligand to CD40 receptor TRAF3IP2 is recruited to the receptor and negatively regulates CD40 intracellular signalling; moreover, TRAF3IP2 mediates elimination of autoreactive clones during B-cells maturation. However, the exact mechanisms of these phenomena are not fully known (289) (290) (291) (292) (293).

Except for its role as an adaptor protein, TRAF3IP2 also acts as an E3 ubiquitin ligase (270) (274). Interestingly, E3 ubiquitin ligase function of TRAF3IP2 is closely connected with its role in interleukin (IL) signalization (270). It was found that the formation of the TRAF3IP2-TARF6 complex in IL-17 pathway requires TRAF3IP2-mediated K63-linked ubiquitination of TRAF6 for the activation of the downstream signalling. In addition to this, it

46 was also found that TRAF3IP2 ubiquitin ligase function is dependent on the TRAF6- TRAF3IP2 interaction, which indicates mutual relation between these proteins (261) (274). Besides above mentioned adaptor and E3 ubiqiuitin ligase function, it was recently found that TRAF3IP2 in complex with TRAF2/TRAF5 proteins behaves as an RNA-binding protein and plays a direct role in mRNA metabolism. The TRAF3IP2 SEFIR domain interacts with the stem-loop structure (SBE) at the 3„UTR of select inflammatory mRNAs. TRAF3IP2-binding results in stabilization of the mRNA encoding key inflammatory proteins (e.g. Cxcl1, Tnf and Csf2) and thereby mediates the proinflmmatory effects of IL-17 signalling (281).

2.5.3 TRAF3IP2 and cancer The connection between TRAF3IP2 and cancer is via its adaptor protein roles in the regulation of several cellular signalling deregulation of whose is associated with tumorignesis (267) (262) (271) (282) (283). For example, up-regulation of TRAF3IP2 leads to constitutive activation of NF-κB and JNK/SAPK signalling (271) (282), which are related to proliferation, resistance to apoptosis, cancer progression and metastasis in melanoma as well as breast, lung or prostate tumors (294) (295) (296). It was also found that persistent activation and crosstalk between NF-κB and AP-1 amplifies inflammation and promotes tumor growth (297) by enhancing the expression of inflammatory mediators, growth factors, anti-apoptotic proteins, and cell cycle regulators (298). Recent study shows that TRAF3IP2 as an up-stream mediator of both, NF-κB and AP-1 transcription factors, is a master regulator of malignant signalling in glioblastoma multiforme (glioblastoma) (299). This study reveals that primary glioblastoma tumors express high levels of TRAF3IP2 and its silencing markedly inhibits NF-κB activation and induction of pro-inflammatory mediators, as well as expression of cell cycle regulators, pro-angiogenic mediators (including VEGF). Silencing of TRAF3IP2 also made the tumor microenvironment less inflammatory, inhibits the spheroid forming ability of malignant glioblastoma cells U87 and U118, inhibits proliferative potential of glioblastoma cells and the growth of cancer stem cells, which contributes to smaller glioblastoma tumors and eliminates recurrence. All these data indicate that TRAF3IP2 could be a potential and promising therapeutic target in glioblastoma (299). Apart from this, it is well known that chronic inflammation poses a risk for the development of cancer. IL-17 as a key inflammatory cytokine was found to play an important role in cancer promotion and progression (300). High IL-17 levels are indicative of poorer prognosis in colorectal, nonsmall cell lung cancer and hepatocellular carcinoma (301) (302). A novel IL-17 inflammatory signalling cascade directly impacting keratinocyte proliferation and tumor formation, which is dispensable for all the

47 other IL-17–induced downstream signalling events described above (e.g. NF-κB, SAPK/JNK etc.), was identified (303). This cascade involves specific interaction of TRAF3IP2 with TRAF4, which mediates MEKK3-dependent extracellular signal-regulated kinase 5 (ERK5) activation (303). Although little is known of the ERK5 pathway, direct evidence has shown that ERK5 reduction is associated with decreased tumor formation in colon cancer and hyperactive ERK5 has also been implicated in breast, lung, cervical, prostate cancer and via TARF3IP2-TRAF4 in keratinocyte proliferation and tumor formation (304) (305) (306). Results also indicate positive feedback regulation of IL-17R-TRAF3IP2-TRAF4-MEKK3- ERK5 cascade in keratinocyte tumor formation. The thing is that IL-17 induces Steap4 expression required for sustained expression of p63 a positive transcription factor of the Traf4 promoter, which leads to persistent activation of TRAF4-ERK5 signalling. Moreover, it was found that TRAF3IP2 and its immediate binding partners TRAF proteins (TRAF6, TRAF4…) are overexpressed in a wide-range of human malignancies (303) (307). In addition to this, amplification at the 6q16-q22 chromosome sub-region where TRAF3IP2 gene is localized (6q21), was associated with various cancers such as malignant melanoma, breast and ovarian carcinomas, leukemia, non-Hodgkin‟s lymphoma or mesothelioma (267) (308).

2.6 Ring finger protein 25 (RNF25)

2.6.1 RNF25 structure and localization RNF25 also known as AO7 is a RING finger protein localized predominantly in the nucleus that exhibits E3 ubiquitin ligase activity encoded by RNF25 gene (249) (309). RNF25 structurally belongs to a RING-finger ubiquitin ligases family and consist of 459 amino acids with a molecular weight about 51 kDa and its coding sequences are localized at the chromosome 2q35. N-terminal part of RNF25 contain two structurally distinct domains, RWD and RING-finger domain. RWD domain located at residues 18-128 is a conserved region with an alpha/beta secondary structure and is thought that might have a function in protein interaction (see Figure 9) (310). A RING consensus sequence is evident between amino acids 135 and 201 and is defined by six cysteine (Cys) and two histidine (His) residues that coordinate two zinc cations. Cys and His residues are arranged in the typical RING-H2 formation with histidine residues in the middle (C3H2C3) (see Figure 9). These Cys and His residues of RNF25 were shown to be crucial for its function as E3 ubiquitin ligase (249) (309) (311). C-terminal part of RNF25 contains highly conserved proline (Pro) rich region at residues 268-411 (see Figure 9) that is involved in protein-protein interactions (249).

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Figure 9: The structure of RNF25: Structurally distinct domains of RNF25 are shown, N-terminally localized RWD, and RING domain and C-terminally localized Proline rich domain. RING consensus sequence arranged ii the typical RING-H2 formation (C2H2C3) is depicted as well (modified from (249) (311)).

2.6.2 Function of RNF25 RNF25 is an enzyme with E3 ubiquitin ligase function mediated by its RING finger domain (309). Ubiquitin ligase function of RNF25 was firstly described in relation to Nkd2 protein. Both, in vitro and in vivo was shown that RNF25 binds and ubiquitinate Nkd2 an inducible antagonist of canonical Wnt pathway (312). Subsequent rapid ubiquitin-mediated proteasomal degradation of Nkd2 leads to the activation of canonical Wnt pathway that orchestrates embryonic development, tissue patterning, cell migration, proliferation, apoptosis, EMT and is frequently implicated in cancer (311) (313) (314). Interestingly, it was found that transforming growth factor α (TGF-α), in an EGFR-independent manner, dose- dependently reduces RNF25 binding to Naked2, thus protecting Naked2 from proteasomal degradation (312). However, RNF25 is able to strengthen Wnt signalling and EMT also in an E3 ubiquitin ligase-independent manner. RNF25 physically interacts with Axin, thereafter forms ternary complex with Nkd1, negative regulator of Wnt signalling, and promotes activation of canonical Wnt pathway (311) (315). RNF25 via the C-terminal amino acid sequence 341–459 (overlapping with the Pro-rich region) also interacts with the transactivation domain (TAD) of p65, and modulates its transcription activity (249) (316). Exact mechanism of this phenomenon is still unclear, however, it was proved that p65 is not directly ubiquitinated by RNF25 and there is no change in the stability of p65 (249). Several possibilities by which RNF25 activates NF-κB were postulated, nevertheless, none of them was experimentally proved so far. For example, RNF25 may mediates ubiquitination and subsequent proteasomal degradation of NF-κB inhibitor proteins IκBs or NF-κB co-repressors such as Sin3A or N-Cor. RNF25 may also act as a scaffold protein for the interaction of transcription factors and/or facilitate binding of transcription activators to TAD domain of p65 (249) (316) (317).

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2.6.3 RNF25 and cancer RNF25 activates Wnt and NF-κB mediated gene expression including expression of many cytokines that are able to induce other signalling pathways (e.g. MAP/ERK) deregulation of which can be pro-tumorigenic (249) (312) (318). Moreover, RNF25 was recently identified as a novel factor related to gefitinib resistance in EGFR-mutant non-small cell lung cancer (NSCLC) cells (318). RNF25 in these cells mediates NF-κB activation, which, in turn, induces reactivation of ERK signalling via the function of cytokines, such as IL-6 or IL-8. Accumulating evidence has demonstrated that both, NF-κB and ERK pathways, are implicated in the induction of drug resistance in EGFR-mutant NSCLC cells (318) (316) (319). It was shown that RNF25 depletion suppressed the NF-κB activation, which subsequently blocked the reactivation of ERK signalling, proving the role of RNF25 as an essential mediator connecting NF-κB and ERK pathways (318). All these results suggest that RNF25 plays a pro-tumorigenic role in cancer and might be a novel therapeutic target in EGFR-mutant NSCLC cells to overcome resistance to tyrosine kinase inhibitors (249) (318).

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3 Hypothesis

Based on the previous data, we defined the following hypothesis:

PDLIM2, TRAF3IP2, and RNF25 proteins have pro-metastatic role in luminal-A breast cancer.

4 Aims of this work

To confirm the above hypothesis, we defined the following research aims:

1. To analyse how the proteomics methods can contribute to understanding of cancer metastasis (review of the literature). 2. To determine how the expression and protein levels of PDLIM2, RNF25 and TRAF3IP2 are associated with lymph node metastasis in luminal-A breast cancer. 3. To analyse PDLIM2 interactome in luminal A breast cancer model. 4. To analyse how the modulated PDLIM2 protein level affect cell migration, invasion, proliferation and adhesion and how it functionally interacts with EMT, hypoxia, TGFβ, p53, NF-κB and other key cancer players related to metastasis in luminal A breast cancer model and compare it with those in normal breast epithelial cells. 5. To analyse how the modulated RNF25 and TRAF3IP2 protein levels affect cell migration, invasion, total proteome profile and protein-protein interactions in luminal A breast cancer model and compare these changes with those caused by PDLIM2 in a network manner.

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5 Materials and methods

5.1 Antibodies, commercial plasmids and siRNAs Mouse anti-PDLIM2 was purchased from Origene, rabbit anti-RNF25 was purchased from LSBio, and rabbit anti-TRAF3IP2 was purchased from Thermo Pierce. For immunoblotting detection horseradish peroxidase conjugated secondary antibodies RAMPx and SWARPx (both purchased by Dako Cytomation) were used. For in house plasmid preparation, pDONR221 “entry” vector and pcDNA3-GW-DEST “destination” vector, both purchased from Invirogen, were used. For specific gene silencing On-Target plus SMART pool human PDLIM2 siRNA and control CTRL siRNA On-Target plus Non-targeting pool control siRNA were used (both from Dharmacon, Thermo Scientific). Dilutions of antibodies and siRNAs as well as other used antibodies and reagents are described in Appendices 2 (including Supplementary Materials), 4 and 5.

5.2 Cell lines and cell counting The breast cancer cell lines MCF7, BT549, and MDA-MB-231 and the human non- tumorigenic breast epithelial cell line MCF10A were purchased from American Type Culture Collection (ATCC) and were maintained according to their protocols. Details of cultivation are described in Appendices 4 and 5. The cells were counted using CASY TT cell counting device standard protocol (Roche, Mannheim, Germany) or Bürker chamber.

5.3 In house plasmids construction

5.3.1 Total RNA isolation and One step RT PCR Total RNA was isolated from BT-549, MDA-MB-231 and MCF7 cell lines using RNeasy Mini Kit 250 (Qiagen) according to the manufacturer protocol. Complementary DNA (cDNA) was synthesized and amplified using One Step RT PCR kit (Invitrogen) with specific primers and resulting products were purified using standard protocol of QIAquick PCR Purification Kit (Qiagen). Details are described in Appendices 4 and 5.

5.3.2 Specific plasmids construction Specific plasmids were prepared according to Gateway® Technology with Clonase® II user guide (Invitrogen, 25-0749 MAN0000470). The cDNA fragments were amplified using PCR with Herculase II DNA polymerase, pair of specific primers and pair of universal primers according to manufacturer protocol. Final products were separated by electrophoresis on 1.5% GoldViewTM stained agarose gel and visualized under UV-light by CCD camera.

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The target fragments were extracted by QIAquick Gel Extraction Kit (Qiagen) according to the manufacturer‟s protocol. 150 ng of extracted fragments were used for BP recombinant reaction with pDONR221 according to Gateway® Technology with Clonase® II user guide, and chemically competent E. coli TOP10 cells (Life Technologies) were used for preparation and amplification of resulting entry vectors. QIAprep Spin Miniprep Kit (Qiagen) was used for vectors purification according to the manufacturer‟s protocol. 150 ng of purified and by sequencing verified entry vectors were used for LR recombination reaction together with 150 ng of pcDNA3-GW-DEST vector according to Gateway® Technology with Clonase® II user guide. Chemically competent E. coli TOP10 were used again for preparation and amplification of resulting destination plasmids that were subsequently purified by Qiagen Plasmid Maxi Kit (Qiagen) according to the manufacturer‟s protocol (details in Appendices 4 and 5).

5.4 Construction of lentiviral plasmids, lentiviruses and stably transfected cell lines Lentiviral vectors were prepared in house at Masaryk Memorial Cancer Institute, Regional center for Applied Molecullar oncology, according to Gateway® Technology with Clonase® II user guide (Invitrogen, 25-0749 MAN0000470), as above. Production of lentiviruses, transduction of MCF7 cells and selection of stably transfected clones were done according to ViraPower™ Lentiviral Expression Systems user manual (Invitrogen, 25-0501 MAN0000273). Detection of recombinant proteins in selected clones was performed using SDS-PAGE and western blot (see below). Details can be found in Appendices 3, 4 and 5.

5.5 Cell transfection For specific post-transcriptional gene silencing cells were transfected using AMAXA Nucleofector technology (Lonza, Switzerland) according to optimized protocols provided by the company (details in Appendix 4). For transient up-regulation of protein levels, plasmid transfection using polyethylene imine (PEI) method based on lipofection was used (details in Appendices 4 and 5).

5.6 TGFβ1 treatment and hypoxia For EMT induction in cells TGFβ1 treatment or hypoxic conditions were used. The protocol of the TGFβ1 treatments is presented in Appendix 4. TGFβ1 tretament was used also for PDLIM2 protein level induction and monitoring (details in Appendix 4). For induction of EMT by hypoxia, cells were cultivated in hypoxic culture hood (Biospherix Xvivo X3,

Biospherix) under 2% concentration of O2 for 48, 72 or 96 hours.

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5.7 Monitoring of cell migration and invasion Cell migration and invasion was monitored using CIM-Plate 16 module of xCELLigence System RTCA DP real-time cell analyzer (Roche, UK) and/or 96 well Transwell assay (Trevigene, USA). The protocols of these experiments are presented in Appendices 4 and 5. Detailed arrangement of CIM-Plate and 96 well plates is shown in Appendix 4 Supplementary Methods.

5.8 Monitoring of cell proliferation and adhesion Cell proliferation in monolayer culture was measured using resazurin treatment (1 mg/ml) and fluorescence measurement by Tecan Infinite M100 Pro (Tecan, Switzerland). Details of the protocol can be found in Appendix 4. Assessment of the cell adhesion was based on amounts of unattached cells counted using Bürker chamber (details in Appendix 4).

5.9 Young’s modulus mapping by Atomic Force Microscopy Young‟s modulus mapping was performed using JPK NanoWizard 3 (JPK, Berlin, Germany) bioAFM microscope, according to protocol described in previous publications (320) (321) (322). The recorded FD curves were fitted with Bilodeau modification (323) of Hertzian model in AtomicJ software (324). Final visualization of the images and stiffness maps was done with Gwyddion software ver. 2.44 (325). Details are provided in Appendix 4 and its Supplementary Methods.

5.10 SDS PAGE and Western blot SDS PAGE and Western blot analysis of cell lysates was used to evaluate the efficiency of transient modification of selected protein levels, to determine levels of selected proteins under particular conditions, to determine levels of recombinant proteins in stable transfected clones, and to test specificity and selectivity of antibodies. The protocols of the procedures are presented in Appendices 2, 4 and 5.

5.11 Pull down assay with LC-SWATH-MS/MS To find optimal conditions for identification of PDLIM2 interactors in breast cancer background, three variants of pull-down assay on streptavidin beads using LC-MS/MS in “Sequential Window Acquisition of all THeoretical fragment ion spectra (SWATH)” mode were compared with LC-SWATH-MS/MS data from surface plasmon resonance (SPR) chips. Lysates of MCF7 breast cancer cells stably transfected with gene encoding N-terminally SBP- tagged PDLIM2 protein and control N-terminally SBP-tagged GFP protein were used for the experiment. Detailed descriptions of individual steps of each method (cell lysis, capture,

54 wash, elution, reduction, alkylation and digestion of potential interaction partners including SWATH-MS and data acquisition and processing) are provided in Appendix 3.

To identify TRAF3IP2, RNF25 and PDLIM2 interaction partners, Method 1 described in Appendix 3 was modified and used according to Collins et al (326). Details of pull down assay, as well as detail description of sample preparation for MS analysis, generation of spectral library, LC-MS analysis and data analysis are provided in Appendix 5.

5.12 Total proteome analysis of MCF7 cell lysates For the total proteome analysis MCF7 cell lysates with transiently augmented RNF25, TRAF3IP2 and PDLIM2 protein levels were used. Cells were prepared using specific plasmid constructs and PEI transfection method (see above). For cell lysis, 8 M urea containing buffer was used (details in Appendix 5) and total protein concentration in supernatants was measured using RC/DC protein assay standard protocol (Bio-Rad, USA). Sample preparation for MS analysis was performed according to previously prescribed protocols (327) (328). Samples for generation of spectral library were pooled together, fractionated using HILIC in peak- dependent manner and measured by SWATH-MS as described previously (328). LC-MS analysis of whole cell lysate peptide samples was performed using Eksigent Ekspert nanoLC 400 liquid chromatograph (SCIEX, Dublin, California) coupled to TripleTOF 5600+ (SCIEX, Toronto, Canada) mass spectrometer. Data dependent acquisition (DDA) and SWATH measurements were performed in positive polarity as was previously described by Bouchal et al. (328). LC-MS data analysis in DDA runs was performed with MaxQuant (www.maxquant.org) version 1.5.3.30 using Andromeda database search algorithm against UniProt/SwissProt human database version 2015_02. SWATH assay library was generated in Spectronaut software (Biognosys, Zurich, Switzerland) version 8.0 based on the results of MaxQuant database searches, quantitative peptide level information was extracted from SWATH data using Spectronaut 9.0. Statistical analysis of total proteome samples was performed in mapDIA 2.4.1 software at fragment level. Detail descriptions of individual steps can be found in Appendix 3.

5.13 Gene set enrichment analysis of total proteome data Gene set enrichment analysis (GSEA) in GSEA Java desktop application (http://software.broadinstitute.org/gsea/downloads.jsp) was performed for SWATH results of complete proteomes. Details can be found in Appendix 5.

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5.14 Statistical analysis STATISTICA software version 12 was used for all remaining statistical analyses not mentioned in chapters 5.11, 5.12 and 5.13. Data were reported as mean +/- 1.96*S.D. Student‟s t-test was used to assess the significance of differences between two groups, and p- values<0.05 were considered statistically significant.

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6 Results

The results section comments publications stated above in the list of selected original publications and listed in Appendices 1 to 5.

6.1 Proteomics in cancer metastasis (review paper 1) Maryas J, Faktor J, Dvorakova M, Struharova I, Grell P, Bouchal P. Proteomics in investigation of cancer metastasis: functional and clinical consequences and methodological challenges. Proteomics. 2014;14(4-5):426-40. (Appendix 1)

Cancer is the second most common cause of death for both men and women in developed countries. More than primary tumors, cancer metastasis, causing dissemination of tumor cells, are responsible for the deaths of cancer patients. Metastasis formation is driven via multistep and complex process called metastatic cascade, which includes EMT as a fundamental step in cancer progression and metastasis. Numerous structural, catalytic, signalling proteins and signalling pathways like Wnt, TGF-β, NF-κB and others are involved in EMT and/or metastatic cascade and can be investigated as potential therapeutic and diagnostic targets. In our review we analysed the methodological aspects of the proteomics approaches and summarized contribution of current proteomics in the field of discovery novel protein biomarkers of cancer metastasis.

Up to now, proteomics has enabled the identification of number of metastasis-associated proteins and potential biomarkers. We highlighted a fact that number of cancer markers for prognosis and therapy prediction has been recommended by ASCO. However, only limited number of these is specifically associated with metastatic disease (e.g. uPA, PAI-1, CA 19-9) (149) (150). Besides, specificity and sensitivity of current clinically used biomarkers is relatively low in general (329). From the clinical point of view, the process of novel biomarker identification and validation is highly desirable and thus we focused on the key achievements of biomarker discovery studies using a toolbox of proteomics methods.

Proteome analysis workflow for metastatic associated biomarkers discovery is typically performed using proteomic quantitation methods such as iTRAQ/TMT labelling, label-free quantification (LFQ), with Super-SILAC internal standard (330) for LC-MS/MS-based proteomics or within 2D DIGE image analysis step (331) (332). Verification and/or validation of proteomics data is typically performed using IHC, western blot, ELISA by comparison with transcriptomics data (qRT-PCR), or using in vitro or in vivo functional characterization

57 of candidate proteins. Verification and validation of pro-metastatic biomarkers is also a field opened for targeted proteomics like selected reaction monitoring mass spectrometry (SRM- MS) (333), or novel multiplexed SRM-like approaches like parallel reaction monitoring on quadrupole-Orbitrap systems (334) and SWATH-MS on QqTOF instrument (335). Overview of studies focused on metastasis-associated biomarkers published in higher impact factor journals, (i) based on LC-MS/MS or (ii) using 2DE based strategy, are summarised in Tables 2 and 3.

Table 2: Overview of proteomics studies focused on discovery of metastatic disease biomarkers performed with LC-MS/MS based proteomics.

Cancer type No. of tissue Discovery Proteins Verification/ Verified/ Reference samples in proteomics identified validation validated the discovery method method(s) (No. of metastasis step (number tissue samples in associated targets of samples the verification/ pooled validation step) together, if performed)

Breast 12 (pooling SDS- 5010 IHC (12), WBT (9); STAT1, MX1, CD74 Greenwood et al. N=6) PAGE/LC- bioinformatics [21]* MS/MS analysis, invasion (Orbitrap), assay LFQ Breast 90 iTRAQ-LC- 853 Bioinformatics CTNBB1, STAT1 Gormley et al. MALDI- analysis [22]* MS/MS

Breast 6 (pooling SDS- 2000 IHC (14), WBT (14), MFGM Alldridge et al. N=3) PAGE/LC- bioinformatics [23]* MS/MS (IT), analysis LFQ Pancreatic 14 (pooling 2DLC-MS/MS 1504 IHC (55), SFN, S100P Naidoo et al. [24]* N=3-4) (Orbitrap), bioinformatics LFQ analysis

Gastric 7 (pooling LCM, ICAT- 757 IHC (120), WBT (7), LGALS1 Jung et al. [25]* N=3-4) 2DLC-MS/MS bioinformatics (Orbitrap) analysis

Lung 4 iTRAQ-2DLC- 353/326 IHC (150), WBT FLOT1 Zhang et al. [26]* MS/MS (36), bioinformatics analysis Cutaneous 24 LC-MS/MS 1557 IHC, bioinformatics PKM2, HSP90AA1, Huang SK et al. (LIT), LFQ analysis MIF, STAT1 [27]* Oral cavity 6 LCM, iTRAQ- 2901/2546 IHC (88), migration PRDX4, P4HA2, Chang et al. [28]* squamous 2DLC-MS/MS and invasion assay, CNN3, CALD1 (Orbitrap) bioinformatics analysis * Stated numbers determine references in the Appendix 2. Note: IHC, immunohistochemistry; LCM, laser capture microdissection; MIF, migration inhibitory factor; IT, ion trap; LIT, linear ion trap; WBT, western blot on tissue samples.

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The success of biomarker research depends not only on the analytical method, but also on protein extraction and abundance of the biomarker in the cancerous tissue. The metastatic tumor tissue or metastases itself are the biological materials with the highest abundance of metastasis associated proteins and frozen tissues can be easily processed for proteomics analysis, however, their use is generally limited by their availability. On the other hand, formalin fixed paraffin embedded tissue (FFPE) are much more accessible, nonetheless, recovery of proteins highly depends on used protocol. Laser capture microdissection (LCM) recovers only a part of the tumor, which leads to cpecific information about microdissected cell type (e.g. stromal or tumor cells), however, it increases the material consumption. Other sources of metastatic biomarkers are model systems, most commonly cell lines derived from the primary tumors. Research on model systems is also very often accompanied with uncovering of pro-metastatic biomarkers situated on the cell surface. Due to the fact that surface proteins are responsible for cell-to-cell, cell-to matrix, and cell-to-environment interactions and their alterations are associated with the changes in cell adhesion, one of the key events in cancer metastasis, they might be considered as potential drug targets (336). Another source of metastasis associated biomarkers are secreted proteins, which are also covered in our review. Secreted proteins from primary tumors could be potentially quantified as serum markers; however, the determination of their clinical need is complicated (i) by protein dilution in 5–6 L of human blood and (ii) by existence of other tissues that may secrete the same protein into blood (337). Attention we also paid to post-translational modification of proteins because differential protein modification, for example phosphorylation, might indicate deregulation of metastasis associated pathways (e.g Wnt, TGF-β, NF-κB).

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Table 3: Overview of proteomics studies focused on discovery of metastatic disease biomarkers performed with 2-DE-MS technology.

Cancer type No. of tissue samples in the Discovery proteomics Identified Verification/ validation Verified/validated Reference discovery step (number of method differentially method(s) (No. of tissue samples metastasis associated samples pooled together, if regulated in the verification/ validation targets performed) proteins step)

Breast 38 2-DE-MS 13 WBT (2) CALR, TPM3, HSP70, Lee et al. [29]* PRKCSH, PDI, PDIA3

Colorectal 36 2-DE-MS 31 IHC (126), WBT (9) RhoGDI, S100A9, LASP-1 Zhao et al. [30]* Pancreatic 15 (pooling N=7-8) LCM, 2-D-DIGE-MS 33 IHC (74), WBT (15) RDX, MSN, C14orf166 Cui et al. [31]* Gastrointestinal 17 2-D-DIGE-MS 25 IHC (72 ), WBT (17) DDX39 Kikuta et al. [32]*

Hepatocellular 24 2-DE-MS 13 IHC (72), WBL, cell migration RHOA Gou et al. [33]* assay, wound healing assay

Hepatocellular 12 2-DE-MS 16 IHC (4), WBT (12), qRT-PCR HSP27 Song et al. [34]* Lung 21 (pooling N=3-4) LCM, 2-D-DIGE-MS 20 IHC (125), WBT (21) ANXA3 Liu et al. J [35]* Lung 8 LCM, 2-D-DIGE-MS 23 IHC (253) ANXA1, ANXA2, ANXA3 Liu et al. [36]*

Prostate 27 2-D-DIGE-MS N/A IHC (85), WBT (27), qRT-PCR (48), CRMP4 Gao et al. [37]* cell invasion assay, metastasis

Prostate 27 (pooling N=7-10) LCM, 2-D-DIGE-MS 58 IHC (105), WBT (27), qRT-PCR e-FABP5, MCCC2, PPA2, Pang et al. [38]* (48), ELISA (70 blood samples) Ezrin, SLP2, SM22

Endometrial 8 2-D-DIGE-MS 10 IHC (33), WBT (15) DESM, MSN, SERPINH1, Monge et al. FSCN1, SOD1, TPM4 [39]*

Nasopharyngeal 84 (pooling N=14) LCM, 2-DE-MS 36 IHC (148), WBT (24), WBL (4) STMN1, SFN, ANXA1 Cheng et al.[40]*

Nasopharyngeal 84, pooling N=7 LCM (stromal cells) 20 IHC (182), WBT (24), WBL (5), POSTN Li et al. [41]* 2-D-DIGE-MS invasion assay

Oral squamous 20 2-DE-MS 52 IHC (144), WBT (20), qRT-PCR RACK1 Wang et al. (20), colony formation assay [42]*

Head and neck 20 LCM, 2-D-DIGE-MS 34 IHC (4), WBT (20), stable isotope CK19 Bleijerveld et al squamous dimethyl labeling (2) [43]*

Giant cell tumor of 10 2-DE-MS 14 IHC (155), WBT (10) GPX1, PRX, AIF1, and Conti et al. bone UBE2N [44]*

Synovial sarcoma 13 2-D-DIGE-MS 17 IHC (45), WBT (13), qRT-PCR (31), SCRN1 Suehara et al. [45]* Osteosarcoma derived 10 2-D-DIGE-MS 17 IHC (46), qRT-PCR (28) CRYAB, EZR1 Folio et al. [46]* primary cell lines * Stated numbers determine references in the Appendix 2.

Note: LCM, laser capture microdissection; WBL, western blot of cell line lysates; WBT, western blot of tissue lysates.

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Our review also covers the functional proteomics in investigation of the function of particular proteins in the pro-metastatic processes. We summarized functional studies trying to reveal exact mechanisms of local invasion (76) and EMT activation (338). These studies revealed plenty of potential EMT triggering stimuli including hypoxia or oxidative stress (338), MMPs as key players in local invasion of tumor cells, and many other stimuli that are involved in invasiveness and migration of tumor cells (96). Last but not least important part of our review is dedicated to studies of protein–protein interactions involved in pro-metastatic mechanisms. Two basic principles of affinity purification of protein complexes (pull-down- MS and co-immunoprecipitation (co-IP-MS) have been combined with MS identification of interacting partners (339). We focused here on studies of protein-protein interactions involved in the actin-based motility and rearrangement, adhesion of the tumor cells to endothelium, focal adhesion, stress fibre assembly and chemoresistance, which all have an indispensable role in a cancer metastasis (340) (341) (342) (343). Beside, we mentioned an interactome of a potential tumor suppressor 14–3–3σ, which influenced invasiveness of the cells as well (344).

To sum up, our review shows that proteomics analysis of tissues, model systems, secretome, or surface proteins revealed number of proteins unambiguously associated with cancer metastasis. Moreover, proteomic techniques have also contributed to quantitative and/or functional verification and description of such targets either as a part of functional studies, or by interactome analysis. Despite these facts, it is evident that translation of these findings into routine clinical diagnostics and therapy becomes demanding, and not always a successful task, in term of its duration comparable with drug development. Nonetheless, increasing proteome coverage achievable with latest untargeted proteomics technologies, together with the improvement of accuracy, easy use and increasing sample capacity of new targeted proteomics assays is able to shift the cancer proteomics closer to clinical applications in the near future (345).

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6.2 Identification of PDLIM2, RNF25 and TRAF3IP2 as proteins associated with lymph node metastasis of grade 1 luminal A breast cancer (research paper 1) Bouchal P, Dvorakova M, Roumeliotis T, Bortlicek Z, Ihnatova I, Prochazkova I, Ho J.T.C, Maryas J, Imrichova H, Budinska E, et al. Mol. Cell.Combined Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor κB (NF-κB) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer. Moll Cell Proteomics. 2015;14:1814-30. (Appendix 2)

Current prognostic factors are insufficient for precise risk-discrimination in breast cancer patients specifically with low grade luminal-A breast tumors. Despite their very favourable prognosis a few percentage of these tumors develop early lymph node metastasis. The molecular mechanism of this phenomenon is not known and current clinical practice lacks the markers for predicting its occurrence. The aim of this study was to identify new biomarkers for low grade breast cancer patients, whose, in disagreement with theoretical prognosis, form early lymph node metastasis. These markers would serve for better risk-discrimination in this group of patients resulting in more intensive follow-up and more personalized therapy.

To identifying biomarkers that can distinguish high risk individuals within the predominantly low risk population of patients with low grade breast cancers, we used shotgun proteomics with isobaric tags for relative and absolute quantification (iTRAQ) in combination with 2D-LC-MS/MS. This established approach for quantification of proteins related to cancer metastasis (345) (327) was employed to 24 lymph node positive and 24 lymph node negative well characterized (ER+, PR+, HER2-) grade 1 luminal-A primary breast tumors. To investigate similarities and difference in the mechanism of metastasis and metastatic biomarkers in high and low grade breast cancers, a second sample set was collected of 48 grade 3 carcinomas, 24 of them node positive and 24 node negative. Using iTRAQ-2DLC- MS/MS we identified a total of 4405 proteins based on at least one tryptic peptide (FDR<5%). On the basis of the observed changes at the proteome level, we selected 65 proteins and added an additional 30 gene products related to pro-metastatic mechanisms according to the literature. To further elucidate the mechanisms of protein alterations in low grade breast cancer and verify shotgun proteomic data, we analysed expression of these 95 genes at transcript level using a custom-designed TaqMan Low Density Array (Microfluidic card). The combining of protein and transcript level profiles allowed us to interrogate independent large patient data sets for validation and their impact on survival. To connect proteomics and transcriptomics data, only targets that exhibited statistically significant changes at both

62 protein and transcript levels in lymph node positive versus negative grade 1 tumors were selected. This group involved carboxypeptidase B1 (CPB1), PDLIM2, RNF25, NF-κB transcription factor p65 (RELA), 14–3-3η (YWHAH), stathmin 1 (STMN1) and thymosin beta 10 (TMSB10), see Table 4. TRAF3IP2 protein was up-regulated in lymph node positive versus negative tumors regardless of grade (see Table 4) and integrin beta-1 (ITGB1) was up- regulated in grade 3 but not grade 1 tumors with metastasis.

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Table 4: Clinicopathological selectivity of top targets correlating at both, proteomics and transcript levels, with lymph node metastasis in grade 1 breast tumors. Cases with low number of observations (N≤10) in proteomics study are in italics. Significant changes in protein levels and expression are indicated in bold

G1: N1-2/N0 G3:N1-2/N0 N1-2/N0 G3/G1 N1-2:G3/G1 ER+/ER- HER2+/HER2- fold change fold change fold change fold change fold change fold change fold change p-value p-value p-value p-value p-value p-value p-value (IHC trend) (IHC trend) (IHC trend) (IHC trend) (IHC trend) (IHC trend) (IHC trend) CPB1 protein 4.34 0.002 0.87 0.741 1.95 0.114 0.55 0.063 0.26 0.013 2.22 0.007 0.56 0.248 transcript 6.737 0.025 0.197 0.073 1.199 0.779 0.192 0.009 0.034 0.000 15.571 0.001 1.266 0.799 IHC ↑ 0.096 ↓ 0.109 ↑ 0.739 ↓ 0.315 ↓ 0.021 ↑ 0.109 ~ 1.000 RNF25 protein 1.44 0.053 1.18 0.354 1.30 0.089 0.84 0.379 N<3 N<3 N<3 N<3 N<3 N<3 transcript 1.267 0.002 0.870 0.218 1.050 0.512 0.763 0.000 0.632 0.000 1.418 0.002 1.168 0.160 IHC tumor cells staining ~ 0.817 ~ 0.766 ↑ 0.570 ~ 0.626 ~ 0.905 ↓ 0.286 ↑ 0.825 IHC stromal cell staining ~ 1.000 ↑ 0.742 ~ 0.905 ↓ 0.521 ~ 1.000 ↓ 0.439 ↓ 0.433 PDLIM2 protein 1.30 0.009 1.06 0.866 1.18 0.415 0.71 0.008 0.64 0.063 1.42 0.208 0.65 0.060 transcript 1.287 0.007 0.851 0.215 1.047 0.571 1.038 0.645 0.844 0.109 1.159 0.258 1.056 0.671 STMN1 protein 1.24 0.002 1.02 0.720 1.12 0.012 1.14 0.039 1.04 0.762 1.01 0.949 0.77 0.036 transcript 1.283 0.050 1.116 0.608 1.197 0.211 2.042 0.000 1.905 0.000 1.250 0.296 1.086 0.705 IHC total intensity ↑ 0.454 ↓ 0.879 ↑ 0.750 ↑ 0.006 ~ 0.417 ~ 0.879 ~ 0.110 IHC % of stained cells ~ 0.415 ~ 0.350 ~ 0.327 ↑ 0.000 ↑ 0.000 ~ 0.73 ~ 0.246 IHC membrane staining ↑ 0.108 ↓ 0.370 ~ 1.000 ↑ 0.000 ↑ 0.171 ~ 0.562 ↓ 0.210 IHC stained lymphocytes ~ 1.000 ↓ 0.659 ↓ 0.434 ↑ 0.203 ↑ 0.490 ↓ 0.05 ↓ 1.000 TMSB10 protein 1.32 0.003 0.97 0.668 1.13 0.066 1.26 0.005 1.07 0.603 0.89 0.161 1.09 0.540 transcript 1.245 0.050 0.990 0.951 1.110 0.364 1.727 0.000 1.540 0.002 0.738 0.066 1.925 0.000 RELA protein 1.30 0.020 1.10 0.246 1.20 0.007 0.85 0.029 0.78 0.066 1.26 0.161 0.80 0.066 transcript 1.215 0.012 1.020 0.835 1.111 0.850 1.097 0.133 1.007 0.945 1.094 0.337 1.050 0.601 YWHAH protein 1.20 0.012 1.04 0.382 1.12 0.007 0.84 0.000 0.79 0.000 1.17 0.022 0.83 0.001 transcript 1.200 0.024 0.877 0.330 1.026 0.750 1.226 0.011 1.048 0.662 1.056 0.687 1.075 0.583 IHC tumor cells staining ~ 1.000 ~ 0.145 ~ 0.348 ↑ 0.002 ↑ 0.304 ↓ 0.336 ~ 0.767 IHC membrane staining in tumor cells ↓ 0.246 ↓ 0.552 ↓ 0.269 ↑ 0.060 ↑ 0.085 ↓ 0.126 ~ 1.000 IHC stromal cell staining ~ 0.866 ~ 0.789 ~ 0.788 ↑ 0.070 ↑ 0.457 ~ 0.529 ~ 0.840 TRAF3IP2 protein 1.24 0.387 1.52 0.001 1.37 0.002 0.63 0.000 0.70 0.012 1.32 0.042 1.09 0.403 transcript 1.404 0.003 1.084 0.505 1.233 0.012 0.858 0.070 0.754 0.010 1.088 0.486 1.335 0.023 IHC percentage of stained cells ~ 0.928 ~ 0.220 ~ 0.357 ~ 0.494 ~ 0.261 ~ 0.403 ~ 0.342 IHC stromal fibroblasts stained ~ 1.000 ↑ 0.755 ~ 0.825 ~ 0.825 ~ 1.000 ↑ 0.063 ↓ 0.191

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Protein levels and gene expression were also analysed together using hierarchical clustering, which revealed three clusters of gene products in cold maps related to lymph node metastasis in grade 1 (G1) tumors. Firstly, cluster 1 of G1 tumors metastasis related proteins (CPB1, PDLIM2 and RNF25) that had low levels in grade 3 (G3) tumors. Secondly, cluster 2 of G1 tumors metastasis related proteins that had also high levels in G3 tumors (STMN1, TMSB10, and ITGB1). This cluster involves other metastasis-related genes EPCAM, KISS1 and MTA1 as well, and neighbours with a cluster containing PLAU and SERPINE1. Thirdly, RELA and YWHAH together with Plasminogen activator inhibitor 1 RNA-binding protein (SERBP1) and gelsolin (GSN) form cluster 3, again associated with lymph node metastasis of G1 tumors, however, with no significant differences between G1 and G3 tumors.

Moreover, IHC staining was performed for the top targets for which IHC compatible, specific antibodies were available (CPB1, RNF25, STNM1, ITGB1, YWHAH). Despite the semi-quantitative nature of IHC, the results confirmed key trends observed in proteomics and transcriptomics data: (1) up-regulation of CPB1 in lymph node positive versus negative grade 1 tumors and (2) up-regulation of STMN1 in grade 3 versus grade 1 tumors. Additionally, the most promising target CPB1 was tested in an independent set of G1 luminal-A tumors (n = 64) and similar trend, but without the statistical significance, was observed.

To further investigate correlations with metastatic behaviour of luminal-A tumors an independent published data set SUPERTAM_HGU133A of gene expression data on 856 patients, including 348 luminal-A-like breast tumors (346), was analysed. The analysis confirmed statistically significant up-regulation of CPB1, PDLIM2 and RELA in lymph node positive luminal-A tumors. Moreover, we tested the relationship with patient survival in publically searchable microarray database of 4142 breast cancer tissues (347), which showed statistically significant connection with relapse free survival in luminal-A group of tumors (n = 1678) for CPB1, PDLIM2, RNF25, STMN1, and TMSB10.

In conclusion, a combination of state of the art proteomics, transcriptomics and IHC, together with validation in independent database sets confirmed selectivity of proteins CPB1, RNF25, PDLIM2, STMN1, TMSB10, RELA and YWHAH for lymph node metastasis in low grade breast cancer. Protein TRAF3IP2 was then up-regulated in lymph node positive tumor samples regardless of the grade, and ITGB1 protein was up-regulated in high grade but not in low grade breast tumors with metastasis (348).

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6.3 Analysis of PDLIM2 interactome using pull-down assay on streptavidin beads and surface plasmon resonance chips (research paper 2) Maryas J, Faktor J, Skladal P, Bouchal P. Pull-down assay on streptavidin beads and surface plasmon resonance chips for SWATH mass spectrometry based interactomics. Cancer Genomics Proteomics. 2018;15(5):395-404 (Appendix 3)

Protein–protein interactions (PPI) play a fundamental role in a wide range of biological processes (349) and their identification is important to infer the protein function within the cell and in the inter-cellular communication (350). Pull-down assay represents a powerful in vitro screening tool for identifying previously unknown PPIs via an antibody-free approach (351). It is considered as a form of affinity purification similar to immunoprecipitation, except that an immobilised "bait" protein is used instead of an antibody. A number of affinity tags including enzymes, protein domains or small polypeptides have been developed to prepare a fusion bait protein. Of these, 38 amino acids long streptavidin binding peptide (SBP) tag was chosen because of its with high affinity to streptavidin that enables a fast, efficient, and relatively specific one-step method for isolation and studying protein complexes. Moreover, it allows simple competitive elution by biotin under mild conditions (352).

Effectiveness of the pull-down assay always depends on the optimal binding, washing and elution conditions. The aim of this study was to find optimal conditions for identification of PDLIM2 interactors in breast cancer cells by comparing four different pull-down methods. PDLIM2 was chosen because study of its interactome might provide new insights into molecular machineries important in re-arrangement of the cell in various phases of tumour development and clarify its previously described context dependent role in tumorigenesis (246) (for more details see chapter 2.4.3 and 6.4). We compared three modifications of conventional pull-down assays on streptavidin beads with the streptavidin modified surface plasmon resonance (SPR) chips, using lysates of stably-transfected MCF7 breast cancer cell line expressing a fusion construct consisting of N-terminal SBP tag and PDLIM2 or a corresponding control cell line expressing N-terminal SBP–GFP fusion protein (see Figure 10).

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Figure 10: Experimental design and overall workflow: Overview of major steps of four methods used for identification of protein–protein interactions (Methods 1-4).

To ensure consistent peptide and protein quantification across the samples LC-MS/MS in “Sequential Window Acquisition of all Theoretical fragment ion spectra (SWATH)” was used for all methods. Individual methods differed in (i) solid supports for tagged protein capture (streptavidin agarose beads, SPR chip), (ii) mechanics of the washing and elution steps (centrifugation, gravity flow, microflow) and (iii) type and concentration of detergents in lysis, wash and elution buffers (Tween 20, CHAPS or NP40 in various concentrations, detailed in Table 5). The efficacy of each pull-down assay was evaluated in Spectronaut software using a custom spectral library containing 128 protein groups based on 675 peptides. Quantitative data were obtained for 120 consistently quantified proteins, which were statistically evaluated in mapDIA software.

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Table 5: Overview of methods under comparison

Method 1 Method 2 Method 3 Method 4

Lysis buffer 0.5% Tween 20, 150 0.5% CHAPS, 100 mM 0.5% NP-40, 150 mM 0.1% Tween 20, 150 mM NaCl, 50 mM KAc, 50 mM HEPES NaCl, 50 mM HEPES pH mM NaCl, 50 mM HEPES pH 7.5, 2 mM pH 7.5, 2 mM MgCl 1 7.5, protease and HEPES pH 7.5, 2 mM 2, MgCl 25 U/µl phosphatase inhibitors MgCl 25 U/µl 2, mM DTT, avidin 10 2, benzonase, avidin 10 µg/ml, protease and 10 µg/ml both benzonase, avidin 10 µg/ml, protease and phosphatase inhibitors µg/ml, protease and phosphatase inhibitors 10 µg/ml both phosphatase inhibitors 10 µg/ml both 10 µg/ml both

Solid support Streptavidin agarose Streptavidin agarose Streptavidin agarose SPR streptavidin chip beads in microtube beads in microtube beads on column

Washing and Centrifugation Centrifugation Gravity flow Microflow elution mechanism

Wash buffer 0.1% Tween 20, 150 0.1% CHAPS, 100 mM 150 mM NaCl, 50 mM 0.1% Tween 20, 150 mM NaCl, 50 mM KAc, 50 mM HEPES pH HEPES pH 7.5, mM NaCl, 50 mM HEPES pH 7.5, 2 mM 7.5, 2 mM MgCl 1 mM 50 mM NaF HEPES pH 7.5, 2 mM 2, MgCl MgCl 2 DTT 2 Elution buffer Wash buffer + 1 mM 50 mM HEPES pH 7.5, Wash buffer + 2.5 mM 0.005% Tween 20, 150 biotin 100 mM KAc, 1 mM biotin mM NaCl, 50 mM biotin HEPES pH 7.5, 2 mM MgCl 1 mM biotin 2, Protein quant. SWATH-MS SWATH-MS SWATH-MS SWATH-MS

Our data showed high up-regulation of PDLIM2 for both types of solid support, streptavidin beads and SPR chip, indicating a sufficient capacity to bind, identify and quantify SBP-PDLIM2 for all compared pull-down approaches. The highest binding capacity of PDLIM2 protein was obtained by Method 3 followed by Method 2, Method 4 and Method 1. These results show that the most efficient mechanism of wash and elution is gravity flow and the least efficient way is centrifugation. Interestingly, despite lower binding capacity of PDLIM2 protein, signal intensities of many potential interactors in Method 4 were comparable or higher than in Method 3. It may indicate comparable or even better binding conditions for some specific interacting proteins in Method 4 than in Method 3. Importantly, Method 1 and Method 4 provided different potential interactors. Since they were based on similar lysis, wash and elution buffers but different solid support and mechanics of elution, we conclude that buffer composition has only a minor effect on our results, in contrast to solid support and mechanics of elution, where gravity flow and microflow provided better results than centrifugation.

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We were also interested in the biological relevance of our data. As biologically relevant interacting protein partners we considered only proteins statistically significantly more abundant (log2FC>1 and FDR<0.05) in PDLIM2 positive purifications by at least two methods in parallel. Ten significantly abundant proteins were quantified by Method 4, six by Method 3 and three proteins by Methods 1 and 2. The largest overlap (6 interactors) was found between Method 3 and Method 4, involving biologically interesting interactions with Shroom3 (SHROOM3), serine/threonine protein kinase Nek 10 (NEK10) and CREB3 regulatory factor (CREBRF). Another interactor confirmed by two methods (Method 1 and Method 4) was calmodulin. Our data also correspond to previously published PDLIM2 interaction partners: Torrado et al. (243) identified components of stress fibres, including filamin A, as PDLIM2 interactors. Filamin A and other components of stress fibres (actin, tropomyosin alpha-3 chain, transgelin-2 and contractility regulator calmodulin) were detected by Method 4, which further supports PDLIM2 interaction with stress fibre proteins and thus biological relevance of our data. Above described results indicate that in terms of biological relevance pull-down on SPR chips is the most efficient assay among compared methods.

In conclusion, we for the first time compared conventional pull-down-LC-MS/MS approaches with SPR-LC-MS/MS system and our data show that both represent potent tools in interactomic studies. Moreover, efficient binding conditions, lower sample consumption, real-time observation of binding/ washing/elution steps, time-saving and fully automated operation, are the major benefits of SPR-based system, which do not exist in the alternative procedures (353).

6.4 Functional analysis of PDLIM2 in breast cancer (research paper 3) Maryas J, Pribyl J, Bouchalova P, Skladal P, Bouchal P. PDZ and LIM domain protein 2 plays dual and context dependent roles in breast cancer development. BioRxiv. 2020; doi: 10.1101/2020.01.27.920199 (BMC Cancer under review) (Appendix 4)

PDZ and LIM domain containing protein 2 (PDLIM2), member of the actinin-associated LIM (ALP) family of proteins (241), acts as an E3 ubiquitin ligase and as such regulates the stability and activity of NF-κB, STAT and other transcription factors in hematopoietic and epithelial cells (242) (245). Deregulation of PDLIM2 has been associated with several malignancies (249) (250) and its expression has been connected with both tumor suppression and tumorigenesis (246). PDLIM2 levels are epigenetically suppressed in different cancers due to promoter hypermetylation and its re-expression is able to inhibit tumorigenicity and

69 induces tumor cell death both in vitro and in vivo (254) (255) (256). On the other hand, PDLIM2 is highly expressed in cancer cell lines derived from metastatic cancer and its expression is associated with tumor progression and metastasis formation (261) (262) (for more details please see chapter 2.4.3).

Controversial literature results on the role of PDLIM2 in cancer development and the results of our previous combined proteomics-transcriptomics study (see chapter 6.2) led us to assumptions about dual and context-dependent role of PDLIM2 (265). The aim of this study was to test this hypothesis, performing a series of in vitro experiments at the molecular and cellular levels using MCF7 breast cancer cells (model of luminal-A breast cancer (354) (355) (356)) and MCF10A immortalized normal epithelial breast cells (model of normal breast epithelium).

Our experiments showed positive co-regulation of PDLIM2 protein levels with epithelial- to-mesenchymal transition (EMT) in MCF7 cells. Overexpression of PDLIM2 decreased the epithelial markers and increased levels of the β1-integrin pathway regulator FAK, indicating EMT induction. On the other hand, PDLIM2 expression suppression by small interference RNA (siRNA) had opposing effect, indicating mesenchymal-to-epithelial transition (MET), see Figure 11A. Moreover, optical microscopy showed the ability of PDLIM2 to affect cell morphology. MCF7 cells with stably overexpressed PDLIM2 (MCF7-PDLIM2) acquire similar morphology as MCF7 cells after TGFβ1-induced EMT, see Figure 11B. A similar pattern was evident from the atomic force microscopy (AFM) data, showing MCF7-PDLIM2 cells had higher stiffness based on Young‟s modulus relative to parental MCF7 cells, similarly as after TGFβ1-induced EMT, see Figure 11C. From the opposite point of view, it was shown that EMT induction by TGFβ1 treatment (1 ng/ml for 24 hours) or by long-term exposure to hypoxia (2% O2 for 96 hours) was able to increased PDLIM2 protein levels, see Figure 11D.

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Figure 11:Relation between PDLIM2 and EMT in MCF7 cells: A) Effect of up-regulated (PDLIM2 pl.) or down-regulated (PDLIM2 siRNA) PDLIM2 levels on EMT markers E-cadherin (E-cad.), keratin-18 (KRT18) and β-catenin as well as focal adhesion kinase (FAK) in MCF7 cells compared to control cells (CTRL pl. and CTRL siRNA). B) Representative photos of MCF7 parental cells and stably transduced MCF7-PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). Magnification 50x. C) Average height and Young‟s modulus of the cells measured by AFM: MCF7 and MCF7-PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). D) Effect of EMT induction by TGFβ1 treatment (1 ng/ml for 24 hours) and by long term hypoxia (96 h) on PDLIM2 protein level. Successful EMT induction was confirmed by changes in EMT markers. PCNA was used as a loading control. Numbers under the protein bands represent their integral optical density (INT*mm2).

Interestingly, our data showed that not only EMT but also hypoxia itself up-regulates PDLIM2 levels in MCF7 cells, and PDLIM2 overexpression in these cells increased levels of

71 carbonic anhydrase-9 (CA9), a marker of the response to hypoxic conditions. Moreover, our results revealed that PDLIM2 overexpression in MCF7 cells decreased levels of key tumor suppressor p53 at both total protein level as well as its serine 20 phosphorylated, active form (p-p53 (S20)). Above indicated pro-tumorigenic role of PDLIM2 in MCF7 cells was confirmed also on cellular levels. We examined that PDLIM2 overexpression statistically significant increased the migration and invasion capabilities of MCF7 cells as observed by real-time measurement using the xCELLigence system (p=2.8x10-4 for migration, p=3.9x10-4 for invasion, see Figure 12A and 12B) and independently confirmed using a Transwell assay (p=3.6x10-5 for migration, p=1x10-5 for invasion). Furthermore, we found out that PDLIM2 overexpression significantly diminished cell adhesion (p=1x10-5) and increased proliferation (p=1x10-5) of MCF7 cells, while suppression of PDLIM2 had the opposite effect (p=1x10-5 for both), all of which confirm oncogenic role of PDLIM2 in MCF7 cells.

Figure 12: Effect of PDLIM2 protein level modulations on migration and invasion: A) Effect of PDLIM2 overexpression (PDLIM2 pl.) on migration of MCF7 cells (in comparison with control cells with endogenous PDLIM2 levels (CTRL pl.)) measured by xCELLigence system. B) Effect of PDLIM2 overexpression (PDLIM2 pl.) on invasiveness of MCF7 cells measured by xCELLigence system.

On the contrary, the potential tumor suppressive role of PDLIM2 was revealed in our experiments using immortalized normal epithelial MCF10A cells. Our data indicated that PDLIM2 is important for maintenance of the epithelial phenotype. PDLIM2 overexpression led to augmentation of the epithelial marker E-cadherin and decreases in the mesenchymal markers, as well as the β1-integrin pathway regulator FAK, and PDLIM2 suppression had the opposite effect (see Figure 13).

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Figure 13: Effects of PDLIM2 protein level modulations on EMT markers in MCF10A cells:. Effect of PDLIM2 protein levels up-regulation (PDLIM2 pl.) and down-regulation (PDLIM2 siRNA) on EMT markers E- cadherin (E-cad.), N-cadherin (N-cad.) and vimentin (Vim.), as well as FAK levels, compared to control cells (CTRL pl. and CTRL siRNA). PCNA was used as a loading control. Numbers under the protein bands represent their integral optical density (INT*mm2).

Additionally, EMT induction by TGFβ1 treatment (1 ng/ml for 24 hours) led to decrease PDLIM2 levels in MCF10A cells, which further confirms the importance of PDLIM2 in the maintenance of the epithelial phenotype in these cells. Interestingly, hypoxic conditions were not able to induced EMT as well as changes in PDLIM2 protein levels in MCF10A cells, and no changes were observed in CA9 levels after PDLIM2 modulation in these cells. These data indicate that PDLIM2 does not play a significant role in the response to hypoxic conditions in MCF10A cells in comparison with MCF7 cells. Differences were also observed in case of PDLIM2 effect on p53 levels. In contrast to MCF7 cells, overexpression of PDLIM2 had no effect on p53 levels; nevertheless, the active form of this protein (p-p53 (S20)) was augmented, again suggesting tumor-suppressive role of PDLIM2 in MCF10A cells. To further validate the distinct role of PDLIM2 in MCF10A cells at the cellular level, we examined its effect on the migration and adhesion of MCF10A cells. Transwell assay experiments revealed that PDLIM2 augmentation significantly decreased (p<1x10-5) and PDLIM2 suppression increased (p<1x10-5) the migration abilities of the MCF10A cells (see Figure 14). Besides that PDLIM2 suppression significantly decreased the adhesion of MCF10A cells (p<1x10-5), while overexpression of this protein considerably augmented the adhesion of MCF10A cells

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(p<1x10-5). All these results further confirm the tumor-suppressive role of PDLIM2 in MCF10A cells.

Figure 14: Effect of PDLIM2 protein level modulations on migration of MCF10A cells: A) Effect of PDLIM2 protein level modulations on MCF10A cell migration. PDLIM2 protein levels were modulated by siRNA suppression (PDLIM2 siRNA) compared to the control (CTRL siRNA) or by PDLIM2 overexpression (PDLIM2 pl.) compared to control (CTRL pl.) B) Verification of PDLIM2 protein level modulations in MCF10A cells migration measurement. PCNA was used as a loading control. Numbers under the protein bands represent their integral optical density (INT*mm2).

In conclusion, our data revealed that PDLIM2 promotes EMT in MCF7 breast cancer cells, but facilitates maintenance of the epithelial phenotype in normal breast MCF10A cells. Our findings also showed that PDLIM2 supports migration, invasion and proliferation in MCF7 cells, but blocks migration and supports the adhesion of MCF10A cells. Take it together, we for the first time demonstrated that PDLIM2 has the potential to act as a tumor suppressor in MCF10A cells (a model of normal epithelial breast cancer) but as an oncoprotein in MCF7 cells (model of luminal-A breast cancer).

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6.5 Analysis of functional and interaction network of RNF25, TRAF3IP2 and PDLIM2 (research paper 4) Maryas J, Faktor J, Capkova L, Muller P, Bouchal P. RNF25, TRAF3IP2 and PDLIM2 are promising NF-κB modulators associated with metastasis of luminal A breast cancer (in preparation). (Appendix 5)

RNF25 also known as AO7 is a RING finger protein that (249) (309) activates Wnt signalling (312), positively regulates epithelial-to-mesenchymal transition (EMT) (311) (313) (314) (315) and activates NF-κB mediated gene expression (249) (316) (317). Moreover, deregulation of RNF25 is associated with several malignancies and RNF25 was recently identified as a novel factor related to gefitinib resistance in EGFR-mutant non-small cell lung cancer (NSCLC) cells (318) (for more details see chapter 2.6). TRAF3IP2 also known as ACT1 or CIKs act as an adaptor protein and E3 ubiquitin ligase that plays an important role in several cellular processes (268) (270) (274) including NF-κB (280) (282) and MAPK pathways activation (278) (280) (281) and negative regulation of JAK-STAT signalling (284). In addition to this, TRAF3IP2 could be a potential and promising therapeutic target in glioblastoma (299), and its amplification was associated with various cancers (267) (308) (for more details see chapter 2.5). PDLIM2 was described in detail above (see chapters 2.4 and 6.4).

Our previous study revealed RNF25, TRAF3IP2 and PDLIM2 as putative biomarkers associated with lymph node metastasis in low grade breast cancer (see chapter 6.2). The aim of this study was to uncover the molecular and cellular basis of pro-metastatic role of RNF25, TRAF3IP2 and PDLIM2 proteins in breast cancer; performing series of in vitro experiments using MCF7 breast cancer cells as a model of luminal-A low grade breast tumors. At first, we focused on the study of migration and invasion capabilities of these cells with altered levels of above mentioned proteins. Our experiments revealed that RNF25 and TRAF3IP2 overexpression significantly increased the migration capabilities of MCF7 cells as observed by real-time measurement using the xCELLigence (p=0.00586 for RNF25 and p=0.00903 for TRAF3IP2) system and independently confirmed using a Transwell assay (p=3.1.10-7 for RNF25 and p=8.4.10-8 for TRAF3IP2). Overexpression of RNF25 and TRAF3IP2 increased also the invasion capabilities of MCF7 cells, determined by Transwell assay only (p=2.5.10-8 for RNF25, p=2.1.10-9 for TRAF3IP2). Role of PDLIM2 overexpression on migration and invasion capabilities of MCF7 cells was described in our previous study (see chapter 6.4).

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In view of fact that proteins hardly act as an isolated species, our effort was also devoted to determination of the total proteome changes and pathway analysis of total proteome data, and to detection of potential interacting partners of RNF25, TRAF3IP2 and PDLIM2.

MCF7 cell lysates with up-regulated RNF25, TRAF3IP2 and PDLIM2 levels were collected 24, 48 and 72 hours after cell transfection and total proteome levels changes were analysed using SWATH-MS. For better understanding of the observed molecular changes, gene set enrichment analysis (GSEA) of total 1834 identified proteins (FDR=0.01) was also performed. Only proteins statistically up or down-regulated in all three time intervals and pathways with Log2FC>0 (average from three time points) were considered as relevant. In MCF7 cell lysates with elevated RNF25 levels two statistically up-regulated (Log2FC>1, FDR<0.05) and biologically relevant proteins were detected compared to control cells. Tumor protein D52 (TPD52) and Jupiter microtubule associated homolog 1 (JPT1), both previously associated with migration, invasion and metastasis in breast cancer (357) (358). Moreover, GSEA analysis of total proteome data revealed significant up-regulation (ES>0.7, p<0.1) of MAPK and p38 MAPK pathway (see Figure 15, marked in bold), deregulation of both pathways is associated with cancer development. No significantly down-regulated biologically relevant proteins (Log2FC<-1, FDR<0.05) and/or pathways (ES<-0.7, p<0.1) were detected. In lysates of cells with elevated TRAF3IP2 levels four tubulin subunits (tubulin alpha-1C chain, tubulin beta 3 chain, tubulin beta-4B chain and tubulin alpha-1B chain) and S100-A14 protein were assessed as biologically relevant and significantly up- regulated. On the other hand, 26S proteasome non-ATPase regulatory subunit 7 (PSMD7) was evaluated as biologically interesting and significantly down-regulated. GSEA, analysis revealed fourteen up-regulated pathways, including biologically relevant MET, p53, MAPK, p38 MAPK, and cell cycle signalling pathway, and three down-regulated pathways counting TID signalling, which was also evaluated as biologically pertinent (see figure 15, marked in bold). Deregulation of all above mentioned proteins and pathways is associated with cancer development, progression, metastasis formation and imperfection in cell cycle signalling. In MCF-7 cell lysates with up-regulated PDLIM2 Cullin-associated NEDD8-dissociated protein 1 (CAND1), Cell division control protein 42 (CDC42), Basigin, three tubulin subunits (tubulin alpha-1C chain, tubulin beta 3 chain, tubulin beta-4B chain), Alpha-actinin-4 (ACTN4), Actin and Actin-related protein 3 (ARP3) were detected, indicating significant role of PDLIM2 in cytoskeleton regulation and cell motility. No biologically relevant down- regulated proteins were detected. Among up-regulated pathways p38 MAPK, MET and

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CDC42 RAC signalling were detected as biologically relevant, TID pathway was then detected as biologically interesting down-regulated signalization (see Figure 15, marked in bold). Already mentioned roles of p38 MAPK, MET and TID pathway are in association with known role of CDC42 RAC pathway in actin cytoskeleton regulation and association with cancer migration and invasiveness.

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Figure 15: Up- and down-regulated pathways revealed by GSEA: all statistically up- (ES>0.7, p<0.1) and down- (ES<-0.7, p<0.1) regulated pathways revealed by GSEA analysis of total proteome data, which were obtained by examination of MCF-7 lysates with elevated RNF25, TRAF3IP2 or PDLIM2 protein levels. For each protein, enrichment score of detected pathways is reflected by the size of the circle and the statistical significance (p-value) by shades of black for up-regulated or by shades of blue for down-regulated pathways. Pathways evaluated as biologically relevant are marked in bold, and for every single pathway, most significantly changed proteins (core proteins) are also included.

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Identification of potential interaction partners was done by pull-down assay in combination with LC-SWATH-MS/MS, using lysates of MCF7 cells stably transfected with gene encoding N-terminally SBP-tagged RNF25, TRAF3IP2 or PDLIM2 protein. Among 431 total identified proteins (FDR=0.01), forty one potential interaction partners of RNF25 were detected (AVG.Log2.Ratio>1, p-value<0.05) including Cingulin-like protein 1 (Paracingulin), proteins S100-A8 and S100-A9, and members of WAVE complex - Cytoplasmic FMR1- interacting protein 1 (p140sra-1) and Nck-associated protein 1 (NAP 1). In case of TRAF3IP2, forty nine probable interaction partners (AVG.Log2.Ratio>1, p-value<0.05) including DNA-dependent protein kinase catalytic subunit (DNA-PKcs), Paracingulin, S100- A8 and S100-A9 proteins and member of WAVE complex p140sra-1 were detected. Finally, thirty two potential interaction partners of PDLIM2 (AVG.Log2.Ratio>1, p-value<0.05) were detected, among them also ARP3, Leucine-rich PPR motif-containing protein (LRP 130), and p140sra-1. Well-known roles of above mentioned potential interactors indicate connection between RNF25, TRAF3IP2 or PDLIM2 and cytoskeleton regulation, cell motility, survival or cell proliferation in MCF7 cells.

In conclusion, our data confirmed pro-migration and pro-invasion roles of the aforementioned proteins in MCF7 cells. Moreover, total proteome data and GSEA analysis revealed alterations of proteins and pathways associated with cytoskeleton regulation, cell proliferation, migration, invasion, cancer development and metastasis. This was also confirmed by pull-down assay that identified biologically interesting interactors connected with cytoskeleton regulation, tumor progression, and metastasis. All these results thus strongly support pro-metastatic roles of RNF25, TRAF3IP2 and PDLIM2 in MCF7 cells, model of low grade luminal-A breast tumors.

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7 Discussion

Our proteomic-transcriptomic study revealed a list of new putative biomarkers for low grade breast cancer patients, whose, in disagreement with theoretical prognosis, form early lymph node metastasis (see Research paper 1). We used quantitative proteomics approach to identify such proteins by comparing a group of 24 lymph node positive luminal-A grade 1 tumors with a corresponding set of 24 lymph node negative tumors. Another group of 48 high-grade tumors (24 lymph node negative, 24 lymph node positive) was also analyzed to investigate marker specificity for grade 1 luminal-A tumors. In addition, proteomics data in combination with targeted transcriptomic, IHC and validation on independent data sets revealed statistically significant up-regulation of RNF25 and PDLIM2 proteins in lymph node positive low grade luminal-A breast tumors. However, markers that indicate metastasis in all breast cancer patients regardless of the grade are also very important, and one of them – TRAF3IP2 protein – was also identified in our study. Due to the potential pro-oncogenic role of the abovementioned proteins in low grade luminal-A breast cancer, all of these proteins were selected as promising targets for further study.

7.1 The roles of PDLIM2, RNF25 and TRAF3IP2 proteins at cellular level To validate presumed pro-oncogenic role of PDLIM2, RNF25 and TRAF3IP2 proteins, we performed a series of in vitro experiments at the cellular level using MCF7 breast cancer cells, model of low grade luminal-A tumors (see Research papers 3 and 4). Positive effect of all three proteins on MCF7 cell migration and invasion, as was revealed by xCELLigence and/or Transwell assay, indicates their role in the regulation of migration and invasiveness. These results correlate with elevated levels of PDLIM2, RNF25 and TRAF3IP2 in subgroup of lymph node positive low grade breast tumors, and with their assumed pro-oncogenic role, because migration and invasion are the key steps in metastasis formation (76) (88). Moreover, our results showed that up-regulation of PDLIM2 also increase MCF7 proliferation and decrease their adhesion abilities. Disruption of cell adhesion and increased cell proliferation are the key signatures of oncogenesis (359), which further supports assumed pro-oncogenic role of PDLIM2 in MCF7 cells. PDLIM2 also significantly affects morphology of MCF7 cells. Optical microscopy revealed that MCF7 cells with stably overexpressed PDLIM2 acquire similar morphology as MCF7 cells after EMT induction, indicating connection between PDLIM2 and EMT. A similar pattern was evident from the AFM data, because the stiffness of MCF7 cells with stably overexpressed PDLIM2 was significantly augmented. All

80 these data correlate with assumed pro-oncogenic role of PDLIM2 in MCF7 cells as well as with previously discussed results of our migration and invasion experiments because both EMT like morphology and increased cell stiffness are typical for cells with mesenchymal properties (360), which are crucial for motility and invasiveness augmentation and metastasis formation (143).

Nonetheless, controversial literature results on the role of PDLIM2 in cancer development led us to assumptions about its dual and context-dependent role. We hypothesized that PDLIM2 may act as an oncoprotein in luminal-A breast tumors and as a tumor suppressor in normal breast epithelium. To test our hypothesis, we performed another series of in vitro experiments with MCF10A cells, immortalized model of normal breast epithelium (see Research paper 3). Our results revealed that PDLIM2 negatively regulates migration and positively regulates adhesion of MCF10A cells, which indicate tumor suppressive role of PDLIM2 in MCF10A and supports our hypothesis about its dual and context-dependent role in breast cancer development.

7.2 The roles of PDLIM2, RNF25 and TRAF3IP2 proteins at molecular level To uncovered molecular basis of the aforementioned effects in MCF7 cells, a series of in vitro experiments at the molecular level were performed. We studied the effect of upregulation of all three proteins on MCF-7 total proteome in three time intervals (24, 48, 72 hours after transient cell transfection), which allowed us to observe continual proteome changes over time (see Research paper 4).

Up-regulation of RNF25 led to significant increase of TPD52 and JPT1 protein levels. Previous studies revealed association of both with breast cancer cell migration, invasion, proliferation and metastasis formation (358) (361) (362), which correlate with our functional experiments and assumed pro-oncogenic role of RNF25 in MCF7 cells. Moreover, GSEA analysis of total proteome data uncovered connection between RNF25 up-regulation and augmentation of MAPK and p38 MAPK pathways. These pathways are involved in regulation of cell proliferation, migration, invasion, gene expression, differentiation, mitosis, cell survival, and apoptosis (363) (364) and their deregulation is associated with cancer progression, development and metastasis formation (365) (366) (367). These data further confirmed pro-oncogenic role of RNF25, which seems to be mediated by increased activity of MAPK pathways.

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Up-regulation of TRAF3IP2 was associated with increase of tubulin subunits, S100-A14 protein and with down-regulation of PSMD7 protein levels. Increase levels of tubulin subunits suggest connection between TRAF3IP2 and regulation of dynamic instability of microtubules required for the crawling way of migration (368). Up-regulation S100-A14 protein was previously associated with the regulation of cell survival, apoptosis, cell proliferation, migration and invasion and also modulation of p53 protein levels (369) (370). On the other hand, down-regulation of PSMD7, component of 26S proteasome (371), was associated with aberrant activity of the proteasome, which affects the cell cycle regulation, apoptosis and other cellular processes related to cancer (372). Furthermore, GSEA analysis revealed up- regulation of MET, p53, MAPK, p38 MAPK, and cell cycle signalling pathways and down- regulation of TID signalling. As already mentioned, deregulation of MAPK and p38 MAPK pathways is associated with cancer development. Defects in cell cycle regulation are known to be implicated in cancer development (373) and up-regulation of MET pathway leads to cell proliferation, protection from apoptosis, angiogenesis, invasion and metastasis formation as well (374). In addition to that, deregulation of p53 pathway can commit cancer cells to a survival path in response to stress such as DNA damage (375), and down-regulation of TID signalling pathway, members of which act as NF-κB suppressors (376), was previously associated with malignant transformation as well (377). These data strongly support the results of our migration and invasion experiments, and correlates with previously determined implication of TRAF3IP2 in NF-κB and MAPK pathways activation (267) (269) (278) (280). In general, our results strongly support assumed pro-oncogenic role of TRAF3IP2 in MCF7 cells.

Increase of CAND1, CDC42, Basigin, ARP3, ACTN4, Actin and three tubulin subunits protein levels in MCF7 cell with up-regulated PDLIM2 indicates connection between PDLIM2 and cytoskeleton regulation. As described above, up-regulation of tubulin subunits indicates potential role of PDLIM2 in regulation of microtubule cytoskeleton dynamic. Furthermore, up-regulation of CDC42, ARP3, ACTN4 and Actin indicates connection between PDLIM2 and actin cytoskeleton regulation. CDC42 is involved in epithelial cell polarization processes, plays a role in the extension and the formation of filopodia, thin actin- rich surface projections, and mediates cell migration (378) (379). ARP3 as a component of Arp2/3 complex is involved in regulation of actin polymerization and formation of branched actin networks (380), and its expression is related to tumor progression, metastasis, and poor prognosis of gallbladder cancer, adenosquamous carcinomas and adenocarcinomas (381).

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ACTN4 acts as an actin bundling protein and is then associated with cell motility and cancer invasion (382), all of which indicate possible connection between PDLIM2 and regulation of cell motility, cell signalling, cell shape and tumor progression (383). Another up-regulated protein Basigin is also connected with cancer invasion and tumor progression, as was previously described (384), so as CAND1 protein that is associated with lung cancer cell proliferation and cell migration via COP9 signalosome (385), whose connection with PDLIM2 was already described (261) (272). These results strongly support our assumptions about pro-oncogenic and pro-metastatic roles of PDLIM2 in MCF7 cells. Moreover, GSEA revealed statistically significant up-regulation of MAPK and MET pathway and down- regulation of TID signalling pathway, which were detected also in case of TRAF3IP2 and further support pro-metastatic role of PDLIM2 and its previously described role in NF-κB regulation (249) (250). Except for these pathways, GSEA revealed up-regulation of CDC42RAC pathway that governs cancer malignancy, including cell polarity, migration, and cell-cycle progression as well (386) (387) (388). All these results correlate with our functional experiments, and with previously published and known roles of PDLIM2 in cancer cells (261) (348) (389) (390) and strongly support its expected pro-oncogenic role in MCF7 cells.

Pro-oncogenic roles of RNF25, TRAF3IP2 and PDLIM2 proteins in MCF7 cells were confirmed also by studying the protein-protein interactions (PPIs) using streptavidin agarose beads pull-down assay in combination with LC-SWATH-MS/MS (see Research paper 4). Among forty one potential interacting partners of RNF25, Paracingulin, S100-A8 and S100- A9 proteins, and members of WAVE complex - p140sra-1 and NAP 1 - were detected as biologically interesting interactors based on the current level of knowledge. Paracingulin regulates GTPase mediated angiogenesis (391) the process fundamental to the development of malignancy (392) (393) and is also involved in anchoring tight junction to actin-based cytoskeletons (394). S100-18 and S100-A9 proteins are predominantly found as dimer complex calprotectin (S100A8/A9), and play wide plethora of intra- and extracellular functions. Both of these are involved in the modulation of the tubulin cytoskeleton during migration (395), regulate apoptosis via p53 (396), and activate NF-κB and MAPK pathways (397), whose deregulation is associated with cancer progression, development and metastasis formation as was already mentioned above (365) (366) (367). p140sra-1 and NAP 1 as members of WAVE complex regulate actin filament reorganization as well via interaction with the Arp2/3 complex (398). Moreover, these proteins also inactivate WASF3 complex and are essential for breast cancer metastasis (399). These results support our functional

83 experiments and total proteome data and further confirmed our hypothesis about pro- oncogenic role of RNF25 in MCF7 cells. Among forty nine detected potential interaction partners of TRAF3IP2, DNA-PKcs, p140sra-1, Paracinguin, S100-A8 and S100-A9 proteins were evaluated as biologically relevant. DNA-PKcs protein which plays a major role in DNA damage signalling (400) (401) is frequently overexpressed in tumor metastasis and exhibits pro-metastatic activity via modification of the tumor microenvironment by regulation of matrix metalloproteases secretion (402). Detected potential interactors – p140sra-1, Paracingulin, S100-A8 and S100-A9 proteins, functions of which were described above, together with DNA-PKcs suggest possible role of TRAF3IP2 in regulation of DNA damage response mechanisms and in the regulation of cell cytoskeleton and motility. Moreover, these experiments strongly support results of our functional experiments, as well as confirmed the results of total proteome analysis and our presumptions about pro-oncogenic role of TRAF3IP2 in MCF7 cells. Streptavidin agarose beads pull-down assay revealed thirty two potential interaction partners of PDLIM2. Among them, ARP3, LRP 130 and p140sra-1 proteins were assessed as biologically interesting PDLIM2 interactors. All these potential interactors are involved in actin cytoskeleton regulation, tumor progression and metastasis. Moreover, previous reports also show that protein LRP 130 is highly expressed in most cancers and associated with tumorigenesis and invasion (403) and could be used as a predictive marker of poorer overall survival rate for patient with gastric cancer (404). These data strongly support our results from our functional experiments and total proteome analysis as well as our assumption about pro-oncogenic role of PDLIM2 in MCF7 cells.

Moreover, in our technical study (see Research paper 2) SHROOM3, CREBRF, NEK10 and calmodulin were detected as potential PDLIM2 interactors, previously published studies revealed that SHROOM3 is involved in Rho signalling and epithelial cell remodelling (405), NEK10 regulates MAPK pathway (406) and phosphorylation-mediated contractility of stress fibres, and CREBRF regulates NF-ĸB pathway via CREB3 protein (407), which further confirm results of our total proteome analysis and pro-oncogenic role of PDLIM2 in MCF7 cells. However, the main aim of our technical brief was to compare conventional pull-down assays with SPR-LC-MS/MS system and find optimal conditions for identification of PDLIM2 interactors. From this point of view, we proved that bottlenecks for successful PPIs detection are choices of solid supports and mechanics of wash and elution. Moreover, our study revealed that SPR based system is comparable or even better for PDLIM2 PPIs identification than conventional pull-down assays. Nonetheless, potential interaction partners

84 detected in our technical brief require further validation. In general, all potential interaction partners detected in screening experiments such as affinity purification-MS require further validation using an independent approach before considered as “true” interactors.

On the top of that, our experiments at the cellular level (optical microscopy and AFM) indicate potential connection between PDLIM2 and EMT in MCF7 cells. To confirm this relationship, series of experiment at molecular level were performed (see Research paper 3). According to the expectations, our data indicate a positive role for PDLIM2 in EMT induction; furthermore, overexpression of PDLIM2 denotes disturbance of the β1-integrin pathway and loss of the epithelial phenotype via increased levels of FAK, the regulator of the β1-integrin pathway (246). On the other hand, successful EMT induction, by TGFβ1 treatment or long term hypoxia (96 hours), increased PDLIM2 protein levels. Our experiments also revealed a reciprocal connection between PDLIM2 and the response to hypoxia, which represents completely new information that deserves further examination. Moreover, the observed negative effects of PDLIM2 on the p53 and especially p-p53 (S20) protein levels indicate the relation between PDLIM2, DNA repair and cell cycle regulation in MCF7 cells. Involvement of PDLIM2 regulation of EMT induction, in the response to hypoxia and the effect of PDLIM2 overexpression on key cancer molecular players suggest a pro-oncogenic role of PDLIM2 in MCF7 cells, and further validates our previous data.

Discrepancies in published data led us to the hypothesis about dual and context-dependent role of PDLIM2 in breast cancer development, which was also tested at the molecular level using MCF10A cells. Our data indicate a negative role of PDLIM2 in EMT induction, its importance for maintaining the epithelial phenotype of MCF10A cells and the negative effect of EMT on PDLIM2 protein levels, suggesting tumor suppressive role of PDLIM2 in these cells. Overexpression of PDLIM2 in MCF10A was further associated with increased p53 (S20) phosphorylation, which indicates a positive connection between PDLIM2, DNA repair and cell cycle regulation in these cells. Interestingly, no observed effect of PDLIM2 on hypoxia and no effect of hypoxia on PDLIM2 levels suggest different and context-dependent roles of PDLIM2 in the response to hypoxic conditions. It seems that in contrast to MCF7 cells PDLIM2 may not be involved in hypoxic response regulation in MCF10A cells. In a broader context, PDLIM2 function in MCF10A cells is evidently highly distinct from that in MCF7 cells, being rather tumor suppressing.

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Dual and context-dependent roles of PDLIM2 in breast cancer development were also confirmed by the comparison of PDLIM2 protein levels in MCF7, MCF10A and highly invasive triple negative breast cancer cells MDA-MB-231. Similar levels of PDLIM2 protein in invasive triple negative breast cancer cells (MDA-MB-231) and normal breast epithelial cells (MCF10A) indicated a significant role for PDLIM2 not only in MCF10A but also in invasive breast cancer cells. On the other hand, substantially lower PDLIM2 levels in low invasive MCF7 cells, suggested that PDLIM2 is suppressed in a gap between neoplastically transformed and invasive breast cancer cells that undergo EMT.

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8 Conclusions

Current prognostic factors are insufficient for precise risk-discrimination in breast cancer patients specifically with low grade breast tumors, which, in disagreement with theoretical prognosis, form early lymph node metastasis. Our combined proteomics and transcriptomics study revealed list of proteins including PDLIM2, RNF25 and TRAF3IP2 as putative biomarkers of metastasis in low grade (luminal-A) breast tumors. Our subsequent effort aimed to clarify the roles of these proteins in vitro in MCF7 cells, a model of low grade luminal-A tumors. To understand the molecular basis of this phenomenon, we have realized a broad panel of functional analyses. Their results confirmed pro-migration and pro-invasion roles of PDLIM2, RNF25 and TRAF3IP2 in MCF7 cells and moreover, the positive role of PDLIM2 in cell proliferation and negative one in cell adhesion. Pro-oncogenic roles of all these proteins in MCF7 cells were further confirmed in total cell proteomics complemented by the analysis of PPIs. These results revealed connections between these proteins and cytoskeleton regulation, apoptosis regulation, cell motility, migration, invasion and metastasis, the key factors of cancer progression. Technically, we for the first time showed that combination of SPR chips with SWATH-MS represents a potent tool in interactomics. For PDLIM2, we further used another cellular model, MCF10A immortalized normal epithelial breast cells for a subset of the functional analyses described above and showed its opposite role in cell migration, adhesion and phenotype maintenance compared to MCF7 cells. These data confirmed another working hypothesis on dual and context-dependent role of PDLIM2 in breast cancer development. Our work contributes to understanding the molecular network involved in luminal-A breast cancer metastasis as well as its impact on cancer cell phenotype and provides a basis for further interesting investigations to determine whether PDLIM2 blocking might have potential therapeutic implications in luminal-A breast cancer.

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389. Maryáš J, Přibyl J, Skládal P et al. PDZ and LIM 400. Helleday T, Petermann E, Lundin C, et al. DNA domain protein 2 plays dual and context dependent repair pathways as targets for cancer therapy. Nat roles in breast cancer development. BioRxiv. 2020; Rev Cancer. 2008;8(3):193-204. doi: https://doi.org/10.1101/2020.01.27.920199 401. Lieberman HB. DNA damage repair and (BMC Cancer under review). response proteins as targets for cancer therapy. 390. Sistani L, Dunér F, Udumala S, et al. Pdlim2 is Curr Med Chem 2008;15(4):360-7. a novel actin-regulating protein of podocyte foot 402. Kotula E, Berthault N, Agrario C, et al. DNA- processes. Kidney Int. 2011;80(10):1045-54. PKcs plays role in cancer metastasis through 391. Chrifi I, Hermkens D, Brandt MM, et al. Cgnl1, regulation of secreted proteins involved in an endothelial junction complex protein, regulates migration and invasion. Cell Cycle. GTPase mediated angiogenesis. Cardiovasc Res. 2015;14(12):1961-72. 2017;113(14):1776-88. 403. Tian Tian, Ikeda Jun-ichiro, Wang Yi, et al. Role 392. Bergers G, Benjamin LE. Tumorigenesis and of leucine-rich pentatricopeptide repeat motif- the angiogenic switch. Nat Rev Cancer. containing protein (LRPPRC) for anti-apoptosis and 2003;3(6):401-10. tumourigenesis in cancers. Eur J Cancer. 2012;48(15):2462–73. 393. Bid HK, Roberts RD, Manchanda PK, et al. RAC1: an emerging therapeutic option for targeting 404. Li X, Lv L, Zheng J, et al. The significance of cancer angiogenesis and metastasis. Mol Cancer LRPPRC overexpression in gastric cancer. Med Ther. 2013;12(10):1925-34. Oncol. 2014;31(2):818.

394. Elbediwy A, Zihni C, Terry SJ, et al. Epithelial 405. Nishimura T, Takeichi M. Shroom3-mediated junction formation requires confinement of Cdc42 recruitment of Rho kinases to the apical cell activity by a novel SH3BP1 complex. J Cell Biol. junctions regulates epithelial and neuroepithelial 2012;198(4):677-93. planar remodeling. Development. 2008;135(8):1493-502. 395. Vogl T, Ludwig S, Goebeler M, et al. MRP8 and MRP14 control microtubule reorganization during 406. Moniz LS, Stambolic V. Nek10 mediates G2/M transendothelial migration of phagocytes. Blood cell cycle arrest and MEK autoactivation in 2004;104:4260-68. response to UV irradiation. Mol Cell Biol. 2011;31(1):30-42. 396. Li C, Chen H, Ding F, et al. A novel p53 target gene, S100A9, induces p53-dependent cellular 407. Xue H, Zhang J, Guo X, et al. CREBRF is a apoptosis and mediates the p53 apoptosis potent tumor suppressor of glioblastoma by pathway. Biochem J 2009;422:363-72. blocking hypoxia-induced autophagy via the CREB3/ATG5 pathway. Int J Oncol. 2016;49(2):519- 397. Riva M, Kaellberg E, Bjoerk P, et al. Induction 28. of nuclear factor-kappaB responses by the S100A9 protein is Toll-like receptor-4-dependent. Immunology 2012;137:172-82.

105

10 List of all publications related to the topic of dissertation thesis

Faktor J, Dvorakova M, Maryas J, Struharova I, Bouchal P. Identification and characterization of pro-metastatic targets, pathways and molecular complexes using a toolbox of proteomictechnologies. Klin Onkol. 2012;25 (Suppl. 2):70-77. (no IF). https://www.prolekare.cz/en/journals/clinical-oncology/2012-supplementum-2/identification- and-characterisation-of-pro-metastatic-targets-pathways-and-molecular-complexes-using-a- toolbox-of-proteomic-technologies-40338

Maryas J, Faktor J, Dvorakova M, Struharova I, Grell P, Bouchal P. Proteomics in investigation of cancer metastasis: functional and clinical consequences and methodological challenges. Proteomics. 2014;14(4-5):426-40. (IF2014 = 3.807, Q1 in Biochemical Research Methods, Q1 in Biochemistry and Molecular Biology). https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.201300264

Bouchal P, Dvorakova M, Roumeliotis T, Bortlicek Z, Ihnatova I, Prochazkova I, Ho J.T.C, Maryas J, Imrichova H, Budinska E, et al. Combined Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor κB (NF-κB) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer. Mol. Cell. Proteomics MCP.

2015;14:1814-30. (IF2015 = 5.912, Q1/92.857 percentile in Biochemical Research Methods). https://www.mcponline.org/content/mcprot/14/7/1814.full.pdf

Maryas J, Bouchal P. PDLIM2 and Its Role in Oncogenesis-Tumor Suppressor or Oncoprotein? Klin Onkol. 2015;28 (Suppl. 2):40-46. (no IF). https://www.prolekare.cz/casopisy/klinicka-onkologie/2015-supplementum-2/pdlim2-a-jeho- role-v-onkogenezi-tumor-supresor-nebo-onkoprotein-55684

Maryas J, Faktor J, Skladal P, Bouchal P. Pull-down assay on streptavidin beads and surface plasmon resonance chips for SWATH mass spectrometry based interactomics. Cancer

Genomics and Proteomics. 2018;15(5):395-404. (IF2018 = 3.147, Q2 in Oncology, Q2 in Genetics and Heredity). http://cgp.iiarjournals.org/content/15/5/395.full.pdf+html

Maryas J, Pribyl J, Bouchalova P, Skladal P, Bouchal P. PDZ and LIM domain protein 2 plays dual and context dependent roles in breast cancer development. BioRxiv. 2020; doi: 10.1101/2020.01.27.920199 (BMC Cancer, under review). https://www.biorxiv.org/content/10.1101/2020.01.27.920199v1 106

Maryas J, Faktor J, Capkova L, Muller P, Bouchal P. RNF25, TRAF3IP2 and PDLIM2 are promising NF-κB modulators associated with metastasis of luminal A breast cancer (in preparation)

107

11 List of contributions at conferences and symposia

Maryas J, Bouchal P. Protein interactions and networks. In: Abstract Book: Wellcome Trust Advanced Course: Protein Interactions and Networks 04 Aug 2014-12 Aug 2014. 2014, Hinxton, England.

Maryas J, Dvorakova M, Struharova I, Nenutil R, Vojtesek B, Bouchal P. Functional characterization of potentially pro-metastatic proteins in breast cancer. In: Book of abstracts: XXXIX. brněnské onkologické dny. 2015, ISSN 0862-495X, Brno, Czech Republic.

Maryas J, Dvorakova M, Struharova I, Nenutil R, Bouchal P. Investigation of pro-metastatic role of PDLIM2 protein in breast cancer using functional proteomics.. In: Abstract Book: 9th European Summer School ADVANCED PROTEOMICS. 2015, Brixen/Bressanone, Italy.

Maryas J, Dvorakova M, Struharova I, Nenutil R, Bouchal P. Investigation of the role of PDLIM2 in breast cancer metastasis. In: Book of abstracts: 6th RECAMO joint meeting. 2015, ISBN 978-80-86793-37-5, Brno, Czech Republic.

Galoczová M, Maryas J, Faktor J, Bouchal P. The dual role of PDLIM2 protein in breast cancer development. In: Book of abstracts: XL. brněnské onkologické dny. 2016, ISSN 1802- 5307, Brno, Czech Republic.

Galoczova M, Maryas J, Kovarikova P, Capkova L, Faktor J, Bouchal P. A new panel of proteins associated with metastasis in low-grade breast cancer: a role in cell migration and invasiveness. In: Book of abstracts: Goodbye Flat Biology: Models, Mechanisms and Microenvironment. 2016, Berlin, Germany.

108

12 Appendices

12.1 Appendix 1

Review paper 1

Proteomics in investigation of cancer metastasis: functional and clinical consequences and methodological challenges.

Maryas J, Faktor J, Dvorakova M, Struharova I, Grell P, Bouchal P. Proteomics. 2014;14(4- 5):426-40. https://onlinelibrary.wiley.com/doi/epdf/10.1002/pmic.201300264

109 12.2 Appendix 2

Research paper 1

Combined Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor κB (NF-κB) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer.

Bouchal P, Dvorakova M, Roumeliotis T, Bortlicek Z, Ihnatova I, Prochazkova I, Ho J.T.C, Maryas J, Imrichova H, Budinska E, et al. Mol. Cell. Proteomics MCP. 2015;14:1814-30. https://www.mcponline.org/content/mcprot/14/7/1814.full.pdf Research

© 2015 by The American Society for Biochemistry and Molecular Biology, Inc. This paper is available on line at http://www.mcponline.org Combined Proteomics and Transcriptomics Identifies Carboxypeptidase B1 and Nuclear Factor ␬B (NF-␬B) Associated Proteins as Putative Biomarkers of Metastasis in Low Grade Breast Cancer*□S

Pavel Bouchal‡§, Monika Dvorˇa´ kova´‡§, Theodoros Roumeliotis¶, Zbyneˇk Bortlícˇekʈ, Ivana Ihnatova´ ʈ, Iva Procha´zkova´‡, Jenny T. C. Ho**, Josef Marya´sˇ ‡§, Hana Imrichova´‡‡, Eva Budinska´‡ʈ, Rostislav Vyzula‡, Spiros D. Garbis§§, Borˇivoj Vojteˇsˇ ek‡, and Rudolf Nenutil‡¶¶

Current prognostic factors are insufficient for precise level. For CPB1, these differences were also observed by risk-discrimination in breast cancer patients with low immunohistochemical analysis on tissue microarrays. Up- grade breast tumors, which, in disagreement with theo- regulation of putative biomarkers in lymph node positive retical prognosis, occasionally form early lymph node me- (versus negative) luminal A tumors was validated by gene tastasis. To identify markers for this group of patients, we expression analysis of an independent published data set (0.02027 ؍ PDLIM2 (p ,(0.00155 ؍ for CPB1 (p (343 ؍ employed iTRAQ-2DLC-MS/MS proteomics to 24 lymph (n Moreover, statistically significant .(0.00015 ؍ node positive and 24 lymph node negative grade 1 luminal and RELA (p A primary breast tumors. Another group of 48 high-grade connections with patient survival were identified in an- Our findings indicate .(1678 ؍ tumors (luminal B, triple negative, Her-2 subtypes) was other public data set (n also analyzed to investigate marker specificity for grade 1 unique pro-metastatic mechanisms in grade 1 tumors that luminal A tumors. From the total of 4405 proteins identi- can include up-regulation of CPB1, activation of NF-␬B fied (FDR<5%), the top 65 differentially expressed to- pathway and changes in cell survival and cytoskeleton. gether with 30 previously identified and control markers These putative biomarkers have potential to identify the were analyzed also at transcript level. Increased levels of specific minor subpopulation of breast cancer patients carboxypeptidase B1 (CPB1), PDZ and LIM domain pro- with low grade tumors who are at higher than expected tein 2 (PDLIM2), and ring finger protein 25 (RNF25) were risk of recurrence and who would benefit from more in- associated specifically with lymph node positive grade 1 tensive follow-up and may require more personalized tumors, whereas stathmin 1 (STMN1) and thymosin beta therapy. Molecular & Cellular Proteomics 14: 10.1074/ 10 (TMSB10) associated with aggressive tumor phenotype mcp.M114.041335, 1814–1830, 2015. also in high grade tumors at both protein and transcript

From the ‡Masaryk Memorial Cancer Institute, Regional Centre for Breast cancer is the most common form of cancer in Applied Molecular Oncology, Brno, Czech Republic; §Masaryk Uni- women worldwide and distant metastases are the main rea- versity, Faculty of Science, Department of Biochemistry, Brno, Czech sons for patient mortality. Cancer emerges as a consequence Republic; ¶Proteomics Mass Spectrometry, The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK; ʈMasaryk University, of multiple genetic aberrations, whereas metastatic charac- Faculty of Medicine, Institute of Biostatistics and Analyses, Brno, teristics may be predisposed or acquired during disease de- Czech Republic; **ThermoFisher Scientific, Hemel Hempstead, UK; velopment and are governed by a number of genetic and ‡‡Laboratory of Computational Biology, Center for Human Genetics, biochemical mechanisms (1, 2). In clinical practice, both tra- University of Leuven, Belgium; §§University of Southampton, School ditional and molecular prognostic markers are used for risk- of Medicine, Cancer Sciences Division, Institute for Life Sciences– Center for Proteomic Research, Southampton, UK group discrimination and determination of metastatic poten- Received May 19, 2014, and in revised form, April 8, 2015 tial. Traditional prognostic markers in breast cancer involve Published, MCP Papers in Press, April 22, 2015, DOI 10.1074/ age at diagnosis, tumor size and grade, lymph node status, mcp.M114.041335 and presence of distant metastasis. Tumor size is a potent Author contributions: P.B., E.B., R.V., S.D.G., B.V., and R.N. de- prognostic factor predicting higher probability of metastatic signed research; P.B., M.D., T.R., I.P., J.M., S.D.G., and R.N. per- formed research; J.T.H. and S.D.G. contributed new reagents or behavior for larger tumors. More differentiated tumors (e.g. analytic tools; P.B., Z.B., I.I., H.I., E.B. and S.D.G. analyzed data; P.B., grade 1) have low dissemination potential in general, although B.V., and R.N. wrote the paper. less differentiated, more proliferative high grade tumors (e.g.

1814 Molecular & Cellular Proteomics 14.7 Metastasis in Low Grade Breast Cancer grade 3) form metastases much more frequently. Low grade FT-Orbitrap detector technology, have significantly advanced breast tumor cells spread predominantly via lymph vessels the discovery proteomics field (8). We have used this untar- and lymph nodes are therefore the first site of tumor cell geted quantitative approach to identify proteins correlating dissemination prior to eventual spread into distant organs with lymph node metastasis in low grade breast cancer. A such as lung or bone (3). Molecular prognostic markers in- complementary targeted transcriptomics study was per- volve hormonal receptors (estrogen receptor (ER)1, proges- formed on the same sample set to identify those proteins that terone receptor (PR)), Her-2/neu receptor, and expression exhibited correlation with gene expression. The combining of panels like Oncotype DX and MammaPrint. Also, the Ameri- protein and transcript level profiles allowed us to interrogate can Society for Clinical Oncology (ASCO) has recommended independent large patient data sets for validation and their urokinase plasminogen activator (PLAU) and urokinase plas- impact on survival. In addition, we compared G1 and G3 minogen activator inhibitor (SERPINE1) as indicative factors tumors with and without metastasis to see whether the meta- for metastatic potential in breast cancer (4, 5), however their static process is the same or different between these groups, use in clinical practice has not been generally accepted (4). providing fundamental information into the mechanisms of Currently available markers are not sufficient for precise tumor progression in different tumor grades. risk-group or individual assessment specifically in low grade luminal-A tumors, whose general prognosis is very favorable, EXPERIMENTAL PROCEDURES resulting in treatment by less aggressive adjuvant therapy and Tissue Procurement and Patient Characteristics—Patient informed no chemotherapy. However, a low percentage of these tu- consent forms along with tissue procurement procedures were ap- mors develop early lymph node metastases. The molecular proved by the ethics committee of the Masaryk Memorial Cancer mechanism of this phenomenon is not known and current Institute (MMCI) (see supplemental File 1 for ethics documents). Tis- clinical practice lacks the means for predicting its occurrence. sues were frozen in liquid nitrogen within 20 min after surgical removal and stored at Ϫ180 °C in tissue bank of MMCI. A complementary New knowledge is thus essential for identifying biomarkers formalin fixed, paraffin embedded tissue block was available for each that can identify high risk individuals within the predominantly sample for histological evaluation and immunohistochemical (IHC) low risk population of patients with low grade breast cancers. analysis. A set of 96 preoperatively untreated breast carcinomas of These high risk patients should then receive more intensive 11–20 mm maximum diameter (pT1c) was selected for analysis. The follow-up and could be considered for more aggressive ther- sample set characteristics are shown in supplemental File 1 in detail and the study design is summarized in Fig. 1. The sample set included apy, which cannot be achieved currently in view of the detri- 48 grade 1 tumors positive for both ER and PR, without HER2 am- mental effects of therapy on the majority of patients who will plification; 24 of these had lymph node metastases at the time of not show benefit. In addition, understanding the mechanisms operation, selected from a total collection of about 4000 carcinoma of metastasis of low grade breast cancer may lead to the cases, as they are quite rare. The matching node negative pT1c grade identification of new therapeutic targets. 1 tumors exhibiting identical profiles of ER, PR, and HER2 were selected randomly from the total collection. Shotgun proteomics with isobaric tags for relative and ab- To investigate similarities and difference in metastasis biomarkers solute quantification (iTRAQ) is an established approach for in high and low grade breast cancers, a second sample set was quantification of proteins related to cancer metastasis (6, 7). collected of 48 pT1c grade 3 carcinomas, 24 of them node positive Moreover, recent developments made to multidimensional and 24 node negative. These were selected to ensure representation ϭ liquid chromatography and mass spectrometry, including the of the following immunophenotypes: triple negative (n 16); tumors with HER2 amplification (n ϭ 16), of which 8 were ER positive and 8 ER negative; and ER positive tumors without HER2 amplification (n ϭ 1 The abbreviations used are: ER, estrogen receptor; ASCO, Amer- 16), always half of them with and half without metastases. ican Society for Clinical Oncology; CPB1, carboxypeptidase B1 pro- For all samples, frozen tumor samples were cut into two pieces tein encoded by CPB1 gene; ESR1, estrogen receptor alpha protein used for (1) RNA isolation for confirmation of RNA integrity and for encoded by ESR1 gene; FCH, fold change; G1, grade 1; G3, grade 3; qRT-PCR analyses on TaqMan Low density arrays, and (2) protein HER2, Her2/neu receptor encoded by ERBB2 gene; iTRAQ, isobaric isolation for iTRAQ-2DLC-MS/MS analysis. The set of the same 96 tags for relative and absolute quantification; LA, luminal A subtype tumors was used for proteomics, transcriptomics, and IHC analyses. breast tumors; MMCI, Masaryk Memorial Cancer Institute; N0, lymph An independent set of 64 additional grade 1 luminal A breast carci- node negative tumors; N1–2, lymph node positive tumors; PDLIM2, nomas was used for IHC validation of CPB1 protein levels. These PDZ and LIM domain protein 2 encoded by PDLIM2 gene; PLAU, samples were preoperatively untreated, ER positive, PR positive, urokinase plasminogen activator encoded by PLAU gene; qRT-PCR, HER2 negative tumors of 11–30 mm maximum diameter (T1c-T2); 43 quantitative real-time polymerase chain reaction; PR, progesterone were lymph node negative and 21 lymph node positive. receptor protein encoded by PGR gene; RELA, NF-␬B transcription RNA Isolation, RNA Integrity Control and Reverse Transcription— factor p65 protein encoded by RELA gene; RNF25, ring finger protein After homogenization in a MM301 mechanical homogenizer (Retsch, 25 protein encoded by RNF25 gene; SERPINE1, plasminogen acti- Haan, Germany) using a metal ball for 2 ϫ 2 min at 25 sϪ1 in 600 ␮l vator inhibitor 1 (also known as PAI-1) encoded by SERPINE1 gene; of RLT buffer (Qiagen, Hilden, Germany) with 1% ␤-mercaptoethanol, STMN1, stathmin 1 protein encoded by STMN1 gene; TN, triple total RNA was isolated using RNeasy Mini Kit (Qiagen) following the negative subtype breast tumors; TRAF3IP2, TRAF3-interacting pro- manufacturer’s protocol. RNA was eluted with 30 ␮l of RNase-free tein 2 encoded by TRAF3IP2 gene; TMSB10, thymosin beta 10 pro- water, quantified at 260 nm using NanoDrop ND-1000 (Thermo Sci- tein encoded by TMSB10 gene; YWHAH, 14–3-3␩ protein encoded entific, Wilmington, DE) and quality checked by measurement of RNA by YWHAH gene. integrity number (RIN) on Agilent 2100 Bioanalyzer (Agilent Technol-

Molecular & Cellular Proteomics 14.7 1815 Metastasis in Low Grade Breast Cancer

ogies, Waldbronn, Germany). Samples that did not pass the criterion containing 0.1% formic acid (mobile phase A) at 10 ␮l/min. Peptide of RNA quality (RINϾ7) were excluded and replaced by other tissues separation was performed on a pulled tip column (15 cm ϫ 100 ␮m id) with the same clinicopathological characteristics for the whole study containing C18 Reprosil, 5 ␮m particles (Nikkyo Technos, Tokyo, (transcriptomics, proteomics, and IHC). The final sample set is pre- Japan) using increasing amounts of acetonitrile containing 0.1% for- sented in supplemental File 1. Isolated RNA was stored at Ϫ80 °C. mic acid (mobile phase B) at 300 nl/min. Gradient conditions were: Total RNA (0.9 ␮g) was reverse transcribed using H Minus M-MuLV 5–40% B (0 to 40 min), 40–80% B (5 min), 80% B held for 4 min, Reverse Transcriptase (Fermentas, Vilnius, Lithuania) according to the 80–5% B (1 min). manufacturer’s protocol with the use of random hexamer primers Protein identification and quantification was based on accurate (Fermentas). precursor mass measurements and high resolution/accurate frag- Proteomics Sample Preparation, Pooling, Digestion, and Label- mentation data. The mass spectrometer was operated in positive ion ing—One hundred and fifty microliters lysis solution (0.5 M triethylam- mode and a data-dependent “Top 10” method was employed. In monium bicarbonate, pH 8.5; 0.05% w/v SDS) was added to each each cycle a full scan spectrum was acquired in Orbitrap Velos tissue and homogenized in a mechanical homogenizer (Retsch Tech- (Thermo Fisher Scientific) (m/z range 400–2000) at a target value of nology, Haan, Germany) using a metal ball for 2 ϫ 2 mins at 25 sϪ1. 1 ϫ 106 ions (two microscans) with resolution r ϭ 30.000 at m/z 400 The homogenates were then subjected to needle sonication (Bandelin followed by higher energy collision dissociation (HCD) on the 10 most 2200 Ultrasonic homogenizer, Bandelin, Germany, 30 ϫ 0.1 s pulses intense ions with a target value of 5 ϫ 104 ions (1 microscan). at 50 W), kept on ice for 1 h, centrifuged at 14000 ϫ g for 20 min at Fragment ions were measured in the Orbitrap mass analyzer with 4 °C and the total protein in the supernatant quantified using a mod- resolution r ϭ 7500 at m/z 400. The ‘lock mass’ function was enabled ified Bradford assay (Bio-Rad, Hercules, CA) according to manufac- for the MS mode, where the background ion at m/z 445.1200 was turer’s instruction. used as the lock mass ion. General mass spectrometric conditions To match the sample set size with the sample capacity of iTRAQ- were as follows: spray voltage, 1.75 kV; no sheath or auxiliary gas 2DLC-MS/MS approach, sample pooling was performed as outlined flow; S-lens, 60%. FT preview mode was disabled, charge state in Fig. 1 with details presented in supplemental File 1:25␮g (total screening enabled and rejection of singly charged ions enabled. Ion protein) aliquots of tumor lysates from four patients with the same selection thresholds were 500 counts for MS2, isolation width 1.2 Th, clinicopathological characteristics (tumor grade, lymph node status, HCD normalized collision energy was 42. Dynamic exclusion was ER and HER2 status) were pooled together. The pooled lysates employed and Ϯ5 ppm window of the selected m/z was excluded for containing 100 ␮g of protein in 20 ␮l of 0.5 M triethylammonium 30 s. bicarbonate, pH 8.5 and 0.05% w/v SDS were subjected to reduction Proteomics Data Analysis—Protein identification and quantification of cysteine S-S bridges by the addition of 2 ␮lof50mM tris-2- in the iTRAQ experiments was performed with Proteome Discoverer™ carboxyethyl phosphine (TCEP), followed by incubation for1hat version 1.1 software (Thermo Fisher Scientific, Bremen, Germany) 60 °C. Cysteines were blocked by adding 1 ␮l 200 mM methyl meth- using the Mascot database search algorithm (Mascot server version anethiosulfonate in isopropanol and 10 min incubation at room tem- 2.2.4, Matrixscience, London, UK). The data analysis parameters perature. For trypsin digestion, 6 ␮l of freshly prepared trypsin were as follows: Spectrum properties filter: Peptide mass range: (Roche, Mannheim, Germany) solution (500 ng/␮l) were added and 800–7000 Da. Peak filters: S/N ϭ 3. Input data: Protein database: incubated for 12 h at RT (protein/trypsin ratio ϳ1:30). Labeling with SwissProt (version 2010_04), enzyme name: Trypsin (cleaving poly- iTRAQ eight-plex (ABSCIEX, Darmstadt, Germany) was performed at peptides at the carboxyl side of lysine or arginine except when either RT for 2 h according to the manufacturer’s instructions (see Fig. 1 for is followed by proline), max. missed cleavage sites 2, taxonomy: the design of three iTRAQ experiments and supplemental File 1 for Homo sapiens (20279 human protein entries were searched in total). the tissue specimens involved). The samples in each eight-plex were Peptide scoring options: Peptide cut-off score: 10 (default by Pro- then mixed and evaporated in a centrifugal vacuum concentrator to teome Discoverer). Protein scoring options: Use MudPIT Scoring: 100 ␮l final volume. Yes. Protein relevance threshold: 20. Decoy database search: True. ZIC-HILIC Peptide Fractionation—A Dionex P680 HPLC Pump with Target FDR 0.05 (as calculated by Proteome Discoverer). Tolerances: PDA-100 photodiode Array Detector and SeQuant ZIC-pHILIC col- 5 ppm precursor mass tolerance and 0.8 Da fragment mass tolerance. umn 150 ϫ 4.6 mm, 5 ␮m with a corresponding precolumn (20 ϫ 2.1 Modifications: Dynamic (variable): Phosphorylation (STY), oxidation mm, 5 ␮m) were used for peptide fractionation. Mobile phase (A) (M), deamidation (NQ), acetylation (K). Static (fixed): iTRAQ eight-plex comprised 0.1% ammonium hydroxide in acetonitrile. Mobile phase (K, N-term), methylthio (C). Quantitation method: iTRAQ eight-plex (B) comprised 10 mM ammonium formate in water adjusted with (Thermo Scientific Instruments). Protein quantification was based on ammonium hydroxide to pH 10. The mixture of iTRAQ labeled unique peptides (supplemental File 2) with at least three quantitative peptides (100 ␮l) was diluted with 100 ␮l ZIC-HILIC mobile phase ratios using statistical analysis described below. Protein grouping (A) and centrifuged at 14000 ϫ g 10 min at RT. Sample injection function was disabled. volume was 190 ␮l. Separation conditions were: isocratic 10% B for Statistical Analysis of Proteomics Data—Loess and global median 10 min; gradient up to 40% B over 40 min; gradient up to 100% B normalization was used to process the proteomics data. Data were over 40 min; isocratic 100% B for 10 min and gradient down to 10% log2-transformed and analyzed on both peptide and protein level. B over 5 min. Flow rate was 0.4 ml/min, column temperature 30 °C Statistical significance of observed fold-change ratios was deter- and UV detection at 280 and 254 nm. Thirteen to fourteen fractions mined by one sample t test. p values were adjusted for multiple were collected from each fractionation and dried with centrifugal hypothesis testing by Benjamini-Hochberg procedure. To select dif- vacuum concentrator. ferentially expressed proteins for further validation, three criteria were LC-MS/MS Analysis—All LC-MS/MS experiments were performed applied in parallel: (1) Fold change higher than 1.2 for up-regulation or by nanoscale reversed phase liquid chromatography (Easy-nLCII, lower than 0.8 for down-regulation, (2) lower limit of the fold change Thermo Fisher Scientific, Bremen, Germany) coupled on-line to an confidence interval above 1.0 for up-regulation and upper limit below LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific). 1.0 for down-regulation; (3) FDR adjusted p values Ͻ 0.05 were Individual ZIC-HILIC fractions were redissolved in 15 ␮l 0.1% formic considered as statistically significant for proteins with high and me- acid (aq) and 12 ␮l of each fraction was loaded onto a peptide dium number of observations (NϾ10) and FDR adjusted p values of cartridge (CapTrap, Michrom Bioresources, Auburm, CA) using water 0.1 for proteins with low number of observations (N Յ 10). The

1816 Molecular & Cellular Proteomics 14.7 Metastasis in Low Grade Breast Cancer

rationale for choosing the above criteria was to filter out the false clinicopathological characteristics were assessed by Wilcoxon rank positive protein level changes caused by (1) technical method vari- sum test for continuous variables and by Pearson’s Chi-squared for ability up to 20% (criterion 1), (2) inconsistent protein levels accross categorical variables (Fisher’s exact test was used when the number biological replicates (criteria 2 and 3), and (3) variability in levels of of observations was less than 5). All statistical tests were two-sided. various peptides representing a single protein (criteria 2 and 3). Higher All analyses were performed in R (9). threshold of FDR adjusted p values (0.1) was allowed to not filter out Analysis of Gene Expression and Connection of Gene Expression proteins whose quantification relied on low numbers of peptides; this With Patient Survival in Independent Published Sample Sets—Pub- criterion was chosen based on the HER2/ERBB2 protein change licly available gene expression data set SUPERTAM_HGU133A in- between HER2-positive and HER2-negative carcinomas (P04626, cluding data from four studies (all platform Affymetrix Human Ge- n ϭ 4, FCH ϭ 5.27, p ϭ 0.099, Supplemental file 4, HER2ϩ versus nome U133A, 856 samples in total) was downloaded in a log2 HER2- sheet and supplemental File 5); HER2 tumor positivity/nega- normalized form that was used in (11). Samples were classified into 4 tivity was independently determined by IHC. All calculations were breast cancer subtypes using a subtype classification model based performed in R 2.10 (9) using packages from Bioconductor (www. on gene modules called SCMOD2 (12) resulting in 348 luminal A bioconductor.org). samples. Information on lymph node status was available for 343 qRT-PCR Gene Expression Analysis Using Low Density Arrays— cases (n ϭ 76 node positive, n ϭ 267 node negative). Association Gene expression analysis of 95 genes (see supplemental File 6 for between gene expression and lymph node status within luminal A detailed information on the TaqMan assays used) in 96 primary breast samples was assessed by Wilcoxon rank sum test (also known as cancer tissues was performed using Low Density Arrays (Micro Flu- Mann-Whitney test). idic Card System, 384 qRT-PCR reactions/card) on 7900HT Fast Survival analysis was performed using Kaplan-Meier Plotter (http:// Real-Time PCR System (Applied Biosystems, Foster City, CA). Each kmplot.com) for both relapse-free survival (RFS) and distant metastasis RNA isolated from an individual tissue piece was analyzed in three free survival (DMFS) involving a microarray data set from 4142 breast analytical replicates that were run independently in three different cancer tissues (2014 database version) (13). For each gene, the popu- cards. 100 ␮l of sample mix containing 200 ng of cDNA and 1ϫ lation was split according to upper quartile (based on approximate TaqMan Universal PCR Master Mix UNG was used in each loading proportion of lymph node positive patients in luminal A grade 1 group) reservoir. All analysis parameters were electronically provided by the and 15 years follow up threshold was applied. Each gene was repre- manufacturer together with the custom MicroFluidic Cards. Gene sented by user-defined probe set, Affymetrix IDs were as follows:

expression was evaluated by the comparative CT method of relative 205509_at (CPB1), 219165_at (PDLIM2), 218861_at (RNF25), 200783_ quantification using RQ Manager 1.2 software (Applied Biosystems) s_at (STMN1), 217733_s_at (TMSB10), 215411_s_at (TRAF3IP2), with 18S rRNA as an endogenous control. The baseline was estab- 201783_s_at, and 209878_s_at (RELA), 201020_at (YWHAH). The ⌬ lished manually to 0.25 for all samples and CT values were exported following target group of patients were analyzed: luminal A (data from for external statistical evaluation. 1678 patients were available for analysis of RFS and from 918 pa-

Statistical Analysis of qRT-PCR Data—The comparative Ct method tients for DMFS), luminal A restricted to grade 1 (n ϭ 228 for RFS and ⌬⌬ ϭ Ϫ⌬⌬Ct (10) was used to calculate Ct, r 2 and their standard n ϭ 140 for DMFS), luminal A restricted to N0 patients (n ϭ 933 for deviations. Discrepancies in observation numbers for each gene were RFS and n ϭ 546 for DMFS), luminal A restricted to grade 1 N0

inspected by Fisher exact test. Ct values were then compared by two patients (n ϭ 159 for RFS and n ϭ 109 for DMFS) and luminal B (n ϭ sample t test, the results were considered significant when p Յ 0.05. 989 for RFS and n ϭ 360 for DMFS). Correlation Analysis and Hierarchical Clustering—Fisher Z-trans- formed Pearson’s correlation was used as the distance measure RESULTS between samples coupled with average linkage criterion. Spearman correlation coefficients for each pair of samples in qRT-PCR study Untargeted Proteomics Screening—To identify metastasis- were computed. Additionally, qRT-PCR profiles from samples pooled related proteins in low grade breast cancer, we performed a in proteomic experiments were averaged and Spearman correlation high-resolution proteomics discovery study on a set of 48 clini- coefficients for combined proteomics/qRT-PCR profiles calculated. Immunohistochemistry (IHC) on Tissue Microarrays (TMA)—IHC copathologically well characterized small primary grade 1 was used to evaluate protein levels and staining patterns of selected luminal A (ERϩ,PRϩ, HER2-) breast tumors; 24 lymph node putative biomarkers in TMAs prepared from formaldehyde fixed, par- positive and 24 lymph node negative. A similar matched set of affin embedded blocks, taken in parallel with the frozen tissue. Anti- 48 high grade tumors was used to investigate the selectivity of bodies and protocols are presented in supplemental File 9. The proteins for the low grade luminal A tumor group and to gain antibody dilutions were optimized at test TMA from breast carcino- mas from the same sample set. The paraffin sections were placed on potential insight into common versus distinct metastatic pro- Superfrost Plus slides (Gerhard Menzel GmbH, Braunschweig, Ger- cesses during progression of low and high grade tumors and many), slides were deparaffinized by three changes of xylene (5 min of different breast cancer subtypes. The workflow of the pro- each step), followed by rehydration in 95%, 70%, 50% ethanol (5 min teomic experiment together with all follow-up studies is each step) and brought to distilled water. After blocking endogenous shown in Fig. 1. A total of 4405 proteins were identified based peroxidase (3% hydrogen peroxide in phosphate buffered saline on at least one tryptic peptide (FDRϽ0.05). Protein and peptide (PBS), pH 7.5, for 15 min) antigen retrieval was performed in 10 mM sodium citrate buffer, pH 6.0 for 30 min at 95 °C. The primary anti- identification data together with peptide fractionation chromato- bodies diluted in DAKO antibody diluent (DAKO, Glostrup, Denmark) grams are available in supplemental File 2. Peptide spectra are were incubated overnight at 4 °C followed by detection by DAKO accessible through MS-Viewer at http://prospector2. EnVision™/peroxidase kit (cat. No K4007 for anti-mouse and K4011 ucsf.edu/prospector/cgi-bin/msform.cgi?formϭmsviewer, for anti-rabbit secondary antibody, DAKO) and counterstained with Gill’s hematoxylin (Sigma-Aldrich, St. Louis, MO). All slides were search key access dd7asd2je5. The mass spectrometry raw scored by the same pathologist who was blinded to other data. proteomics data have been deposited to the Proteome- Associations between staining intensity of each selected protein and Xchange Consortium (http://proteomecentral.proteomexchange.

Molecular & Cellular Proteomics 14.7 1817 Metastasis in Low Grade Breast Cancer

FIG.1.Experimental design based on three iTRAQ eight-plex experiments, overall workflow and overview of key data outputs across the study. The design allowed analysis of 24 samples (each pooled from 4 patients’ lysates, 24 ϫ 4 ϭ 96 patients involved); these 24 samples are represented by 24 rectangles in three rows corresponding to three iTRAQ eight-plex experiments (E1, E2, E3). Each pooled sample in each

1818 Molecular & Cellular Proteomics 14.7 Metastasis in Low Grade Breast Cancer org)(14) via the PRIDE partner repository with the data set both differential expression and different protein levels be- identifier PXD000029. tween other subgroups of breast tumors that were involved in The quantitative data at peptide level and protein level are the study to investigate clinicopathological selectivity of iden- presented in supplemental Files 3 and 4, respectively, where tified targets. detailed comparisons of protein levels between the key breast Protein levels and gene expression were also ana- tumor characteristics (grade, lymph node status, estrogen lyzed together using hierarchical clustering (Fig. 2 and and HER2 receptors) are available. Forty-two proteins whose supplemental Files 7A–7J). Three clusters of gene products in levels correlated with lymph node metastasis either positively cold maps related to lymph node metastasis in grade 1 tu- or negatively were selected for further verification. An addi- mors were revealed (Fig. 2): G1 metastasis related cluster 1 tional 23 proteins connected to metastasis according to the involves CPB1, PDLIM2 and RNF25. It neighbors a large literature and that exhibited dysregulation in other parameters cluster of estrogen receptor related gene products composed under comparison (Table I) were also involved in the verifica- of ESR1, PGR and anterior gradient proteins 2 and 3 (AGR2 tion process to investigate their correlation with low-grade and AGR3). Among the genes within the second lymph-node cancer metastasis. positivity related cluster in grade 1 tumors (G1 metastasis Targeted Transcriptomics—To further elucidate the mech- related cluster 2 in Fig. 2) there are STMN1, TMSB10, and anisms of protein alterations in low grade breast cancer, we ITGB1. This cluster involves other metastasis-related genes designed a custom TaqMan Low Density Array (Microfluidic EPCAM, KISS1 and MTA1, and neighbors with a cluster con- card). The arrays were made to monitor (1) 65 gene transcripts taining PLAU and SERPINE1. Two other, RELA and YWHAH selected on the basis of the observed changes at proteome are involved in the third G1 metastasis related cluster 3, level (shown in Table I), (2) an additional 30 genes related to together with other metastasis-associated Plasminogen acti- prometastatic mechanisms according to the literature that vator inhibitor 1 RNA-binding protein (SERBP1) and gelsolin were not detected at protein level, or that internally validated (GSN) (Fig. 2). the sample set design (see list in supplemental File 6). Some Immunohistochemistry—IHC staining was performed for genes within the array were also members of Oncotype DX the top targets from the previous analysis and for which IHC (15) (MMP11, ESR1, PGR, ERBB2, ACTB) and MammaPrint compatible, specific antibodies were available: CPB1, RNF25, (16) (TGFB1, STMN1 and MMP9) gene expression arrays. Complete results of transcriptomics experiment are presented STMN1, ITGB1, and YWHAH. Data confirming specificity of in supplemental File 6. antibodies are available in supplemental File 8, including ef- Connecting Proteomics and Transcriptomics Data—The fects of siRNA silencing on protein level using Western blot targets that exhibited statistically significant changes at both and protein profiles in breast cancer cell lines. Representative protein and transcript levels in lymph node positive versus IHC images showing protein staining in low grade breast negative grade 1 tumors were then selected. This group in- tumors for CPB1 are shown in Fig. 3, for other proteins see volved carboxypeptidase B1 (CPB1), PDZ and LIM domain supplemental File 9. Although IHC is by nature a rather semi- protein 2 (PDLIM2), ring finger protein 25 (RNF25), NF-␬B quantitative approach, the data confirmed key trends ob- transcription factor p65 (RELA), 14–3-3␩ (YWHAH), stathmin served in proteomics and transcriptomics data: (1) Up-regu- 1 (STMN1) and thymosin beta 10 (TMSB10). TRAF3 interact- lation of CPB1 in lymph node positive versus lymph node ing protein 2 (TRAF3IP2) was up-regulated in lymph node negative grade 1 tumors, (2) up-regulation of STMN1 in grade positive versus negative tumors regardless of grade and in- 3 versus grade 1 tumors. Data are available in Table III, with tegrin beta-1 (ITGB1) was up-regulated in grade 3 but not further details in supplemental Files 9 and 10. The most prom- grade 1 tumors with metatasis (see Tables I to III, in bold). ising target, CPB1, was tested in an independent set of grade Table II also summarizes genes and proteins that exhibited 1 luminal A tumors (n ϭ 64), see Fig. 3.

of three eight-plex experiments was labeled by one of eight iTRAQ labels (113, 114, 115, 116, 117, 118, 119, 121). Patient’s numbers correspond with their data in Supplemental file 1. The colors represent grouping of samples according to tumor grade and lymph node status: Lymph node positive grade 1 (red) were labeled with iTRAQ 113 and 114 labels in all three eight-plex experiments, lymph node negative grade 1 (yellow) were labeled with iTRAQ 115 and 116 labels, lymph node positive grade 3 (all tones of blue) were labeled with iTRAQ 117 and 118 labels and lymph node negative grade 3 (all tones of green) were labeled with iTRAQ 119 and 121 labels. Stratification of the samples according to estrogen receptor (ER), progesterone receptor (PR) and HER2 receptor across the sample set is also shown in the figure: All grade 1 tumors (luminal A subtype) were ER positive, PR positive, and HER2 negative (red and yellow color, depending on lymph node status). The grade 3 tumor group involved subgroups with different ER, PR, and HER2 status: (i) Triple negative (ER, PR, and HER2 negative), (b) luminal B type (ER positive, PR positive or negative, and HER2 negative), (c) luminal B HER2 positive (ER positive, PR positive or negative, and HER2 positive), (g) HER2 positive (and ER, PR negative) and luminal B HER2 positive; all subtypes both lymph node positive and negative. These subgroups are distinguished by different tones of blue (lymph node positive) or green (lymph node negative). Samples with the same color tone belong to the same cancer subtype with the same lymph node status and are thus considered as biological replicates. For more clinical characteristics of individual patients see Supplemental file 1.

Molecular & Cellular Proteomics 14.7 1819 1820 Cancer Breast Grade Low in Metastasis

TABLE I Top 65 targets in proteomics study with statistically significant changes (with major focus on proteins related to lymph node positivity of grade 1 breast tumors) that were selected for analysis of gene expression at transcript level (right part of the table). Proteins with low number of observations (NՅ10) in the proteomics study are in italics (see Experimental procedures for details). Agreements in statistically significant changes at protein and transcript levels are indicated in bold

Proteomics Transcriptomics Annotation Gene Fold Lower limit Upper limit p p Protein level Acc. No. symbol Protein name change of CI of CI value Rsd value Protein function NR

CPB1 12 4.34 2.86 6.59 0.002 6.74 5.53 0.025 Protease, Metalloprotease, Select calcium ؍ Homo sapiens GN ؍ G1: N1–2/N0 P15086 CPB1 Carboxypeptidase B OS CBPB1_HUMAN͔ binding protein͓-4 ؍1SV ؍ PE G1: N1–2/N0 Q14161 GIT2 ARF GTPase-activating protein GIT2 OS ϭ Homo sapiens 3 1.53 1.49 1.56 0.028 1.15 0.11 0.138 Select regulatory molecule, G-protein modulator, GN ϭ GIT2 PE ϭ 1SVϭ 2-͓GIT2_HUMAN͔ Other G-protein modulator G1: N1–2/N0 P31947 SFN 14–3-3 protein sigma OS ϭ Homo sapiens GN ϭ SFN PE 20 1.45 1.28 1.64 0.001 1.17 0.14 0.215 Miscellaneous function, Other miscellaneous ϭ 1SVϭ 1-͓1433S_HUMAN͔ function protein Homo sapiens 3 1.44 1.39 1.48 0.053 1.27 0.09 0.002 Molecular function unclassified ؍ G1: N1–2/N0 Q96BH1 RNF25 E3 ubiquitin-protein ligase RNF25 OS RNF25_HUMAN͔͓-1 ؍1SV ؍ RNF25 PE ؍ GN G1: N1–2/N0 Q9Y592 CCDC41 Coiled-coil domain-containing protein 41 OS ϭ Homo 10 1.41 1.21 1.64 0.050 1.17 0.11 0.088 Molecular function unclassified sapiens GN ϭ CCDC41 PE ϭ 1SVϭ 2- ͓CCD41_HUMAN͔ G1: N1–2/N0 Q9NZT2 OGFR Opioid growth factor receptor OS ϭ Homo sapiens GN ϭ 18 1.39 1.24 1.56 0.002 1.13 0.09 0.124 Receptor, Other receptor OGFR PE ϭ 1SVϭ 3-͓OGFR_HUMAN͔ G1: N1–2/N0 Q96JB5 CDK5RAP3 CDK5 regulatory subunit-associated protein 3 OS ϭ 10 1.33 1.14 1.55 0.082 1.02 0.10 0.877 Molecular function unclassified Homo sapiens GN ϭ CDK5RAP3 PE ϭ 1SVϭ 2- ͓CK5P3_HUMAN͔ TMSB10 32 1.32 1.18 1.49 0.003 1.25 0.14 0.050 Cytoskeletal protein, Actin family cytoskeletal ؍ Homo sapiens GN ؍ G1: N1–2/N0 P63313 TMSB10 Thymosin beta-10 OS TYB10_HUMAN͔ protein, Non-motor actin binding protein͓-2 ؍1SV ؍ PE Transcription factor, Other transcription 0.012 0.09 1.22 0.020 1.47 1.16 1.30 18 ؍ Homo sapiens GN ؍ G1: N1–2/N0 Q04206 RELA Transcription factor p65 OS TF65_HUMAN͔ factor, Nucleic acid binding͓-2 ؍1SV ؍ RELA PE Homo sapiens 8 1.30 1.21 1.40 0.009 1.29 0.11 0.007 Cytoskeletal protein, Actin family cytoskeletal ؍ G1: N1–2/N0 Q96JY6 PDLIM2 PDZ and LIM domain protein 2 OS PDLI2_HUMAN͔ protein, Non-motor actin binding protein͓-1 ؍1SV ؍ PDLIM2 PE ؍ GN G1: N1–2/N0 P35442 THBS2 Thrombospondin-2 OS ϭ Homo sapiens GN ϭ THBS2 68 1.25 1.10 1.43 0.043 0.92 0.27 0.765 Cell adhesion molecule, Extracellular matrix, PE ϭ 1SVϭ 2-͓TSP2_HUMAN͔ Extracellular matrix glycoprotein Miscellaneous function, Other miscellaneous 0.052 0.16 1.28 0.002 1.33 1.15 1.24 18 1 ؍ STMN1 PE ؍ Homo sapiens GN ؍ G1: N1–2/N0 P16949 STMN1 Stathmin OS STMN1_HUMAN͔ function protein͓-3 ؍ SV G1: N1–2/N0 P67936 TPM4 Tropomyosin alpha-4 chain OS ϭ Homo sapiens GN ϭ 130 1.23 1.16 1.31 0.000 1.16 0.14 0.209 Cytoskeletal protein, Actin family cytoskeletal TPM4 PE ϭ 1SVϭ 3-͓TPM4_HUMAN͔ protein, Non-motor actin binding protein YWHAH 63 1.20 1.10 1.32 0.012 1.20 0.09 0.024 Miscellaneous function, Other miscellaneous ؍ Homo sapiens GN ؍ G1: N1–2/N0 Q04917 YWHAH 14–3-3 protein eta OS 1433F_HUMAN͔ function protein͓-4 ؍1SV ؍ PE G1: N1–2/N0 P01042 KNG1 Kininogen-1 OS ϭ Homo sapiens GN ϭ KNG1 PE ϭ 1 24 0.60 0.45 0.79 0.050 1.12 0.09 0.186 Signaling molecule, Other signaling molecule, SV ϭ 2-͓KNG1_HUMAN͔ Select regulatory molecule, Protease inhibitor G1: N1–2/N0 P00747 PLG Plasminogen OS ϭ Homo sapiens GN ϭ PLG PE ϭ 1SV 38 0.74 0.64 0.85 0.007 NA NA NA Protease, Serine protease ϭ 2-͓PLMN_HUMAN͔ G1: N1–2/N0 P06396 GSN Gelsolin OS ϭ Homo sapiens GN ϭ GSN PE ϭ 1SVϭ 1 172 0.82 0.74 0.91 0.016 1.13 0.14 0.327 Select calcium binding protein, Other select - ͓GELS_HUMAN͔ calcium binding proteins, Cytoskeletal protein

oeua ellrPoemc 14.7 Proteomics Cellular & Molecular G3: N1–2/N0 P22676 CALB2 Calretinin OS ϭ Homo sapiens GN ϭ CALB2 PE ϭ 1SV 8 2.32 1.86 2.91 0.003 2.00 0.82 0.100 Select calcium binding protein, Calmodulin ϭ 2-͓CALB2_HUMAN͔ related protein G3: N1–2/N0 Q9Y2M0 MTMR15 Coiled-coil domain-containing protein MTMR15 OS ϭ 4 1.66 1.47 1.86 0.034 0.93 0.12 0.584 Molecular function unclassified Homo sapiens GN ϭ MTMR15 PE ϭ 2SVϭ 4- ͓MTMRF_HUMAN͔ G3: N1–2/N0 P21926 CD9 CD9 antigen OS ϭ Homo sapiens GN ϭ CD9 PE ϭ 1SV 9 1.43 1.22 1.68 0.026 1.23 0.23 0.261 Cell adhesion molecule, Other cell adhesion ϭ 4-͓CD9_HUMAN͔ molecule G3: N1–2/N0 P58107 EPPK1 Epiplakin OS ϭ Homo sapiens GN ϭ EPPK1 PE ϭ 1SV 59 1.32 1.21 1.44 0.000 1.26 0.37 0.433 Cytoskeletal protein, Actin family cytoskeletal ϭ 1-͓EPIPL_HUMAN͔ protein, Non-motor actin binding protein G3: N1–2/N0 Q15063 POSTN Periostin OS ϭ Homo sapiens GN ϭ POSTN PE ϭ 1SV 244 1.31 1.24 1.38 0.000 1.51 0.34 0.070 Cell adhesion molecule, Other cell adhesion ϭ 2-͓POSTN_HUMAN͔ molecule G3: N1–2/N0 Q15582 TGFBI Transforming growth factor-beta-induced protein ig-h3 72 1.30 1.17 1.44 0.000 1.35 0.28 0.157 Signaling molecule, Cell adhesion molecule, OS ϭ Homo sapiens GN ϭ TGFBI PE ϭ 1SVϭ 1- Other cell adhesion molecule ͓BGH3_HUMAN͔ G3: N1–2/N0 P60903 S100A10 Protein S100-A10 OS ϭ Homo sapiens GN ϭ S100A10 16 1.28 1.17 1.39 0.001 1.16 0.15 0.256 Select calcium binding protein, Calmodulin PE ϭ 1SVϭ 2-͓S10AA_HUMAN͔ related protein G3: N1–2/N0 P31949 S100A11 Protein S100-A11 OS ϭ Homo sapiens GN ϭ S100A11 74 1.26 1.19 1.33 0.000 1.19 0.18 0.279 Signaling molecule, Select calcium binding PE ϭ 1SVϭ 2-͓S10AB_HUMAN͔ protein, Calmodulin related protein G3: N1–2/N0 O15173 PGRMC2 Membrane-associated progesterone receptor component 28 1.25 1.15 1.36 0.000 1.03 0.12 0.782 Receptor, Other receptor 2OSϭ Homo sapiens GN ϭ PGRMC2 PE ϭ 1SVϭ 1 - ͓PGRC2_HUMAN͔ G3: N1–2/N0 O43707 ACTN4 Alpha-actinin-4 OS ϭ Homo sapiens GN ϭ ACTN4 PE ϭ 167 1.25 1.21 1.29 0.000 1.04 0.13 0.767 Cytoskeletal protein, Actin family cytoskeletal 1SVϭ 2-͓ACTN4_HUMAN͔ protein, Non-motor actin binding protein G3: N1–2/N0 P04083 ANXA1 Annexin A1 OS ϭ Homo sapiens GN ϭ ANXA1 PE ϭ 1 111 1.24 1.18 1.30 0.000 1.28 0.23 0.175 Signaling molecule, Select calcium binding SV ϭ 2-͓ANXA1_HUMAN͔ protein, Annexin,Transfer/carrier protein oeua ellrPoemc 14.7 Proteomics Cellular & Molecular

TABLE I—continued

Proteomics Transcriptomics Annotation Gene Fold Lower limit Upper limit p p Protein level Acc. No. symbol Protein name change of CI of CI value Rsd value Protein function NR

G3: N1–2/N0 P40763 STAT3 Signal transducer and activator of transcription 3 OS ϭ 38 1.24 1.14 1.35 0.000 1.25 0.16 0.086 Transcription factor, Other transcription factor, Homo sapiens GN ϭ STAT3 PE ϭ 1SVϭ 2- Nucleic acid binding ͓STAT3_HUMAN͔ G3: N1–2/N0 Q05682 CALD1 Caldesmon OS ϭ Homo sapiens GN ϭ CALD1 PE ϭ 1 134 1.24 1.18 1.31 0.000 1.13 0.27 0.624 Cytoskeletal protein, Actin family cytoskeletal SV ϭ 2-͓CALD1_HUMAN͔ protein, Non-motor actin binding protein Extracellular matrix, Extracellular matrix 0.048 0.19 1.33 0.002 1.35 1.13 1.24 33 ؍ Homo sapiens GN ؍ G3: N1–2/N0 P55268 LAMB2 Laminin subunit beta-2 OS LAMB2_HUMAN͔ linker protein͓-2 ؍1SV ؍ LAMB2 PE Receptor, Other receptor, Cell adhesion 0.034 0.14 1.28 0.000 1.30 1.14 1.22 29 ؍ ITGB1 PE ؍ Homo sapiens GN ؍ G3: N1–2/N0 P05556 ITGB1 Integrin beta-1 OS ITB1_HUMAN͔ molecule͓-2 ؍1SV N1–2/N0 P31944 CASP14 Caspase-14 OS ϭ Homo sapiens GN ϭ CASP14 PE ϭ 1 10 1.74 1.32 2.29 0.064 2.30 1.41 0.179 Protease, Cysteine protease SV ϭ 2-͓CASPE_HUMAN͔ N1–2/N0 Q96HF1 SFRP2 Secreted frizzled-related protein 2 OS ϭ Homo sapiens 8 1.48 1.23 1.79 0.097 1.31 0.24 0.148 Signaling molecule, Other signaling molecule GN ϭ SFRP2 PE ϭ 1SVϭ 2-͓SFRP2_HUMAN͔ Molecular function unclassified 0.012 0.10 1.23 0.002 1.55 1.21 1.37 28 ؍ Homo sapiens GN ؍ N1–2/N0 O43734 TRAF3IP2 Adapter protein CIKS OS CIKS_HUMAN͔͓-2 ؍2SV ؍ TRAF3IP2 PE N1–2/N0 Q14993 COL19A1 Collagen alpha-1(XIX) chain OS ϭ Homo sapiens GN ϭ 10 1.36 1.27 1.46 0.001 1.20 0.77 0.779 Cell adhesion molecule, Extracellular matrix, COL19A1 PE ϭ 1SVϭ 3-͓COJA1_HUMAN͔ Extracellular matrix structural protein N1–2/N0 P06756 ITGAV Integrin alpha-V OS ϭ Homo sapiens GN ϭ ITGAV PE ϭ 38 1.23 1.12 1.35 0.010 1.10 0.09 0.206 Cell adhesion molecule, Other cell adhesion 1SVϭ 2-͓ITAV_HUMAN͔ molecule N1–2/N0 P07951 TPM2 Tropomyosin beta chain OS ϭ Homo sapiens GN ϭ 80 1.21 1.12 1.32 0.002 1.11 0.13 0.353 Cytoskeletal protein, Actin family cytoskeletal TPM2 PE ϭ 1SVϭ 1-͓TPM2_HUMAN͔ protein, Non-motor actin binding protein N1–2/N0 P20962 PTMS Parathymosin OS ϭ Homo sapiens GN ϭ PTMS PE ϭ 1 378 1.12 1.07 1.17 0.000 1.17 0.15 0.220 Molecular function unclassified SV ϭ 2-͓PTMS_HUMAN͔ S100A9 30 1.83 1.43 2.34 0.001 4.27 2.15 0.006 Select calcium binding protein, Calmodulin ؍ Homo sapiens GN ؍ N1–2: G3/G1 P06702 S100A9 Protein S100-A9 OS S10A9_HUMAN͔ related protein͓-1 ؍1SV ؍ PE N1–2: G3/G1 P51153 RAB13 Ras-related protein Rab-13 OS ϭ Homo sapiens GN ϭ 10 1.30 1.10 1.54 0.090 0.70 0.07 0.001 Select regulatory molecule, G-protein, Small RAB13 PE ϭ 1SVϭ 1-͓RAB13_HUMAN͔ GTPase N1–2: G3/G1 P60709 ACTB Actin, cytoplasmic 1 OS ϭ Homo sapiens GN ϭ ACTB 4 1.21 1.14 1.28 0.052 1.045 0.120 0.701 Cytoskeletal protein, Actin family cytoskeletal PE ϭ 1SVϭ 1-͓ACTB_HUMAN͔ protein, Actin and actin related protein N0: G3/G1 P08670 VIM Vimentin OS ϭ Homo sapiens GN ϭ VIM PE ϭ 1SVϭ 4 881 0.74 0.73 0.76 0.000 1.076 0.119 0.511 Cytoskeletal protein, Intermediate filament - ͓VIME_HUMAN͔ G3/G1 Q9NZT1 CALML5 Calmodulin-like protein 5 OS ϭ Homo sapiens GN ϭ 116 1.69 1.37 2.07 0.000 1.44 0.46 0.248 Select calcium binding protein, Calmodulin CALML5 PE ϭ 1SVϭ 2-͓CALL5_HUMAN͔ related protein G3/G1 P23528 CFL1 Cofilin-1 OS ϭ Homo sapiens GN ϭ CFL1 PE ϭ 1SVϭ 72 1.25 1.16 1.35 0.000 1.11 0.08 0.168 Cytoskeletal protein, Actin family cytoskeletal 3-͓COF1_HUMAN͔ protein, Non-motor actin binding protein G3/G1 Q8NC51 SERBP1 Plasminogen activator inhibitor 1 RNA-binding protein OS 72 1.22 1.15 1.30 0.000 1.09 0.08 0.237 Nucleic acid binding, Other RNA-binding protein ϭ Homo sapiens GN ϭ SERBP1 PE ϭ 1SVϭ 2- ͓PAIRB_HUMAN͔ Cancer Breast Grade Low in Metastasis G3/G1 O95433 AHSA1 Activator of 90 kDa heat shock protein ATPase 52 1.22 1.10 1.35 0.003 1.206 0.093 0.017 Molecular function unclassified ؍ AHSA1 PE ؍ Homo sapiens GN ؍ homolog 1 OS AHSA1_HUMA5N2͔͓-1 ؍1SV G3/G1 P26447 S100A4 Protein S100-A4 OS ϭ Homo sapiens GN ϭ S100A4 PE 151 1.28 1.18 1.38 0.000 1.206 0.125 0.074 Select calcium binding protein, Calmodulin ϭ 1SVϭ 1-͓S10A4_HUMAN͔ related protein G3/G1 P12830 CDH1 Cadherin-1 OS ϭ Homo sapiens GN ϭ CDH1 PE ϭ 1SV 48 0.83 0.75 0.92 0.006 1.16 0.16 0.265 Cell adhesion molecule, Cadherin ϭ 3-͓CADH1_HUMAN͔ ALCAM PE 28 0.84 0.73 0.96 0.050 0.43 0.07 0.000 Cell adhesion molecule, CAM family adhesion ؍ Homo sapiens GN ؍ G3/G1 Q13740 ALCAM CD166 antigen OS CD166_HUMAN͔ molecule͓-2 ؍1SV ؍ G3/G1 P02753 RBP4 Retinol-binding protein 4 OS ϭ Homo sapiens GN ϭ 24 0.72 0.62 0.83 0.001 0.948 0.326 0.877 Transfer/carrier protein, Other transfer/carrier RBP4 PE ϭ 1SVϭ 3-͓RET4_HUMAN͔ protein Homo 231 0.47 0.42 0.53 0.000 0.082 0.030 0.000 Ion channel, Voltage-gated ion channel ؍ G3/G1 Q96NY7 CLIC6 Chloride intracellular channel protein 6 OS -3 ؍2SV ؍ CLIC6 PE ؍ sapiens GN ͓CLIC6_HUMAN͔ Homo 8 3.19 1.84 5.54 0.023 25.81 17.52 0.000 Miscellaneous function, Surfactant ؍ ER؉/ER- O95994 AGR2 Anterior gradient protein 2 homolog OS -1 ؍1SV ؍ AGR2 PE ؍ sapiens GN ͓AGR2_HUMAN͔ Molecular function unclassified 0.000 1.30 5.29 0.010 4.06 2.31 3.07 5 ؍ Homo sapiens GN ؍ ER؉/ER- P50238 CRIP1 Cysteine-rich protein 1 OS CRIP1_HUMAN͔͓-3 ؍1SV ؍ CRIP1 PE Homo 10 2.21 1.70 2.88 0.002 72.69 46.42 0.000 Miscellaneous function, Surfactant ؍ ER؉/ER- Q8TD06 AGR3 Anterior gradient protein 3 homolog OS -1 ؍1SV ؍ AGR3 PE ؍ sapiens GN ͓AGR3_HUMAN͔ ,THBS4 4 2.16 1.73 2.70 0.031 3.03 1.17 0.006 Cell adhesion molecule, Extracellular matrix ؍ Homo sapiens GN ؍ ER؉/ER- P35443 THBS4 Thrombospondin-4 OS TSP4_HUMAN͔ Extracellular matrix glycoprotein͓-2 ؍1SV ؍ PE 1821 1822 Cancer Breast Grade Low in Metastasis

TABLE I—continued

Proteomics Transcriptomics Annotation Gene Fold Lower limit Upper limit p p Protein level Acc. No. symbol Protein name change of CI of CI value Rsd value Protein function NR

ERϩ/ER- Q96S44 TP53RK TP53-regulating kinase OS ϭ Homo sapiens GN ϭ 4 1.57 1.43 1.71 0.014 1.28 0.19 0.110 Protease, Metalloprotease TP53RK PE ϭ 1SVϭ 2-͓PRPK_HUMAN͔ Homo 5 1.40 1.31 1.51 0.005 1.52 0.19 0.002 Transcription factor, Zinc finger transcription ؍ ER؉/ER- Q9BTC8 MTA3 Metastasis-associated protein MTA3 OS factor, Other zinc finger transcription factor -2 ؍1SV ؍ MTA3 PE ؍ sapiens GN ͓MTA3_HUMAN͔ Molecular function unclassified 0.000 0.60 2.54 0.004 1.56 1.21 1.38 11 ؍1SV ؍ CGN PE ؍ Homo sapiens GN ؍ ER؉/ER- Q9P2M7 CGN Cingulin OS 2-͓CING_HUMAN͔ ERϩ/ER- Q01995 TAGLN Transgelin OS ϭ Homo sapiens GN ϭ TAGLN PE ϭ 1SV 59 1.26 1.22 1.30 0.000 0.94 0.19 0.759 Cytoskeletal protein, Actin family cytoskeletal ϭ 4-͓TAGL_HUMAN͔ protein, Non-motor actin binding protein ERϩ/ER- P16070 CD44 CD44 antigen OS ϭ Homo sapiens GN ϭ CD44 PE ϭ 1 20 0.82 0.73 0.94 0.035 1.00 0.15 0.974 Receptor, Other receptor, Cell adhesion SV ϭ 2-͓CD44_HUMAN͔ molecule, Cell junction protein ,Homo 4 5.27 3.12 8.89 0.099 22.866 5.402 0.000 Receptor, Protein kinase receptor, Kinase ؍ HER2؉/ P04626 ERBB2 Receptor tyrosine-protein kinase erbB-2 OS Protein kinase, Tyrosine protein kinase -1 ؍1SV ؍ ERBB2 PE ؍ HER2- sapiens GN ͓ERBB2_HUMAN͔ receptor Homo 20 2.92 1.80 4.77 0.013 7.97 3.39 0.000 Molecular function unclassified ؍ HER2؉/ Q9UGT4 SUSD2 Sushi domain-containing protein 2 OS -1 ؍1SV ؍ SUSD2 PE ؍ HER2- sapiens GN ͓SUSD2_HUMAN͔ HER2ϩ/ Q9UNE0 EDAR Tumor necrosis factor receptor superfamily member 7 0.66 0.56 0.78 0.058 0.69 0.27 0.346 Receptor, Other receptor HER2- EDAR OS ϭ Homo sapiens GN ϭ EDAR PE ϭ 1SVϭ 1-͓EDAR_HUMAN͔ oeua ellrPoemc 14.7 Proteomics Cellular & Molecular HER2ϩ/ P06703 S100A6 Protein S100-A6 OS ϭ Homo sapiens GN ϭ S100A6 PE 20 0.76 0.68 0.84 0.004 0.73 0.16 0.170 Signaling molecule, Growth factor, Select HER2- ϭ 1SVϭ 1-͓S10A6_HUMAN͔ calcium binding protein, Calmodulin related protein Metastasis in Low Grade Breast Cancer

TABLE II Expression profiles of the top 65 gene products screened at both protein and transcript level and of 30 additional transcripts based on the literature in all breast cancer immnophenotypes involved in the study. Proteins with low number of observations (N Յ 10) in the proteomics study are in italics (see Experimental procedures for details). Agreements in statistically significant changes at protein and transcript levels are indicated in bold. Up ϭ up-regulation, down ϭ down-regulation. Gene products monitored only at transcript level are underlined G1: N1–2/N0 Protein up: CPB1, GIT2, SFN, RNF25, OGFR, TMSB10, RELA, PDLIM2, THBS2, STMN1, TPM4, YWHAH ʈ down: KNG1, CALML5, PLG Transcript up: RNF25, TRAF3IP2, PPP5C, PDLIM2, RELA, TIMP1, YWHAH, CPB1, RAB13, ITGAV, AGR3, CDH5, TMSB10, STMN1 ʈ down: - G3: N1–2/N0 Protein up: CALB2, SFRP2, MTMR15, TRAF3IP2, CD9, COL19A1, EPPK1, POSTN, TGFBI, S100A10, TPM2, S100A11, PGRMC2, ACTN4, ANXA1, STAT3, CALD1, LAMB2, PTMS, ITGB1, TAGLN ʈ down: S100A9 Transcript up: SERPINE1, ITGB1, THBS2, SPP1, CGN, LAMB2 ʈ down: - N1–2/N0 Protein up: SFRP2, TRAF3IP2, COL19A1, RNF25, OGFR, ITGAV, TPM2, TPM4, LAMB2 ʈ down: - Transcript up: TRAF3IP2, SERPINE1, CFL1, CGN, ITGB1, ANXA1, TPM4, RAB13, POSTN, SPP1 ʈ down: - N1–2: G3/G1 Protein up: CALML5, CALB2, S100A9, AHSA1, CFL1, S100A4, ANXA1, ACTB ʈ down: CPB1, THBS4, CLIC6, CCDC41, COL19A1, AGR3, EDAR, PDLIM2, TP53RK, POSTN, TPM2, THBS2, TRAF3IP2, SFN, MTA3, TAGLN, TPM4, YWHAH, LAMB2 Transcript up: STMN1, MMP9, MMP1, TMSB10, S100A9, EPCAM, CASP14 ʈ down: LAMB2, ESR1, TIMP1, PGR, HGF, CLIC6, ITGAV, ERBB3, MUC1, RNF25, CPB1, AGR3, MTA3, ALCAM, POSTN, RAB13, FLT4, SFRP2, MTMR15, CDH5, PGRMC2, CGN, TRAF3IP2, VEGFC, GIT2, STAT3, AGR2, GSN, THBS10, CCDC41, CRIP1, FLT1, CD44, PPP5C, THBS2, CASP14 N0: G3/G1 Protein up: S100A9, TMSB10, SERBP1, S100A4, STMN1 ʈ down: CLIC6, POSTN, CRIP1, TRAF3IP2, COL19A1, TAGLN, EPPK1, TPM2, KNG1, TP53RK, RBP4, THBS4, CALD1, S100A10, ITGB1, ITGAV, VIM, LAMB2, PLG, PTMS, RAB13, GSN, CCDC41, TGFBI, THBS2 Transcript up: COL19A1, TMSB10, FASLG, STMN1, S100A9, YWHAH, MMP9, MMP1, CXCR4, RELA, S100A4, OGFR, CASP14, PDLIM2 ʈ down: LAMB2, PGR, CLIC6, MUC1, SFRP2, POSTN, STAT3, THBS2, ERBB3, ESR1, CRIP1, ALCAM, TAGLN, CGN, AGR3, GSN, MTA3, THBS4, ITGAV, RAB13, HGF, TPM4, ITGB1, TIMP1, TPM2, AGR2 G3/G1 Protein up: S100A9, CALML5, SUSD2, S100A4, TMSB10, CFL1, SERBP1, AHSA1 ʈ down: THBS4, POSTN, COL19A1, CRIP1, CCDC41, TRAF3IP2, AGR3, TP53RK, TPM2, TAGLN, PDLIM2, EPPK1, RBP4, EDAR, THBS2, CALD1, S100A10, ITGAV, LAMB2, MTA3, TPM4 Transcript up: STMN1, TMSB10, MMP9, MMP1, S100A9, FASLG, COL19A1, CASP14, EPCAM, CXCR4, YWHAH, AHSA1, OGFR, EGFR, MET ʈ down: LAMB2, PGR, CLIC6, ERBB3, ESR1, SFRP2, POSTN, AGR3, TIMP1, ALCAM, ITGAV, HGF, MTA3, STAT3, THBS2, CGN, RAB13, CRIP1, RNF25, GSN, THBS4, FLT4, MTMR15, AGR2, TAGLN, GIT2, VEGFC, CCDC41, TPM4, CPB1, PGRMC2, MMP11, CDH5, CD9, TPM2 -ER؉/ER Protein up: AGR2, CRIP1, CLIC6, CPB1, AGR3, THBS4, EDAR, COL19A1, CCDC41, TP53RK, PTMS, SUSD2, POSTN, MTA3, ALCAM, CGN, TRAF3IP2, TPM2, TAGLN, EPPK1, TPM4, THBS2 ʈ down: CASP14, S100A9, CALML5, S100A4 Transcript up: ESR1, CRIP1, AGR3, ERBB3, PGR, AGR2, PPP5C, CGN, CPB1, RNF25, MTA3, ALCAM, MTMR15, PGRMC2, CDH1, THBS4, PTMS, LAMB2, GIT2, OGFR, CDK5RAP3, CLIC6, RAB13 ʈ down: CD82, EGFR, S100A9, S100A10, ANXA1, VIM, MMP1, S100A6, CALD1, CALB2 HER2؉/HER2- Protein up: ERBB2, CRIP1, SUSD2, KNG1 ʈ down: CLIC6, THBS4, SFN, PDLIM2, EDAR, S100A6, STMN1, PTMS Transcript up: ERBB2, SUSD2, MUC1, CDH1, TMSB10, TP53RK, S100A11, MMP11, CD9, POSTN, EPPK1, GIT2, KISS1, TRAF3IP2, PGRMC2, CASP14, RAB13, AGR2, MTA3 ʈ down: - TN/LA Transcript only up: TMSB10, S100A9, EGFR, MMP1, FASLG, COL19A1, CD82, MMP9, STMN1, EDAR, MET, ANXA1, S100A10, CASP14 ʈ down: ESR1, PGR, CLIC6, MTA3, LAMB2, POSTN, AGR3, CRIP1, ERBB3, AGR2, ERBB2, MTMR15, CPB1, MUC1, ALCAM, THBS2, CGN, ITGAV, RNF25, PGRMC2, FLT4, MMP11, RAB13, THBS4, SFRP2, STAT3, GIT2, VEGFC, FASLG, HGF, TPM4, CD9, PPP5C, TAGLN, TRAF3IP2, CCDC41, TIMP1, TP53RK, SUSD2, GSN, CDH1

Evaluation of Clinicopathological Selectivity of Gene of these potential biomarkers was based on their differential Products Correlating with Metastasis of Low Grade Tu- expression between grade 3 and grade 1 tumors (G3/G1). mors—Analysis of clinicopathological selectivity within Multiple comparisons in Table III clearly show that higher breast cancer subtypes was enabled by analysis of grade 1 level of CPB1, RNF25 and PDLIM2 proteins and transcripts and grade 3 tumors in the same study design and was were typical of highly differentiated grade 1 tumors. On the based on agreements between proteomics, transcriptomics other hand, STMN1 and TMSB10 that were up-regulated in and IHC data. Table III shows clinicopathological selec- grade 1 lymph node positive versus negative tumors, how- tivity of proteins/mRNAs that were up-regulated in lymph ever, were up-regulated also in grade 3 versus grade 1 node positive versus lymph node negative grade 1 tissues: tumors. The above protein stratification into groups accord- CPB1, RNF25, PDLIM2, STMN1, TMSB10, RELA, YWHAH, ing to the clinicopathological selectivity was fully reflected and TRAF3IP2. Up-regulation of these proteins and corre- in hierarchical clustering (Fig. 2). sponding transcripts in lymph node positive tissues was Expression of Metastasis-correlating Genes and Patient observed in grade 1 tumors (G1:N1–2/N0 in Table III) but not Survival Data in Independent Public Data Sets—To further in grade 3 tumors (G3:N1–2/N0), with the exception of investigate correlations with metastatic behavior of luminal A TRAF3IP2 that was up-regulated in lymph node positive tumors in independent sample sets, we analyzed a published tissues regardless of grade (Table III). Further stratification (11) data set SUPERTAM_HGU133A of gene expression data

Molecular & Cellular Proteomics 14.7 1823 Metastasis in Low Grade Breast Cancer

FIG.2.Heatmap of expression patterns of proteins and transcripts in connection with tumor grade, lymph node status, ER and HER2 status. Genes marked with full green dots were up-regulated whereas those marked with empty green dots were down-regulated at protein level in G1 N1–2 versus G1 N0 tumors. Genes marked with full orange dots were statistically significantly up-regulated at transcript level in G1 N1–2 versus G1 N0 tumors (no down-regulated transcripts were found here). The genes up-regulated at both levels are considered as core genes of G1 metastasis related clusters 1 (CPB1, PDLIM2, RNF25, associated with lymph node metastasis of grade 1 tumors and had low levels in grade 3 tumors), G1 metastasis related clusters 2 (STMN1, TMSB10 associated with lymph node metastasis of grade 1 tumors and had high levels in grade 3 tumors) and G1 metastasis related clusters 3 (RELA, YWHAH associated with lymph node metastasis of grade 1 tumors with no significant difference between low and high grade tumors). Clusters related to estrogen receptor (ESR1) and HER2 receptor, and PLAUϩSERPINE1 genes are also highlighted. on 856 patients, including 348 luminal A-like breast tumors. DISCUSSION The analysis confirmed up-regulation of CPB1 (p ϭ 0.00155), Methodological Strategy—The main aim of this study was PDLIM2 (p ϭ 0.02027) and RELA (p ϭ 0.00015) in lymph node to identify novel proteins correlating with early lymph node positive luminal A tumors, see Fig. 4. metastasis in low grade breast cancer that could be easily Moreover, we tested the relationship with patient survival in incorporated into clinical use. We used quantitative proteo- publically searchable microarray database of 4142 breast mics approach to identify such proteins by comparing a group cancer tissues (13). The analysis showed statistically signifi- of 24 lymph node positive luminal A grade 1 tumors with a cant connection with relapse free survival in luminal A group corresponding set of 24 lymph node negative tumors. We (n ϭ 1678) for CPB1 (p ϭ 0.0073), PDLIM2 (p ϭ 0.00013), selected small (pT1c, 11–20 mm) tumors to keep potential RNF25 (p ϭ 0.016), STMN1 (p ϭ 0.0038), and TMSB10 (p ϭ sampling error as low as possible, to capture metastasis- 0.0011) (Fig. 5A–5E). In addition, we obtained the highest responsible cell population. Although this approach is suffi- significance of CPB1 connection with relapse free survival cient for this purpose, we also wished to study whether low (p ϭ 0.00091) and distant metastasis free survival (p ϭ grade metastasis-associated proteins differ from those con- 0.00062) for luminal A grade 1 tumors, Fig. 5F–5G.To nected to metastasis in high grade tumors. The distinctive investigate prognostic value of the putative biomarkers, we identification of proteins associated specifically with meta- further restricted these groups to lymph node negative (N0) static spread of tumors of different grades would imply differ- patients and received statistically significant connection of ent mechanisms employed during their progression, requiring CPB1 (p ϭ 0.0248), PDLIM2 (p ϭ 0.0034) and TMSB10 (p ϭ different therapeutic approaches for each. Identifying markers 0.018) with relapse free survival, Fig. 5H–5J. Control anal- of potential metastasis in low grade tumors specifically is also ysis of relapse-free survival in high grade luminal B tumors important clinically because these are less sensitive to chem- showed similar trends as in low grade luminal A tumors for otherapy than high grade tumors and may therefore require STMN1 and opposite trends for CPB1, in agreement with different targeted therapeutics. However, markers that indi- their clinicopathological selectivity observed in our discov- cate metastasis in all breast cancer patients regardless of ery set. grade are also very important clinically and are discovered as

1824 Molecular & Cellular Proteomics 14.7 Metastasis in Low Grade Breast Cancer

FIG.3.A, Representative examples of CPB1 IHC in luminal A grade 1 tumors. B, CPB1 levels in lymph node negative and positive luminal A grade 1 tumors in discovery (n ϭ 48) and validation (n ϭ 64) set of patients. The difference in CPB1 levels between lymph node positive and negative tumors was statistically significant in discovery but not in validation set although the trend is visible. Combining of both data sets (n ϭ 112) would however lead to statistical significance at p ϭ 0.02742. of potential general applicability through this methodological DX and MammaPrint) or for use in current routine diagnostic strategy. histopathology laboratories. For mRNA analysis, discrepancies We also analyzed individual patients at the transcript level between mRNA and protein levels are not uncommon, but for a large panel of putative protein biomarkers and using IHC selecting targets correlating at two distinct biological levels for a more limited set for which antibodies are available. In reduces false positive findings inherent to a single screening addition to providing independent verification of the shotgun approach and allowed us to interrogate independent large pa- proteomic data, these approaches are imperative for the tient data sets for further validation and investigation of clinical adoption of biomarkers into clinical practice, either through correlation (Fig. 4) and impact on survival (Fig. 5), which is not incorporation into multiplex expression assays (e.g. Oncotype otherwise possible from our limited number of samples.

Molecular & Cellular Proteomics 14.7 1825 1826 Cancer Breast Grade Low in Metastasis

TABLE III Top targets related to lymph node metastasis in grade 1 breast tumors and their clinicopathological selectivity. Proteins with low number of observations (N Յ 10) in the proteomics study are in italics (see Experimental procedures for details). Significant changes in protein levels and expression (according to criteria defined in the Experimental Procedures section) are indicated in bold G1: N1–2/N0 G3:N1–2/N0 N1–2/N0 G3/G1 ERϩ/ER- HER2ϩ/HER2- fold change fold change fold change fold change fold change fold change (IHC trend) p value (IHC trend) p value (IHC trend) p value (IHC trend) p value (IHC trend) p value (IHC trend) p value CPB1 protein 4.34 0.002 0.87 0.741 1.95 0.114 0.55 0.063 2.22 0.007 0.56 0.248 transcript 6.737 0.025 0.197 0.073 1.199 0.779 0.192 0.009 15.571 0.001 1.266 0.799 IHC total intensity 1 0.096 ϳ 0.514 ϳ 0.607 ϳ 0.206 1 0.514 ϳ 0.722 IHC histoscore 1 0.039 ϳ 0.475 ϳ 0.406 ϳ 0.246 0.449 ϳ 0.589 RNF25 protein 1.44 0.053 1.18 0.354 1.30 0.089 0.84 0.379 NϽ3NϽ3NϽ3NϽ3 transcript 1.267 0.002 0.870 0.218 1.050 0.512 0.763 0.000 1.418 0.002 1.168 0.160 IHC tumor cells staining ϳ 0.817 ϳ 0.766 1 0.570 ϳ 0.626 2 0.286 1 0.825 IHC stromal cell staining ϳ 1.000 1 0.742 ϳ 0.905 2 0.521 2 0.439 2 0.433 PDLIM2 protein 1.30 0.009 1.06 0.866 1.18 0.415 0.71 0.008 1.42 0.208 0.65 0.060 transcript 1.287 0.007 0.851 0.215 1.047 0.571 1.038 0.645 1.159 0.258 1.056 0.671 STMN1 protein 1.24 0.002 1.02 0.720 1.12 0.012 1.14 0.039 1.01 0.949 0.77 0.036 transcript 1.283 0.050 1.116 0.608 1.197 0.211 2.042 0.000 1.250 0.296 1.086 0.705 IHC total intensity 1 0.454 2 0.879 1 0.750 1 0.006 ϳ 0.879 ϳ 0.110 IHC % of stained cells ϳ 0.415 ϳ 0.350 ϳ 0.327 1 0.000 ϳ 0.73 ϳ 0.246 IHC membrane staining 1 0.108 2 0.370 ϳ 1.000 1 0.000 ϳ 0.562 2 0.210 IHC stained lymphocytes ϳ 1.000 2 0.659 2 0.434 1 0.203 2 0.05 2 1.000 TMSB10 protein 1.32 0.003 0.97 0.668 1.13 0.066 1.26 0.005 0.89 0.161 1.09 0.540 transcript 1.245 0.050 0.990 0.951 1.110 0.364 1.727 0.000 0.738 0.066 1.925 0.000 RELA protein 1.30 0.020 1.10 0.246 1.20 0.007 0.85 0.029 1.26 0.161 0.80 0.066 oeua ellrPoemc 14.7 Proteomics Cellular & Molecular transcript 1.215 0.012 1.020 0.835 1.111 0.850 1.097 0.133 1.094 0.337 1.050 0.601 YWHAH protein 1.20 0.012 1.04 0.382 1.12 0.007 0.84 0.000 1.17 0.022 0.83 0.001 transcript 1.200 0.024 0.877 0.330 1.026 0.750 1.226 0.011 1.056 0.687 1.075 0.583 IHC tumor cells staining ϳ 1.000 ϳ 0.145 ϳ 0.348 1 0.002 2 0.336 ϳ 0.767 IHC membrane staining in tumor cells 2 0.246 2 0.552 2 0.269 1 0.060 2 0.126 ϳ 1.000 IHC stromal cell staining ϳ 0.866 ϳ 0.789 ϳ 0.788 1 0.070 ϳ 0.529 ϳ 0.840 TRAF3IP2 protein 1.24 0.387 1.52 0.001 1.37 0.002 0.63 0.000 1.32 0.042 1.09 0.403 transcript 1.404 0.003 1.084 0.505 1.233 0.012 0.858 0.070 1.088 0.486 1.335 0.023 IHC percentage of stained cells ϳ 0.928 ϳ 0.220 ϳ 0.357 ϳ 0.494 ϳ 0.403 ϳ 0.342 IHC stromal fibroblasts stained ϳ 1.000 1 0.755 ϳ 0.825 ϳ 0.825 1 0.063 2 0.191 Metastasis in Low Grade Breast Cancer

FIG.4.Validation of expression of se- lected genes on SUPERTAM_HGU133A .(343 ؍ independent sample set (n Log2 intensity of gene expression of CPB1, PDLIM2, and RELA genes is plot- ted for lymph node negative and positive luminal A samples. Names of specific probes are shown in brackets.

Proteins Correlating With Lymph Node Positivity in Grade 1 was ring finger protein 25 (RNF25, or AO7), which binds to the Tumors and Their Potential Prometastatic Functions—As transactivation domain of p65/RELA and enhances its tran- shown in Table I–III, CPB1 exhibited the largest increase in scriptional activity (21). In parallel, RNF25 has a pro-meta- lymph node positive versus lymph node negative tumors static role as an E3-ubiquitin ligase of Naked2, an antagonist grade 1 (G1:N1/N0 FCH ϭ 4.34, p ϭ 0.002); transcript level of the pro-metastatic Wnt pathway (22). Moreover, levels of (FCH ϭ 6.737, p ϭ 0.025); supported by IHC staining in these NF-␬B associated proteins were down-regulated in discovery set (p ϭ 0.03809). IHC in an independent sample grade 3 tumors compared with grade 1 tumors, indicating the set (n ϭ 64) of luminal A grade 1 tumors showed similar trend components of NF-␬B pathway being more typical of grade 1 (Fig. 3) but was not statistically significant (p ϭ 0.13055); this tumors (Table III and supplemental Files 9, 10). may also reflect the semiquantitative nature of the method as Other proteins correlating with lymph node metastasis are well as the increased proportion of N0 to N1–2 samples in the STMN1 and TMSB10 which were linked more generally with validation set. Moreover, high protein levels and expression an aggressive phenotype because of their overexpression in were specific for grade 1 and not grade 3 tumors. Kaplan- grade 3 and triple negative tumors (Table I). STMN1 (also Meier plots on independent samples support both the clin- known as oncoprotein 18, OP-18) is a microtubule destabiliz- ical impact of CPB1 (Fig. 5) and its selectivity for luminal A ing phosphoprotein with a key role in the control of mitosis. tumors, because different association with patient survival Recent evidence supports a role for STMN1 in advanced for luminal B tumors of higher grade was found (Fig. 5K). invasive and metastatic cancer because of its pro-survival CPB1 is a secreted tissue protease and its potential pro- role (23) and is associated with high risk and lymph node metastatic role in lymph node positive tumors might be re- metastasis (24, 25). High STMN1 expression also negatively lated to matrix metalloprotease activity (http://www.uniprot. influences tamoxifen response in estrogen positive breast org/uniprot/P15086). In this regard, its homolog, plasma car- cancer (26). These observations support the role of STMN1 as boxypeptidase B2, has been implicated in the pro-metastatic a marker of poor prognosis and a target for antitumoral and urokinase plasminogen-activator/inhibitor system (17). anti-metastatic therapies (23, 27). On the other hand, it was Another target correlating with lymph node metastasis of reported that STMN1 plays a protumorigenic role in early grade 1 tumors identified here was PDLIM2, a protein with stages of carcinogenesis but may act as a tumor suppressor pro-metastatic, pro-survival, pro-angiogenic and pro-trans- and inhibit metastasis formation in later stages (28). The role formation functional properties through the NF-␬B pathway of TMSB10 in cancer is mainly related to cytoskeletal altera- (18). PDLIM2 inhibits the central transcription regulator of the tions. TMSB10 is part of a gene expression signature for NF-␬B pathway, p65/RELA, via its nuclear ubiquitin ligase predicting lymph node metastasis of early stage cervical car- activity. Both PDLIM2 and RELA were up-regulated in discov- cinomas (29) and is up-regulated in metastatic papillary thy- ery and validation (Fig. 4) sample sets, indicating activation of roid carcinomas (30). The roles of STMN1 and TMSB10 in NF-␬B pathway (Fig. 4). In addition, the inhibition effect of breast cancer metastasis are supported by their correlation PDLIM2 may be reflected in Kaplan-Meier plots, where asso- and clustering with other metastasis-associated gene prod- ciation of higher PDLIM2 expression with better survival was ucts within G1 metastasis related cluster 2, e.g. epithelial cell found (Figs. 5B and 5I). PDLIM2 was recently proposed as a adhesion molecule (EPCAM), metastasis-suppressor KiSS-1 potential therapeutic target in breast cancer (19). A recent (KISS1) and metastasis-associated protein 1 (MTA1). Further- study also revealed that PDLIM2 regulates transcription factor more, their neighboring cluster in Fig. 2 contains two markers activity in epithelial-to-mesenchymal transition via COP9 sig- recommended for prediction of breast cancer metastasis (4), nalosome (20). The third target identified in the study as PLAU and SERPINE1. The last marker identified here is 14– up-regulated in grade 1 tumors with lymph node involvement 3-3␩ (YWHAH). Bergamaschi and Katzenellenbogen recently

Molecular & Cellular Proteomics 14.7 1827 Metastasis in Low Grade Breast Cancer

FIG.5. Kaplan-Meier plots of CPB1, PDLIM2, RNF25, STMN1 and TMSB10 expression and patient survival derived from http:// kmplot.com . Relapse free survival of CPB1 (A), PDLIM2 (B), RNF25 (C), STMN1 (D), and TMSB10 (E) in luminal A (LA) tumors. Relapse free survival (F) and distant metastasis free survival (G) of CPB1 in luminal A grade 1 tumors. Relapse free survival of CPB1 (H) in luminal A grade 1 N0 tumors. Relapse free survival of PDLIM2 (I) and TMSB10 (J) in luminal A N0 tumors. Relapse free survival of CPB1 (K) and STMN1 (L)in luminal B (LB) tumors. The associations not shown here were not statistically significant.

1828 Molecular & Cellular Proteomics 14.7 Metastasis in Low Grade Breast Cancer reported high YWHAH protein levels in correlation with early acterization and validation toward clinically usable diagnostic breast cancer recurrence and regulation by tamoxifen via and therapeutic targets in low grade breast cancer patients down-regulation of microRNA-451 (31). and may be useful to predict those rare low grade luminal A Different Prometastatic Proteins and Mechanisms Observed breast cancer patients that should receive more regular fol- in Grade 3 Tumors—In contrast to low grade tumors, fewer low-up and intensive therapy. proteins correlated with lymph node status in grade 3 can- Acknowledgments—We thank the women who provided their tis- cers. The protein and corresponding transcript most signifi- sue for this research and all of the clinically related staff involved in cantly up-regulated in lymph node positive versus negative their treatment. We thank Dana Knoflícˇkova´ for RIN determination, grade 3 tumors was integrin B1 (ITGB1) which functions in Alice Hlobilkova´, M.D. for her help with TMA preparation, Zina Han- cell-to-cell and cell-to-extracellular matrix (ECM) adhesion, zelkova´ for her excellent technical assistance with TMA analysis and transducing signals from the ECM to the cell and vice versa to Andrea Janotova´ for the statistical analysis of TMA data. We also thank Dr. Michaela Sˇ cˇigelova´ and Dr. Martin Hornshaw for their kind influence cell migration and invasion (32). Overexpression of cooperation with mass spectrometry measurement of peptide frac- plasminogen activator inhibitor 1 (SERPINE1) in lymph node tions, Dr. Peter Baker (University of California, San Francisco) for positive versus negative tumors supported the validity of the displaying MS/MS data in MS-Viewer and PRIDE Team for public experiment and was specific for grade 3 tumors (p ϭ 0.024 for accessibility of raw MS data. We are very grateful to Dr. Philip J. grade 3 versus p ϭ 0.241 for grade 1, Table II). The same Coates for critical reading of the manuscript and correction of language. applied for osteopontin (SPP1), a known pro-metastatic and pro-survival protein from previous breast cancer studies (33, * This work was supported by Czech Science Foundation (project 34). No. 14-19250S), European Regional Development Fund and the State To determine prognosis in early stage breast cancer, two Budget of the Czech Republic (RECAMO; CZ.1.05/2.1.00/03.0101), Czech Ministry of Education, Youth and Sports (BBMRI:LM2010004), gene expression-based tests are available (MammaPrint and by the project MEYS - NPS I - LO1413, MH CZ - DRO (MMCI, Oncotype DX). In our study, we analyzed several genes in- 00209805), Amgen, the Wessex Cancer Trust and Medical Research, cluded in these tests (MMP11, ESR1, PGR, ERBB2, ACTB, UK, Hope for Guernsey and the University of Southampton “Annual TGFB1, STMN1, and MMP9) and observed a significant rela- Adventures in Research.” □ tion to metastasis only in the case of STMN1. Changes in S This article contains supplemental Files 1 to 11. ¶¶ To whom correspondence should be addressed: Masaryk Me- MMP11, MMP9, ESR, and PGR were more related to differ- morial Cancer Institute, Regional Centre for Applied Molecular On- ences between breast cancer subtypes with different tumor cology, Zluty kopec 7, 65653 Brno, Czech Republic. Tel.: ϩ420- grade (Table II). 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1830 Molecular & Cellular Proteomics 14.7 12.3 Appendix 3

Research paper 2

Pull-down assay on streptavidin beads and surface plasmon resonance chips for SWATH mass spectrometry based interactomics

Maryas J, Faktor J, Skladal P, Bouchal P. Cancer Genomics and Proteomics. 2018;15(5):395- 404. http://cgp.iiarjournals.org/content/15/5/395.full.pdf+html CANCER GENOMICS & PROTEOMICS 15 : 395-404 (2018) doi:10.21873/cgp.20098

Pull-down Assay on Streptavidin Beads and Surface Plasmon Resonance Chips for SWATH-MS-based Interactomics JOSEF MARYÁŠ 1,2 , JAKUB FAKTOR 1,2 , LENKA ČÁPKOVÁ 1, PETR MÜLLER 2, PETR SKLÁDAL 1 and PAVEL BOUCHAL 1

1Masaryk University, Faculty of Science, Department of Biochemistry, Brno, Czech Republic; 2Masaryk Memorial Cancer Institute, Regional Centre for Applied Molecular Oncology, Brno, Czech Republic

Abstract. Background/Aim: Pul-down assay is a popular in PPIs is important to infer the protein function within the cell vitro method for identification of physical interactors of and in the inter-cellular communication (4). The large-scale selected proteins. Here, for the first time, we compared three studying of affinity protein interactions is often called conventional variants of pull-down assay with the streptavidin- interactomics (5) and the importance of this field is reflected by modified surface plasmon resonance (SPR) chips for the many studies that have been performed up to now. Methods for detection of PDZ and LIM domain protein 2 (PDLIM2) PPIs identification can be classified according to their interaction partners. Materials and Methods: PDLIM2 principles: in vitro (involving tandem affinity purification, co- protein –protein interactions were analysed by three variants of immunoprecipitation and pull-down assays), in vivo (methods pull-down assay on streptavidin beads using LC-MS/MS in based on yeast two-hybrid system and synthetic lethality) and “Sequential Window Acquisition of all Theoretical fragment in silico (e.g. chromosome proximity, phylogenetic tree) (4). All ion spectra (SWATH)” mode and compared with LC-SWATH- of them have their own intrinsic advantages and disadvantages, MS/MS data from SPR chips. Results: The results showed that as recently reviewed (4). This study focuses on the pull-down (i) the use of SPR chip led to comparable data compared to assay, a powerful in vitro screening tool for identifying on-column streptavidin beads, (ii) gravity flow and microflow previously unknown PPIs via an antibody-free approach (4, 5). in wash and elution steps provided better results than To isolate and study PPIs using pull-down assay, fusion proteins centrifugation, and (iii) type and concentration of detergent did of a target protein with various tags are constructed to enable not significantly affect the interactome data of cancer- capture of the target protein onto a solid support (5, 6). A associated PDLIM2. Conclusion: Our study supports further number of affinity tags including enzymes, protein domains or application of SPR-based affinity purification with SWATH small polypeptides has been developed (7). Of these, a mass spectrometry for reproducible and controlled streptavidin binding peptide (SBP)-based, 38 amino acids long –9 characterization of cancer-associated interactomes. tag, with high affinity to streptavidin ( KD ~2.5 ×10 M) enables a fast, efficient, and relatively specific one-step method for Protein –protein interactions (PPI) play a fundamental role in a isolation and study protein complexes (8-10). Moreover, it wide range of biological processes (1). Typically, proteins provides better affinity, higher purity and higher yields over hardly act as isolated species while performing their functions other commonly used tags like His tag or maltose binding (2); it has been revealed that over 80% of proteins do not protein and allows simple competitive elution by biotin under operate alone, but in complexes (3). Therefore, the studying of mild conditions (11) (biotin affinity to streptavidin is –14 characterized by KD ~1 ×10 M) (9). In a practical set-up, every pull-down assay comprises five main steps: i) cell lysis, ii) capture of tagged protein onto solid support and wash off This article is freely accessible online. unspecific interacting biomolecules, iii) elution of specific interaction partners, iv) protein digestion and v) mass Correspondence to: Pavel Bouchal, Ph.D., Masaryk University, spectrometry (MS) identification and quantification of Faculty of Science, Department of Biochemistry, Kotlarska 2, interacting partners in comparison with the control assay (Figure 61137 Brno, Czech Republic. Tel: +420 549493251, Fax: +420 549492690, e-mail: [email protected] 1). Effectiveness of the experiment, however, always depends on the optimal binding, washing and elution conditions, and Key Words: LC-SWATH-MS/MS, pull-down assay, PDLIM2, resulting specificity and compatibility for the PPIs of interest. protein-protein interactions, SPR. PPIs can be also quantified using surface plasmon resonance

395 CANCER GENOMICS & PROTEOMICS 15 : 395-404 (2018)

Table I . Overview of methods under comparison.

Method 1 Method 2 Method 3 Method 4

Lysis buffer 0.5% Tween 20, 0.5% CHAPS, 0.5% NP-40, 0.1% Tween 20, 150 mM NaCl, 100 mM KAc, 150 mM NaCl, 150 mM NaCl, 50 mM 50 mM HEPES 50 mM HEPES 50 mM HEPES HEPES pH 7.5, 2 mM pH 7.5, 2 mM MgCl 2, pH 7.5, 2 mM MgCl 2, pH 7.5, protease and MgCl 2, 25 U/ μl b 25 U/ μl benzonase, 1 mM DTT, avidin phosphatase inhibitors enzonase, avidin 10 μg/ml, avidin 10 μg/ml, protease 10 μg/ml, protease and 10 μg/ml both protease and phosphatase and phosphatase phosphatase inhibitors inhibitors 10 μg/ml both inhibitors 10 μg/ml both 10 μg/ml both Solid support Streptavidin agarose Streptavidin agarose Streptavidin agarose SPR streptavidin beads in microtube beads in microtube beads on column chip Washing and Centrifugation Centrifugation Gravity flow Microflow elution mechanism Wash buffer 0.1% Tween 20, 0.1% CHAPS, 100 mM 150 mM NaCl, 0.1% Tween 20, 150 mM 150 mM NaCl, 50 mM KAc, 50 mM HEPES 50 mM HEPES pH 7.5, NaCl, 50 mM HEPES pH 7.5, 2 mM pH 7.5, 2 mM 50 mM NaF HEPES pH 7.5, MgCl 2 MgCl 2, 1 mM DTT 2 mM MgCl 2 Elution buffer Wash buffer + 50 mM HEPES Wash buffer + 0.005% Tween 20, 1 mM biotin pH 7.5, 100 mM 2.5 mM biotin 150 mM NaCl, 50 mM KAc, 1 mM biotin HEPES pH 7.5, 2 mM MgCl 2, 1 mM biotin Protein quant. SWATH-MS SWATH-MS SWATH-MS SWATH-MS

(SPR) (12, 13), which has been used almost exclusively in GFP. Lentiviral vectors were prepared in house according to ® ® validation experiments with purified proteins up-to-date (14- Gateway Technology with Clonase II user guide (Invitrogen, 25- 18), with a single exception (19). 0749 MAN0000470). Production of lentiviruses, transduction of MCF7 cells and selection of stably transfected clones were done PDZ and LIM domain protein 2 (PDLIM2) is a low- according to ViraPower™ Lentiviral Expression Systems user abundant protein that plays a role during breast cancer manual (Invitrogen, 25-0501 MAN0000273). Detection of oncogenesis (20), with both tumour suppressor and oncoprotein recombinant proteins in selected clones was performed using SDS- contribution to breast cancer development, depending on the PAGE and western blot (see below). Each variant of cells was biological context (21). Identification of PDLIM2 interaction grown on two 15 cm dishes to 80% confluence in DMEM partners is expected to provide new insights into molecular supplemented with 10% FBS, 1.25 mM pyruvate, 0.172 mM machineries that are important in re-arrangement of the cell in streptomycin, 100 U/ml penicillin and 10 μg/ml blasticidin. Cells were then harvested into pellet as follows: Media were aspirated various phases of tumour development. Up to now, only three from Petri dishes and attached cells were rinsed two times with cold studies (22-24) were focused on PDLIM2 interactome, of PBS solution (2.68 mM KCl, 0.137 M NaCl, 6.45 mM which two dealt with viral proteins (22, 23) biologically Na 2HPO 4.12H 2O, 1.47 mM KH 2PO 4). Cells from each dish were irrelevant to human background. In this study, we attempted to scratched into 1 ml cold PBS and cells of the same cell line were find optimal conditions for identification of PDLIM2 pooled together, transferred in a test tube, centrifuged (5 min/3000 interactors by comparing four different pull-down methods (for g/4˚C) and the supernatant was removed. overview see Table I) in breast cancer cells. This was Cell lysis. Cell lysis varied depending on the pull-down method as represented by a stably-transfected MCF-7 breast cancer cell follows: line expressing a fusion construct consisting of N-terminal SBP Method 1: Cell pellets were washed three times using wash tag and PDLIM2 or a corresponding control cell line buffer containing 0.1% Tween 20, 150 mM NaCl, 50 mM HEPES expressing N-terminal SBP–GFP fusion protein. pH 7.5, 2 mM MgCl 2 at 10,000 × g for 1 min at RT. Washed cells were lysed by addition of 200 μl (~three volumes) of lysis buffer (0.5% Tween 20, 150 mM NaCl, 50 mM HEPES pH 7.5, 2 mM Materials and Methods MgCl 2, 25 U/ μl benzonase, avidin 10 μg/ml, protease and phosphatase inhibitors 10 μg/ml both) vortexing, centrifugation at Cell lines. MCF-7 breast cancer cells stably transfected with gene 10,000 × g for 1 min at RT, three times sonication at 15 kHz encoding N-terminally SBP-tagged PDLIM2 protein and control N- frequency for five sec, and incubation for 30 min on ice. Cell terminally SBP-tagged GFP protein were prepared using lentiviral lysates were then centrifuged at 10,000 × g for 15 min at 4˚C, vectors pLENTI-N-SBP-PDLIM2-IRES-GFP and pLENTI-N-SBP- supernatants were transferred to a new low-binding microtube.

396 Maryáš et al : SPR-based Affinity Purification with SWATH MS and Interactomics

Figure 1. Overview of major steps of identification protein –protein interactions using four methods compared in this study (Methods 1-4). Individual methods differed in (i) solid supports for tagged protein capture (streptavidin agarose beads, SPR chip), (ii) mechanics of the washing and elution steps (centrifugation, gravity flow, microflow) and (iii) type and concentration of detergents (detailed in Table I). Protein quantification was performed using SWATH-MS for all methods.

Method 2: Cell lysates were prepared analogically to Method 1 20, 150 mM NaCl, 50 mM HEPES pH 7.5, 2 mM MgCl 2 at 10,000 with the following modifications: more effective zwitterionic × g for 1 min at RT and then incubated for 10 min on ice. A total of detergent CHAPS was used instead of Tween 20 in lysis buffer (0.5% 555 μg of total protein lysate was then added to the beads and CHAPS, 100 mM KAc, 50 mM HEPES pH 7.5, 2 mM MgCl 2, 1 mM incubated together for 1 h at 4˚C on a rotating wheel. The suspension DTT, avidin 10 μg/ml, protease and phosphatase inhibitors 10 μg/ml was then centrifuged at 10,000 × g for 1 min at 4˚C, supernatants both) and in wash buffer (0.1% CHAPS, 100 mM KAc, 50 mM were removed and the beads were washed three times with wash HEPES pH 7.5, 2 mM MgCl 2, 1 mM DTT). Cell lysates were then buffer containing (detailed composition see above) at 10,000 × g for centrifuged at 10,000 × g for 15 min at 4˚C, supernatants were 1 min at 4˚C. The beads were then incubated with 50 μl elution transferred to a new low-binding microtube. buffer (1 mM biotin, 0.1% Tween 20, 150 mM NaCl, 50 mM Method 3: Unlike remaining methods, cell pellets were not HEPES pH 7.5, 2 mM MgCl 2) for 5 min on ice and centrifuged at washed before lysis. A total of 300 μl of lysis buffer containing 10,000 × g for 1 min at 4˚C. Supernatants were then transferred to a 0.5% NP-40, 150 mM NaCl, 50 mM HEPES pH 7.5, protease and new low-binding microtube. phosphatase inhibitors (10 μg/ml both) were added to pellets and Method 2: Each step of the procedure was analogical to Method incubated for 10 min on ice. Cell lysates were then centrifuged at 1 (see above); however different wash and elution buffers were 10,000 × g for 20 min at 4˚C, supernatants were transferred to a used. Wash buffer was composed of 0.1% CHAPS, 100 mM KAc, new low-binding microtube. 50 mM HEPES pH 7.5, 2 mM MgCl 2 and 1 mM DTT. Elution Method 4: Cell lysates were prepared analogically to Method 1, buffer composition was 50 mM HEPES pH 7.5, 100 mM KAc and however a lower, SPR compatible concentration of Tween 20 was 1 mM biotin. used in lysis buffer (0.1% Tween 20, 150 mM NaCl, 50 mM HEPES Method 3: A volume of 100 μl of streptavidin agarose beads pH 7.5, 2 mM MgCl 2, 25 U/ μl benzonase, avidin 10 μg/ml, protease (High Capacity Streptavidin Agarose Resin, Thermo Scientific) were and phosphatase inhibitors 10 μg/ml both). equilibrated for 30 min on ice with 750 μl lysis buffer (0.5% NP-40, Total protein concentration in all lysates was determined using 150 mM NaCl, 50 mM HEPES pH 7.5, protease and phosphatase RC-DC Protein assay (Bio-Rad, Hercules, CA, USA) according to inhibitors 10 μg/ml both). 200 μl of this slurry and 555 μg of total manufacturer’s instructions and 555 μg of total protein was used for protein lysate were then mixed in low binding microtube and each interaction assay. incubated for 15 min at 4˚C on rotating wheel. The suspension was then packed onto Bio-Spin Disposable Chromatography Columns Capture, wash and elution of interaction partners (Bio-Rad, USA) previously washed on ice with 250 μl lysis buffer Method 1: 20 μl of streptavidin agarose beads (High Capacity (composition see above), to prevent formation of air bubbles. The Streptavidin Agarose Resin, Thermo Scientific, Waltham, MA, USA) beads on column were washed two times with 1 ml lysis buffer and were washed three times with wash buffer containing 0.1% Tween three times with 1 ml wash buffer containing 150 mM NaCl, 50 mM

397 CANCER GENOMICS & PROTEOMICS 15 : 395-404 (2018)

HEPES pH 7.5 and 50 mM NaF (gravity flow). The beads were then three technical replicates (injections). Up to 20 most intensive incubated three times on the column with 66.6 μl of elution buffer precursor ions with intensity exceeding 50 cps were fragmented in containing 150 mM NaCl, 50 mM HEPES pH 7.5, 50 mM NaF and each cycle. Cycle time was 2.3 sec, m/z range was set to 400-1250 2.5 mM biotin (gravity flow). Eluates were pooled together and for MS and 200-1600 for MS/MS. Precursor exclusion time was set transferred to a new low-binding microtube. to 12 sec. Method 4: The SA SPR chips (streptavidin immobilized on the CM5 carboxymethylated dextran matrix) were used with the Data acquisition in data independent SWATH mode. All digested Biacore 3000 system (GE Healthcare). The chip was equilibrated in eluates were measured in a positive high sensitivity product ion scan the running buffer (0.01 M HEPES pH 7.4, 0.15 M NaCl, 0.005% in two technical replicates (injections). Precursor ion range was set Tween-20, 2 mM MgCl 2) for 5 min. The flow rate 5 ul/min was from m/z 400 up to 1200 and further divided into 67 SWATH always used. A zone of the total protein lysate (555 μg) diluted windows, each 12 Da wide with 1 Da overlap. Accumulation time twice in the running buffer (composition see above) was then per SWATH window was 50.8 msec, resulting in 3.5 sec cycle time. injected and allowed to interact in flow for 10 min. The chip was Rolling collision energy with 15 V spread was set. Product ions washed for 10 min, the mean bound amount was 183±19 RU; 1 RU were scanned from 360 m/z up to 1360 m/z . was stated to be approximately equivalent to a change in surface 2 concentration of 1 pg/mm (anon, Biacore Assay Handbook, GE Data processing. Protein identification in DDA runs was performed Healthcare, Piscataway, NJ 2012, p. 9). Then, elution buffer with MaxQuant 1.5.3.30 (www.maxquant.org) using Andromeda (0.01 M HEPES pH 7.4, 0.15 M NaCl, 0.005% Tween-20, 2 mM database search algorithm against UniProt/SwissProt human MgCl 2, 1 mM biotin) was injected and stopped inside the flow cell database version 2015_02 downloaded on 19.3.2015 containing for 2 min. The eluted proteins (7 μl) were captured in a vial and 20,198 sequences, complemented by iRT protein database frozen. The mean unbound amount was 166±80 RU. The whole (Biognosys, Zurich, Switzerland) and internal database of common process was controlled by a custom script based on the protein contaminants in Andromeda, using default settings for MICRORECOVER procedure from the Biacore programming Sciex Q-TOF instrument. Enzyme name: Trypsin (cleaving language. Finally, the chip was washed twice with 10 mM NaOH polypeptides at the carboxyl side of lysine or arginine except when (2 min pulses) and for 5 min with the running buffer (composition either is followed by proline), max. missed cleavage sites: 2, see above). An example sensorgram from this procedure is provided taxonomy: Homo sapiens. Decoy database search: True. PSM FDR in Figure 2. 0.01, protein FDR 0.01, site FDR 0.01 Tolerances: precursor mass tolerance 0.07 Da/0.006 Da (first search/main search), fragment Proteomic identification of protein-protein interacting partners: mass tolerance 40 ppm. Modifications: Dynamic (variable): protein digestion. Proteins were digested with trypsin using filter Oxidation (M); Acetyl (Protein N-term). Static (fixed): aided sample preparation and desalted using C18 spin columns as Carbamidomethyl (C). SWATH assay library was generated in previously described (25) and dried under vacuum. Spectronaut 8.0 software (Biognosys, Zurich, Switzerland) based on the results of MaxQuant database search of all DDA analyses. LC-MS/MS. Prior to the analysis, the dried peptides were dissolved Quantitative peptide level information was extracted from SWATH in 40 μl of 5% acetonitrile, 0.05% TFA. A volume of 0.8 μl of 1x data using Spectronaut 8.0, the peptides detected significantly HRM peptides (Biognosys, Zurich, Switzerland) was added and (q<0.01) at least once across all SWATH runs were involved in the 4 μl of the resulting solution was loaded on LC-MS/MS. Tryptic final dataset (“Qvalue sparse” setting in Spectronaut software). The digests were separated on Eksigent Ekspert nanoLC 400 liquid quantitative information was extracted for all corresponding chromatography (SCIEX, Dublin, CA, USA) on-line coupled to proteins/peptides/transitions and for all conditions using algorithm TripleTOF 5600+ (SCIEX, Toronto, Canada) mass spectrometer. implemented in Spectronaut, with data normalization between runs. Sample pre-concentration and desalting was performed on a Statistical analysis of intensities of all proteins identified at least cartridge trap column (300 μm i.d. × 5 mm) packed with C18 once across the SWATH dataset with q-value<0.01 was performed PepMap100 sorbent with 5 μm particle size (Thermo Scientific) in mapDIA 2.3.1 software at fragment level as follows: The data using a mobile phase composed from 0.05% trifluoroacetic acid was log2 transformed, normalized by dividing by the total intensity (TFA) in 2% acetonitrile (ACN). Subsequently, peptides were sum and analyzed in “ReplicateDesign“ setting. Only peptides with separated on a capillary analytical column (75 μm i.d. × 500 mm) 3 to 6 fragments were used, data from 1 to 5 peptides per protein packed with C18 PepMap100 sorbent, 2 μm particle size (Thermo were used for protein level quantification, standard deviation factor Fisher Scientific, Waltham, MA, USA). Mobile phase A composed was set to 2 and minimal intra-protein correlation of peptides was of 0.1% (v/v) formic acid (FA) in water while mobile phase B set to 0.2. composed of 0.1% (v/v) FA in ACN. Analytical gradient started from 2% B, the proportion of mobile phase B increased linearly SDS-PAGE and western blotting. SDS-PAGE and western blotting up to 40% B in 120 min, flow was 300 nl/min. The analytes were were used to determine the expression of PDLIM2 and streptavidin ionized in nano-electrospray ion source, where nitrogen was used binding peptide fusion protein in the cells. Cell lysates were as a drying and nebulizing gas. Temperature and flow of drying prepared using hot (95˚C) sample buffer (10% glycerol, 2% gas was set to 150ºC and 12 psi. Voltage at the capillary emitter bromophenol blue, 62.5 mM Tris HCl pH 6.8, 2% SDS pH 6.8, 5% was 2.65 kV. mercaptoethanol). SDS-PAGE with 5% stacking gel (126.67 mM Tris HCl pH 6.8, 5% acrylamide, 0.6% TEMED, 1.2% APS) and Data acquisition in data-dependent mode. To generate a SWATH 10% running gels (373 mM Tris HCl pH 6.8, 10% M acrylamide, assay library, a pooled mixture of all digested protein samples was 0.6% TEMED, 1.2% APS) were used for separation. 20 μg of total measured in a positive data dependent acquisition (DDA) mode in protein as determined by RC-DC Protein Assay (Bio-Rad) were run

398 Maryáš et al : SPR-based Affinity Purification with SWATH MS and Interactomics

Figure 2. Typical sensorgram from interaction of the lysate with streptavidin-modified SPR chip and elution of the captured proteins using the MICRORECOVERY procedure of Biacore.

in gels and wet transferred onto PVDF membranes. Membranes (GFP). Expression of PDLIM2 and SBP was confirmed were than blocked for 1 h in PBS+0.1% Tween 20 (2.68 mM KCl, using western blotting as shown in Figure 3. To compare 0.137 M NaCl, 6.45 mM Na 2HPO 4.12H 2O, 1.47 mM KH 2PO 4, 0.89 mM Tween 20) containing 5% non-fat milk, washed two times different experimental conditions that may play a role in in PBS and once in PBS+0.1% Tween 20 and incubated with identification of PDLIM2 interactors, we used four different primary antibody in 4 ˚C overnight. Mouse anti-PDLIM2 antibody pull-down assay protocols adopted and/or modified from (OriGene Cat. No TA50270, dilution 1:250) was used for PDLIM2 previous publications: Method 1 (26) and Method 3 (27), of detection, Streptavidin-Peroxidase polymer (Sigma-Aldrich Cat. which the first one was also modified by the use of No. S2438-250UG, dilution 1:2,000) was used for SBP detection zwitterionic detergent (28) (Method 2) and for the use on and in-house prepared PC10 antibody supernatant in dilution SPR chip (Method 4), see Figure 1 and Table I for overview corresponding to concentration 1 μg/ml was used to detect proliferating cell nuclear antigel (PCNA) as a loading control. After of the details. In all methods, the eluted proteins were the incubation, membranes were washed again. Membranes reduced, alkylated, digested by trypsin and the resulting incubated with anti-PDLIM2 antibody or PC10 antibody peptides were analysed by LC-MS/MS in SWATH mode to supernatant were subsequently incubated with corresponding ensure consistent peptide and protein quantification across secondary antibody (RAMPx, DakoCytomation, dilution 1:1,000) the samples (27), with quantitative data extraction in at room temperature for 1 h. After incubation with secondary Spectronaut software (29) using a custom spectral library antibody, the membranes were washed again and incubated for containing 128 identified protein groups based on 675 5 min with enhanced chemiluminescence (ECL) solution (10 mM luminol, 0.5 mM EDTA, 405 μM coumaric acid, 200 mM Tris pH peptides. Quantitative data were obtained for 120 9.4, 8 mM sodium perborate tetrahydrate, 50 mM sodium acetate pH consistently quantified proteins across SWATH dataset that 5). Membranes incubated with Streptavidin-Peroxidase polymer were were statistically evaluated in mapDIA software (30) (see directly incubated with ECL solution for 5 min. In both cases, Materials and Methods for details). Importantly, significantly immunoreactive proteins were visualized by ECL using CCD camera. high augmentation of PDLIM2 protein levels were detected by all four methods. Among them, the highest log2 fold Results change (log 2FC) of PDLIM2 protein against control pull- down assay, log 2FC=7.692, was obtained by Method 3, To identify PDLIM2 interacting partners in human breast originating from both the highest signal in PDLIM2-positive cancer cells, we generated a cell line stably transfected with sample (log2 protein intensity was 5.007) and the lowest one sequences encoding N-terminally SBP tagged PDLIM2 in a control sample ( –2.766, Table II). This was followed by protein as well as control cell line stably transfected with log 2FC obtained by Method 2 (log 2FC=3.049), Method 4 sequences encoding SBP tagged green fluorescence protein (log 2FC=2.578) and Method 1 (log 2FC=1.590).

399 CANCER GENOMICS & PROTEOMICS 15 : 395-404 (2018)

Figure 3. Verification of successful incorporation of pLENTI-N-SBP-PDLIM2-IRES-GFP and pLENTI-N-SBP-GFP vectors in MCF7 cells. A) Detection of PDLIM2 protein levels in non-transfected MCF7 cells and stably transfected MCF7 N-SBP-PDLIM2 cells and MCF7 N-SBP-GFP cells. B) Detection of Streptavidin-binding peptide in non-transfected MCF7 cells and stably-transfected MCF7 N-SBP-PDLIM2 cells and MCF7 N-SBP-GFP cells. Proliferating cell nuclear antigen (PCNA) was used as a loading control.

As biologically relevant interacting protein partners we Discussion considered only proteins statistically significantly more abundant (log 2FC >1 and FDR <0.05) in PDLIM2 positive In this study, we attempted to find optimal conditions for purifications by at least two methods in parallel (see Table identification of PDLIM2 interactors in breast cancer II). This provides an initial validation corresponding to the background drawing a comparison between three technical aims of this study. The largest overlap (6 conventional pull-down-LC-MS/MS approaches and pull- interactors) was found between Method 3 and Method 4, down assay on the streptavidin-modified SPR chips. We involving biologically interesting interactions with Shroom3 mainly focused on the following key steps: (i) solid support (SHROOM3), serine/threonine protein kinase Nek 10 on which the fusion proteins are bound, (ii) the mechanics (NEK10) and CREB3 regulatory factor (CREBRF). A single of the washing and elution, and (iii) detergents used for cell interaction confirmed by both Method 1 and Method 4 was lysis, wash and elution of interacting proteins. between PDLIM2 and calmodulin. These proteins may Our data show that both types of solid support, represent novel potential interaction partners of PDLIM2 in streptavidin beads and SPR chip, enabled a sufficient breast cancer cells. Interestingly, additional interactors that capacity to bind, identify and quantify SBP-PDLIM2 support previously identified PDLIM2 interactions with protein using pull-down MS approach because significant stress fibres (actin, tropomyosin alpha-3 chain, transgelin-2 augmentation of PDLIM2 protein levels was detected by and contractility regulator calmodulin) were confirmed all four methods (see log2FC values in Table II). This is almost exclusively by SPR-chip based Method 4 as shown especially important for the SPR chip, whose capacity is in Table III. considered significantly lower than the capacity of the

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y p i t 1 1 6 5 1 7 8 6 5 0 7 i

t

a e i n 0 4 8 2 3 2 4 3 7 1 2 e d

t m ...... t

s o e

a d o 0 0 0 6 3 7 7 1 0 2 1 o

n

s r

e C r

m

e

r

p

t r p

e i

n , f

i e d e e

e

4 n r

l v 0 4 1 2 8 9 2 5 9 0 s g

2 3 i o o

p 8 2 9 1 3 0 1 5 0 0 a a t

c g 0 i r

2 1 4 5 8 9 5 5 2 9 . b e m s o ...... 1 s e r 0 a

a o r 1 2 0 6 2 6 7 0 2 1 t v L a

– s

d e

P

a a

o s n

d

, t

h n

r e t t i

r

a e e o 4 4

t a

r p 6 6 2 9 1 2 5 9 6 0 0

o R

M

- - 0 0 4 4 8 2 9 2 2 P w

r g i t

1 6 4 2 1 1 2 3 1 3 3 D f

n ...... P n

0 1 i

F o

. . 0 0 0 0 0 0 0 0 0 t .

s U

2 2 c

n

a

o o

A

t i r

C 9 3 7 6 9 I

t

4 0 7 1 7 0 e

F 4 6 7 4 5 s t

a D 6 9 5 7 5 7

2 e 3 5 3 8 4 n

c

p 2 5 2 0 1 6 . . . . .

i i g c ......

f - a 0 0 0 0 0

o n i 1 1 0 0 0 0

2 t

– – – – –

e m L

n r

M

e a y

I f

u

b

y

- L - e

e

3 l r

q

r I

e e I s s 7

n

,

n B D d I y o e

i

e g n ) i m a

r t u 1

n s e - 5 6 M i o m l n e P e

D

a n i t 2 e

a

o 1 I o o i

n a

t

o l m i

n I n l l o y p f l l

i

e

h a p n o e

r

n

o e

a L a t s a d A o l o u x y a r 0 r M a

u c

i l t

r

y o a t n t n t

d

i n

o I o e n u t i g o h i o 1 h

c

d e

k t e d

e p e n i r r o

e

t

, l y c

e l l i

g c e L b , k

u

o r n S n n s m g l b p h t r c

e

r n v c e

n e e i n e

t n c e -

n a i u o o a i t a a D i r o

a / i

l k

a t f m u e k

n t

t r r

f

3 o t r c i l e t i o s a N c e e A s

P

y o s a n I t t e

e

Z h

a r

b a (

e t o r o

- B P t n h

o

h p i r

o e t o

b c m t N r

i p

t

t

n e e

r

A D C

r

.

e E V f

r 2 u o

o y

e P g y e

R

m o l

r p

P d

e o

K I

H c

X d

R

c

K

a

e

m P

C S

t

M

v

C

n

s

g

-

i

i

l

a

p

L

r

D r

5

0

f

I

. e

6

d 2

6

I

H

5

C

d

8 1 2 9 9

e

e I

n 7

R

9

t

i Y

n

5 5 0 5 5

d

C

W F

s

e i J 6

e

u

1 6 6 1 2 U

l

t

t

u 6 I Z T 4 H

2 9 1 8 4

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9 8 6 8 0 9

6 r 0 0 3 0

a r e d

T P P a Q p a P Q Q Q P Q P Q P

401 CANCER GENOMICS & PROTEOMICS 15 : 395-404 (2018) . . t ) p 4 5 5 3 1 1 e

2

streptavidin beads. An important difference between ( P

n

2

.

Method 3 (that provided both the highest log FC and 2 g M

I a 9 0 6 r 6 3

L

2 3 1

vs PDLIM2 protein intensities) . Methods 2 and 1 was the F

D

n

P

l use of different mechanics of wash and elution, which was f

e 4 o o y l 2 4 9 5

t r 0 p i t 9 0 1 8 s 3

done by gravity flow in Method 3, while by centrifugation s r n 4 4 3 6 . m . . . . n e o 0 a 0 0 1 2 e n

– s t C

t

in Methods 1 and 2. Also, Method 4 based on SPR chip

r n

i

e a

e l v 2 p 3 1 2 1 and micro-flow provided very similar data as Methods 1 1 i

p 1 g 8 7 3 7 t n i 9 o 8 0 2 2 4 s m . . . . . o

i L a

and 2 in term of signal intensities of PDLIM2, despite the o 2 1 3 2 5 d

t

s

P

c o

a h

lower binding capacity of SPR chip. However, signal t r

5 3 4

e e

1 0 0 t

R - - -

M n

0 intensities of many potential interactors in Method 4 were 0

7 7 6 i

D

3 9 5

F

. . .

d

2

e 6 3

comparable or higher than in Method 3, which may

i

f

i

t C

7 2 7 3

6

indicate comparable or even better binding conditions for n

F

1 9 6 2 8

e 2

4 4 8 3 4

d g

. . . . .

i

some specific proteins in Method 4 than in Method 3 o

1 1 2 2 3

y

L

l

s

(Table II). Importantly, Method 1 and Method 4 provided

l u

e

y o o l

4 1 8 0 6 t i r

i p

t 2 6 3 5 8 different potential interactors. Since they were based on v

s

n 5 3 6 8 3 e m

n . . . . . r

o a e

3 1 5 2 1 p

t

s

similar lysis, wash and elution buffers but different solid C

n

h

i

t e

e i 9 8 7

2 l v 6 support and mechanics of elution, we conclude that buffer 8

i 6 7 8 g w p 2 4 t

9 3 8 i 3 o t . 8 1 . .

s m . . n L 0 0 composition has only a minor effect on our results, in 1 d a o

0 0 e

–

– – s o

P

m h

2 t 7

contrast to solid support and mechanics of elution, where

e

e 1 0

e

R -

-

r

0 0 2 9

M

D g

gravity flow and microflow provided better results than

1

3

a F

.

.

l 2

1

a

centrifugation. From the practical point of view, lower

C

5 8 3 7

p

i

F 1 3 3 9

c

2 5 2 0 3

sample consumption, time-saving, and more efficient

. . . .

n

g

i

4 2 4 4

o r

– – – –

p L

binding conditions are the major benefits of SPR. The

a

l

e

6 5 7 o l

fully automated operation significantly enhancing 4 9 n y r

2 3 1 i t p

t 1 3

i

6 0 0

n . 6 0 . . e s

m . . r

reproducibility of assays and minimized workload 1 o 0 1 n a

0 2 a

–

– – e s C

t

t

n

represent additional experimental benefits of the use of a

i

e

e h 0 3 3

l v t 9 7 2

i 4 8

8

p 3 7 t g

SPR chip. ) 4 1 4 i 2 . 8 7 . . o 5 s m . .

1 0 0 0 L a o 0 0 d .

–

– – In the next step, we focused on biological relevance of our s

P

o

0

h

< t

4

e data. Only proteins statistically significantly more abundant R

6 1 3

4 0

R -

2 3 9

0 D M

0

4 2 4 0 D

F .

. . (log2FC >1 and FDR <0.05) in PDLIM2 positive .

0

F

.

0

0 0 0 ,

2

1

purifications by at least two methods in parallel were

>

C

6 9

9 1 0

F C 7 1

3 3 7

2

2 5

considered as a biologically relevant and among them F

. 1 2 7 .

g

2 . . .

0 1

o

g 0 0 0

–

–

L

o SHROOM3, CREBRF, NEK10 and calmodulin were

l

(

l

e

o

4 l 9 7 9 5 detected. SHROOM3 is involved in Rho signalling and 6

r

p y t 5 2 7 1 1

d t

n 0 6 8 3 2 i

o m . . . . .

s epithelial cell remodelling (31), CREBRF regulates NF-ĸB o

a h 0 0 1 0 3

n

t

s

C

e

e

t

pathway via CREB3 protein (32), NEK10 regulates MAPK

n

M

e i

e

l v 9 2 9 7 0 y

2 i

p 0 4 2 5 8 pathway (33) and regulates phosphorylation-mediated t b

g i

2 3 2 2 2 o m s . . . . . 1 d

a o 1 0 1 0 3 e L

contractility of stress fibres (34). However, all potential

s t d

P

c

o

e

h

t

t

interactions detected in screening experiments such as AP-

e

e 5 4

d

8 5 3 0 0

R

M

- - 2 9 5 MS generally require further validation using an independent s

1 4 e 4 3 2 D

. . . r

0 4 F

. . 0 0 0

b

approach before considered as “true” interactions. Our data

i

2 3 .

f

I

I

s

C

6 1 5

s 1 also correspond to previously published PDLIM2 interactors: 4

e F 2 6 2

l e 4 6

2 3 4 1 r

b 2 0 . . .

t

g . .

a s 0 0 Torrado et al. (24) identified components of stress fibres, 0

o 1

0

T

– – – f

L

o

r

including filamin A, as PDLIM2 interactors. Filamin A and

o

s

f

t

n

d

e 2 other components of stress fibres (actin (35), tropomyosin

n e n n

2

i i n

n

- e c

i

a s m A o i

l

n g

-

a h o , i

alpha-3 chain, transgelin-2 and contractility regulator p

u e l c

n m n y n l

d

i

e i

s m

t

o

3

n e m g a o

m c i - calmodulin) were detected by Method 4 (see Table III), l

s e

o c

m a

e a p

s

n l

l A t p f

h

i o a

a

o o s t

o

r

p F r which further supports PDLIM2 interaction with stress fibre r

l C n

y

t

T

P

T

a

o c

s

i

i

t

l

proteins and thus biological relevance of our data.

a

A

n

D

.

a

In conclusion, we for the first time compared conventional

I

l I

I

p

3 1 3 2

I 8

n

x

i

3 6 5 0

5

pull-down-LC-MS/MS approaches with SPR-LC-MS/MS e

e

e

3 2 7 8

1

l

t

r

1 3 6 7

2 b

o

o

r 2 6 0 3

6 a

system. Both pull-down-LC-SWATH-MS/MS approach with

P T P P P P F gravity flow and SPR-LC-SWATH-MS/MS system represent P

402 Maryáš et al : SPR-based Affinity Purification with SWATH MS and Interactomics potent tools in interactomics studies, where SWATH-MS 13 Stockley PG and Persson B: Surface plasmon resonance assays enables consistent and reliable protein quantification (27, of DNA-protein interactions. Methods Mol Biol 543 : 653-669, 36). Moreover, the SPR-based system provides efficient 2009. 14 Borch J and Roepstorff P: Combinations of SPR and MS for binding conditions, real-time observation of binding/ characterization of native and recombinant proteins in cell washing/elution steps and fully automated operation, which lysates. Mol Biotechnol 33 : 179-190, 2006. do not exist in the alternative procedures. 15 Reverté L, de la Iglesia P, del Río V, Campbell K, Elliott CT, Kawatsu K, Katikou P, Diogène J and Campàs M: Detection of Conflicts of Interest tetrodotoxins in puffer fish by a self-assembled monolayer-based immunoassay and comparison with surface plasmon resonance, LC- The Authors have declared no conflicts of interest. MS/MS, and mouse bioassay. Anal Chem 87 : 10839-10847, 2015. 16 Nedelkov D, Tubbs KA and Nelson RW: Surface plasmon resonance-enabled mass spectrometry arrays. Electrophoresis 27 : Acknowledgements 3671-3675, 2006. 17 Boucher LE and Bosch J: Development of a multifunctional tool This work was supported by Czech Science Foundation (Project No. for drug screening against plasmodial protein-protein 17-05957S), JM and JF were supported by grant MEYS-NPS-LO1413. interactions via surface plasmon resonance. J Mol Recognit 26 : 496-500, 2013. References 18 Bécsi B, Dedinszki D, Gyémánt G, Máthé C, Vasas G, Lontay B and Erdődi F: Identification of protein phosphatase interacting 1 Braun P and Gingras AC: History of protein-protein interactions: proteins from normal and UVA-irradiated HaCaT cell lysates by from egg-white to complex networks. Proteomics 12 : 1478- surface plasmon resonance based binding technique using biotin- 1498, 2012. microcystin-LR as phosphatase capturing. molecule. J 2 Yanagida M: Functional proteomics; current achievements. J Photochem Photobiol B 138 : 240-248, 2014. Chromatogr B Analyt Technol Biomed Life Sci 771 : 89-106, 19 Hayano T, Yamauchi Y, Asano K, Tsujimura T, Hashimoto S, 2002. Isobe T and Takahashi N: Automated SPR-LC-MS/MS system 3 Berggard T, Linse S and James P: Methods for the detection and for protein interaction analysis. J Proteome Res 7: 4183-4190, analysis of protein-protein interactions. Proteomics 7: 2833- 2008. 2842, 2007. 20 Bouchal P, Dvořáková M, Roumeliotis T, Bortlíček Z, Ihnatová 4 Rao VS, Srinivas K, Sujini GN and Kumar GN: Protein-protein I, Procházková I, Ho JT, Maryáš J, Imrichová H, Budinská E, interaction detection: methods and analysis. Int J Proteomics Vyzula R, Garbis SD, Vojtěšek B and Nenutil R: Combined 2014 : 147648, 2014. Proteomics and Transcriptomics Identifies Carboxypeptidase B1 5 Kool J, Jonker N, Irth H and Niessen WM: Studying protein- and Nuclear Factor ĸB (NF-ĸB) Associated Proteins as Putative protein affinity and immobilized ligand-protein affinity Biomarkers of Metastasis in Low Grade Breast Cancer. Mol Cell interactions using MS-based methods. Anal Bioanal Chem 402 : Proteomics 14 : 1814-1830, 2015. 1109-1125, 2011. 21 Maryas J and Bouchal P: PDLIM2 and its Role in Oncogenesis - 6 Takahashi N, Kaji H, Yanagida M, Hayano T and Isobe T: Tumor Suppressor or Oncoprotein? Klin. Onkol 28 : 40-46, 2015. Proteomics: advanced technology for the analysis of cellular 22 Yu J, Li X, Wang Y, Li B, Li H, Li Y, Zhou W, Zhang C, Wang function. J Nutr 133 : 2090-2096, 2003. Y, Rao Z, Bartlam M and Cao Y: PDlim2 selectively interacts 7 Zhao X, Li G and Liang S: Several affinity tags commonly used with the PDZ binding motif of highly pathogenic avian H5N1 in chromatographic purification. J Anal Methods Chem 2013 : influenza A virus NS1. PLoS One 6: e19511, 2011. 581093, 2013. 23 Fu J, Yan P, Li S, Qu Z and Xiao G: Molecular determinants of 8 Barrette-Ng IH, Wu SC, Tjia WM, Wong SL and Ng KK: The PDLIM2 in suppressing HTLV-I Tax-mediated tumorigenesis. structure of the SBP-Tag-streptavidin complex reveals a novel Oncogene 29 : 6499-6507, 2010. helical scaffold bridging binding pockets on separate subunits. 24 Torrado M, Senatorov VV, Trivedi R, Fariss RN and Tomarev Acta Crystallogr D Biol Crystallogr 69 : 879-887, 2013. SI: Pdlim2, a novel PDZ-LIM domain protein, interacts with 9 Wu SC and Wong SL: Structure-guided design of an engineered alpha-actinins and filamin A. Invest Ophthalmol Vis Sci 45 : streptavidin with reusability to purify streptavidin-binding 3955-3963, 2004. peptide tagged proteins or biotinylated proteins. PLoS One 8: 25 Dvořáková M, Jeřábková J, Procházková I, Lenčo J, Nenutil R e69530, 2013. and Bouchal P: Transgelin is upregulated in stromal cells of 10 Stotland A, Pruitt L, Webster P and Wolkowicz R: Purification lymph node positive breast cancer. J Proteomics 132 : 103-111, of the COP9 signalosome complex and binding partners from 2016. human T cells. OMICS 16 : 312-319, 2012. 26 Trcka F, Durech M, Man P, Hernychova L, Muller P and 11 Keefe AD, Wilson DS, Seelig B and Szostak JW: One-step Vojtesek B: The assembly and intermolecular properties of the purification of recombinant proteins using a nanomolar-affinity Hsp70-Tomm34-Hsp90 molecular chaperone complex. 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28 Görg A, Weiss W and Dunn MJ: Current two-dimensional 33 Moniz LS and Stambolic V: Nek10 mediates G 2/M cell cycle electrophoresis technology for proteomics. Proteomics 4: 3665- arrest and MEK autoactivation in response to UV irradiation. 3685, 2004. Mol Cell Biol 31 : 30-42, 2011. 29 Bruderer R, Bernhardt OM, Gandhi T, Miladinović SM, Cheng 34 Tojkander S, Gateva G and Lappalainen P: Actin stress fibers-- LY, Messner S, Ehrenberger T, Zanotelli V, Butscheid Y, Escher assembly, dynamics and biological roles. J Cell Sci 125 : 1855- C, Vitek O, Rinner O and Reiter L: Extending the limits of 1864, 2012. quantitative proteome profiling with data-independent 35 Arany I, Clark JS, Reed DK, Ember I and Juncos LA: Cisplatin acquisition and application to acetaminophen-treated three- enhances interaction between p66Shc and HSP27: its role in dimensional liver microtissues. Mol Cell Proteomics 14 : 1400- reorganization of the actin cytoskeleton in renal proximal tubule 1410, 2015. cells. Anticancer Res 32 : 4759-4763, 2012. 30 Teo G, Kim S, Tsou CC, Collins B, Gingras AC, Nesvizhskii AI 36 Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, and Choi H: mapDIA: Preprocessing and statistical analysis of Bonner R and Aebersold R: Targeted data extraction of the quantitative proteomics data from data independent acquisition MS/MS spectra generated by data-independent acquisition: a mass spectrometry. J Proteomics 129 : 108-120, 2015. new concept for consistent and accurate proteome analysis. Mol 31 Nishimura T and Takeichi M: Shroom3-mediated recruitment of Cell Proteomics 11 : O111.016717, 2012. Rho kinases to the apical cell junctions regulates epithelial and neuroepithelial planar remodeling. Development 135 : 1493- 1502, 2008. 32 Xue H, Zhang J, Guo X, Wang J, Li J, Gao X, Guo X, Li T, Xu S, Zhang P, Liu Q and Li G: CREBRF is a potent tumor suppressor of glioblastoma by blocking hypoxia-induced Re ceived July 4, 2018 autophagy via the CREB3/ATG5 pathway. Int J Oncol 49 : 519- Revised July 26, 2018 528, 2016. Accepted July 27, 2018

404 12.4 Appendix 4

Research paper 3

PDZ and LIM domain protein 2 plays dual and context dependent roles in breast cancer development

Maryas J, Pribyl J, Bouchalova P, Skladal P, Bouchal P. BioRxiv. 2020; doi: 10.1101/2020.01.27.920199 (BMC Cancer under review) https://www.biorxiv.org/content/10.1101/2020.01.27.920199v1 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

1 PDZ and LIM domain protein 2 plays dual and context-dependent roles in breast cancer

2 development

3

4

5 Josef Maryas1,2, Jan Pribyl3, Pavla Bouchalova1, Petr Skladal1,3 and Pavel Bouchal1*

6 1Masaryk University, Faculty of Science, Department of Biochemistry, Brno, Czech Republic

7 2Masaryk Memorial Cancer Institute, Regional Centre for Applied Molecular Oncology, Brno, Czech

8 Republic

9 3Masaryk University, Central European Institute for Technology, Brno, Czech Republic

10

11 *Corresponding author: 12 Pavel Bouchal, Ph.D. 13 Masaryk University 14 Faculty of Science 15 Department of Biochemistry 16 Kamenice 5 17 62500 Brno 18 Czech Republic 19 Phone: +420-549493251 20 E-mail: [email protected]

21

1 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

22 ABSTRACT

23 Background

24 PDZ and LIM domain protein 2 (PDLIM2) is a cytoskeletal and nuclear effector that regulates the

25 activity of several transcription factors (e.g., NF-κB, STAT), and its deregulation has been associated

26 with oncogenesis. Our recent study identified PDLIM2 as a protein associated with the lymph node

27 metastasis of low grade luminal A breast cancer tissues. Here, we aim to understand this association at

28 the molecular and cellular levels.

29 Methods

30 To investigate the link between PDLIM2 and epithelial-to-mesenchymal transition (EMT), stably

31 transduced MCF7-PDLIM2 cells, and MCF7 or MCF10A cells with PDLIM2 protein levels modified

32 using siRNA or PDLIM2 gene carrying plasmid, were used. Additionally, MCF7 and MCF10A cells

33 were exposed to hypoxic conditions and TGFβ1 treatment. EMT was monitored using immunoblotting

34 of EMT markers and atomic force microscopy (AFM). The role of PDLIM2 in cell migration and/or

35 invasion was investigated using Transwell assay and xCELLigence system.

36 Results

37 First, we observe a positive effect of PDLIM2 overexpression on EMT in MCF7 cells, a model of

38 luminal A tumors, using EMT markers and AFM. On the other hand, PDLIM2 helps to maintain the

39 epithelial phenotype in MCF10A cells, a model of normal breast epithelial cells. Second, we find that

40 exposure of the MCF7 cells to hypoxic conditions increases levels of PDLIM2 and carbonic

41 anhydrase-9 (CA-9), a marker of the response to hypoxia. However, none of these effects are observed

42 in the MCF10A cells. Third, PDLIM2 overexpression promotes migration, invasion, and proliferation

43 and decreases adhesion of the MCF7 cells, but an opposite effect is observed in the MCF10A cells.

2 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

44 Conclusions

45 Our data indicate that PDLIM2 plays a dual role: (i) as an EMT-supporting and hypoxia-responding

46 oncoprotein in luminal breast cancer cells, and (ii) as an epithelial phenotype-maintaining tumor

47 suppressor in normal epithelial breast cells.

48 KEYWORDS

49 PDLIM2; EMT; metastasis; luminal A breast cancer; hypoxia; MCF10A cells

50

3 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

51 BACKGROUNDS

52 Breast cancer is the most common form of cancer and the second cause of death of women worldwide.

53 Distant metastases represent the main reason for patient mortality [1, 2]. For risk group discrimination

54 and determination of the metastatic potential of breast tumors in clinical practice, both traditional and

55 molecular prognostic markers have been used. However, currently available markers are not sufficient

56 for precise determination of metastatic potential [3, 4]. This insufficiency is well demonstrated by the

57 low grade luminal A breast cancer subtype, whose general prognosis is favorable; nevertheless,

58 approximately one-third of these tumors exhibit early lymph node metastases in disagreement with the

59 initial prognosis. The molecular mechanism responsible for this lapse is unknown [3]. To improve

60 understanding, we identified a panel of proteins associated with lymph node metastasis in low grade

61 luminal A breast cancer using combined proteomics and transcriptomics [5]. PDZ and LIM domain

62 protein 2 (PDLIM2), also known as Mystique or SLIM, was one of the key proteins in this panel, and

63 its mRNA and protein levels were upregulated in a set of 24 lymph node-positive luminal A grade 1

64 tumors compared to their 24 lymph node-negative counterparts, specifically in this subtype.

65 Additionally, the PDLIM2 protein level was higher in grade 1 vs. grade 3 tumors and in HER2- vs.

66 HER2+ breast cancer in a set of 96 breast tumors in total, indicating its connection with the luminal A

67 subtype, low tumor grade, and high metastatic potential.

68 PDLIM2 is a member of the actinin-associated LIM protein (ALP) family, which contains a single N-

69 terminal PDZ domain and C-terminal LIM domains [6]. All members of the ALP family are known to

70 interact with the actin cytoskeleton [7] and play essential roles in its organization, cell differentiation,

71 organ development, and neural signalization and have been associated with oncogenesis in general [8].

72 PDLIM2 acts as an E3 ubiquitin ligase and as such regulates the stability of NF-κB and other

73 transcription factors in hematopoietic and epithelial cells. Its deregulation has been associated with

74 several malignancies [7, 9-10]. The PDLIM2 gene is localized on chromosome 8p21, a region

75 frequently disrupted in various cancers [10-11], and its expression has been previously connected with

76 both tumor suppression and oncogenesis [10]. PDLIM2 expression is epigenetically suppressed by

77 promoter hypermethylation in different cancers [10], and its re-expression is able to inhibit

4 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

78 tumorigenicity and induce tumor cell death both in vitro and in vivo [12]. It was first identified in

79 corneal epithelial cells [6] and in fibroblasts transformed by overexpression of the insulin-like growth

80 factor 1 receptor (IGF-1R), T-lymphocytes, macrophages, dendritic cells and epithelial cancer cells [6-

81 7, 9-11]. On the other hand, PDLIM2 is highly expressed in invasive cancer cells, and its expression is

82 associated with tumor progression and metastasis [5, 9].

83 Controversial literature results on the role of PDLIM2 in cancer development led us to assumptions

84 about its dual and context-dependent role [13]. To test this hypothesis, we performed a series of in

85 vitro experiments at the molecular and cellular levels using MCF7 breast cancer cells and MCF10A

86 immortalized normal epithelial breast cells. Comparison of migration and invasion abilities as well as

87 association with epithelial-to-mesenchymal transition (EMT) and hypoxia of different cell lines with

88 modulated levels of PDLIM2 confirmed our hypothesis regarding the dual and context-dependent

89 roles of PDLIM2 in breast cancer. Based on our data, we postulated a precise hypothesis about the

90 cyclic changes of PDLIM2 protein levels during the oncogenesis of breast cancer, which also explains

91 the inconsistency among previously published studies.

92 MATERIALS AND METHODS

93 Reagents and antibodies

94 Pyruvate, bromophenol blue, glycerol, mercaptoethanol, acrylamide, TEMED, APS, Tween 20, SDS,

95 Tris HCl, HEPES, PEI, TGFβ1, luminol, coumaric acid, resazurin, insulin, CH3COONa,

96 NaBO3.4H2O, NaCl, KCl, Na2HPO4.12H2O, KH2PO4, MgCl2, and NaH2PO4 were purchased from

97 Sigma-Aldrich. Streptomycin/penicillin, trypsin and EGTA were purchased from Invitrogen. Fetal

98 bovine serum and horse serum were purchased from Biochrom AG, GoldViewTM was obtained from

99 Viswagen, EDTA from Serva, EGF from Millipore, Calcein AM from Biotium, crystal violet from

100 Merck, hydrocortisone from VUAB Pharma, and formaldehyde from Penta. Mouse anti-E-cadherin

101 (diluted 1:100) antibody (Ab) and anti-vimentin Ab (1:1000) were purchased from DakoCytomation,

102 mouse anti-p65 Ab (1:500) was purchased from Santa Cruz Biotechnology, mouse anti-N-cadherin Ab

103 was purchased from Invitrogen, mouse anti-actin Ab (1:250) was purchased from Sigma Aldrich and

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104 mouse anti-PDLIM2 Ab (1:250) was purchased from Origene. Rabbit anti-FAK Abs (1:500), anti-β-

105 catenin Ab (1:500) and anti-p-p53 (phosphorylated on S20) Ab (1:500) were purchased from Cell

106 Signaling. Mouse anti-CA9 Ab (M75, diluted 1:3), anti-KRT18 Ab (DC10, 1:10), anti-p53 (DO-1,

107 1:10) and anti-PCNA Ab (PC10, 1:10) were prepared in-house. Horseradish peroxidase-conjugated

108 RAMPx and SWARPx Abs (both DakoCytomation, dilution 1:1000) were used as secondary Abs. All

109 antibodies were diluted in PBS with 0.1% Tween 20 containing 5% nonfat milk.

110 Cell lines, cell culture and cell counting

111 The human ER-positive breast cancer cell line MCF7 and ER-negative breast cancer cell lines MDA-

112 MB-231 and BT-549 were maintained in DMEM supplemented with 10% FBS, 1.25 mM pyruvate,

113 0.172 mM streptomycin and 100 U/ml penicillin. The human nontumorigenic breast epithelial cell line

114 MCF10A was cultivated in DMEM/F12 supplemented with 5% horse serum, 1.25 mM pyruvate,

115 0.172 mM streptomycin, 100 U/ml penicillin, 20 ng/ml EGF, 0.5 mg/ml hydrocortisone and 10 μg/ml

116 insulin. All cell lines were purchased from the American Type Culture Collection (ATCC). The cells

117 were counted using a CASY TT cell counting device (Roche, Mannheim, Germany) or Bürker

118 chamber for adhesion, proliferation, migration and invasion experiments.

119 Commercial plasmids and siRNAs

120 For specific plasmid preparation, the pENTR221 “entry” vector and pcDNA3-GW-DEST

121 “destination” vector were used (both from Invitrogen). Empty pcDNA3-GW-DEST served also as a

122 CTRL plasmid in the experiments. On-Target plus SMART pool human PDLIM2 siRNA was used for

123 PDLIM2 suppression (600 nM concentration, cat. no. L-005152-00). As a control, CTRL siRNA On-

124 Target plus Nontargeting pool control siRNA (cat. no. D-001810-10-20) was used also at a

125 concentration of 600 nM (both siRNAs from Dharmacon, Thermo Scientific).

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126 Plasmid construction

127 Total RNA isolation and One step RT PCR

128 Total RNA was isolated from the BT-549, MDA-MB-231 and MCF7 cell lines using an RNeasy Mini

129 Kit 250 (Qiagen) according to the manufacturer’s protocol. The isolated RNA was pooled, eluted into

130 50 μl of RNase-free water, and stored at -80 °C. Complementary DNA (cDNA) was synthesized and

131 amplified using a One Step RT PCR kit (Invitrogen) with PDLIM2-specific primers: forward 5’-

132 CGGCGCCGGGCTCCTCTC-3‘ and reverse 5’-CCCTGGCCCACCCCTCTCCTTCC-3’. The

133 reaction mixture contained 1 M betain, 1x Reaction Mix, 0.36 M trehalose, 1 μl of SuperScriptTM III

134 RT/Platinum Taq Hi Fi Enzyme Mix, 0.5 μg of total RNA, and primers at a concentration 0.4 μM, and

135 nuclease-free water was added to the final volume of 50 μl. The PCR program included two steps of

136 cDNA synthesis (50 °C for 30 min and 80 °C for 3 min), predenaturation at 95 °C for 30 s, and 10

137 amplification cycles - each consisting of denaturation at 95 °C for 5 s, annealing at 55 °C for 30 s and

138 extension at 68 °C for 3 min, followed by further extension at 68 °C for 1 min. The resulting product

139 was purified using the standard protocol of the QIAquick PCR Purification Kit (Qiagen).

140 Construction of the pcDNA3-PDLIM2-GW-DEST plasmid

141 Gateway technology (Thermo Fisher Scientific) was used to construct the PDLIM2-carrying plasmid.

142 The cDNA fragment of PDLIM2 was amplified using PCR with Herculase II DNA polymerase, a pair

143 of specific primers, PDLIM2 TEV forward (5’-

144 GGCTCTGAGAACCTGTACTTCCAGAGCATGGCGTTGACGGTGGATGTG-3’, bearing a region

145 coding the sequence recognized by TEV protease) and PDLIM2 GWs reverse (5’-

146 GTACAAGAAAGCTGGGTTTCAGGCCCGAGAGCTGAGG-3’, bearing a stop codon), and a pair

147 of universal primers, TEV forward (TEV F: 5’-

148 GGGGCTGCTTTTTTGTACAAACTTGTCCGAGACTCTTGG-3’) and ATTB2 reverse (ATTB2 R:

149 5’-GGGGCAGCTTTCTTGTACAAAGTGGGACATGTTCTTTCG-3’). The reaction mixture

150 contained 1 M betain, 20 μl of cDNA, 1x reaction buffer, 1 mM dNTP, 1 μl of Herculase II, specific

151 primers at 0.1 μM and universal primers at 0.3 μM, and nuclease-free water was added to the final

152 volume of 50 μl. The PCR program included predenaturation at 95 °C for 1 min and 30 amplification 7 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

153 cycles - each consisting of denaturation at 95 °C for 10 s, annealing at 50 °C for 20 s and extension at

154 70 °C for 90 s, followed by further extension at 70 °C for 5 min. The final product was separated by

155 electrophoresis on a 1.5% GoldViewTM stained agarose gel and visualized under UV light by a CCD

156 camera. The target fragment was extracted by a QIAquick Gel Extraction Kit (Qiagen) according to

157 the manufacturer’s protocol, and 150 ng were used for the BP recombinant reaction with pDONR221

158 according to the Gateway technology manual (Thermo Fisher Scientific). Chemically competent

159 E. coli TOP10 (Life Technologies) cells were used for the preparation and amplification of resulting

160 entry vector (pENTR221-PDLIM2). The QIAprep Spin Miniprep Kit (Qiagen) was used for vector

161 purification according to the manufacturer’s protocol. A total of 150 ng of purified and sequencing-

162 verified entry vector together with 150 ng of pcDNA3-GW-DEST vector were used for preparation of

163 the pcDNA3-PDLIM2-GW-DEST destination plasmid, and the LR recombination reaction was

164 performed according to the Gateway technology manual (Thermo Fisher Scientific). Chemically

165 competent E. coli TOP10 cells were used again for preparation and amplification of the resulting

166 destination plasmid that was subsequently purified by the Qiagen Plasmid Maxi Kit (Qiagen). The

167 resulting destination plasmid was stored at -20 °C.

168 Construction of lentiviral plasmids and lentiviruses and generation of a stably transduced

169 MCF7-PDLIM2 cell line

170 The lentiviral vector pLENTI-PDLIM2 was prepared in-house according to Gateway® Technology

171 with the Clonase® II user guide (Invitrogen, 25-0749 MAN0000470). The production of lentiviruses

172 was performed according to the ViraPower™ Lentiviral Expression Systems user manual (Invitrogen,

173 25-0501 MAN0000273). Transduction of MCF-7 cells and selection of stably transfected clones were

174 performed according to the ViraPower™ Lentiviral Expression Systems user manual (Invitrogen, 25-

175 0501 MAN0000273).

176 Cell transfection

177 For PDLIM2 suppression, cells were transfected using the Amaxa cell line nucleofector kit

178 V (LonzaBio). A total of 1.0×106 cells cultivated to 70% confluence were harvested and resuspended

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179 in 100 μl of AMAXA buffer (4 mM KCl, 10 mM MgCl2, 120 mM NaH2PO4/Na2HPO4, 10 mM

180 HEPES pH 7.2) together with either anti-PDLIM2 siRNA or control siRNA at a concentration of 600

181 nM. T024 and P020 transfection programs were used for the transfection of MCF 10A and MCF7

182 cells, respectively. Plasmid transfection using the polyethylene imine (PEI) method based on

183 lipofection was used. Cells were cultivated on 6-cm Petri dishes to 60-70% confluence, and 5 μg of

184 specific or control plasmid was resuspended together with 15 μl of PEI (working solution 1 μg/μl in

185 water, pH 7) in 0.5 ml of serum-free medium, incubated for 15 minutes at room temperature, poured

186 onto a dish and further cultivated for 24, 48 or 72 hours.

187 SDS PAGE and immunoblotting

188 Cell lysates for SDS PAGE were prepared using hot (95 °C) sample buffer (10% glycerol, 2%

189 bromophenol blue, 62.5 mM Tris HCl pH 6.8, 2% SDS pH 6.8, 5% mercaptoethanol). SDS PAGE

190 with a 5% stacking gel and 10% running gel was used for separation as described previously [14].

191 Protein lysates in the amount of 20 µg, determined by an RC-DC Protein Assay (Bio-Rad), were run in

192 the gels and wet-transferred onto PVDF membranes. Membranes were then blocked for 1 hour in

193 PBS+0.1% Tween 20 (2.68 mM KCl, 0.137 M NaCl, 6.45 mM Na2HPO4.12H2O, 1.47 mM KH2PO4,

194 0.89 mM Tween 20) containing 5% nonfat milk and incubated with primary antibody (at the

195 appropriate dilution, see above) at 4 °C overnight. After incubation, membranes were washed two

196 times in PBS and once in PBS+0.1% Tween 20 and subsequently incubated with the corresponding

197 secondary antibody (1:1000) at room temperature for 1 hour and washed again. After 5 min of

198 incubation of membranes with ECL solution (10 mM luminol, 0.5 mM EDTA, 405 μM coumaric acid,

199 200 mM Tris pH 9.4, 8 mM sodium perborate tetrahydrate, 50 mM sodium acetate), immunoreactive

200 proteins were visualized by enhanced chemiluminescence (ECL) solution using a CCD camera (Alpha

201 Innotech FluorChemTMSP, Quansys Biosciences, USA).

202 Young’s modulus mapping by Atomic Force Microscopy

203 Young’s modulus mapping was performed using JPK NanoWizard 3 (JPK, Berlin, Germany) on a

204 bioAFM microscope, similar to previous publications [15-17]. A non-coated silicon nitride AFM

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205 probe Hydra 2R 100N (AppNano, Mountain View, CA, USA) equipped with a pyramidal silicon tip

206 was used for all experiments (side angle 18°). The sensitivity and cantilever spring constant were

207 calibrated by the recommended routine procedure. The 64x64 point maps of force distance curves

208 (setpoint 1 nN, Z length 15 µm, time per curve 0.5 s) were measured. The recorded FD curves were

209 fitted with the Bilodeau modification [18] of the Hertzian model in AtomicJ software [19]. Final

210 visualization of the images and stiffness maps was performed with Gwyddion software ver. 2.44 [20].

211 Further details are provided in the Additional file 15: Methods S1.

212 TGFβ1 treatment and hypoxia

213 EMT in cells was induced by TGFβ1 treatment or hypoxic conditions. For PDLIM2 protein level

214 induction and monitoring, TGFβ1 was added to the complete culture medium to a final concentration

215 of 1 ng/ml, and the cells were cultivated for 24 hours. For cell morphology changes and monitoring,

216 TGFβ1 at 10 ng/ml was added to serum-free medium after 16 hours of serum starvation, and the cells

217 were cultivated for 48 hours [21]. Control cells were cultivated in medium without TGFβ1. For

218 induction of EMT by hypoxia, cells were cultivated in a hypoxic culture hood (Biospherix Xvivo X3,

219 Biospherix) under a 2% concentration of O2 for 48, 72 or 96 hours.

220 Migration and invasion assay

221 Real-time measurements of cell migration and invasion were performed using the CIM-Plate 16

222 module of an xCELLigence System RTCA DP real-time cell analyzer (Roche, UK). For invasion

223 measurement, the top side of the polycarbonate membrane in the upper chamber wells was coated with

224 Culturex®Basement membrane extract (Trevigen, USA) diluted 1:50 with Coating buffer (Trevigen,

225 USA) 4 hours prior to the experiment. The CIM plates were then prepared by the addition of 170 µL

226 of corresponding medium (with 10% FBS as the chemoattractant) into twelve wells of the lower

227 chamber; four wells filled with 170 µL of serum-free medium served as controls to determine the

228 background signal. Each well of the upper chamber was filled with 30 µL of serum-free medium. The

229 plates were inserted into the xCELLigence station in the culture hood (21% O2, 5% CO2, 37 °C), and

230 the baseline impedance was measured after 1 hour of equilibration at 37 °C. Cells (5×104 cells per well

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231 for migration and 1×105 cells per well for invasion) were then added to the wells of the upper chamber

232 in 100 µL of serum-free medium, and the plate was equilibrated again for 30 min at room temperature

233 and inserted into the xCELLigence once again before the start of measurements. The detailed

234 arrangement of the plates is shown in Additional file 1: Figure S1. The xCELLigence analyzer was set

235 to measure the impedance every 15 min for 24 hours. Normalized cell indexes (CI) were then

236 calculated as a variable corresponding to the number of migrating or invading cells and statistically

237 evaluated (part “Statistical analysis”).

238 Migration and invasion of cells were also measured using a 96-well Transwell assay (Trevigene,

239 USA). The Transwell assay consists of upper and lower chambers (each with 96 wells) separated by a

240 porous polyethylene terephthalate (PET) membrane (pore size 8 μm) localized on the bottom of the

241 upper chamber wells. For migration measurement, 100 μl of medium containing 10% FBS (used as a

242 chemoattractant) was applied to the lower chamber wells, and wells were filled with 100 μl of serum-

243 free medium were used as controls. Cells (5×104 cells per well for migration and 1×105 cells per well

244 for invasion; see Additional file 1: Figure S1 for plate design) were resuspended in serum-free

245 medium and added to the upper chamber wells. After incubation in a culture hood (21% O2, 5% CO2,

246 37 °C) for 24 hours, upper and lower chamber wells were rinsed, cells in the upper chamber wells

247 were washed away and the migrated or invasive cells on the lower membrane surface were stained

248 using Calcein AM according to the manufacturer’s protocol. After 1 hour of incubation in the culture

249 hood in the dark, the fluorescence of converted calcein was measured using a Tecan Infinite M100 Pro

250 (Life Sciences) with an excitation wavelength of 495 nm and emission wavelength of 515 nm. The

251 number of cells migrating or invading across the membrane was quantified according to fluorescence

252 values and statistically evaluated (part “Statistical analysis”). The invasion assay was performed

253 equivalently to the migration measurement assay but with coating of the top side of the polycarbonate

254 filter in the upper chamber wells with Culturex® Basement membrane extract.

255 Each experiment was performed with two biological replicates (cells grown on independent plates) per

256 condition with three technical replicates (wells) for each biological replicate. Two independent

257 experiments were performed per comparison.

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258 Adhesion assay

259 To measure adhesion in monolayer culture, cells in corresponding medium were seeded at 1×105 cells

260 per well in triplicate in a 6-well plate. After 30 min, the medium was completely removed from the

261 wells, and unattached cells in the medium were counted using a Bürker chamber. The adhesion of cells

262 was based on the amount of unattached cells, which was statistically evaluated (part “Statistical

263 analysis”). Each experiment was performed three times on independent plates per comparison.

264 Proliferation assay

265 To measure proliferation in monolayer culture, cells in corresponding medium were seeded at

266 2×104 cells per well in triplicate in a 96-well plate. After 24 hours, cells were treated with resazurin

267 (1 mg/ml) and incubated for 150 min. After the incubation period, the fluorescence of resorufin was

268 measured using a Tecan Infinite M100 Pro, and measurement was performed for 16 spots per well

269 (excitation wavelength 573 nm/emission wavelength 583 nm) and statistically evaluated. Each

270 experiment was performed three times on independent plates per comparison.

271 Statistical analysis

272 STATISTICA software version 12 was used for the statistical analyses for Figs. 3A-B and 5A. Data

273 were reported as the mean +/- 1.96*(standard deviation) corresponding to 95% confidence intervals;

274 Student’s t-test was used to assess the significance of differences between two groups, and p-values

275 below 0.05 were considered significant. For AFM evaluation in Fig. 1E, the Mann-Whitney test was

276 performed in R version 3.5.3., and p-values below 0.05 were considered significant. For the remaining

277 statistical analyses, Student’s t-test in Microsoft Excel was applied, and p-values below 0.05 were

278 considered significant.

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279 RESULTS

280 PDLIM2 protein levels are positively coregulated with epithelial-to-mesenchymal transition in

281 MCF7 breast cancer cells

282 Based on the results of our previous combined proteomics-transcriptomics study [5], we hypothesized

283 that PDLIM2 may act as an oncoprotein in luminal A breast tumors. To test this hypothesis in vitro,

284 we selected MCF7 human breast cancer cells as a model of luminal A breast cancer [22-24],

285 modulated PDLIM2 protein levels and evaluated molecular and cellular changes caused by these

286 modifications. First, overexpression of PDLIM2 decreased the epithelial markers E-cadherin, β-

287 catenin and keratin-18 (KRT18) and increased levels of the β1-integrin pathway regulator FAK (Fig.

288 1A and Additional file 2: Figure S2), indicating epithelial-to-mesenchymal transition (EMT) was

289 connected with PDLIM2. On the other hand, PDLIM2 expression suppression by small interference

290 RNA (siRNA; Fig. 1A and Additional file 2: Figure S2) increased E-cadherin, β-catenin and KRT18

291 and decreased FAK levels, indicating mesenchymal-to-epithelial transition (MET) was initiated by

292 PDLIM2 suppression. The ability of PDLIM2 to affect cell morphology was observed in MCF7 cells

293 stably transduced with PDLIM2 vector (compared to parental MCF7 as a control) using optical

294 microscopy, showing that MCF7-PDLIM2 cells acquire similar morphology as MCF7 cells after

295 TGFβ1-induced EMT (Fig. 1B). A similar pattern was evident from the atomic force microscopy

296 (AFM) data, showing MCF7-PDLIM2 cells had higher stiffness based on Young’s modulus relative to

297 parental MCF7 cells (Fig. 1E), similarly as after TGFβ1-induced EMT (Figs. 1B-F, Additional file 12:

298 Table S1 and Additional file 16: Dataset S1). All of these data suggest that PDLIM2 might play a

299 significant role in the regulation of EMT in MCF7 cells.

300 EMT induction by TGFβ1 and hypoxia increases PDLIM2 levels, and PDLIM2 overexpression

301 augments CA9 and reduces p53 levels as well as p53 phosphorylation (S20) in MCF7 breast

302 cancer cells

303 To investigate how PDLIM2 is affected by EMT, we induced EMT by TGFβ1 (1 ng/ml for 24 hours)

304 or by long-term exposure to hypoxia (2% O2 for 96 hours) and monitored how PDLIM2 responds.

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305 Both treatments led to elevated PDLIM2 protein levels, and successful induction of EMT was

306 confirmed by decreased levels of the epithelial markers E-cadherin, β-catenin and KRT18 (Fig. 2A

307 and Additional file 4: Figure S4). Notably, we observed that shorter exposure times to hypoxic

308 conditions (48 and 72 hours) were still not able to decrease levels of epithelial markers and induce

309 EMT (see Additional file 5: Figure S5); however, PDLIM2 levels increased (Fig. 2B and Additional

310 file 5: Figure S5), indicating that not only EMT but also hypoxia itself upregulates PDLIM2 levels.

311 Moreover, levels of carbonic anhydrase-9 (CA9), a marker of the response to hypoxic conditions, were

312 elevated in MCF7 cells with overexpressed PDLIM2 (Fig. 2C and Additional file 6: Figure S6).

313 In addition to TGFβ1 and hypoxia-induced EMT, we were interested in whether PDLIM2 functionally

314 interacts with other key cancer players, including p53 and p65. We found that PDLIM2

315 overexpression decreased both the p53 total protein level as well as its serine 20 phosphorylated,

316 active form (p-p53 (S20)) (Fig. 2D and Additional file 7: Figure S7). On the other hand, the level of

317 p65 protein, a key member of the canonical NF-κB pathway, was not affected by any of these

318 treatments, nor by overexpression or suppression of PDLIM2 (see Fig. 2D and Additional file 7:

319 Figure S7).

320 PDLIM2 overexpression increases both migration and invasion of MCF7 breast cancer cells

321 To further verify the pro-tumorigenic role of PDLIM2 on cellular levels, we examined its effect on the

322 migration and invasion of the MCF7 cells. PDLIM2 overexpression increased the migration

323 capabilities of these cells as observed by real-time measurement using the xCELLigence system

324 (p=2.8x10-4) and independently confirmed using a Transwell assay with end-point detection

325 (p=3.6x10-5) (Fig. 3A). Overexpression of PDLIM2 also had a similar effect on the invasion of MCF7

326 cells measured using xCELLigence (p=3.9x10-4) and the Transwell assay (p=1x10-5) (Fig. 3B). Our

327 results suggest that PDLIM2 is relevant for regulation of the migration and invasiveness of MCF7

328 cells.

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329 PDLIM2 overexpression diminishes cell adhesion and increases proliferation of MCF7 cells,

330 while suppression of PDLIM2 has the opposite effect

331 Next, we investigated the consequences of PDLIM2 modulation on the adhesion and proliferation of

332 MCF7 cells. Suppression of PDLIM2 by siRNA significantly augmented the adhesion of MCF7 cells

333 (*p=1x10-5) compared to control cells; on the other hand, specific overexpression of PDLIM2

334 considerably decreased adhesion (p=1x10-5) (Fig. 3C). PDLIM2 suppression also substantially

335 diminished the proliferation of MCF7 cells (p=1x10-5), in contrast to the dramatic increase in the

336 proliferation of the MCF7 cells with overexpressed PDLIM2 (p=1x10-5) (Fig. 3D). These results

337 indicate involvement of PDLIM2 in the regulation of MCF7 cell adhesion and proliferation.

338 PDLIM2 overexpression is important for maintenance of the epithelial phenotype in MCF10A

339 human immortalized epithelial breast cells, while PDLIM2 suppression has opposite

340 consequences

341 To ascertain whether the effects related to PDLIM2 levels described in sections above are of general

342 validity or are context-dependent, we selected another model, MCF10A immortalized human

343 epithelial breast cells. We either overexpressed or suppressed PDLIM2 in these cells and monitored

344 the effects of these changes on molecular and cellular levels. The effects of overexpression and

345 suppression of PDLIM2 on EMT markers are shown in Fig. 4A and Additional file 8: Figure S8:

346 overexpression led to augmentation of the epithelial marker E-cadherin and decreases in the

347 mesenchymal markers vimentin and N-cadherin, as well as the β1-integrin pathway regulator FAK,

348 indicating MET and maintenance of the epithelial phenotype. PDLIM2 suppression had the opposite

349 effect: E-cadherin levels were decreased, and levels of the mesenchymal markers vimentin and N-

350 cadherin as well as FAK were increased, indicating EMT. These results suggest that PDLIM2 is

351 important for maintaining the epithelial phenotype of MCF10A cells.

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352 EMT induction by TGFβ1 decreases PDLIM2 levels, but hypoxia has no effect on PDLIM2

353 levels in MCF10A cells

354 To investigate how EMT affects PDLIM2 in MCF10A cells, we induced EMT by treatment with

355 TGFβ1 (1 ng/ml for 24 hours) and monitored PDLIM2 protein levels. As shown in Fig. 4B and

356 Additional file 9: Figure S9, PDLIM2 protein levels were downregulated after EMT induction in

357 MCF10A cells, in a distinct manner compared to that of MCF7 cells. Successful EMT induction was

358 confirmed by decreased levels of E-cadherin and increased levels of vimentin and N-cadherin. We also

359 attempted to induce EMT by exposing the MCF10A cells to hypoxic conditions; however, we did not

360 observe any changes in E-cadherin and vimentin levels as well as in PDLIM2 protein levels (see

361 Additional file 10: Figure S10A and S10B). Additionally, we did not observe any change in CA9

362 levels after PDLIM2 modulation (see Additional file 10: Figure S10C). These results confirm the

363 importance of PDLIM2 in the maintenance of the epithelial phenotype and indicate that PDLIM2 does

364 not play a significant role in the response to hypoxic conditions in MCF10A cells.

365 PDLIM2 overexpression decreases p53 (S20) phosphorylation in MCF10A cells

366 To investigate mechanisms of the potential tumor suppressive role of PDLIM2 in MCF10A cells, we

367 analyzed its effect on p53 levels, p53 phosphorylation (S20) and p65 protein levels. In contrast to

368 MCF7 cells, overexpression of PDLIM2 had no effect on p53 levels (see Fig. 4A and Additional file

369 8: Figure S8); nevertheless, the active form of this protein, p-p53 (S20), was augmented (Fig. 4A and

370 Additional file 8: Figure S8). The level of p65 was not affected by overexpression or suppression of

371 PDLIM2 (see Fig. 4A and Additional file 8: Figure S8). These results suggest a tumor suppressive role

372 of PDLIM2 in MCF10A cells.

373 PDLIM2 levels negatively regulate migration and positively regulate the adhesion of MCF10A

374 cells

375 To validate the distinct role of PDLIM2 in MCF10A cells at the cellular level, we examined its effect

376 on the migration and adhesion of MCF10A cells. PDLIM2 suppression by siRNA was accompanied

377 by significant augmentation of the migration abilities of the MCF10A cells relative to cells with

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378 endogenous protein levels, as revealed by Transwell assay (p<1x10-5) (Fig. 5A, conditions A, C and

379 E). Conversely, overexpression of PDLIM2 significantly decreased the migration abilities of the

380 MCF10A cells (p<1x10-5) relative to cells with endogenous protein levels (Fig. 5A, conditions B, D

381 and E). As shown in Fig. 5B, PDLIM2 suppression significantly decreased the adhesion of MCF10A

382 cells (p<1x10-5), while on the other hand, overexpression of this protein considerably augmented the

383 adhesion of MCF10A cells (p<1x10-5) relative to control cells. These results further confirm the

384 tumor-suppressive role of PDLIM2 in MCF10A cells and its context-dependent behavior.

385 Analysis of breast cancer cell lines and tissues supports the dual role of PDLIM2 in breast

386 cancer development

387 Finally, comparison of PDLIM2 protein levels in other breast cancer cell lines (Additional file 11:

388 Figure S11) showed that PDLIM2 levels were low in low invasive MCF7 breast cancer; however,

389 higher PDLIM2 levels were found in highly invasive triple negative MDA-MB-231, supporting the

390 connection of PDLIM2 to metastatic potential. Higher levels of PDLIM2 were also found in normal

391 MCF10A cells (Additional file 11: Figure S11), in agreement with its tumor suppressor role in normal

392 breast cells. This is in principal agreement with our breast cancer tissue data that showed a high

393 PDLIM2 level in primary tumors forming lymph node metastases, but specifically in small (T1)

394 luminal A grade 1 tumors (Additional file 13: Table S2), which represent the early phase of tumor

395 development. Conversely, the epithelial marker KRT18 was significantly downregulated in PDLIM2-

396 rich luminal A tumors forming lymph node metastases (Additional file 14: Table S3), further

397 supporting the PDLIM2 connection with mesenchymal phenotype in tissues. Taken together, these

398 data suggest a connection between PDLIM2 and metastatic phenotype in luminal A tumors and its

399 distinct roles in different phases of cancer development.

400 DISCUSSION

401 Deregulation of PDLIM2 has been associated with oncogenesis, including lymph node metastasis of

402 breast cancer [5]. The PDLIM2 gene is repressed in different cancers, which implies its tumor

403 suppressive role. Repression and a tumor suppressive role of PDLIM2 were observed in ATL induced

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404 by HTLV1 virus [26-29], Kaposi sarcoma [30], ovarian cancer [31], gastric cancer [32], colon cancer

405 [12], Hodgkin lymphoma [33] and breast cancer [34, 35]. In contrast to the above studies, a pro-

406 oncogenic role and enhanced expression of PDLIM2 were observed in castration-resistant prostate

407 cancer cells [5, 9, 36], invasive breast cancer cell lines and breast carcinomas [9, 11]. The aim of the

408 study presented here was to understand the role of PDLIM2 at both the molecular and cellular levels in

409 the breast cancer context in more detail using in vitro experiments.

410 PDLIM2 role in MCF7 breast cancer cells

411 Our data indicate a positive role for PDLIM2 in EMT induction (Fig. 1A and Additional file 2: Figure

412 S2), which was previously described in DU145 prostate cancer cells and MDA-MB-231 highly

413 invasive breast cancer cells [9, 36]. We for the first time report this role in MCF7 cells, a model of

414 luminal A tumors, confirming the previous data from our clinical set of luminal A breast cancer tissues

415 [5]. Overexpression of PDLIM2 also denotes disturbance of the β1-integrin pathway and loss of the

416 epithelial phenotype via increased levels of FAK, the regulator of the β1-integrin pathway [10]. Apart

417 from the above, the stiffness of MCF7 cells was significantly augmented after PDLIM2

418 overexpression (Fig. 1B-C). Increased cell stiffness is typical for cells with a mesenchymal phenotype

419 [28], which again supports the PDLIM2 role in EMT induction. Moreover, EMT induction by TGFβ1

420 treatment and/or long-term hypoxia increases PDLIM2 levels (Fig. 2A, Additional file 4: Figure S4).

421 Our experiments also revealed a reciprocal connection between PDLIM2 and the response to hypoxia

422 (Fig, 2B, 2C, Additional file 5: Figure S5 and Additional file 6: Figure S6), which represents

423 completely new information that deserves further examination. Furthermore, the observed negative

424 effects of PDLIM2 on the levels of proteins involved in DNA repair and cell cycle regulation via p53

425 and especially p-p53 (S20) [39-41] indicate the relation between PDLIM2 and these processes in

426 MCF7 cells (Fig. 2D and Additional file 7: Figure S7). All of these results confirm the involvement of

427 PDLIM2 in the regulation of EMT induction and in the maintenance of the cellular phenotype in

428 MCF7 cells. Additionally, involvement of PDLIM2 in the response to hypoxia and the effect of

429 PDLIM2 overexpression on key cancer molecular players suggest a pro-oncogenic role of PDLIM2 in

430 MCF7 cells, further validating our previous data from clinical tissues [5].

18 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

431 At the cellular level, we have clearly proven the positive effect of PDLIM2 on MCF7 cell migration,

432 invasiveness, and proliferation and the negative effect on MCF7 adhesive abilities (Fig. 3A-D).

433 Similar results were previously obtained with MCF7 [11], DU145 and MDA-MB-231 cells for

434 migration [9] and with CRPC cells for both migration and invasion [36]. A negative effect of PDLIM2

435 on adhesion and a positive effect on proliferation were already described in DU145 and MDA-MB-

436 231 cells [9], and the positive effect of PDLIM2 on proliferation was observed in CRPC cells [36].

437 Migration and invasion are closely connected to EMT induction, and both are the key steps in

438 oncogenesis and metastasis formation [42-44]. The results observed at the cellular level are thus in

439 good agreement with the molecular-level data. Additionally, disruption of cell adhesion and increased

440 cell proliferation are the key signatures of oncogenesis [42]. In view of these facts, and in agreement

441 with previous studies, the cellular-level results bring additional support for the pro-oncogenic role of

442 PDLIM2 in luminal A breast cancer and functionally further validate our previous data from clinical

443 tissues [5].

444 PDLIM2 role in MCF10A normal epithelial breast cells

445 On the other hand, the potential tumor suppressive role of PDLIM2 was revealed in our experiments

446 using immortalized normal epithelial MCF10A cells. Our data indicate a negative role of PDLIM2 in

447 EMT induction, its importance for maintaining the epithelial phenotype of MCF10A cells and the

448 negative effect of EMT on PDLIM2 protein levels (Figs. 4A, 4B, Additional file 8: Figure S8 and

449 Additional file 9: Figure S9), distinct from MCF7 cells. These new findings are in good agreement

450 with the known role of PDLIM2 in maintaining breast epithelial cell polarity [10]. Overexpression of

451 PDLIM2 in MCF10A was further associated with increased p53 (S20) phosphorylation. This was

452 revealed for the first time and indicates a positive connection between PDLIM2, DNA repair and cell

453 cycle regulation in these cells (Fig. 4A, Additional file 8: Figure S8). Interestingly, no effect of

454 PDLIM2 on hypoxia and no effect of hypoxia on PDLIM2 levels were observed, suggesting different

455 and context-dependent roles of PDLIM2 in the response to hypoxic conditions (see Additional file 10:

456 Figure S10). It seems that PDLIM2 helps to induce a hypoxic response and supports the proliferation

457 of MCF7 cells; however, it may not be involved in hypoxic response regulation in MCF10A cells. In a

19 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

458 broader context, PDLIM2 function in MCF10A cells is evidently highly distinct from that in MCF7

459 cells, being rather tumor suppressing. Furthermore, similar levels of PDLIM2 protein in invasive triple

460 negative breast cancer cells (MDA-MB-231) and normal breast epithelial cells (MCF10A) (Additional

461 file 11: Figure S11) indicate, in agreement with previous data [34], a significant role for PDLIM2 not

462 only in MCF10A but also in invasive breast cancer cells. On the other hand, PDLIM2 levels were

463 substantially lower in low invasive MCF7 cells (Additional file 11: Figure S11 and [34, 11]),

464 suggesting that PDLIM2 is suppressed in a gap between neoplastically transformed and invasive

465 breast cancer cells that undergo EMT. The tumor suppressive function of PDLIM2 in MCF10A cells

466 was also evident in experiments at the cellular level. As shown for the first time here, PDLIM2

467 negatively regulates migration and positively regulates the adhesion of MCF10A cells (Fig. 5A-B).

468 These effects are in agreement with the expected role of PDLIM2 in the maintenance of the epithelial

469 phenotype and with its tumor-suppressive function in MCF10A cells. Altogether, these results give

470 evidence for the dual and context-dependent role of PDLIM2, which acts as a possible tumor

471 suppressor in normal epithelial cells (MCF10A) and as a potential oncoprotein in luminal A breast

472 cancer (MCF7 cells), and suggest that PDLIM2 levels may fluctuate during oncogenesis.

473 Context-dependent role of PDLIM2 in breast cancer progression

474 Data presented in this study led us to the hypothesis that changes in PDLIM2 protein levels during

475 breast cancer oncogenesis are context-dependent (Fig. 6). According to our data and several previous

476 studies [9-11, 36, 37], we assume that newly transformed low-invasion breast cancer cells have

477 reduced levels of PDLIM2. Overexpression of PDLIM2 levels in these cells led to a dramatic increase

478 in their metastatic potential and cancer progression, demonstrating the oncoprotein role of PDLIM2

479 (Figs. 1-3, Additional file 2: Figure S2, Additional file 4: Figure S4, Additional files 5-7: Figures S5-

480 S7). According to our data (Additional file 11: Figure S11) and in agreement with others [9, 34], we

481 further assume that highly invasive breast cancer cells express high levels of PDLIM2, and we expect

482 that this could support them to form metastasis. However, these cells may undergo MET in the next

483 step of the metastatic cascade, and PDLIM2 levels may diminish after successful metastatic

484 colonization, supporting growth of the cells to build secondary tumors. In agreement with our previous

20 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

485 tumor tissue data [5], luminal A grade 1 tumors with high levels of PDLIM2 represent a group of

486 tumors with increased metastatic potential. Inhibition of PDLIM2 protein in these tumors has the

487 potential to reduce migration, invasion and proliferation and augment adhesion properties due to

488 phenotypic changes toward MET, which makes PDLIM2 an interesting target for further studies on its

489 possible therapeutic application.

490 CONCLUSION

491 We demonstrate that PDLIM2 plays dual and context-dependent roles in breast cancer development:

492 PDLIM2 facilitates maintenance of the epithelial phenotype in normal breast MCF10A cells, but it

493 also promotes EMT in MCF7 breast cancer cells. PDLIM2 supports migration, invasion and

494 proliferation in MCF7 cells but blocks migration and supports the adhesion of MCF10A cells. Our

495 findings thus show that PDLIM2 has the potential to act as a tumor suppressor in MCF10A cells (a

496 model of normal epithelial breast cancer) but as an oncoprotein in MCF7 cells (model of luminal A

497 breast cancer). These observations are complementary and unravel previous contradictory findings in

498 the literature, leading us to the hypothesis that changes in PDLIM2 protein levels during oncogenesis

499 of breast cancer have a context-dependent nature. These data provide a basis for further interesting

500 investigations to determine whether PDLIM2 blocking might have potential therapeutic implications

501 in luminal A breast cancer.

502 ABBREVIATIONS

503 AFM: atomic force microscopy

504 ALP: actinin-associated LIM protein

505 CA9: carbonic anhydrase-9

506 E-cad: E-cadherin

507 EMT: epithelial-to-mesenchymal transition

508 FAK: focal adhesion kinase

21 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

509 IGF-1R: insulin-like growth factor 1 receptor

510 KRT18: keratin-18

511 MET: mesenchymal-to-epithelial transition

512 N-cad: N-cadherin

513 PCNA: proliferating cell nuclear antigen

514 PDLIM2: PDZ and LIM domain protein 2

515 Vim: Vímentin

516 DECLARATIONS

517 Ethics approval and consent to participate

518 Not applicable.

519 Consent for publication

520 Not applicable.

521 Availability of data and materials

522 All the datasets used and/or analysed during the current study are available from the corresponding

523 author on reasonable request.

524 Competing interests

525 The authors declare that they have no competing interests.

526 Funding

527 This work was supported by the Czech Science Foundation, project No. 17-05957S. Parts of the work

528 were further supported by the Ministry of Education, Youth and Sports of the Czech Republic: the

529 experiments by J.M. at Masaryk Memorial Cancer Institute (MEYS - NPS I - LO1413), the work of

22 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

530 J.P. and P.S. (CEITEC 2020, LQ1601) and the AFM measurements at CF Nanobiotechnology (CIISB

531 research infrastructure, LM2015043).

532 Authors’ contributions

533 J.M. designed, performed and evaluated experiments and drafted the manuscript, J.P. performed and

534 evaluated AFM experiments, P.B.1 prepared cells for optical microscopy and AFM and generated Fig.

535 S3, P.S. initiated AFM experiments and contributed to manuscript preparation, P.B.2 supervised the

536 study, manuscript preparation, acquired funding and approved the final manuscript.

537 Acknowledgements

538 We thank Dr. Petr Müller for his help with preparation of the plasmids and stably transduced cell line,

539 Zuzana Bertova for her technical assistance with AFM and Anna Pospisilova for data processing in

540 Fig. 1E.

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652 ADDITIONAL INFORMATION

653 Additional file 1: Figure S1.pdf. A common schema of xCELLigence and/or Transwell experiments

654 for the measurement of cell migration and invasion.

655 Additional file 2: Figure S2.pdf. Effects of altered PDLIM2 protein levels on EMT markers in MCF7

656 cells.

657 Additional file 3: Figure S3.pdf. Confirmatory immunoblotting of EMT induction in MCF7-

658 PDLIM2 and parental MCF7 cells after TGFβ1 treatment and before AFM measurements.

659 Additional file 4: Figure S4.pdf. Effect of EMT induction on PDLIM2 protein levels in MCF7 cells.

27 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

660 Additional file 5: Figure S5.pdf. Effects of short-term exposure to hypoxia (2% O2 for 48 and 72

661 hours) on EMT markers and PDLIM2 protein levels in MCF7 cells.

662 Additional file 6: Figure S6.pdf. Effects of altered PDLIM2 protein levels on the hypoxic marker

663 carbonic anhydrase 9 (CA9) in MCF7 cells.

664 Additional file 7: Figure S7.pdf. Effects of altered PDLIM2 protein levels on p53 levels, p53 (S20)

665 phosphorylation and p65 levels in MCF7 cells.

666 Additional file 8: Figure S8.pdf. Effect of altered PDLIM2 protein levels on EMT markers, p53, p-

667 p53 (S20) and p65 in MCF10A cells.

668 Additional file 9: Figure S9.pdf. Effects of EMT induction on PDLIM2 protein levels in MCF10A

669 cells.

670 Additional file 10: Figure S10.pdf. Effect of short term and long term expositions to hypoxic

671 conditions on EMT induction and effect of PDLIM2 alterations on CA9 in MCF10A cells.

672 Additional file 11: Figure S11.pdf. PDLIM2 protein levels in different breast cell lines (MCF7,

673 MCF10A and MDA MB 231).

674 Additional file 12: Table S1.docx. Statistics of average height and Young’s modulus of the cells

675 measured by AFM.

676 Additional file 13: Table S2.docx. Connection between PDLIM2 and clinicopathological parameters

677 of breast cancer based on our previous combined proteomics and transcriptomics study (n=96 in total)

678 [5].

679 Additional file 14: Table S3.docx. iTRAQ-2DLC-MS/MS quantitative protein-level data for

680 PDLIM2 and epithelial and mesenchymal marker proteins in lymph node-positive (n=24) vs. negative

681 (n=24) luminal A grade 1 tumors in our previous combined proteomics and transcriptomics study

682 (n=96 in total) [5].

683 Additional file 15: Methods S1.pdf. Young’s modulus mapping by Atomic Force Microscopy.

28 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

684 Additional file 16: Dataset S1.pdf. AFM data for all measurements.

685 Additional file 17: Dataset S2.pdf. xCELLigence data for MCF7 migration and invasion

686 measurements.

687 Additional file 18: Dataset S3.xls. Transwell assay data for MCF7 migration and invasion

688 measurements.

689 Additional file 19: Dataset S4.xls. Transwell assay data for MCF10A migration measurements.

29 bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 1: Effect of PDLIM2 protein level modulations on EMT markers and MCF7 cell stiffness. (A) Effect of modulated PDLIM2 levels on the EMT markers E-cadherin (E-cad.), keratin-18 (KRT18) and β-catenin as well as focal adhesion kinase (FAK) in MCF7 cells. PDLIM2 protein levels were modulated by siRNA suppression (MCF7 PDLIM2 siRNA) compared to the control (MCF7 CTRL siRNA) or by PDLIM2 overexpression (MCF7 PDLIM2 pl.) compared to the control (MCF7 CTRL pl.). Proliferating cell nuclear antigen (PCNA) was used as a loading control [25]. Numbers bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

under the protein bands represent their integral optical density (INT*mm2). Blots are representative of two independent experiments (biological replicates), see Additional file 2: Figure S2 for both biological replicates. (B) Representative photos of MCF7 parental cells and stably transduced MCF7- PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). Magnification 50x. (C) Distribution of cell height measured by atomic force microscopy (AFM) over the MCF7 parental cells and stably transduced MCF7-PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). Scale bar inside the image is equal to 20 µm. Figures are representative of 11 AFM measurements (biological replicates) per group, see Additional file 16: Dataset S1 for all measurements (also applies to Fig. 1D). (D) Distribution of Young’s modulus measured by AFM over the MCF7 parental cells and stably transduced MCF7- PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). E) Average height and Young’s modulus of the cells measured by AFM: MCF7 and MCF7-PDLIM2 cells after TGFβ1 treatment (TGFβ1+) in comparison with control untreated cells (TGFβ1-). Statistics for the key Young’s modulus comparisons are shown, see Tab. S1 for full statistics. Eleven AFM measurements (biological replicates) per group. F) Histograms show distribution of Young’s modulus in MCF7-PDLIM2 and TGFβ1-treated MCF7 cells compared to parental/untreated MCF7 cells.

bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 2: Effect of EMT on PDLIM2 levels and, conversely, PDLIM2 modulations on selected proteins (MCF7 cells). (A) Effect of EMT induction by TGFβ1 treatment (1 ng/ml for 24 hours) and by long-term hypoxia (96 h) on PDLIM2 protein levels. Successful EMT induction was confirmed by changes in the EMT markers E-cadherin (E-cad.), keratin-18 (KRT18) and β-catenin. (B) Effect of short-term hypoxia (48 and 72 hours) on PDLIM2 protein levels. Carbonic anhydrase-9 (CA9) was monitored as a control marker of hypoxia induction. (C) Effect of PDLIM2 overexpression on CA9 protein levels. (D) Effect of PDLIM2 protein level modulations on p53 protein levels, p53 (S20) phosphorylation (p-p53 S20), and on p65. Proliferating cell nuclear antigen (PCNA) was used as a bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

loading control. Numbers under the protein bands represent their integral optical density (INT*mm2). Blots are representative of two independent experiments (biological replicates), see Additional files 4 - 7: Figures S4 - S7 for both biological replicates.

bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 3: Effect of PDLIM2 protein level modulations on migration, invasion, adhesion and proliferation of MCF7 cells. (A) Effect of PDLIM2 overexpression (PDLIM2 pl.) on the migration of MCF7 cells (in comparison with control cells with endogenous PDLIM2 levels (CTRL pl.)) measured by the xCELLigence system and Transwell assay. Conditions without fetal bovine serum as the chemoattractant (-FBS) serve as negative controls in A and B. (B) Effect of PDLIM2 overexpression (PDLIM2 pl.) on the invasiveness of MCF7 cells measured by the xCELLigence bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

system and by Transwell assay. (C) Effect of PDLIM2 protein level modulations on the adhesion of MCF7 cells. PDLIM2 protein levels were modulated by siRNA suppression (MCF7 PDLIM2 siRNA) compared to the control (MCF7 CTRL siRNA) or by PDLIM2 overexpression (MCF7 PDLIM2 pl.) compared to the control (MCF7 CTRL pl.) (D) Effect of PDLIM2 protein level modulations on the proliferation (viability) of MCF7 cells. (E) Verification of PDLIM2 protein level modulations in MCF7 cells measured in C and D; please see separate verification blots for independently cultivated cells in A and B figure sections/measurements. PCNA was used as a loading control. Numbers under the protein bands represent their integral optical density (INT*mm2). All data were obtained from at least two independent experiments (see Additional files 17 - 18: Datasets S2 - S3 for migration and invasion experiments). For the detailed design of the xCELLigence and Transwell assay plates see Additional file 1: Figure S1.

Fig. 4: Effects of PDLIM2 modulations on EMT, and vice versa (MCF10A cells). (A) Effects of PDLIM2 protein level modulations on the EMT markers E-cadherin (E-cad.), N-cadherin (N-cad.) and vimentin (Vim.) as well as FAK levels, p53, p53 (S20) phosphorylation and p65. (B) Effects of EMT induction by treatment with TGFβ1 (1 ng/ml for 24 hours) on PDLIM2 protein levels and EMT markers. Numbers under the protein bands represent their integral optical density (INT*mm2). Blots are representative of two independent experiments (biological replicates), see Additional files 8 - 9: Figures S8 - S9 for both biological replicates. bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 5: Effect of PDLIM2 protein level modulations on the migration and adhesion of MCF10A cells. (A) Effects of PDLIM2 protein level modulations on MCF10A cell migration. PDLIM2 protein levels were modulated by siRNA suppression (PDLIM2 siRNA) compared to the control (CTRL siRNA) or by PDLIM2 overexpression (PDLIM2 pl.) compared to the control (CTRL pl.) Conditions without fetal bovine serum as a chemoattractant (-FBS) serve as negative controls in A. (B) Effects of PDLIM2 protein levels on the adhesion of MCF10A cells. See legend to A for explanation. (C) and (D) Verification of PDLIM2 protein level modulations in MCF10A cells for A and B measurements, respectively. PCNA was used as a loading control. Numbers under the protein bands represent their integral optical density (INT*mm2). All data were obtained from at least two independent experiments (see Additional file 19: Dataset S4 for migration experiments). bioRxiv preprint doi: https://doi.org/10.1101/2020.01.27.920199. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. All rights reserved. No reuse allowed without permission.

Fig. 6: Model of context-dependent changes in PDLIM2 protein levels during breast oncogenesis based on the data presented here. High PDLIM2 protein levels in normal epithelial breast cells are decreased during neoplastic transformation and are kept low in newly transformed tumor cells. However, PDLIM2 levels increase after EMT and are kept high in invasive tumor cells. Finally, PDLIM2 levels go down in secondary tumor cells that undergo MET and are kept as low as in newly transformed tumor cells.

12.5 Appendix 5

Research paper 4

RNF25, TRAF3IP2 and PDLIM2 are promising NF-κB modulators associated with metastasis of luminal A breast cancer

Maryas J, Faktor J, Capkova L, Muller P, Bouchal P.(in preparation)