Studying the Impact of Tspan8 on Extracellular Vesicles in Breast Cancer and Application of a Novel Tool for Their Detection

Studying the impact of Tspan8 on extracellular vesicles in breast cancer and application of a novel tool for their detection

Inaugural-Dissertation zur Erlangung der Doktorwürde der Fakultät für Biologie der Albert-Ludwigs-Universität Freiburg im Breisgau

Vorgelegt von Richa Khanduri geboren in Delhi, Indien

Freiburg im Breisgau Dezember 2018 Die vorliegende Arbeit entstand in der Arbeitsgruppe Exosomen und Tumorbiologie des Instituts für Infektionsprävention und Krankenhaushygiene am Universitätsklinikum Freiburg unter der Anleitung von PD Dr. Irina Nazarenko.

Dekan der Fakultät für Biologie: Prof. Dr. Wolfgang Driever Promotionsvorsitzender: Prof. Dr. Andreas Hiltbrunner Betreuerin der Arbeit: PD Dr. Irina Nazarenko Betreuerin der Fakultät für Biologie: Prof. Dr. Annegret Wilde

Referentin: Prof. Dr. Annegret Wilde Koreferent: Prof. Dr. Thomas Reinheckel Drittprüfer: Prof. Dr. Dr. h.c. Christoph Borner Datum der mündlichen Prüfung: 25.02.2019

DECLARATION

I hereby declare, that I am the sole author and composer of my Thesis and that no other sources or learning aids, other than those listed, have been used. Furthermore, I declare that I have acknowledged the work of others by providing detailed references of said work. I hereby also declare, that my Thesis has not been prepared for another examination or assignment, either wholly or excerpts thereof.

Freiburg, 28.02.2019 ______Place, date Signature

Richa Khanduri

Life consists in penetrating the unknown and fashioning our actions in accord with the new knowledge thus acquired. -Leo Tolstoy

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Table of Contents

1 Summary ...... 1 2 Introduction ...... 2 2.1 Breast cancer ...... 2 2.2 Extracellular Vesicles ...... 3 2.2.1 EV types ...... 4 2.2.2 Biogenesis and secretion of exosomes ...... 6 2.2.3 Biogenesis of microvesicles ...... 9 2.2.4 Molecular composition of exosomes...... 9 2.3 Function of EVs in physiology and pathology ...... 12 2.3.1 EVs and tumor microenvironment ...... 13 2.3.2 Diagnostic applications of EVs ...... 15 2.3.3 Role of EVs in breast cancer ...... 16 2.4 Tetraspanin 8: characteristic features and its role in cancer ...... 19 2.4.1 Structure of Tetraspanins...... 20 2.4.2 Tspan8 and its role in cancer ...... 21 2.5 Human Epidermal Growth Factor Receptor 2 ...... 23 2.6 WGM lasers ...... 26 3 AIM of the study ...... 28 4 Materials & Methods...... 29 4.1 Materials ...... 29 4.1.1 Cell lines ...... 29 4.1.2 Media and supplements ...... 29 4.1.3 Chemicals and Reagents ...... 30 4.1.4 Buffers and solutions ...... 31 4.1.5 Antibodies ...... 33 4.1.6 Assay Kits ...... 35 4.1.7 Equipments ...... 35 4.1.8 Consumables ...... 37 4.1.9 Softwares ...... 38 4.2. Methods ...... 38 4.2.1 Cell culture ...... 38 4.2.2 3D cell culture ...... 40 4.2.3 Plasmid DNA isolation ...... 40

4.2.4 Transient transfection ...... 42 4.2.5 Generation of stable cell lines ...... 43 4.2.6 Flow Cytometry with cells ...... 43 4.2.7 Fluorescence-activated cell sorting ...... 44 4.2.8 Preparation of cell lysate ...... 44 4.2.9 Western Blotting ...... 45 4.2.10 Cell proliferation assays ...... 45 4.2.11 Adhesion Assay ...... 46 4.2.12 Invasion Assay ...... 46 4.2.13 EVs isolation from 2D cell culture ...... 49 4.2.14 EVs Isolation from 3D cell culture ...... 50 4.2.15 EVs isolation from patients’ sera ...... 52 4.2.16 Beads-assisted flow cytometry with EVs ...... 53 4.2.17 Electron microscopy ...... 53 4.2.18 Nanoparticle Tracking Analysis and Zeta Potential ...... 54 4.2.19 DLS ...... 54 4.2.20 Protein quantification ...... 55 4.2.21 Proteomics (LC-MS/MS)...... 55 4.2.22 HER2 ELISA ...... 56 4.2.23 WGM Laser measurements ...... 56 4.2.24 Statistical Analysis ...... 57 5 Results ...... 58 5.1 Investigating the role of Tspan8 in tumor progression in breast cancer cells ...... 58 5.1.1 Establishment of Tspan8 overexpressing breast cancer cell models...... 58 5.1.2 Tspan8 regulates the expression of other tetraspanins breast cancer cells ...... 63 5.1.3 Examining the effect of ectopic expression of Tspan8 on cell proliferation and adhesion properties of the breast cancer cells ...... 66 5.1.4 Tspan8 regulates expression of integrins in breast cancer cells ...... 69 5.1.5 Effect of Tspan8 on the expression of tetraspanins and integrins under hypoxic environments ...... 74 5.2 Analysis of extracellular vesicles obtained from breast cancer cells cultured in 2D and 3D environments ...... 78 5.2.1 Characterization of different subpopulations of EVs ...... 78 5.2.2 Optimizing 3D cell culture method of breast cancer cells using agarose microwell array for EVs isolation ...... 81 5.2.3 Effect of Tspan8 on proliferation and invasion properties in the 3D environment ...... 86 5.2.4 Characterization of EVs isolated from breast cancer cells in 2D and 3D environments ...... 90

5.3 EVs bound HER2 as a putative diagnostic marker in breast cancer patients ...... 108 5.3.1 HER2 is recruited to EVs derived from breast cancer cells ...... 108 5.3.2 Detection limit of EVs by western blotting in breast cancer cells ...... 109 5.3.3 HER2 selectivity as a target ...... 110 5.3.4 Comparing EV5, EV12 and EV120 derived from breast cancer patients ...... 112 5.3.5 Characterization of EVs (EV120 fractions) derived from breast cancer patients ...... 112 5.3.6 EV-HER2 levels correlate well with HER2 score as determined by immunohistochemistry ...... 115 5.3.7 Whispering-gallery mode lasers for detection of EVs ...... 116 6 Discussion ...... 118 6.1 Tspan8 has diverse effects on cell morphology, proliferation, invasion and adhesion properties in triple-negative breast cancer cells ...... 118 6.2 Tspan8 regulates expression of integrins in breast cancer cells and in EVs derived from them ...... 120 6.2.1 Tspan8 regulates expression of integrins, especially α3β1 in triple-negative breast cancer cells ...... 120 6.2.2 Tspan8 regulates integrins content in EVs ...... 122 6.3 Tspan8 enhances release of EVs in breast cancer ...... 123 6.4 EVs bound HER2 can be a potential biomarker in diagnosis and prognosis of breast cancer ...... 124 6.5 Conclusion and Future outlook ...... 126 7 Appendix ...... 128 7.1 Supplementary figures ...... 128 7.2 References ...... 137 7.3 List of Abbreviations ...... 153 Acknowledgments ...... 155

1 Summary

Extracellular vesicles (EVs) play an essential role in cell-cell communication through their cargo consisting of proteins, lipids and nucleic acids. They are released by various cell types and orchestrate different pathophysiological processes in recipient cells. Multiple recent studies have shown that EVs contribute to tumor progression. They are abundant in tetraspanin proteins like CD63 and CD9 which play a role in the biogenesis of EVs and sorting of EV cargo. Tspan8 is one such tetraspanin which is known as tumor-associated protein. It is involved in cell motility, angiogenesis and invasion. It cooperates with other tetraspanins like CD151, CD9, and α3β1 & α6β4 integrins. It is overexpressed in melanoma, glioma and hepatic, colon, pancreatic and gastric carcinoma. Its role in breast cancer has not been addressed so far. However, it has been recently identified as a marker for a subset of highly quiescent stem cells of the mammary gland. We wanted to study its role on the regulation of cell behavior in breast cancer and its impact on EVs derived from breast cancer cells.

We established a human in vitro model. Tspan8 was overexpressed in luminal A type MCF7, triple negative MDA-MB-231 and BT-549 breast cancer cells. Luminal B type MDA-MB-361 cells which express Tspan8 endogenously were also included in the study. We observed Tspan8 had a significant effect on cell proliferation, cell invasion and adhesion properties of triple negative breast cancer cells. Furthermore, Tspan8 modulated the expression of certain integrins and tetraspanins. The expression of tetraspanin-CD63 was upregulated while the surface expression of CD9 was downregulated. The expression of α3β1 integrin was upregulated.

To study the impact of Tspan8 on EVs content and properties, the EVs were isolated from the established cell model. Since the 3D cell culture better simulates the in vivo tumor environment; EVs were isolated from an agarose-based microwell array supporting 3D environment. It is a recently established and commercialized know-how of the Medical Centre, University of Freiburg. EVs were isolated under normoxic and hypoxic conditions and characterized using transmission electron microscopy, nanoparticle tracking analysis, dynamic light scattering, and bead-assisted flow cytometry. Interestingly, we observed Tspan8 boosted the release of EVs in breast cancer cells. The effect was especially significant under 3D hypoxic environments with a 2-6-fold increase in EVs released per cell in Tspan8+ cells. Tspan8 also upregulated the expression of α3β1 integrin in EVs derived from triple negative breast cancer cells. For the detection of EVs obtained from breast cancer cells and patients’ sera, the application of a novel tool, ‘whispering gallery mode (WGM) lasers’ was studied. This was done in collaboration with Prof. Dr. Christian Koos, IPT, Karlsruhe Institute of Technology, Germany.

Conclusively, this study provides the first indicaton on the function of Tspan8 in breast cancer, showing its impact on cell behavior, EV content and EV release. It also indicates that Tspan8 function might differ within a breast cancer subtype. And, EVs can be a potential diagnostic and prognostic tool in breast cancer.

Keywords: Extracellular vesicles, tetraspanins, Tspan8, breast cancer, integrins, 3D culture, hypoxia, WGM laser

Introduction

2 Introduction

2.1 Breast cancer

Breast cancer is the leading cause of death in women amongst other cancer types (http://www.who.int/features/factfiles/cancer/en/). A minority of breast cancer cases have also been reported in males. It is known that primary tumors don’t account for the deaths; it is the systemic spread to other metastatic sites which lead to deaths. Breast cancer is a very heterogeneous disease that exhibits diverse histological, molecular and clinical phenotypes. These characteristics also help in determining the incidence, survival and multimodality treatment approaches required for the patients. The treatment approaches include surgery, chemotherapy, hormone therapy, and other targeted therapies. In recent times, breast cancer has been classified to different subtypes based on the gene expression profiling. The five breast cancer subtypes are Luminal A, Luminal B, HER2-enriched, Triple-negative (including Claudin- low and Basal-like) and Normal breast-like (Prat and Perou, 2011).

These are mainly divided based on the presence or absence of markers like hormone receptors (ER, PR), HER2, Ki-67 (cell proliferation marker) and claudin as described in Table 1. The basal- like triple-negative (ER- PR- HER2-) subtype highly expresses cell proliferation gene cluster including Ki-67, cytokeratins CK5/6, CK14 and EGFR which are also called basal markers. On the other hand, claudin-low triple-negative tumors have low expression of proteins involved in cell adhesion and tight junctions. Among the 20 gene clusters recognized in this group include claudins 3, 4, 7 and occludin which are involved in tight junctions. They also express low levels of E-Cadherin which contribute to calcium-dependent cell adhesion. They have high mesenchymal features and low epithelial differentiation. Triple negative cancer is found more in women with the BRCA1 gene mutation. The normal breast-like tumors account for 5-10% of all breast cancer and have expression profile resembling that of normal breast tissue (Yersal and Barutca, 2014). They are poorly characterized, show low tumor cellularity and are rich in stromal cells like normal breast tissue. The existence of this subtype is debatable as some researchers believe that they could be a technical artifact due to contamination with normal tissue during microarray (Weigelt et al., 2010).

Introduction

Table 1: Molecular profile of different breast cancer subtypes (Cho, 2016; Prat and Perou, 2011; Reis-Filho and Pusztai, 2011)

Breast cancer subtype Molecular profile Disease outcome Therapy

Luminal A ER+, PR+/-, HER2 low, Good Endocrine therapy mostly, cytotoxic low Ki-67 therapy sometimes *Basal markers -

Luminal B ER+, PR+/- HER2+/- Intermediate or poor Endocrine + cytotoxic therapy Ki-67high Basal markers -

HER2-enriched ER-/low, PR-/low HER2high Poor (but the therapy Cytotoxic + anti- response to HER2 HER2 targeted high Ki-67 targeted treatment is therapy Basal markers -/+ good)

Triple-negative: ER-, PR-, HER2-/low, Poor cytotoxic therapy Basal-like Ki-67high Basal markers+

(CK5/6 + EGFR high)

Triple-negative: ER-, PR-, HER2-/low, Intermediate cytotoxic therapy Claudin- Low Ki-67Intermediate Basal markers+/-

Normal breast-like ER-/ low, PR-/ low, Intermediate undetermined

HER2-/ low, Ki-67low/- Basal markers -/+

*Basal markers include cell proliferation markers CK5/6, EGFR. EGFR: epidermal growth receptor, ER: Estrogen receptor, PR: progesterone receptor, HER2: human epidermal growth factor receptor2

+= positive, - = negative, -/+ = predominantly negative, +/- = predominantly positive.

2.2 Extracellular Vesicles

Extracellular vesicles (EVs) have recently emerged as a means of communication between cells. They are released into the extracellular space by many eukaryotic cell types in vitro. They are also naturally found in body fluids including blood, saliva, urine, breast milk (Théry et al., 2006) and act as messengers, carrying information in the form of lipids, proteins, RNA and DNA to the recipient cells (Simons and Raposo, 2009; Valadi et al., 2007). Thus, they play a significant role in cell-cell communication, representing a recently recognized mechanism of the horizontal

Introduction transfer of genetic information (Lee et al., 2012). EVs act locally and on the system level, modifying properties of the recipient cells (Fig. 1). From being initially considered as just membrane debris without any biological significance, EVs are now known to play a significant role not only in physiological processes but also in pathology underlying several diseases like cancer, infectious diseases and neurodegenerative disorders (EL Andaloussi et al., 2013). They can be secreted as a response to external factors such as changes in pH, hypoxia, irradiation, injury and cellular stress (Xu et al., 2018).

Figure 1: Schematic representation of horizontal transfer of information from the Donor cell to recipient cell via extracellular vesicles (EV) including exosomes. Exosomes (in red) originate from multivesicular bodies (MVB) formed by invagination of endosomal membrane. MVBs fuse with the plasma membrane to release exosomes. Microvesicles (in blue) on the other hand, are formed from the budding of the plasma membrane which encapsulates the loading cargo. Microvesicles are then released or shed from the cell surface. These EVs are uptaken by the recipient cells. The cargo of the extracellular vesicles which includes proteins, nucleic acids, lipid molecules is released into the recipient cells bringing about various phenotypic changes such as cell growth, migration and survival. Adapted with permission (Desrochers et al., 2016).

2.2.1 EV types

Currently, the two major classes of extracellular vesicles (EVs) that have been investigated are exosomes and microvesicles (also called as ectosomes, shedding vesicles or microparticles) (The´ry et al., 2009; Beyer and Pisetsky, 2010; Mathivanan et al., 2010). The vesicles are

Introduction distinguished based on their size, density, subcellular origin and protein markers (Table 2). Exosomes are 50–150 nm in size and originate from multivesicular endosomes/bodies (MVB) formed by intraluminal budding of endosomal membrane. These MVB either subject to proteasomal degradation in lysosomes or they fuse with the plasma membrane and release their content. Hence, they’re also referred to as exocytic MVBs. Microvesicles, on the other hand, are larger membranous vesicles (50 nm – 1000nm) which are shed directly from the plasma membrane by outward blebbing and engulfing the cytoplasmic content in the process. Another class of EVs, apoptotic blebs are 800 – 5000 nm sized vesicles released during apoptosis by dying cells. While microvesicles can be released during early phases, apoptotic blebs are released during later stages of apoptosis. Exosomes are small, comparatively more homogenous and float at a density of 1.10–1.21 g/mL on a sucrose gradient. While non-exocytic vesicles are irregular shaped and float at a density higher than (>1.23 g/mL). It has been recognized in the community that all EV preparations are heterogeneous and the current methods of isolation only permit enrichment of one over the other (The´ry et al., 2009 Mathivanan et al., 2010). These variances in isolation methods cause the heterogeneity in preparations and need standardization (Lötvall et al., 2014). Additionally, the population of EVs obtained from available methods do not determine their origin. In my work, I have focussed on small extracellular vesicles which were earlier referred to as exosomes.

Table2: Classes of EVs Features Exosomes Ectosomes/Microvesicles Apoptotic bodies Reference

van Niel et al., *Size 50-150 nm 50nm – 1000nm 800 – 5000nm 2018; Todorova et al., 2017

van Niel et al., Origin Endosome Plasma membrane Plasma membrane 2018

Théry et al., Density 1.07-1.18 g/ml Unspecified 1.16-1.28 g/ml 2001

CD63, CD9, Garcia- ARF6, VCAMP 3, CD81, HSP70, Caspase 3, Annexin V, Contreras et al., Markers integrins, selectins, CD40 ALIX, TSG101, phosphatidylserine, 2017; Todorova ligand flotillin histones et al., 2017

*Due to lack of complete standardization procedures, slight variation in the exact sizes of extracellular vesicle types can be seen in literature.

Introduction

2.2.2 Biogenesis and secretion of exosomes

The inward budding of plasma membrane leads to the formation of early endosomes. During maturation of early endosomes into late endosomes, the endosomal membrane invaginates to form intraluminal vesicles (ILVs). The ILVs enclose the cytosolic proteins, nucleic acids and lipids within them. These late endosomal compartments with ILVs are referred to as microvesicular bodies (MVBs) or multivesicular endosomes (MVEs) (Colombo et al., 2014; Kowal et al., 2014). The MVBs either fuse with lysosomes advancing to degradation by the lysosomal hydrolases or they fuse with the plasma membrane and release the intraluminal vesicles called as exosomes (Abels and Breakefield, 2016). During the ILV formation, the endosome membrane gets reorganized and enriched in tetraspanins (Pols and Klumperman, 2009) like CD63 and CD9 which are also known to be ubiquitous markers for extracellular vesicles. The cells can have ILVs of different sizes and composition which leads to secretion of different subpopulations of exosomes (Colombo et al., 2013).

2.2.2.1 ESCRT dependent pathways

The machinery involved in the biogenesis pathway is either ESCRT (Endosomal sorting complex required for transport) dependent or ESCRT independent (Fig. 2). ESCRT machinery consists of four complexes: - ESCRT0, ESCRT I, ESCRTII, ESCRTIII and associated proteins like ALIX, TSG101 (Kowal et al., 2014). ESCRT 0 helps in agglomerating ubiquitinated proteins. It has 2 subunits Hrs (hepatocyte growth factor-regulated tyrosine kinase substrate) and STAM (signal transducing adaptor molecule). Hrs binds to ubiquitinated cargoes and form a complex with STAM along with 2 non-ESCRT proteins Ep15 and clathrin. The FYVE domain of Hrs subunit binds to phosphatidylinositol 3-phosphate and directs the whole complex to a pre-MVB endosomal membrane (Henne et al., 2011). The ESCRT 0 domains interact with TSG101 domain of ESCRT I protein. ESCRT I and ESCRT II initiate intraluminal budding by deforming the membrane and induce the intraluminal budding of sorted cargoes. They also have ubiquitin- interacting domains. ESCRT III helps in finishing the intraluminal budding process via invagination and constriction (McGough and Vincent, 2016). ESCRTI binds to the ubiquitinated molecules on endosomes which activates ESCRTII and stabilizes ESCRTIII via adaptor protein Bro1/ALG-2interacting protein X (ALIX). ALIX helps in the association between ESCRTI and ESCRTIII. It binds to tumor susceptibility gene 101 (TSG101) component of ESCRT I and charged MVB protein 4A (CHMP4A) component of ESCRT III (McCullough et al., 2008). In the final step, ATPase Vps4 provides energy for the budding and dissociation of ILVs and recycling of the ESCRTIII complex. ALIX can also act in the sorting of G protein-coupled membrane receptor PAR1 without depending on ubiquitination. ALIX is reported to act in the syndecan-

Introduction syntenin-ALIX pathway which also doesn’t require ubiquitination (Baietti et al., 2012). The intraluminal budding is dependent on the small GTPase adenosine 5-diphosphate-ribosylation factor 6 (ARF6) and its effector molecule the lipid-modifying enzyme phospholipase D2 (PLD2). This mechanism seems to be like the budding mechanism found in some retroviruses such as HIV.

2.2.2.2 ESCRT Independent pathways

It was seen in some studies that vesicles were produced by cells even after inactivation of four key components of ESCRT complex (Stuffers et al., 2009). This suggested the presence of alternative pathways which did not involve ESCRT components. These pathways involved lipids (such as ceramides), tetraspanins, and chaperone proteins. The sphingolipid, ‘ceramide’ was first to be described in ESCRT independent pathway (Trajkovic et al., 2008). It was proposed that ceramide helped in inward budding of the limiting membrane of MVBs to form ILVs. The oligodendrogial cells were able to release exosomes rich in proteolipid protein (PLP) even after inhibition of ESCRT component. The PLP positive exosomes were found to be rich in cholesterol and ceramide. And on inhibiting neutral sphingomyelinase (N-SMase) (involved in the synthesis of ceramide), the number of exosomes was reduced too. Thus, N-SMase is important for ILV formation. In another study with oligodendrogial cells, exosomes carrying Flotillin-2, ALIX, CD63, and cholesterol were secreted in a Flotillin-2 dependent manner (Strauss et al., 2010).

Other lipid molecules which have been studied in exosomes biogenesis are sphingosine-1- phosphate (S1P) and phospholipase D enzyme (PLD). S1P is a by-product of ceramidase action on ceramide substrate. The silencing of S1P receptors or sphingosine kinase 2 (Sphk2) (phosphorylates sphingosine substrate) halted the production of CD63 and CD81 enriched ILVs and exosomes(Kajimoto et al., 2013). Another lipid derivative, PLD2 and not PLD1 is found in abundance in exosomes (Laulagnier et al., 2004). It hydrolyzes phosphatidylcholine to phosphatidic acid (PA). Phosphatidic acid works in a similar way to ceramide by facilitating inward budding of limiting membrane of MVBs and thus formation of ILVs (Ghossoub et al., 2014).

Tetraspanins such as CD63, CD9 and CD81 are found in abundance in exosomes and are used as exosomal biomarkers (Escola et al., 1998). They are known to be involved in the sorting of protein and RNA in exosomes (Andreu and Yáñez-Mó, 2014). The tetraspanins dependent exosomes biogenesis was first highlighted by van Niel G et al. CD63 facilitated sorting of melanosomal proteins into ILVs in human melanoma cells, in a ceramide-independent and ESCRT-independent manner (van Niel et al., 2011). CD81 was also reported to recruit various

Introduction

ligands to the secreted exosomes (Perez-Hernandez et al., 2013). Other molecules found in exosomes are chaperone proteins such as HSP70 and HSC70. HSC70 chaperone has been reported to allow recruitment of transferrin receptors (TFR) to exosomes (Géminard et al., 2004). In one study, it was observed that the protein Nef, an accessory protein coded by HIV-1 induced the release of exosomes. Also, HIV infection of cells led to an increase in the amount of exosomes released which were abundant in CD63 and CD81 proteins (Madison and Okeoma, 2015).

CD63 SDC syntenin

ESCRT

Figure 2: Biogenesis of exosomes. It involves ESCRT dependent and ESCRT independent pathways. The ESCRT dependent pathways have ESCRT I – III components which help in the recruitment of ubiquitinated cargoes to MVBs, intraluminal budding of endosomal membrane, and release of exosomes. There is also a syndecan-syntenin-ALIX pathway which doesn’t depend on ubiquitination of the cargo. The ESCRT independent pathways involve lipids such as ceramide and tetraspanins. HIV infection can also lead to an increase in the exosomes release in a Nef1 protein-dependent manner. Adapted from (Kim et al., 2018), licensed under Creative Commons Attribution Non-Commercial (CC BY-NC 4.0, https://creativecommons.org/licenses/by-nc/4.0/ ).

Introduction

The mechanism of cargoes targeted to exosomes is not very clear. It is suggested that lipids and other proteins having an affinity to lipids bind to the plasma membrane lipids. Some components may also bind to anchors present on the membrane such as palmitoylation, prenylation and myristoylation. The mechanism of targeting nucleic acids is not very clear. One study suggested the conserved zipcode RNA sequence motif in 3’ untranslated regions in mRNA helped it target into MVs (van Niel et al., 2018).

The sorting processes of cargoes are also regulated by RAS-related protein (RAB) GTPases such as RAB5, RAB7 which are known to act in vesicle trafficking among intercellular compartments. RAB proteins such as RAB11, RAB27 and RAB35 are also involved in secretion of exosomes. The actin protein along with RAB proteins helps in the movement of MVBs towards plasma membrane. Finally, the fusion of the MVBs with the plasma membrane is carried out with the help of SNARE proteins (Mittelbrunn and Sánchez-Madrid, 2012). Various external factors can also enhance exosomes secretion such as stress caused due to hypoxia or irradiation (Hessvik and Llorente, 2018).

2.2.3 Biogenesis of microvesicles

Though the site of formation of exosomes and microvesicles are different, they do share the protein complexes involved in sorting during biogenesis which may interfere with distinguishing the two populations (van Niel et al., 2018). The process of biogenesis microvesicles (MVs) isn’t much known and still needs to be elucidated. First, the cargoes must be targeted at the site of production at the plasma membrane. The MVs are released by direct budding from the plasma membrane via ARF6 and RHOA dependent rearrangement of the actin cytoskeleton. Like exosomes, MVs engage ESCRT molecules, for example, TSG101 has been reported to interact with ALIX and arrestin domain-containing protein-1 (ARRDC1) at the plasma membrane. The Gag-mediated budding of HIV virions from cells involves ESCRT III and ALIX. Acid-sphingomyelinase (A-SMase) has also been studied to trigger the release of MVs from glial cells and astrocytes. As described earlier N-SMase also participates in exosomes biogenesis. This indicates that different members of SMase can determine different fates in EV formation.

2.2.4 Molecular composition of exosomes

Exosomes consist of various proteins and lipid molecules involved in their biogenesis and secretion (Fig. 3). They also consist of a specific set of proteins depending on the cells of their origin. The proteomic studies have shown that exosomes consist of endosomal proteins, cytosolic proteins and membrane proteins from the plasma membranes of cells of origin

Introduction

(Simpson et al., 2008; Théry et al., 2001). They are particularly enriched in tetraspanins such as CD63, CD9, and CD81, cell adhesion molecules (EpCAM), endosomal proteins like TSG101 and ALIX. Proteins involved in membrane traffickings such as RAB proteins and annexins. The various proteins reported in the literature can be found in the available databases Vesiclepedia (Kalra et al., 2012) and EVpedia (Kim et al., 2015a).

The lipids found in abundance in exosomes are cholesterol, sphingomyelin, ceramide and its derivatives such as hexosylceramides and generally saturated fatty acids (Llorente et al., 2013; Trajkovic et al., 2008). Cholesterol and sphingomyelin are present in the detergent-resistant subdomains of plasma membrane called lipid rafts. A study revealed that lipid rafts are endocytosed, packed into MVBs and secreted into exosomes in mesenchymal stem cells (Tan et al., 2013). Lipid raft associated proteins such as flotillins and GPI-anchored proteins have also been found in exosomes (Wubbolts et al., 2003). They also consist of phosphatidylserine but lack lysobiphosphatidic acid (LBPA) which is associated with ILVS of MVBs (Matsuo et al., 2004).

Nucleic acids including both DNA and RNA have been found to be present in exosomes. First, mRNA and miRNA were discovered ( Valadi et al., 2007). mRNA was seen to be transferred to human cells via exosomes. Since then, various studies have shown the presence of miRNA in exosomes such as miR-105 (Zhou et al., 2014), miR-214 (van Balkom et al., 2013) and miR92a (Umezu et al., 2013). Other RNA species that have been reported include transfer RNAs (tRNAs), long non-coding RNAs (lncRNA) and very low to undetectable levels of ribosomal 18S and 28S RNA (Crescitelli et al., 2013). Recently, single-stranded DNA (Balaj et al., 2011) and double- stranded DNA (Colombo et al., 2014) have also been discovered in exosomes. Although the thorough understanding of the function of nucleic acids has not been known, some studies have shed some light on the functional role of miRNA where they could affect gene expression in distant cells. For example, a subset of miRNA was found enriched in exosomes derived from hepatocellular carcinoma (HCC) cells which could downregulate the expression of transforming growth factor-beta activated kinase (TAK1) in recipient cells (Hep3B) and inactivate the downstream signaling via JNK1-3 and p38MAPK (Kogure et al., 2011). It further reduced cell viability. Other such studies have been done with miR451 in murine-derived exosomes (Montecalvo et al., 2012). Another interesting observation has been that different types of miRNA carriers seem to transfer different miRNA sequences indicating the possibility of a specific mechanism of RNA cargo selection (Palma et al., 2012) (Wang et al., 2010).

Introduction

Ceramide

Figure 3: Molecular composition of exosomes. On the surface of the exosomes various cell adhesion molecules, lipids, signaling receptors, tetraspanins, and cell-type-specific proteins are present. While the exosomal lumen consists of nucleic acids, enzymes, protein kinases, chaperones, and molecules involved in biogenesis pathways such as ALIX, TSG101. Adapted with permission (Whitehead et al., 2017).

Certain studies have also shown that the EV composition is subjected to variation depending on external factors. For example, hypoxia modifies the protein and/or RNA content of EVs released by endothelial cells (de Jong et al., 2012) and tumor cells (Kucharzewska and Belting, 2013). EV composition can also be affected due to a change in culture conditions mimicking various physiological environments. Inflammatory signals such as TNFα or IFNγ have also been reported to modify the exosomes compositions derived from dendritic cells (Segura et al., 2005), endothelial cells (de Jong et al., 2012) and mesenchymal cells (Kilpinen et al., 2013). Another study showed that at low pH, a hallmark of tumor malignancy, lipid composition of exosomes was altered (Parolini et al., 2009). Studies with oxidative stress situations revealed that it can cause the secretion of immunosuppressive exosomes from leukemia and lymphoma T and B cells (Hedlund et al., 2011). It was also seen to alter the proteomic and genetic profile of exosomes (Biasutto et al., 2013; Eldh et al., 2010). The presence of selective cargoes in the exosomes shows that there is a distinct sorting mechanism and the process is not randomly

Introduction occurring. Also, the exosomes may not have molecular profile completely mirroring that of the cells of their origin. They may also carry specific molecules not found in cells of origin.

There occurs an overlap in the molecular composition of different extracellular vesicle classes such as exosomes and microvesicles (Colombo et al., 2014). For example, CD9 is found in both microvesicles and exosomes while CD63 and CD81 are found more enriched in exosomes. A study by Kowal et al addressed this problem and aimed at identifying specific markers to characterize different EV subtypes. The study did an extensive proteomic profiling of EV populations obtained during exosome isolation and found that many classically used markers such as major histocompatibility complexes, flotillins and HSP70 proteins were similarly present in all the EVs (Kowal et al., 2016). The study suggested that small EVs co-enriched in CD63, CD9, CD81 tetraspanins, and endosome markers are bona fide exosomes. In our study, we have used markers CD63, CD9 and CD81 to characterize all the EVs isolated from breast cancer cells.

2.3 Function of EVs in physiology and pathology

As discussed earlier, the extracellular vesicles including exosomes participate in intercellular communication and can bring about various physiological responses in the recipient cells. They can either interact at the surface level via receptor-mediated interaction or they can be taken up by the cells and deliver cargo molecules such as proteins, mRNA and miRNA. The mechanism of uptake can be endocytosis, direct fusion with the plasma membrane or phagocytosis (Kharaziha et al., 2012). They can also enter the systemic circulation and can be uptaken by cells of a distant tissue (Hoshino et al., 2015).

In normal physiology, their role has not been studied extensively. However, they are known to exchange genetic material between cells, regulate immune responses and induce angiogenesis (Keller et al., 2006; Simons and Raposo, 2009). The original work of Raposo et al showed the role of exosomes derived from B cells in antigen presentation and T cell activation (Raposo et al., 1996). Further studies have shown exosomes from dendritic cells carrying MHC Class I and Class II molecules (Théry et al., 2002). They act as biologically active signals contributing to the development of the nervous system and the regeneration of neurons (De Toro et al., 2015). The exosomes secretion has also been related with ADAM (a disintegrin and metalloprotease) mediated ectodomain shedding (Keller et al., 2006). The transmembrane proteins sometimes shed their extracellular domain generating a soluble form. A study suggested that cells may release soluble forms of proteins via exosomes independent of the ectodomain cleavage by

Introduction sheddase enzymes. It was found that the full-length TNF receptor was secreted in exosomes like vesicles and inhibiting ADAM metalloproteinases blocked exosomes formation (Hawari et al., 2004).

2.3.1 EVs and tumor microenvironment

After cardiovascular diseases, cancer is the second leading cause of mortality followed by respiratory diseases and diabetes (WHO factsheet, http://www.who.int/mediacentre/factsheets/fs355/en/). Cancer cells release high quantities of exosomes when compared to the non-transformed cells (Tickner et al., 2014). Multiple recent studies have shown that exosomes contribute to tumor initiation, progression and metastasis. They promote angiogenesis (Nazarenko et al., 2010), modulate tumor microenvironment and suppress immune response facilitating cancer progression (Steinbichler et al., 2017; Sun et al., 2018; WEIDLE et al., 2016). They have also been reported to impart drug resistance in cancer (Azmi et al., 2013). This is achieved by communication between tumor cells both near and distant via exosomes. This communication also occurs between the tumor cells and their surrounding microenvironment. The exosomes carry various regulatory molecules such as mRNA, proteins and miRNA from cancer cells to stromal cells (Lim et al., 2011; Peinado et al., 2012).

Tumor microenvironment (TME) plays a significant role in the multi-step process of metastasis. It consists of the extracellular matrix, stromal cells, fibroblasts, myofibroblasts, blood and lymphatic vascular network (endothelial cells), bone marrow-derived cells, immune and inflammatory cells (Belli et al., 2018). The cross-talk between the TME and tumor cells allows escape of tumor cells from the primary site and colonization at the secondary metastatic site. To establish themselves at the secondary site the tumor cells need a pre-metastatic niche. (Psaila and Lyden, 2009). The pre-metastatic niche is a conducive micro-environment which allows the circulating tumor cells to successfully dock at the metastatic site and grow. Kaplan et al revealed that bone marrow-derived hematopoietic progenitor cells (BMDCs) house at pre-metastatic sites and help in creating this pre-metastatic niche (Kaplan et al., 2005).

Several studies in the recent past have revealed that exosomes play a vital role in modulating the tumor microenvironment both at the primary tumor and metastatic sites (Peinado et al., 2011; Sceneay et al., 2013). While there is a scarcity of evidence for the contribution of MVs in this regard. The tumor cells escaping primary tumor can home the target site microenvironment by sending out exosomes before their arrival. The exosomes influence the tumor environment of the secondary site and help in the establishment of the pre-metastatic niche (Fig. 4). Hoshino et

Introduction al showed that exosomes carrying specific integrins could direct the tumor cells to metastasize to specific organs in in vivo breast cancer model (Hoshino et al., 2015). α6β4 and α6β1 carrying exosomes were associated with lung metastasis while exosomal αVβ5 integrin contributed to liver metastasis. In another study by Peinado et al observed that melanoma-derived exosomes could mobilize bone marrow-derived cells (BMDCs) via MET onco-receptor which increased the metastatic behavior of primary tumors (Peinado et al., 2012). Another study corroborated these findings where melanoma exosomes mediated preparation of niche promoted lymphatic metastasis (Hood et al., 2011). The conditioning of sentinel lymph nodes was mediated by exosomes which led to the recruitment of melanoma cells, extracellular matrix deposition and vascular proliferation in lymph nodes. Costa-Silva et al demonstrated that pancreatic cancer exosomes induced pre-metastatic niche formation in liver (Costa-Silva et al., 2015). The exosomes derived from pancreatic ductal adenocarcinoma (PDAC) were uptaken by Kupffer cells which induced TGFβ secretion and upregulation of fibronectin production by hepatic stellate cells in the liver. This favorable microenvironment increased recruitment of bone marrow-derived macrophages and promoted metastasis. The macrophage migration inhibitory factor (MIF) was elevated in PDAC exosomes and correlated with liver metastasis development in PDAC patients. Exosomes can also alter tumor microenvironment by affecting the host extracellular matrix through their enzymes (Mathivanan et al., 2010).

Hypoxia is a ubiquitous feature in advanced breast cancer and is related to increased risk of metastasis and poor survival. Hypoxia-inducible factor (HIF) assist in mediating the tumor progression. Recent studies have highlighted the role of exosomes in promoting metastasis and angiogenesis under hypoxia (Park et al., 2010). King et al found that breast cancer cells released a higher number of exosomes under hypoxic conditions and silencing of HIF-1 prevented this effect (King et al., 2012). Kucharzewska et al found that exosomes from highly malignant brain tumor glioblastoma multiforme (GBM) were enriched with hypoxia-related mRNAs and proteins like matrix metalloproteinases, IL-8, PDGFs, caveolin 1, and lysyl oxidase (Kucharzewska et al., 2013). Some of these biomolecules are related to poor prognosis in glioma patients. They found that exosomes from GBM cells under hypoxia were able to induce angiogenesis in endothelial cells ex vivo and in vitro when compared to those derived under normoxia. The hypoxia-derived exosomes were able to programme the endothelial cells to release growth factors and cytokines stimulating PI3K/AKT signaling and migration in pericytes. The study suggested that mRNA and proteomic profiles of exosomes reflects the oxygenation status of glioma cells and patient tumors which can help in disease monitoring and therapeutics.

Introduction

Figure 4: Cancer cells release exosomes which modulate tumor microenvironment. They can secrete exosomes to the surrounding milieu or to the neighboring cells or to the distant cells via blood. The recipient fibroblast and mesenchymal stem cells (MSCs) convert to cancer-associated fibroblasts (CAF), acquiring phenotype supporting tumor growth. They also help in homing of the distant target site and help in establishing a pre-metastatic niche. They can also induce cell proliferation and neo-vasculogenesis in endothelial cells. Further, EVs mediate differentiation of monocytes into macrophage and dendritic cells, releasing pro-tumorigenic cytokines and facilitating immune evasion and tumor escape. Adapted with permission (Sun and Liu, 2014).

2.3.2 Diagnostic applications of EVs

The availability of EVs in various body fluids such as serum, plasma, urine, breast milk, semen, cerebrospinal fluid, saliva, and pleural effusion makes them an excellent candidate for liquid biopsy and detection of biomarkers in cancer. Also, since tumor-derived exosomes carry various proteins and nucleic acids from the cells of their origin, they can give a picture of the disease status in the patient. The levels of biomolecules present in exosomes can determine various processes associated with cancer stage and progression. They can also help in monitoring the disease recurrence and response to therapy (Pakravan et al., 2017). Various exosomal biomolecules have been studied in clinical trials, for example CD34 detected in plasma of acute myeloid leukemia patients, miRNA-21 & miRNA-146a in cervicovaginal lavage of cervical cancer patients, Survivin in serum of breast cancer patients, EDIL-3 in urine of bladder cancer patients

Introduction and miR-21 in cerebrospinal fluid of glioma patients (Sundararajan et al., 2018). Because of the advantages and promise exosomes hold in cancer therapy, they have been included in several clinical trials (Conlan et al., 2017; Sundararajan et al., 2018). The trials that are currently active or are completed target identification of cancer biomarkers, drug delivery via exosomes and cancer vaccines from dendritic cells-derived exosomes. Even though the exosomes show a great promise in diagnostics and therapeutics in cancer, they still pose certain limitations such as there is no standard protocol for isolation and purification of tumor specific exosomes, lack of standard markers for identification of exosomes at clinical level and poor understanding of the mechanisms involved in regulatory control of exosomal content and their secretion (Sun et al., 2018). Other open questions include how cargo loading strategies will affect the heterogeneity and potency in exosomes. Further, there is also lacking the quantitative characterization of exosomes mediated delivery (György et al., 2015).

2.3.3 Role of EVs in breast cancer

Several studies in the recent past have focused on examining the role of EVs in breast cancer (Lowry et al., 2015). O Brien et al first showed the transfer of aggressive phenotypic traits from cells of origin to the secondary cells in triple negative breast cancer. They demonstrated that EVs from an aggressive form of triple-negative breast cancer (TNBC) cell line Hs578Ts(i)8 could transfer their phenotypic characteristics to the secondary breast cancer cells MDA-MB-231, SKBR3, HCC1954 and parental cells Hs578T (O’Brien et al., 2013). They increased the cell proliferation, migration and invasive capacities of all the recipient cells. Hs578Ts(i)8 exosomes also induced more endothelial tubules formation as compared to parental Hs578T exosomes. Further, the cells treated with exosomes derived from TNBC patients’ sera showed more invasive behavior as compared to the exosomes derived from healthy control sera. In a study by Pakravan et al, mesenchymal stem cell-derived exosomes were able to suppress in vitro angiogenesis via miR-100 and modulated the mTOR/HIF-1α/VEGF signaling axis in breast cancer (Pakravan et al., 2017). Exosomes have also been reported to carry full-length signaling- competent EGFR in breast cancer cells MDA-MB-231 (Higginbotham et al., 2011). The EGFR ligand amphiregulin (AREG) increased invasiveness in breast cancer cells via exosomes significantly higher than other EGFR ligands, TGFα and heparin binding EGF like growth factor (HB-EGF). It suggested that EGFR signaling via exosomes may contribute to pre-metastatic niche.

As discussed earlier, exosomes play a significant role in modulating tumor microenvironment. Various studies have demonstrated this in breast cancer as well. It was observed by P.K. Lim et al showed that stromal-derived exosomes could transfer CXCL12-specific miRNAs from bone

Introduction marrow stroma to breast cancer cells resulting in reduced CXCL12 levels and decreased proliferation, further leading to BC cell quiescence (Lim et al., 2011). On the other hand, Luga et al found that exosomes derived from fibroblast cells including carcinoma-associated fibroblasts (CAFs) loaded with Wnt11 were taken up by breast cancer cells (BCC) enhancing their protrusive activity, motility and metastasis. This process was found to depend on the Wnt-PCP pathway in BCC and CD81 component of fibroblasts in an orthotropic mouse model (Luga et al., 2012). As cited earlier in a very interesting study by Hoshino et al showed how exosomal integrins determine the secondary site for colonization by metastatic tumor cells in breast cancer (Hoshino et al., 2015).

To survive, tumor cells develop an immune evasion mechanism which includes T-cell evasion, secretion of tumor proteins, promotion of T regulatory cells and reduction in expression of antigen presenting proteins (Drake et al., 2006). Breast cancer-derived exosomes have also been highlighted in such processes via interaction with dendritic cells, T cells, macrophages, and T regulatory cells. Jang et al found that exosomes derived from mouse breast cancer cells 4T1 treated with epigallocatechin (EGCG) could transfer miR-16 to tumor-associated macrophages (TAMs) (Jang et al., 2013) and inhibited macrophage infiltration suppressing the immune response. In one study, breast cancer cells-derived exosomes targeted CD11b+ myeloid precursors in the bone marrow, accumulating myeloid precursors in the spleen. They also blocked the differentiation of murine myeloid precursor cells and human monocytes into DCs thus facilitating tumor progression. This was mediated by IL-6 expression which was seen to be increased in bone marrow cells after treatment with exosomes (Yu et al., 2007). In another study, exosomes from breast cancer cells MDA-MB-231 and MCF7 were found to stimulate macrophages by activation of NF-kB via Toll-like receptor2 (TLR2) (Chow et al., 2014). Exosomes packaged epidermal growth factor (EGFR) was also found to suppress antiviral innate immunity in dendritic cells through the kinase MEKK2. MEKK2 is known to prevent type1 interferon expression and activation of interferon regulatory transcription factor 3 (IRF3) (Gao et al., 2018). Anti-tumor responses have also been reported with breast cancer-derived exosomes. Kitai et al showed that treating breast cancer cells with topoisomerase inhibitor topotecan (an antitumor chemotherapeutic that triggers DNA double-strand breaks and DNA damage response) significantly increased exosomal DNA production which further stimulated dendritic cells activation through cGAS-STING signaling (an important pathway in cytosolic DNA-mediated innate immune responses). Thus, exosomal DNA can also activate innate antiviral immune cell responses in breast cancer (Kitai et al., 2017).

Exosomes in breast cancer have also been studied as promising diagnostic marker carriers (Sundararajan et al., 2018). Vardaki et al compared the proteomic profiles of exosomes derived

Introduction from metastatic and non-metastatic cell lines from human (MDA-MB-231 & MCF7) and mouse (67NR & 4T1). They found significant differences; especially the protein ‘periostin’ was enriched in metastatic cell-derived exosomes (Vardaki et al., 2016). Its presence was further validated in plasma-derived exosomes and patients with lymph node metastasis had elevated levels of exosomal periostin as compared to those with localized disease. Additionally, exosomes from patients with lymph node metastasis were bigger in size (125nm) and had a higher concentration (10.9 x 109 particles/ml) compared to those with localized breast cancer (95nm and 8.35x109 particles/ml). Periostin is a component of ECM secreted by fibroblast cells and is involved in adhesion of osteoblasts. It’s secreted by cancer cells and interacts with integrins αVβ3 and αVβ5 to promote migration and invasion. It is upregulated in several cancers like breast cancer, ovarian cancer and non-small lung cancer. In breast cancer, it is correlated with tumor progression and poor survival outcome (Nuzzo et al., 2015; Shao et al., 2004). Another study highlighted the clinical relevance of exosomal survivin (Khan et al., 2014). Survivin is an inhibitor of apoptosis and regulates mitosis in cancer cells. It is overexpressed in various carcinomas of breast, prostate, lung, ovarian, and pancreas. Exosomal survivin was analyzed from breast cancer patients’ sera and tissue. Survivin and its splice variants were found to be differentially expressed in breast cancer mimicking the expression pattern of breast cancer tissue. Particularly, an exosomal Survivin-2B variant was found to be significant for early detection of breast cancer. Exosome packaged miR-101 was also studied to be clinically relevant in a cohort of breast cancer patients’ sera where it was found to be elevated in cancer patients compared to the benign and healthy cases. While miR-373 was found to be increased in triple negative breast cancer patients (Eichelser et al., 2014). Another study found that breast cancer exosomes were particularly enriched in miRNA when compared to normosomes (exosomes derived from a non-cancerous sample). It further revealed that breast cancer exosomes were involved in miRNA biogenesis in a cell-independent manner (Melo et al., 2014). The proteins associated with miRNA biogenesis such as Dicer, AGO2, and TRBP along with Pre-miRNA were found in cancer exosomes. Further, exosomes from breast cancer cells and patients’ sera were able to induce tumorigenesis in epithelial cells. EV-associated TGFβ levels have also been studied to have potential as a predictive biomarker of treatment response to HER2 targeted therapy in HER2 overexpressing breast cancer patients (Martinez et al., 2017).

These studies suggest that EVs play a significant role in breast cancer affecting immune response and tumor progression. The preliminary findings also highlight that EVs can be potential diagnostic markers not only to detect but also to assess disease progression and therapy response in breast cancer.

Introduction

2.4 Tetraspanin 8: characteristic features and its role in cancer

Tetraspanins are transmembrane proteins which are present on the surface of exosomes as described earlier. They are also used as exosomal markers and play a significant role in biogenesis, targeting, and function of exosomes. They comprise a large family of four-highly conserved transmembrane domain proteins and their size ranges from 27-34 kDa. There are 33 tetraspanin genes in the human genome. They have also been identified in Drosophila melanogaster (37 proteins) and C. elegans (20 proteins) (Huang et al., 2005).

Figure 5: Tetraspanins interact with other tetraspanins and various cell signaling molecules forming a tetraspanin-enriched domain (TEM). These interactions lead to various processes such as A) exosomes biogenesis B) Sorting of cargoes such as proteins and miRNAs into exosomes C) the exosomes binding and uptake by target cell D) antigen presentation by exosomes, for example, MHC molecules. Adapted from Andreu and Yáñez-Mó (Andreu and Yáñez-Mó, 2014); licensed under Creative Commons Attribution (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).

They are engaged in various cellular processes such as cell adhesion, migration and proliferation and function through tetraspanin-enriched domains (TEM) (Hemler, 2005). Tetraspanins generally do not have direct ligands (with few exceptions) and form complexes

Introduction with other tetraspanins and various cytosolic and transmembrane proteins (Charrin et al., 2009). Thus, besides tetraspanins, TEM contain their main interacting partners which include various receptors of extracellular matrix like integrins, Immunoglobulin superfamily (IgSF) members of adhesion receptors, signaling receptor tyrosine kinases (e.g. EGFR) and metalloproteinases (Fig. 5) (Yáñez-Mó et al., 2009, 2011). Some tetraspanins are widely expressed such as CD81 which is found on most cell types; CD151 on almost all epithelial, endothelial and fibroblastic cells. While, some tetraspanins are found on specific sites like CD37 and CD53 on lymphoid cells (Hemler, 2005).

2.4.1 Structure of Tetraspanins

A tetraspanin protein spans the plasma membrane four times and has 2 extracellular loops; one large (ECL2) and one small (ECL1) (Fig. 6). The full intact protein has about 200—350 amino acids folding up to form a tight rod-like structure. The transmembrane region is most conserved and contains polar residues on its first, third and fourth stretches which stabilizes the tertiary structure (Bienstock and Barrett, 2001; Gratkowski et al., 2001). The small extracellular loop (SEL) is made up of 13-31 amino acids and does not participate in binding interactions. Hence, it cannot be identified by monoclonal antibodies that recognize cell-surface epitopes (Masciopinto et al., 2001; Stipp et al., 2003). The large extracellular loop (LEL) consists of 69 – 132 amino acids comprising the conserved region and variable region (Hemler, 2005). The constant region has 3 conserved α-helices which mediate homodimerization via hydrophobic surface. While the variable region contains all the tetraspanin interacting sites. The constant region also has conserved cysteine residues containing motifs such as CCG which is most well-known. Other such motifs include Pro-Xaa-Xaa-Cys (PXXC) motif (Andreu and Yáñez-Mó, 2014; Hemler, 2014). The variable segment consists of 2-4 disulfide bonds formed between the cysteine residues. The number of cysteine residues depends on the particular tetraspanin. The variable region has the epitopes for anti- tetraspanin antibodies. The tetraspanin structure also has 2 cytoplasmic tails with N- and C- terminal respectively (8-21 amino acids). The C-terminal has different motifs which are essential in sorting and targeting of tetraspanins to specific intracellular locations. For example, Gly-Tyr-Glu-Val-Met (GYEVM) sequence helps in targeting CD63 to late endosomal compartment and Tyr-Xaa-Xaa- φ (YXXφ) motif signals sorting of clathrin-coated vesicles (Bonifacino and Traub, 2003). There is a short intracellular loop present, generally made up of 4 amino acids. Another characteristic feature is that most tetraspanins have post-translational addition of palmitate at cysteine residues near the intracellular border of the transmembrane part. This palmitoylation helps to initiate the tetraspanin-tetraspanin web formation and protects from degradation. It also links gangliosides and cholesterol aiding in the formation of

Introduction

GEM or glycolipid-enriched microdomains (Heiler et al., 2016). Additionally, the tetraspanins undergo N-linked glycosylation at extracellular asparagine residues in the LEL.

Figure 6: Schematic diagram for the typical structure of a tetraspanin protein. It has four highly conserved polar transmembrane domains TM1- TM 4 (yellow circles), two extracellular loops: - one small (ECL1) and one large (ECL2), a small intracellular loop (ICL) and 2 intracellular tails with N and C terminals respectively. The ECL2 consists of two conserved cysteine residues and highly conserved CCG motif (green circles). These cysteine residues can form two disulfide bonds (blue dotted lines) and help in the folding of ECL2. There’re also 2 glycosylation sites present on ECL2 (the purple branched form). The conserved residues in ECL2 include one proline (brown circle) and 2 glycine residues (grey circles). The N-terminal tail also has one conserved lysine residue. Transmembrane 4 has a potential palmitoylation site. Adapted from Lu et al (Lu et al., 2017); licensed under Creative Commons Attribution (CC BY 3.0, https://creativecommons.org/licenses/by/3.0/).

2.4.2 Tspan8 and its role in cancer

Tetraspanin-8 (Tspan8) is an integral transmembrane protein belonging to the tetraspanin superfamily. It was identified as tumor-associated protein, involved in cell motility, cell proliferation, differentiation and metastasis (Szala et al., 1990; Zöller, 2009). In normal physiology, it is found to be expressed in squamous epithelial cells, nerves, smooth and striated muscle cells, capillary endothelial cells, and subpopulations of hematopoietic progenitor cells (Richardson et al., 2011). It has been found to cooperate with tetraspanins CD151, CD9, CD81, integrins α3ß1 & α6ß4 and also associate with other molecules like CD13, EWI-F, intersectin-2, EpCAM, CD49c and CD104 (Nazarenko et al., 2010; Zöller, 2009). Being upregulated in several

Introduction types of cancer, such as melanoma (Berthier-Vergnes et al., 2011), hepatocellular carcinoma (Kanetaka et al., 2001), colon cancer (Greco et al., 2010) and pancreatic cancer (Zöller, 2006); it is known to associate with metastases formation and poor prognosis of gastrointestinal tumor patients.

The first systemic role of Tspan8-enriched exosomes in angiogenesis was described by Gesierich et al, suggesting D6.1A (the rat homolog of Tspan8) may be a potential target for inhibition of angiogenesis (Gesierich et al., 2006). Subsequently, further study of the group has shown that Tspan8 with its cooperating partners associate with induction of angiogenesis in pancreatic adenocarcinomas. Additionally, the exosomes derived from Tspan8-positive tumors acquire pro-angiogenic properties and that only exosomes expressing a Tspan8-CD49d complex preferentially bind and activate endothelial cells (Nazarenko et al., 2010). Following this study, Rana et al found that recruitment of Tspan8 to exosomes differed from that of CD9 and CD151. Also, that Tspan8 internalized in a complex with INS2 (intersectin-2), clathrin and CD49d, independent of CD9 and CD151 (Rana et al., 2011). CD151 and Tspan8 also have been speculated in facilitating uptake of exosomes by cells showing the transfer of exosomes to neighboring non-metastatic tumor cells and contributing to metastatic niche formation (Yue et al., 2015). In pancreatic cancer, Tspan8 has been studied to regulate the content and function of exosomes. Tspan8 along with other interacting proteins such as CD44v6, c-Met, alpha6beta4, CXCR4, CD133, EpCAM, and claudin7 serve as cancer stem cells marker in pancreatic cancer. And these CSC markers cooperate in generating, loading and delivery of exosomes. The exosomes derived from pancreatic cancer stem cells (Pa-CSC) were able to reprogram neighboring non-CSC to undergo epithelial-mesenchymal transition (EMT) and helped in creating a niche for metastasizing tumor cells. The exosomes also interacted with the matrix aiding in tumor cell motility, invasiveness and homing (Heiler et al., 2016). Tspan8 has a strong interaction with α6β4. In gastric cancer, Tspan8-α6β4 association activates the MAPK pathway and increases tumor cell motility (Wei et al., 2015; Yue et al., 2015). It also interacts with matrix metalloproteinases (Fig. 7). In colorectal cancer, it associates with MMP2, MMP9 and indirectly with MMP14 via CD44v6 which accounts for dysregulated adhesion and motility leading to metastasis (Guo et al., 2012). Tspan8 has also been reported to co-operate with ADAM12m (Disintegrin and metalloproteinase domain-containing protein 12) in oesophageal cancer promoting metastasis (Zhou et al., 2008). It has been demonstrated to directly interact with E- cadherin/p120-catenin complex and modulating cell motility on collagen in cells derived from the primary tumor of a patient with metastatic colon cancer via a switch in signaling between α1β1 and α2β1 integrins (Greco et al., 2010). Tspan8 has been proved to be a potential therapeutic target in the colon cancer (Kim et al., 2015b) and ovarian cancer (Park et al., 2016).

Introduction

Antibody targeting the large extracellular loop of Tspan8 was able to reduce invasion in metastatic colorectal cancer cells HCT116 and LoVo (Kim et al., 2015b). In a similar approach, Tspan8 blocking antibody was able to reduce metastasis in an in vivo model of epithelial ovarian cancer (Park et al., 2016). Recently, Tspan8 has been reported to be a marker for quiescent mammary stem cells (Fu et al., 2017). But its function in breast cancer has not been addressed so far.

Figure 7: Tspan8 and its dominant interacting partners. Tspan8 and CD151 associate with receptor tyrosine kinases (RTK) to promote survival in pancreatic cancer. They cooperate with integrins to promote cancer stem cell motility and with metalloproteases to enhance invasiveness in pancreatic cancer. Adapted from Heiler et al (Heiler et al., 2016); licensed under Creative Commons Attribution (CC BY-NC4.0 3.0, https://creativecommons.org/licenses/by- nc/4.0/).

2.5 Human Epidermal Growth Factor Receptor 2

Human epidermal growth factor receptor 2 (HER2) is the member of epidermal growth receptor (EGFR) family of proto-oncogenes, a subclass of receptor tyrosine kinase (RTK) superfamily. The HER/ErbB family consists of four proteins namely HER1/ErbB1 or EGFR, HER2/ ErbB2, HER3/ ErbB3, and HER4/ ErbB4. The typical structural features of EGF receptors include an extracellular domain (ECD), a transmembrane fragment and intracellular tyrosine

Introduction kinase domain (ICD) (except HER3 which lacks ICD). The ICD is critical for the downstream signaling (Yarden and Pines, 2012). The binding of a ligand to the receptor causes conformational changes which allow receptor dimerization at cysteine-rich regions of the ECD leading to autophosphorylation and kinase activation (Rubin and Yarden, 2001). HER2 does not have any known ligands but has an active conformation which helps in forming heterodimers with other three members of the family (Citri and Yarden, 2006). Together with HER3, it activates PI3K-Akt and MAPK downstream pathways. The EGF receptor signaling is involved in critical processes such as cell proliferation, differentiation, and survival.

HER2 is overexpressed in 20–30% breast cancers (Arteaga et al., 2011; Jackson et al., 2013) and is associated with poor prognosis accounting to increased proliferation, metastatic potential and recurrence. But because of its overexpression, HER2 positive cancers are treated with targeted therapy. The three major receptor inhibitors currently used in the clinical management of breast cancer are trastuzumab, lapatinib and pertuzumab (De et al., 2013; Krop, 2013). Trastuzumab is humanized monoclonal antibody that targets the extracellular domain of HER2 disrupting HER2-HER3 dimerization formed when HER2 is overexpressed or in absence of ligand binding to HER3. This inhibits PI3K signaling and Akt activation prohibiting cell proliferation in a ligand-independent manner. While pertuzumab (also humanized monoclonal antibody), inhibits ligand-induced HER2 signaling. Lapatinib is a dual tyrosine kinase inhibitor which prevents tyrosine kinase activity independent of ligand binding activity (Hervent and De Keulenaer, 2012).

HER2 is 185kDa in size and has three isoforms or splice variants. The three splice variants are Δ16HER2, p100 and Herstatin (Jackson et al., 2013). Δ16HER2 gives rise to receptor lacking exon 16 due to which it has constitutively active homodimers triggering oncogenic pathways. It is also associated with therapy resistance as it binds to trastuzumab with less affinity due to changes in its conformation. p100 and Herstatin act as cell growth inhibitors. p100 encodes a secreted protein of size 100kDa which is a truncated inhibitor of tumor cell proliferation and oncogenic signaling. Herstatin transcribes to a secretory protein of about 68kDa which interferes with the dimerization and autophosphorylation of HER2, inhibiting the growth of transformed cells which overexpress HER2. Additionally, HER2 carboxy-terminal fragments (CTFs) were also observed in patients, specially 611-CTF which showed increased metastatic potential and resistance to trastuzumab (Sasso et al., 2011). Currently, HER2 diagnostics tests include tissue-based immunohistochemistry (IHC), in situ hybridization (ISH) and serum-based ELISA method. IHC and ISH have disadvantages as they are invasive and multiple biopsies for regular monitoring of the disease are not feasible, particularly when metastasis occurs in inaccessible tissue. Also, the HER2 status determined depends on when and where the tissue

Introduction sample was taken and can have a high false-negative rate (Wang et al., 2013). The serum HER2 tests pose an advantage over tissue-based tests as they are non-invasive and can provide quick information regarding therapy response. But these tests are not widely used at clinical levels and need improvement in terms of specificity and sensitivity. The sample of volume required needs to be reduced too.

A B . .

HER2 ECD

ECD: Extracellular domain ICD: Intracellular domain TMR: Transmembrane region TMR

ICD

Figure 8: Schematic diagram depicting the role of exosomal HER2 as a potential biomarker in breast cancer. A) HER2 can be released as a soluble protein or on exosomes. B) Structure of HER2 protein.

Exosomes play a significant role in breast cancer as discussed earlier in this chapter. They contribute to the cross-talk between tumor cells and non-malignant cells. HER2 overexpressing exosomes have also been studied to impart resistance against trastuzumab in breast cancer (Ciravolo et al., 2012). In another study, EVs were shown to carry immunosuppressive molecules TGFβ1 and PD-L1 (Martinez et al., 2017). Further, EVs isolated from non-responsive patients’ sera showed significantly higher TGFβ1 levels when compared to those of complete- responders and partial-responders. The study showed EVs-associated TGFβ levels correlated with response to HER2-targeted treatment in HER2-overexpressing breast cancer patients; highlighting the potential of circulating levels of EVs-associated TGFβ1 as a predictive

Introduction biomarker for the treatment response in patients. Fig. 8 shows how HER2 can either be shed in soluble form (HER2 ECD) by the tumor cells or they can be released in the full form on the surface of exosomes. The exosomes carrying HER2 can give a better picture of the cells of their origin and the status of the disease. Hence, in my study, we focus on studying the potential role of exosomal HER2 as diagnostic markers in breast cancer.

2.6 WGM lasers

Whispering-gallery mode (WGM) lasers are label-free optical biosensors which can detect biomolecules with high sensitivity. They are gaining wide interest as detection and analytical tools as they have advantages over the conventional approach of labeled biosensors. They can measure quantitatively in real time without modifying the target molecule. The labeling process can cause interference with the target species and their measurements. It also costs time and resources (Baaske et al., 2014; Fan et al., 2008). WGM lasers can detect biomolecular interactions at very low concentrations (Baaske et al., 2014). They have been used for the detection and quantification of various biomolecules such as oligonucleotides (Scheler et al., 2012), cancer biomarkers (Gohring et al., 2010; Washburn et al., 2009), single viruses and nanoscale particles (He et al., 2011; Su et al., 2016).

Figure 9: Structure of the microdisk whispering-gallery mode resonator. The light propagates along the convex contour of the disk. The image on the right-hand side depicts the electrical energy distribution of the WGM. The electric field principally lies along the y-direction (Ey- mode). The binding of the target molecule to the resonator surface affects the electrical energy in the vicinity which is detected by the transducer. The graph shows the profile of the energy density along XX’ and YY’. Adapted with permission from Wondimu et al, Optics Express (Wondimu et al., 2018).

Introduction

The structure of a WGM resonator is goblet like on an elevated pedestal, allowing the light to propagate along the convex outer contour and interacting with the surrounding medium (Fig 9). It consists of a receptor, a transducer and data processing, and acquisition system. The receptor is a target-specific molecule which interacts with the target. When the target molecule/particle binds to this receptor on the surface of the resonator, there occurs a shift in the resonance of the surrounding of the sensor or change in the refractive index. The transducer converts the shift in resonance to an output signal. The WGM laser cavity is made from a PMMA (poly (methyl methacrylate)) microdisk doped with a fluorescent dye (pyrromethene 597). The microdisk lasers are optically pumped using a pulsed laser emitting at a wavelength of 523nm (in the absorption bandwidth of pyrromethene 597) (Wondimu et al., 2018).

Another advantage of resonators over other optical biosensor devices is that the light makes multiple turns around the device. This allows the light to make multiple interactions with a single particle on the surface, amplifying the alteration in the property of propagating light. Thus, the resonators can detect very low amount of target molecules. They have been shown to detect as low as 5µl volume of analyte (Wondimu et al., 2017). Systems based on surface plasmon resonance and electrochemical biosensors have also been applied for detection of exosomes (Im et al., 2014, 2017; Kilic et al., 2018; Zhu et al., 2014).

In our study, we employed label-free optic based WGM lasers for detection of exosomes. The surface of the resonators was functionalized with biotin molecule. The surface was further treated with streptavidin which acted as a linker to immobilize the biotinylated antibody against HER2. We tested the EVs derived from breast cancer cells MDA-MB-231, BT-549 and MDA-MB-361 targeting EVs bound HER2. Additionally, a pilot study with EVs derived from 10 breast cancer patients’ sera was also conducted.

Aim of the study

3 AIM of the study

Tspan8 has been known to support metastasis in melanoma, ovarian cancer, colon cancer, hepatocellular and pancreatic cancers. There have been no studies addressing the role of Tspan8 in breast cancer so far. From the previous findings of the lab, it was observed that Tspan8 is expressed at the primary tumor and metastases in breast cancer patients. It also showed that Tspan8 supports metastasis in a syngeneic rat breast cancer model and enhances the release of extracellular vesicles (EVs) indicating its potential role in breast cancer. Hence, this study was undertaken to gain insight into the function of Tspan8 in human breast cancer system.

EVs mediate intercellular communication via their cargo which consists of proteins, lipids, and nucleic acids. We also aimed to study how Tspan8 affects EV content and their release in breast cancer under 2D and 3D environments. Furthermore, we wanted to examine the potential of EVs as diagnostic and prognostic biomarkers.

To fulfill my aim following objectives were undertaken:

1. Establishment of Tspan8 overexpressing breast cancer cell model with MDA-MB-231, BT-549, and MCF7 cells. 2. Examination of the impact of Tspan8 on behavior in breast cancer cells. 3. Examination of the impact of Tspan8 on EVs content and their release. 4. Assistance in development of a novel tool for detection of EV-associated oncogene using whispering-gallery mode lasers.

Materials & Methods

4 Materials & Methods

4.1 Materials

4.1.1 Cell lines

Cell line Source Description BT-549 ATCC human breast invasive ductal carcinoma BT-Tspan8 generated in lab by stably transfecting BT- Tspan8 overexpressing BT- 549 cells with pcDNA3.1/Hygro (+) vector 549 cells containing full length human TM4SF3 (encoding Tspan8 protein) and hygromycin resistance genes MCF7 ATCC human breast metastatic adenocarcinoma MCF7-Tspan8 generated in lab by stably transfecting Tspan8 overexpressing MCF7 MCF7 cells with pcDNA3.1/Hygro (+) vector cells containing full length human TM4SF3 (encoding Tspan8 protein) and hygromycin resistance genes MDA-MB-231 ATCC human breast metastatic adenocarcinoma 231-Tspan8 generated in lab by stable transfection MDA- Tspan8 overexpressing MDA- MB-231 cells with pcDNA3.1/Hygro (+) MB-231 cells vector containing full length human TM4SF3 (encoding Tspan8 protein) and hygromycin resistance genes MDA-MB-361 ATCC human breast metastatic adenocarcinoma

4.1.2 Media and supplements

Product Company DMEM Gibco, Life technologies, Darmstadt, Germany DMEM-F12 Gibco, Life technologies, Darmstadt, Germany EDTA Carl Roth, Karlsruhe, Germany Fetal bovine serum PAN Biotech, Aidenbach, Germany Hygromycin B (50mg/ml) Invitrogen, Darmstadt, Germany Opti MEM (no Phenol red) Gibco, Life technologies, Darmstadt, Germany PBS Gibco, Life technologies, Darmstadt, Germany

Materials & Methods

Penicillin-streptomycin Invitrogen, Darmstadt, Germany RPMI1640 without phenol red Gibco, Life technologies, Darmstadt, Germany TrypanBlue (0.4%) Sigma-Aldrich, Steinheim, Germany Trypsin (2.5%) Gibco, Life technologies, Darmstadt, Germany

4.1.3 Chemicals and Reagents

Product Company/Source Acetic acid Applichem, Darmstadt, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Acrylamide/Bis-acrylamide 30% solution Germany Agarose Serva, Heidelberg, Germany Molecular probes, Life technologies, Darmstadt, Aldehyde/Sulfate Latex Beads, 4% w/v, 4 μm Germany Albumin fraction V (pH 7.0) Applichem, Darmstadt, Germany Ammoniumperoxodisulfat (APS) Carl Roth, Karlsruhe, Germany Ampicillin Roche, Mannheim, Germany Bacillol-plus BoDE Chemie, Hamburg, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Brij O10 Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Bromophenol blue sodium salt Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Collagen solution, Type I Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Collagen IV Germany Collagen G Biochrom, Berlin, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Crystal Violet Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Dimethyl sulfoxide (DMSO) Germany Dithiothreitol (DTT) Carl Roth, Karlsruhe, Germany DNA loading dye (6X) Thermo Scientific

Fibronectin Fragment III1-C Sigma-Aldrich, Steinheim, Germany GeneRuler 1kb plus DNA ladder Thermo Scientific Polysciences Europe GmbH, Eppelheim, Glutaraldehyde solution Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Glycerol Germany Glycine Applichem, Darmstadt, Germany HEPES Biochrom AG, Berlin, Germany Human HER2 recombinant protein eBioscience Sigma-Aldrich Chemie GmbH, Schnelldorf, Isopropanol Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Kanamycin sulfate Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Laminin Germany LB agar Carl Roth, Karlsruhe, Germany

Materials & Methods

LB medium Carl Roth, Karlsruhe, Germany Lipofectamine LTX transfection reagent Thermo Scientific ECM gel from Engelbreth-Holm-Swarm murine sarcoma Sigma-Aldrich Chemie GmbH, Schnelldorf, Germany Methanol gradient grade Sigma-Aldrich, Steinheim, Germany Midori green Biozym Scientific GmbH, Oldendorf, Germany Milk powder (for blocking solution for Western Blotting) Gabler Saliter GmbH, Obergüngburg, Germany Paraformaldehyde Carl Roth, Karlsruhe, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Phenyl methyl sulfonyl fluoride (PMSF) Germany Protease inhibitor cocktail tablets, EDTA-free Roche, Mannheim, Germany Protein ruler Plus, pre-stained protein ladder Thermo Scientific RPMI 1640 Gibco, Life technologies, Darmstadt, Germany SDS Carl Roth, Karsruhe, Germany TEMED Serva, Heidelberg, Germany Tris base Applichem, Darmstadt, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Triton X-100 Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Trypan blue (0.4%) Germany Trypsin (2.5%) Gibco, Life technologies, Darmstadt, Germany Sigma-Aldrich Chemie GmbH, Schnelldorf, Tween-20 Germany Uranyl actetate Department of Neuroantaomy, University of Freiburg Sigma-Aldrich Chemie GmbH, Schnelldorf, WST1 Germany

4.1.4 Buffers and solutions

Buffer Composition Blocking buffer 5% skimmed milk powder in 0.1% PBST 25 mM HEPES HEPES buffer (10X) 50 mM NaCl 5 mM MgCl2 pH adjusted to 7.4, followed by autoclaving 1x HEPES buffer 1% Triton X-100 or 1% Brij O10 1x Protease Inhibitors cocktail mixture* HEPES-Lysis buffer 1mM Phenyl methyl sulfonyl fluoride (PMSF) *(prepared according to manufacturer’s guidelines) 300 mM Tris base, pH 6.8 Laemmli buffer (6X) 12% SDS 0.6% Bromophenol blue 20% Glycerol

Materials & Methods

2 g KCl 2.7 g KH2PO4 PBS (10X) 1000ml 80 g NaCl 14.2 g Na2HPO4 1000ml dH20, pH adjusted to 7.4 PBST (0.1%) 1X PBS 0.1% Tween20 30.3 g (250 mM) Tris base SDS-PAGE Running buffer(10X) 1000ml 187.6 g (2.5 M) Glycine 1% SDS 1000ml ddH20, pH adjusted to 8.3 108 g Tris base TBE buffer (10X) 1000ml 55 g Boric acid 9.3 g EDTA 1000ml ddH20, pH adjusted to 8.1 30.3 g Tris base Transfer (Blotting) buffer (10X) 1000ml 144.1 g Glycine 1000ml ddH20

12% (Separating gel) 3.3 ml ddH20 2.5 ml 1.5M Tris pH8.6 SDS-PAGE gel 10ml 0.1 ml 10% SDS 4ml 30% Acrylamide mix 0.1 ml 10% APS 0.005ml TEMED

10% (Separating gel) 4 ml ddH20 2.5 ml 1.5M Tris pH8.6 0.1 ml 10% SDS 3.3 ml 30% Acrylamide mix 0.1 ml 10% APS 0.005ml TEMED

4% (Stacking gel) 6.1ml ddH20 2.5 ml 0.5M Tris pH 6.8 0.1 ml 10% SDS 1.33ml 30% Acrylamide mix 0.1 ml 10% APS 0.005ml TEMED

Materials & Methods

4.1.5 Antibodies

Antibody Host Dilution used Company/Source Application

Primary Antibody 1:800 - 1:1000 R&D Systems, Bio-Techne WB α1 (CD49a) sheep polyclonal 1:20 (for cells) GmbH, Wiesbaden- FC 1:100 (for EVs) Nordenstadt, Germany FC R&D Systems, Bio-Techne WB mouse 1:500 (for cells) α2 (CD49b) GmbH, Wiesbaden- FC monoclonal 1:100 (for EVs) Nordenstadt, Germany FC Santa Cruz Biotechnology, α3 (CD49c) goat polyclonal 1:200 WB Dallas, USA α6 (CD49f) rabbit polyclonal 1:500 Cell Signalling Technology WB rabbit α4 (CD49d) 1:1000 Cell Signalling Technology WB monoclonal rabbit αV (CD51) 1:500 Cell Signalling Technology WB monoclonal rabbit β1 (CD29) 1:1000 Cell Signalling Technology WB monoclonal rabbit β4 (CD104) 1:1000 Cell Signalling Technology WB monoclonal rabbit β5 1:1000 Cell Signalling Technology WB monoclonal mouse Biolegend, Koblenz, β4 (CD104) 1:100 FC monoclonal Germany mouse 1:500 (for cells) β5 Biolegend, Koblenz, GER FC monoclonal 1:100 (for EVS) Prof. Dr. R. Zeidler, αV rat monoclonal 1:100 Helmholtz Zentrum, FC Munich, Germany Prof. Dr. R. Zeidler, 1:150 (for cells) αVβ3 rat monoclonal Helmholtz Zentrum, FC 1:100 (for EVS) Munich, Germany Prof. Dr. R. Zeidler, 1:223(for cells) α3 rat monoclonal Helmholtz Zentrum, FC 1:100 (for EVS) Munich, Germany Prof. Dr. R. Zeidler, 1:2000 (for cells) β1 rat monoclonal Helmholtz Zentrum, FC 1:100 (for EVS) Munich, Germany Prof. Dr. R. Zeidler, α3β1 rat monoclonal 1:100 Helmholtz Zentrum, FC Munich, Germany mouse Santa Cruz Biotechnology, ß-Catenin 1:300 WB monoclonal Dallas, USA mouse Santa Cruz Biotechnology, CD9 (C-4) 1:100 WB monoclonal Dallas, USA mouse Santa Cruz Biotechnology, CD9 (P1/33/2) 1:100 FC monoclonal Dallas, USA

Materials & Methods

mouse BD Biosciences, CD63 1:200 WB & FC monoclonal Heidelberg, Germany mouse Santa Cruz Biotechnology, CD81 (B-11) 1:500 WB monoclonal Dallas, USA mouse Santa Cruz Biotechnology, CD81 (5A6) 1:100 FC monoclonal Dallas,, USA mouse Santa Cruz Biotechnology, CD151 (H-80) 1:200 WB monoclonal Dallas, USA mouse BD Biosciences, E-Cadherin 1:300 WB monoclonal Heidelberg, Germany mouse Santa Cruz Biotechnology, GAPDH 1:1000 WB monoclonal Dallas, USA rabbit HER2, #2242 1:500 Cell Signalling Technology WB monoclonal HER2 (D8F12) XP, rabbit 1:300 Cell Signalling Technology WB #4290 monoclonal mouse Biolegend, Koblenz, HER2 1:200 FC monoclonal Germany mouse WGM laser HER2, #BMS120BT --- eBioscience monoclonal Experiments mouse Santa Cruz Biotechnology, HSP70 1:200 WB monoclonal Dallas, USA mouse BD Biosciences, N-Cadherin 1:300 WB monoclonal Heidelberg, Germany

mouse BD Biosciences, p120 catenin 1:300 WB monoclonal Heidelberg, Germany mouse Santa Cruz Biotechnology, TSG101 (C-2) 1:200 WB monoclonal Dallas, USA Kindly provided by Prof. hTspan8/CO-029 mouse 5-10 ml Dr. M. Zoller, DKFZ, WB & FC hybridoma monoclonal Heidelberg, Germany R&D Systems, Wiesbaden- Tspan8-PE rat 1:2 FC (with EVs) Nordenstadt, Germany

Secondary

Antibody Santa Cruz Biotechnology, Anti-goat HRP donkey 1:2000 WB Dallas, USA Anti-mouse HRP goat 1:1000 Dako, Hamburg, Germany WB Anti-rabbit HRP goat 1:2000 Dako, Hamburg, Germany WB R&D Systems, Wiesbaden- Anti-sheep HRP donkey 1:1000 WB Nordenstadt, Germany Anti- rat Alexa Jackson ImmunoResearch, Fluor 647 donkey 1:500 Dianova GmbH, Hamburg, FC conjugated Germany Anti-mouse R- Jackson ImmunoResearch, 1:200 (for cells) Phycoerythrin goat Dianova GmbH, Hamburg, FC 1:50 (for EVs) conjugated Germany

Materials & Methods

*WB =Western blotting, FC=Flow cytometry; unless specified, the antibody dilutions mentioned have been applied for both cells & EVs

4.1.6 Assay Kits

Assay Kit Application Company Human ErbB2/Her2 Quantikine Measurement of HER2 levels in R&D Systems, Wiesbaden- ELISA sera and EVs Nordenstadt, Germany Micro BCA™ Protein Assay Kit Protein quantitation (for EVs) Thermo Fisher Scientific Routine quality-control for e-Myco™ MycoplasmaPCR Detection Intron Biotechnology, presence of any mycoplasma Kit South Korea contamination in cell culture Pierce™ BCA Protein Assay Kit Protein quantitation Thermo Fisher Scientific QIAGEN plasmid Midi kit Plasmid DNA isolation Qiagen, Hilden, Germany Detection of horseradish Thermo Fisher Scientific, Supersignal™ West Dura Extended peroxidase (HRP) on Life Technologies GmbH, Duration substrate immunoblots Darmstadt, Germany

4.1.7 Equipments

Equipment Name Company Logos Biosystems Inc., Automated bright field Cell counter LUNA™ Automated Cell Counter Annandale, USA Thermo Fisher Scientific, Thermo Electron LED Cell Culture incubator HERA CELL 240i CO2 Incubator GmbH, Langenselbold, Germany Thermo Fisher Scientific, Thermo Electron LED HERA CELL 150i CO2 Incubator GmbH, Langenselbold, Germany Thermo Fisher Scientific, SAFE 2020 (Class II Safety Thermo Electron LED Cell culture Flow Hood Cabinet) GmbH, Langenselbold, Germany AvantiTM J-30I high speed- Beckman Coulter, Krefeld, Centrifuge centrifuge Germany Eppendorf, Wesseling- Centrifuge 5424R Berzdorf, Germany Thermo Fisher Scientific, Heraeus Megafuge 16R Thermo Electron LED

Centrifuge GmbH, Langenselbold, Germany mini G Mini centrifuge IKA, Staufen, Germany OptimaTM LE-80K Beckman Coulter, Krefeld, Ultracentrifuge Germany (SW41Ti Rotor )

Materials & Methods

Concentration chamber Solvent-Resistent Stirred cell Bio-Rad, Munich, Germany Nalgene, Sigma Life Cryo 1°C freezing container Mr. Frosty TM Sciences, Steinheim, Germany Dynamic Light Scattering (DLS) Particle Metrix, NANO-flex® 180° DLS System System Meerbusch, Germany Tecan, Männedorf, ELISA microplate reader Infinite M200 Switzerland BD Becton, Dickinson and Flow Cytometer BD Accuri C6 Company, Heidelberg, Germany Dometic S. à r. l., Hosingen, Freezer -20°C ML 3051C Luxemburg Thermo Fisher Scientific, Thermo Electron LED Freezer -80°C Hera Freeze HFU T Series GmbH, Langenselbold, Germany Merck Millipore, Filtration assembly filtration assembly Darmstadt, Germany Fume hood (Secueflow) Secu Flow Waldner, Germany Molecular Imager® Gel Doc™, Gel and blot imaging apparatus ChemiDoc™, and ChemiDoc XRS Bio-Rad, Munich, Germany Systems Ice Machine EisMaschinen Ziegra, Germany Magnetic stirrer/mixer RH Basic2 IKA®, , Staufen, Germany Hamilton-Bonaduz, Ilinois, Microliter pipette Microliter® USA Axio Vert.A1 Inverted Carl Zeiss Microscopy, Microscopes microscope Jena, Germany Biozero Compact Fluorescence Keyence microscope Zeiss LEO 906E Transmission electron microscope, (with a Carl Zeiss Microscopy,

2kCCDCamera "sharp-eye" from Jena, Germany Tröndle) Microwave Express Sharp MilliQ Water Dispenser Direct QTM Millipore Millipore, Billerica, USA Eppendorf, Wesseling- Multichannel Pipette Research Plus Berzdorf, Germany Nanoparticle tracking analysis Particle Metrix, ZetaView® PMX 110 V3.0 (NTA) System Meerbusch, Germany Heraeus Instruments, Laboratory air circulation oven Function Line Freiburg, Germany Thermo Fisher Scientific, Thermo Electron LED Liquid nitrogen Tanks Locator 6 Plus GmbH, Langenselbold, Germany Liquid nitrogen transport system Apollo/Saturn Cryotherm, Germany pH-Meter Ino Lab pH level 1 WTW, Weilheim, Germany Eppendorf, Wesseling- Pipettes Research Plus Berzdorf, Germany

Materials & Methods

Eppendorf, Wesseling- Pipette Gun Eppendorf EasyPet Berzdorf, Germany Integra Biosciences, Pump Vacusafe Fernwald, Germany Rotator/Mixer Intelli Mixer RM-2L ELMI SDS-PAGE apparatus Mini-PROTEAN® Tetra System Bio-Rad, Munich, Germany Mini-PROTEAN® short Bio-Rad, Munich, Germany plates/Spacers Mini-PROTEAN® Comb-10 well Bio-Rad, Munich, Germany 1 mm & 1.5 mm Western blotting apparatus Criterion™ Blotter Bio-Rad, Munich, Germany Heidolph, Schwabach, Shakers Promax 2020 Germany Heidolph, Schwabach, Polymax 1040 Germany PeQLab Biotechnology, Spectrophotometer Nanodrop 1000 USA Thermal Cycler T100™ Thermal Cycler Bio-Rad, Munich, Germany Eppendorf, Wesseling- Thermomixer Thermomixer Comfort Berzdorf, Germany Voltage supplier Power Pac™ Basic Bio-Rad, Munich, Germany Heidolph, Schwabach, Vortex mixer Reax Top Germany Sartorious, Göttingen, Weighing scales Weighing scale Germany Water Bath Water Bath GFL, Burgwedel, Germany

4.1.8 Consumables

Product Company/Source 0.2 μm filtration membranes Merck Millipore, Darmstadt, Germany 0.2 μm syringe filters Pall LifeSciences 8 well μ-slide chambers ibiTreat (ibidi), Planegg/Martinsried, Germany 20 ml syringes Braun, Melsungen, Germany 300 kDa Ultrafiltration discs (Biomax®) Millipore, Billerica, USA Cell culture flasks (T-25/T-75) Greinar Bio-One, Frickenhausen, Germany Cell scrapers TPP, Trasadingen, Switzerland

Dr. Andreas Thomsen, Department of Radiology, Conical agarose microwell array (CAMA) Medical Centre, University of Freiburg

Cryovials (Cryotube 20) TPP, Trasadingen, Switzerland Disposable serological pipettes (5 ml/ 10 Sigma-Aldrich Labchemicals Ltd, Seelze, Germany ml/ 25 ml) Electron microscopy copper grids Plano GmbH, Wetzlar, Germany Eppendorf tubes (1.5 ml/ 2 ml) Eppendorf, Wesseling-Berzdorf, Germany Filter tips, 20 µl – 1000 µl (Tip One®) Tip one, Star lab, Hamburg, Germany Glassware conical flasks Schott, Mainz, Germany

Materials & Methods

Dr. Andreas Thomsen, Department of Radiology, Invasion matrix (2.8 % agarose) Medical Centre, University of Freiburg Low retention tips (10 μl/200 μl/1000μl) Brand, Wertheim, Germany LUNA™ Cell Counting Slides Logos Biosystems Inc., Annandale, USA Measuring cylinders Brand, Wertheim, Germany Microplates (6-well/24-well/96-well) Greinar Bio-One, Kremsmünster, Austria, Germany Parafilms Brand GmbH + CO KG, Wertheim, Germany Pasteur capillary pipettes WU, Mainz, Germany Polypropylene falcons (15 ml + 50 ml) BD Biosciences, Heidelberg, Germany Precision wipes Kimtech Science Propylene centrifuge tubes (14 x 89 mm, Beckmann Coulter, Brea, USA for Ultracentrifugation) Protection mitts Moufle versilic, France PVDF membranes (Immobilon-P transfer) Millipore, Schwalbach, Germany Whatman filter papers GE Healthcare, Freiburg, Germany

4.1.9 Softwares

Software Company BD Becton, Dickinson and Company, BD CSamplerTM Heidelberg, Germany BZ-8100 Observation Application Keyence i-control 1.6 Tecan, Männedorf, Switzerland Microsoft Office Proffessional Plus 2010 Microsoft, USA MicrotracFLEX 11.0.0.5 Particle Metrix, Meerbusch, Germany ND-1000 V3.8.1 PeQLab Biotechnology Office Suite 2011 Microsoft, USA Photoshop CS6 Adobe, San Jose, USA Prism 5.0 GraphPad, USA Quantity One 4.6.6 Bio-Rad, Munich, Germany Zen2.3 lite Zeiss, Oberkochen, Germany ZetaView 8.4.2 Particle Metrix, Meerbusch, Germany Zotero Reference Manager Roy Rosenzweig Center for History and New Media, USA

4.2. Methods

4.2.1 Cell culture

4.2.1.1 Cell lines and culture conditions

The breast cancer cells MDA-MB-361, MDA-MB-231, MCF7, and BT-549 were obtained from

ATCC. The cells were cultured in an incubator at 37°C, 5% CO2, and 95% humidity conditions. The cell culture medium used was in DMEM-F12 (+L-Glutamine) medium supplemented with

Materials & Methods

10% fetal bovine serum (FBS). Exceptionally, the MDA-MB-361 cells were cultured in DMEM (+4.5 g/L D-glucose, L-Glutamine) medium supplemented with 10% FBS. FBS was heat- inactivated at 56°C for 30 min prior to usage in culture media. The Tspan8 overexpressing cell lines were cultured in selection medium except when being subjected to experiments. The selection medium for 231-Tspan8 and BT-Tspan8 comprised of DMEM-F12 supplemented with 800µg/mL hygromycin. The selection medium for MCF7-Tsapn8 cells was DMEM-F12 supplemented with 600 µg/µl hygromycin. In hypoxia condition, the cells were cultured at 1%

O2, 5% CO2, and 95% humidity conditions. All the cell lines tested negative for Mycoplasma (results not shown). The mycoplasma test was carried out using the e-Myco™ MycoplasmaPCR Detection Kit, at least twice a year.

4.2.1.2 Passaging and freezing of cells

The cells were passaged at 80-90% confluency. And special care was taken that the cells did not reach over-confluency. The cells were washed with 1X PBS and trypsinized with 1-2 mL of Trypsin-EDTA mixture (0.05% Trypsin+1 mM EDTA, prepared in 1X PBS). Complete medium was added to stop the action of trypsin and cell suspension was transferred to the new flask. The split ratio for the wild-type cells was 1:3-1:10. The split ratio for transfected cells and MDA- MB-361 cells was kept not more than 1:2-1:5.

For freezing the cells, freezing medium was prepared as follows: 10% FBS + 10% DMSO in complete medium. The cells were trypsinized, counted and about 1x106 cells were resuspended in the freezing medium per cryovial. The cryovials were kept at -80°C for at least 24h in a freezing container filled with isopropanol with 1°C/min cooling rate, allowing cryopreservation. The frozen stocks were then transferred to liquid nitrogen for long-term preservation.

4.2.1.3 Revival of frozen cells

The frozen stocks from liquid nitrogen were thawed in 37°C water bath for 2-3 min and carefully transferred to flasks containing the complete medium. Cells were allowed to attach overnight in 5% CO2 incubator at 37°C and the medium was changed thereafter.

4.2.1.4 Cell counting

Cells were counted using Luna automated cell counter. After trypsinization, a uniform cell suspension was prepared. 20µl of cell suspension was uniformly mixed with 20µl trypan blue (0.4%) in a ratio of 1:1. 10µl of the resulting mixture was loaded onto the disposable Luna counter slides and the count was measured using Luna automated cell counter with the setting parameters pre-set for specific cell line.

Materials & Methods

4.2.2 3D cell culture

For culturing cells in three-dimensional (3D) environment, the conical agarose microwell array (CAMA) or agarose matrix was employed. The device was developed and kindly provided by Dr. Andreas Thomsen (Department of Radiology, Medical Centre, University of Freiburg). It is made from 2.4% agarose in double distilled water. The device with 1x1 mm geometry was used. It has 950 conical microwells in total with each being spaced at periods of every 1 mm (Fig. 10A). The diameter at the bottom of the microwells is 212 µm. The agarose allows oxygen, nutrients and cellular waste to permeate but is repellent to the cells. Resembling ‘liquid overlay technique’, the cells are seeded onto the microwells. The cells adhere to each other but not the agarose surface. So, depending on their cell-cell attachment properties, they either form spheroids or loose cellular aggregates. The device was solely used to isolate EVs from cells in 3D environment which better mimics the cellular tumor microenvironment.

To culture the cells in the agarose matrix, it was equilibrated with cell culture medium (Fig. 10B). 3 mL of cell culture medium supplemented with 2.5% EVs-depleted FBS was added onto the matrix. The plate was centrifuged at 100g for 1 min to remove any air bubbles. The plate was kept at 37°C in a cell culture incubator for at least 3h or overnight. After equilibration, the medium was removed, and cells were seeded onto the matrix in cell culture medium with 2.5% EVs depleted FBS. The cells were seeded in 9.5 mL medium per matrix and the number was optimized according to each cell type. For isolation of EVs, cells were seeded as follows:

MDA-MB-231 & 231-Tspan8, BT-549 & BT-Tspan8: - 1500 cells per microwell

MCF7 & MCF7-Tspan8, MDA-MB-361: - 2000 cells per microwell

After 24h, the matrix was transferred to a new six-well plate and the medium was changed to starvation medium (medium without FBS). The cells were cultured for 7 days after seeding at 37°C in the cell culture incubator. During optimization of the method, pictures were taken every alternate day till day7 to observe the formation of spheroids. Hematoxylin and Eosin (H&E) staining was also performed with the spheroids on day 7 by Ms. Hannah Fühllgraf (Lab of Dr. med. Peter Bronsert, Institute for Clinical Pathology, Medical Centre, University of Freiburg). The spheroids were sealed with agarose, embedded in wax and sectioned, followed by staining.

4.2.3 Plasmid DNA isolation

The pcDNA3.1/Hygro (+) expression vector containing full length human TM4SF3 gene (HGNC: 11855) encoding Tspan8 protein (previously generated in our laboratory) was isolated using

Materials & Methods

Qiagen Plasmid Midi kit. The procedure based on a modified alkaline lysis method was followed as outlined by the manufacturer’s handbook. Other plasmid DNA isolated included pcDNA3.1 empty vector (mock control) and pEGFPN1 (positive control for transfection). To begin with, a starter culture was prepared for each. 3mL LB medium supplemented with appropriate selective antibiotic (100µg/mL ampicillin for pcDNA3.1/Hygro, & 50µg/mL kanamycin for pEGFPN1) was inoculated with bacterial glycerol stock in a 15mL conical centrifuge tube (loosened cap) and incubated for 8h at 37°C with shaking (300rpm). The starter culture was further expanded into a 100 mL culture and incubated overnight at 37°C with shaking at 300rpm.

Figure 10: Conical agarose microwell array (CAMA) for 3D culture. A) Photographic image of the CAMA, showing its multiple conical microwells. B) Steps involved in equilibration of the CAMA with culture medium. The steps include separating 1 CAMA into a 10cm Petri using a sterilized spatula, removal of PBS, placing the CAMA into the 6-well plate, addition of culture medium and centrifugation to remove any bubbles. Thereafter, the 6-well plate is incubated in 5% CO2 incubator at 37°C for at least 3h or overnight. Image courtesy: Dr. Andreas Thomsen (Department of Radiology, Medical Centre, University of Freiburg).

Materials & Methods

The bacteria were harvested by centrifuging at 3000 g for 20 min at 4°C. The bacterial pellet was resuspended uniformly in 4 mL buffer P1 (50 mM TrisCl, pH=8.0, 10 mM EDTA, 10µg/mL RNAse A) supplied with the kit. The lysis buffer P2 (200 mM NaOH, 1%SDS w/v) was further added and mixed thoroughly by inverting the sealed tube few times followed by 5 min incubation at RT. 4mL of neutralization buffer P3 (3M potassium acetate, pH=5.5) was added, immediately mixed by inversion and incubated on ice for 15 min. The solution was centrifuged at 10 000 g for 30 min at 4°C and the supernatant containing plasmid DNA was quickly transferred to new conical centrifuge tubes. This step was done twice. The supernatant was again centrifuged twice at 10 000 g for 15 min at 4°C and supernatant quickly transferred to a new tube. The Qiagen-tip 100 column was equilibrated by applying 4 mL buffer QBT, emptying the column by gravity flow. The supernatant containing the plasmid DNA was applied to the column followed by washing the column with QC buffer. The plasmid DNA was eluted by running elution buffer through the column into a new tube marked form outside for the glassy DNA pellet obtained in the subsequent step. The DNA was precipitated by adding 3.5 mL room- temperature isopropanol to the eluted DNA. The mixture was immediately centrifuged at 15000 g for 60 min at 4°C and the supernatant was carefully drained without disturbing the DNA pellet. The pellet was then washed with 2 mL room-temperature 70% ethanol and centrifuged at 15 000 g for 10 min a 4°C. The supernatant was drained, and the pellet was air-dried for 5-10 min. The pellet was resuspended in 100 µl elution buffer. The quality and concentration of the eluted plasmid DNA were determined using Nanodrop spectrophotometer.

4.2.4 Transient transfection

The breast cancer cells chosen for study were transfected with pcDNA3.1/Hygro (+) expression vector containing full length human TM4SF3 gene (encoding Tspan8 protein) and hygromycin resistance genes. Lipofectamine LTX Plus was used as the transfecting reagent. The protocol was optimized according to the manufacturer’s guidelines. The amount and ratio of plasmid DNA to Lipofectamine were optimized for each cell line. For MDA-MB-231 and MCF-7 cell lines, DNA (µg): Lipofectamine LTX (µl) ratio used was 1:2 + 1 µl Plus reagent. While for the BT-549 cells, 1:3 + 3 µl Plus ratio was used. Cells were seeded in a 24–well plate, 24-48 h prior to transfection until they reached 70-80% confluency. On the day of transfection, DNA and transfection reagent dilutions were made separately in opti-MEM serum-reduced medium in a 96-well plate and incubated for 5 min. The Plus reagent was added to the DNA directly and incubated for further 5 min. Controls used: untransfected cells, pcDNA3.1/Hygro (+) empty vector as mock control, pEGFPN1 expression vector as a positive control. The DNA and reagent dilutions were mixed together and incubated for 30 min at room temperature. During the 30

Materials & Methods min incubation of the complex, the cells were pre-conditioned with 300µl of opti-MEM. 200µl of the complex was added to the cells and incubated for 24h. After 24h, pEGFPN1 transfected cells were checked under the microscope to examine the efficiency of the transfection.

4.2.5 Generation of stable cell lines

For generating cell lines stably expressing Tspan8, cells were cultured in selective medium containing hygromycin B after transfection. The optimal concentration of the selection marker was pre-determined for each cell line by performing a dose-response at different concentrations and choosing the minimal dose that could kill the cells. The selection medium was changed every 3 days for 10-15 days until all the untransfected cells died. Selection medium used for each cell line, MDA-MB-231: - 800 µg/mL hygromycin B, BT-549: - 800 µg/mL hygromycin B and MCF7: - 600 µg/mL hygromycin B. The transfected pool of cells was expanded by passaging into a T25 flask. Cells were validated for Tspan8 expression by flow cytometry. The cells expressing a high level of Tspan8 were sorted at the Core Facility, University Medical Centre Freiburg (in collaboration with Dr. Marie Follo), as described later. The sorted cells were immediately transferred to T25 flasks and allowed to attach in medium containing 1% Penicillin-Streptomycin mixture. After the cells had attached and were confluent, the cells were expanded in selection medium. The frozen stocks were made and stored in liquid nitrogen for further experiments.

4.2.6 Flow Cytometry with cells

About 200 000 cells were seeded in a 96-well U-bottom plate in 200 µl medium. For MDA-MB- 361, 500 000 cells were seeded. The cells were incubated for an hour at 37°C so as to replenish all the surface proteins. Cells were centrifuged at 1500 g for 3 min at 4°C and washed with 150 µl of cold 1% BSA/PBS. 50µl primary antibody (or 100 µl of Tspan8 hybridoma) was added to the respective wells and incubated for 25 min at 4°C. After incubation, the plate was centrifuged at 1500 g for 3 min and 2 washes were given with 150 µl cold 1X PBS. The cells were then treated with secondary antibody for 30 min at 4°C in dark. Thereafter, the cells were given 2-3 washes with PBS and resuspended in 200µl PBS for measurement. Unstained cells were taken as negative control along with cells incubated only with secondary antibody. The samples were measured with BD AccuriTM C6 flow cytometer. 10 000 events were recorded at medium core speed and 80 000 threshold. The measurements were analyzed using BD CSamplerTM software.

Materials & Methods

4.2.7 Fluorescence-activated cell sorting

About 8-10 million cells were resuspended in 1 mL medium in a 15 mL conical centrifuge tube and incubated for 1h at 37°C. Cells were pelleted down at 1000 g for 5 min at 4°C and washed 3 times with 5 mL PBS. 2mL of primary antibody (Tspan8 hybridoma antibody) was added to the required tube and incubated for 25 min at 4°C on a rotating platform. The cells were then centrifuged at 1000 g for 5 min and washed twice with 5 mL cold PBS. 300 µl of secondary antibody was added to respective tubes and incubated for 30 min in a rotating shaker at 4°C. Dilutions were made in 1% BSA/PBS. This was followed by 2 washes with 5 mL of cold PBS. The samples were resuspended in 500µl of medium with 1% Penicillin-Streptomycin mixture. Unstained cells and cells stained with only secondary antibody were taken as negative control. The cells were sorted with FACS Aria III from Becton Dickinson using a 100 µm nozzle. The sorting was performed at Core Facility, University Medical Centre Freiburg with the kind help of Mr. Dieter Herchenbach and Mr. Klaus Geiger.

4.2.8 Preparation of cell lysate

For all the western blot experiments with transfectants, HEPES lysis buffer with Brij® O10 (1%) was used. While For all other experiments HEPES lysis buffer with Triton X-100 (1%) was used.

Cells were seeded in 10/14.5 cm Petri dishes and incubated at 37°C in a CO2 incubator for 24-48 h until the cells reached a confluency of 70-80%. The cells were then washed twice with 1XPBS followed by a wash with 1X HEPES buffer. 800 µl HEPES lysis buffer was added to each dish so as to cover the complete surface area. The dishes were rotated gently on a shaker at 4°C for 1h. Cells were harvested with cell scrapers and transferred to 2 mL Eppendorf tubes and centrifuged at 12 000 x g for 20 min at 4°C. The pellets were discarded and the supernatant was transferred to fresh tubes. The cell lysates were then stored at -20°C for further experiments. For extracting cell lysate from the stably transfected cells, the cells were trypsinized and incubated at 37°C in a CO2 incubator for 1h for recovery of proteins. The cells were centrifuged at 800 g for 5 min and given a wash with 1X PBS followed by 1X HEPES buffer. The cells were then incubated with lysis buffer consisting of Brij® O10 at 4°C for 2h on rotating shaker. Thereafter, the cell lysate was harvested as described above.

Materials & Methods

4.2.9 Western Blotting

4.2.9.1 SDS PAGE

40µg of protein was loaded and separated using SDS polyacrylamide gels. For small molecular weight proteins like Tspan8 (<100 kDa), 12% SDS PAGE gel was used while for larger proteins (>100 kDa) like integrins 10% gel was used. The proteins were electrotransferred to polyvinylidene fluoride membranes (PVDF) running at constant ampere of 54 Amp (per membrane) for 2.5h. GAPDH was used as loading control.

4.2.9.2 Immunoblotting

The PVDF membranes were then immunoblotted with primary antibodies overnight at 4°C followed by their respective HRP-conjugated secondary antibodies at room temperature for 1hr. The blots were developed using luminol-based enhanced chemiluminescent (ECL) substrate for detection of horseradish peroxidase (HRP) activity from the antibodies. The light output was detected with the help of Bio-Rad Chemi doc XRS system using the Quantity One 4.6.6 software.

4.2.10 Cell proliferation assays

4.2.10.1 Cell count by Trypan blue exclusion assay

Cells were seeded in a 6-well plate to obtain 30-40% confluency after 24h. Cells were counted on day1 – day3. For counting, cells were trypsinized and a uniform cell suspension was prepared. 20 µl of the cell suspension was mixed with 20 µl trypan blue in a ratio of 1:1. 10 µl of the mixture was mounted on the Luna cell counting slide and cells were counted with the help of Luna automated cell counter. The protocol used for counting was optimized in the instrument for each cell line according to their cell size and cell adhesive properties. For example, enhanced declustering was additionally applied for MCF7 & MCF7-Tspan8, and MDA-MB-361 cells.

4.2.10.2 WST-1 Assay

WST-1 Assay was used as an indirect measure of cell proliferation. WST-1 is a stable tetrazolium salt which is cleaved to a soluble formazan by cellular mitochondrial dehydrogenase which can be detected by colorimetric method. Hence, the amount of the formazan dye formed directly correlates to the metabolically active cells present in the culture. For the experiment, 10 000 cells were seeded in a transparent flat bottom 96-well plate. Cells lysed with 1% Triton X-100

Materials & Methods were used as negative control. 100 µl of 5% WST-1 working solution (diluted in RPMI medium without phenol red) was added to the cells at different time points and absorbance was measured with the help of TECAN microplate reader (with the program ‘i-control 1.6.’) at the wavelength of 435 nm. The reference wavelength of 620 nm was subtracted from the reading. The measurements were taken at day 0 (1h after seeding) day 1, day 2 and day 3.

4.2.11 Adhesion Assay

Adhesion assay was performed in 96-well plates. The plates were coated with collagen I, collagen IV, fibronectin, laminin, and basement membrane extract (BME). Uncoated wells and wells coated with 1% BSA/PBS were taken as negative controls. After overnight incubation at 4°C, the plates were gently washed with cold 1X PBS, followed by cold medium (without FBS) to remove unbound matrix. 10 000 cells per well were seeded and incubated at 37°C in CO2 incubator for three time-points: 30 min, 1 h and 1.5 h. After incubation, unbound cells were washed off with PBS. The adherent cells were then fixed with 4% PFA for 15 min at room temperature and stained with 10% crystal violet. The stain was washed after 40 min and the plates were dried overnight. The adhered cells were detached with acetic acid on a shaker and the absorbance was measured with the help of TECAN microplate reader at 595 nm.

4.2.12 Invasion Assay

Brief introduction: The invasion assay was performed using Invasion matrix provided by Dr. Andreas Thomsen from the ‘Department of Radiology’, University Medical Centre Freiburg. The Invasion Matrix (InMx) is made up of 2.8% agarose and has conical recessions. Each matrix has 2 side-bars held together by a bridge in the middle (Fig. 11). The bridge helps in holding and maneuvering the matrix using forceps. Each side-bar has 6 conical wells, i.e. 12 wells per matrix. The conical recesses are 0.6 mm in diameter and are approximately 3 mm deep. The whole matrix measures 28 x 12 mm approximately. Each bar has a common channel through which the cells can be seeded into the wells, followed by the desired extracellular matrix (ECM) layer on top. The common channel has a volume of 15 μl. After seeding the cells and applying the ECM layer, the two bars are cut and separated from the central bridge. The bridge is discarded, and bars are flipped to lie on their sides. The bars then have a size of approx. 20 x 3 mm and observed under the microscope. The images are taken from day 0 (after adding ECM) to day 3.

Procedure: The invasion matrices stored in PBS were first equilibrated with culture medium. The matrices were placed upside down in a 6-well plate (1 InMx per well). About 4 mL of

Materials & Methods starvation medium was added to each well and the plate was centrifuged at 100 g, RT for 3 min. The matrices were placed upright in 6-well plate with 4 mL starvation medium per well. The plate was again centrifuged to remove any bubbles at 100 g, RT for 2 min. The matrices were incubated in the CO2 incubator for at least 15 min. The medium was soaked out of the recessions with the help of sterilized coffee-filter strips. 10 000 cells per recession were seeded dropwise through the common channel. The plate containing matrices was centrifuged for 3 min at 100 g, RT for cells to reach the bottom of the recession. The starvation medium was added to make up the total volume of 4mL. The plate was incubated at 37°C in the CO2 incubator. A day after seeding, extracellular matrices, Matrigel and Collagen G were added on top of the cells. Cells without any ECM served as negative control. The medium was removed by absorbing with a sterilized filter strip leaving some volume so as not to dry the matrix completely. Matrigel was thawed on ice and 15-30 µl was added per channel. The InMx was kept on sterilized square cut Whatman paper strips on a cold Aluminium plate in order to draw the ECM into the wells. The

InMx placed in 6well-plate was then incubated in the CO2 incubator for at least 30 min for the Matrigel to polymerise. Similarly, Collagen G was added 1part Soluton B + 8 parts Collagen G. 15-30 µl was added per channel and incubated for 30-40 min for polymerization. When the ECM was firm, the two bars were carefully separated from the central bridge using scissors. The bars were flipped over to lie on their sides and were kept in a 6-well plate in 2 mL starvation medium. The plate was kept at 37°C in the CO2 incubator. The images were taken from day 0 (the day ECM was added) to day 3 at 5x, 10x and 20x objectives.

Materials & Methods

A. Top view of the pockets Lateral view of the pockets

Agarose pockets

Extracellular matrix

Cells

B.  

 

Figure 11: Schematic diagram and image of the invasion matrix (InMx) used for the invasion assay. A) Left-hand side shows the top view of the 6 conical wells or pockets into which the cells are seeded. There are 2 side-bars connected by a bridge. The bridge helps in maneuvering the matrix using the forceps. The image on the right-hand side shows the lateral view of the wells. The cells were seeded into the wells. The cells were then layered with the extracellular matrix on top. The invasion matrices were placed in 6-well plates using sterilized forceps and incubated in a 5% CO2 incubator at 37°C. B) The behavior of the cells was studied under the microscope. To do that, the side-bars were cut using sterilized scissors and gently flipped over. C) Close-up image of the InMx with top view on the left and lateral view of the cut right side-bar after flipping. The diagram was modified from the images provided by kind courtesy of Dr. Andreas Thomsen (Department of Radiology, Medical Centre, University of Freiburg) who also developed the InMx.

Materials & Methods

4.2.13 EVs isolation from 2D cell culture

Cells were cultured in 14.5 cm dishes in complete medium up to 70-80% confluency. To monitor the cell viability, cells were observed under the microscope to avoid contamination of preparation with apoptotic bodies. Cells were washed with 5 mL 1X PBS and 15 mL starvation medium (complete medium without FBS) was added to the cells. Cells were serum starved for 48h. After 48h, the cell supernatant was collected from the dishes in pre-cooled 250mL high- speed centrifugation bottles and subjected to differential centrifugation (Fig. 12). First, the cell debris was removed by centrifuging at 2000 g for 20 min, followed by 5000 g for 45 min. The pellets obtained at this step if needed were resuspended in 50 µl PBS and named as EV5 (Extracellular vesicles obtained at 5000 g). Similarly, in the next centrifugation step of 12 000 g for 30 min, the pellets were resuspended in 100 µl PBS if required and labeled as EV12. Protease inhibitor was added to all the EVs (one twenty-fifth of the volume of EVs isolated from 25X stock solution). The cell supernatant was transferred to a pre-cooled bottle and kept on ice. It was then filtered through 0.22 µm membrane filter using filtration assembly to eliminate any residual bigger vesicles like microvesicles and apoptotic bodies. The filtered supernatant was then concentrated using pressure based stirred cells unit supplemented with 300 kDa MWCO ultrafiltration discs up to 30-50 mL volume. The flow-through was discarded. The concentrated supernatant was then transferred to ultracentrifuge tubes (11.5 mL per tube) and placed in swinging buckets. The swinging buckets were equally balanced on weighing balance and volume was made up with ice-cold 1X PBS. The buckets were ultracentrifuged at 120 000 g for either 1 h or 4 h followed by 1 h 1X PBS wash. After that, the pellet was resuspended in 100 µl PBS. This suspension solution was used to resuspend the pellets in rest of the tubes and pooled together. This fraction of vesicles obtained was called EV120 or exosomes. Protease inhibitor was added to preparation (one twenty-fifth of the volume of EVs isolated, from stock solution). The EVs were stored at -80°C until further use. For proteomics, the ultracentrifugation step was done overnight (15-16h) to obtain more amounts of vesicles. To estimate the EVs release count by each cell line, an equal number of cells were seeded onto the dishes. 2.0 x 106 cells were seeded per dish in case of MDA-MB-231, BT-549 and their Tspan8 counterparts. While for MCF7 & MCF7-Tspan8, and MDA-MB-361 cells, 2.5 x 106 cells were seeded per dish.

Materials & Methods

Cell Supernatant

Centrifugation at 2 000 x g, 15 min

Supernatant (S2)

Centrifugation at 5 000 x g, 45 min

EV5 Supernatant (S5) (Pellet dissolved in 100µl buffer)

Centrifugation at 12 000 x g, 30min

EV12 (Pellet dissolved in 100µl buffer) Filtration through 0.22µm pore filter

Concentration with 300 kDa MWCO membrane (in case of EVs isolation from 2D culture)

Supernatant (S12)

Ultracentrifugation at 120, 000 x g, 4h

1X PBS/ 1X HEPES buffer wash Ultracentrifugation at 120, 000 x g, 1h

EV120 (Pellet dissolved in 100µl buffer in case of 2D culture and 50µl in case of 3D culture)

Figure 12: Flow-chart depicting the steps involved in the isolation of extracellular vesicles (EVs) by differential centrifugation method from 2D and 3D cell culture supernatants.

4.2.14 EVs Isolation from 3D cell culture

The cells were cultured in conical agarose microwell array (CAMA) or agarose matrix as described earlier. 24 h after seeding, the medium was changed to starvation medium. On the 7th day of culture, the cell conditioned media and spheroids were harvested for EVs isolation. Cell conditioned media was collected from the wells in a 15 mL conical centrifuge tube. 2 mL 1X PBS was added to each well of a new 6-well plate. The agarose matrix was lifted with the help of a sterilized spatula and placed upside down onto the new plate with 1X PBS. The plate was centrifuged at 300 g for 1 min at 4°C. The empty matrices were removed from the wells and cell

Materials & Methods aggregates were harvested with a pipette and added to the tube containing cell conditioned medium. The matrices placed in the 6-well plate were observed under the microscope and if any aggregates remained, the centrifugation step was repeated to harvest all of them. The tubes were centrifuged at 800 g for 10 min at 4°C. The cell pellet was collected for dissociation and the supernatant for EVs isolation. The supernatant was subjected to differential centrifugation. Firstly, the supernatant was centrifuged at 2000 g, 20 min at 4°C. The cell supernatant was kept at 4°C for EVs isolation. The pellet was collected and added to the tube containing harvested cell aggregates. The cells were resuspended in Trypsin-EDTA mixture (0.05% Trypsin+ 1 mM EDTA, prepared in 1X PBS) and incubated at 37°C for trypsinization. The cell suspension was homogenized uniformly by pipetting up and down carefully. The amount of trypsin added, and incubation time was adjusted for each cell line according to their sensitivity to trypsin as follows:

MDA-MB-231 & 231-Tspan8: 1 mL trypsin for 10 min

BT-549 & BT-Tspan8: 100 µl trypsin for 1-2 min followed by adding 900 µl medium

MCF7 & MCF7- Tspan8: 1 mL trypsin for 10 min

MDA-MB-361: 500 µl trypsin 5-10 min followed by adding 500 µl medium

The cells were counted quickly after preparing a uniform cell suspension.

The supernatant obtained after the 2000 g centrifugation step was transferred to a conical centrifuge tube and further centrifuged at 5000 g for 45min at 4°C using Beckmann F50C Rotor. The supernatant was centrifuged at 12 000 g for 30 min at 4°C. The supernatant obtained from the previous step was filtered through 0.22 µm membrane filter with the help of a syringe directly into the ultracentrifuge tubes (11.5 mL per tube). All the tubes were placed in swinging buckets and balanced equally using 1X PBS to make up the volume. The filtrate was then centrifuged at 120 000g for 4h at 4°C followed by 1 h of washing step with PBS. The pellet obtained after ultracentrifugation was resuspended in 50 µl PBS. This suspension solution was used to pool all the tubes together. Protease inhibitor was added (one twenty-fifth of the volume of EVs isolated, from 25x stock solution) and the preparation was added stored at -80°C. Figure 13 shows the experimental layout for EVs isolation from 2D &3D culture under normoxic and hypoxic conditions.

Materials & Methods

Figure 13: Schematic diagram depicting EVs isolation from 2D culture and 3D cultures including the timeline.

4.2.15 EVs isolation from patients’ sera

Blood samples from breast cancer patients were obtained from the ‘Department of Obstetrics and Gynecology’ (University Medical Centre Freiburg), in collaboration with PD Dr. med. Thalia Erbes. The investigation protocol (36/12) was approved by the institutional ethical review board of the University of Freiburg. The written informed consent for participation in the study was given by all the patients involved including the healthy controls. The characteristics of the study population are compiled in Table 7. Blood collected in vacutainers was centrifuged at 2500g for 20min to obtain serum. Serum samples were stored at -80°C until further processing. For EVs isolation, 1mL of serum was diluted in 9mL 0.22µ filtered HEPES solution. The diluted serum was subjected to differential centrifugation as described earlier. Briefly, 2000g for 15min, 5000g for 45min and 12000g for 30min and ultracentrifuged at 120 000g for 4h at 4°C. The pellet at the end of 5000g centrifugation step was labeled as EV5 and pellet at 12000g centrifugation step was labeled as EV12. The pellet obtained at ultracentrifugation step was labeled as EV120 or exosomes. All pellets were resuspended in HEPES buffer supplemented with the protease inhibitor. The supernatant, remaining after ultracentrifugation, was considered as an EV-depleted fraction and designated fc (free-circulating fraction). All the preparations were stored at -80°C until further experiments. For all the experiments, in which exclusively EV120 preparations were applied, the term ‘EVs’ was used in the corresponding description.

Materials & Methods

4.2.16 Beads-assisted flow cytometry with EVs

The EVs (1x109 per condition in case of cellular derived EVs and 20µl in case of patients-derived EVs) were incubated with latex beads for 1hr15min. To block the free aldehyde and sulfate groups of the beads, 100µl of 1M glycine/PBS & 100µl 10% BSA/PBS were added to the existing solution and incubated at RT for 30 min. The latex beads were then pelleted at 10 000 g for 2min at RT and the supernatant was discarded. 2 washes with 300µl of 3%BSA/PBS were given. The supernatant was discarded, and beads were treated with 20 µl of primary antibody for 1 hr at 4°C. The beads were then incubated with secondary antibody for 30 min at 4°C followed by 2 washes with 300µl of 3% BSA/PBS. The supernatant was discarded, and the pellet was resuspended in 300µl of 3%BSA/PBS for measurement. The samples were measured using BD AccuriTM C6 flow cytometer instrument and analyzed with BD CSamplerTM software. A threshold of 80,000 FSC-H (T1) and 10 SSC-H (T2) were used for measurement. 100 000 events were recorded for each sample at medium core speed. For direct fluorophore-labeled primary antibody, respective isotype control was used as the control. While for unconjugated primary antibody, the respective secondary antibody was used as the control.

4.2.17 Electron microscopy

For evaluating the morphology of EVs, they were imaged using a TEM (transmission electron microscope). The exosome samples were prepared in a 10cm Petri dish layered with Parafilm. All the reagents and exosome samples were places as droplets onto the parafilm. 10µl of EVs were mounted on copper grids and incubated for 5min. Ultra-fine tweezers were used very carefully for handling the grids. The EVs were then fixed with 20µl of 1% glutaraldehyde (in HEPES) and incubated for 5min. The grids were washed four times with 20µl double distilled water (also placed as droplets on the Parafilm). After washing, the EVs were negatively stained with 10µl uranyl acetate for 1min in a separate Petri dish covered with Aluminum foil. The grids were then dried on Whatman paper and stored in the grid box till measurements were done. The EVs were then imaged with TEM (transmission electron microscope) Zeiss LEO 906E, with a 2kCCDCamera "sharp-eye" using the software ISProfessional. The grids were imaged at the Institute for Anatomy and Cell Biology, Faculty of Medicine, Albert-Ludwigs University Freiburg (Core facility, Microscopy and Image Analysis Platform).

Materials & Methods

4.2.18 Nanoparticle Tracking Analysis and Zeta Potential

Nanoparticle tracking analysis (NTA) was used to determine the size distribution, the concentration of EVs per mL and their zeta potential. The technique records the Brownian motion of the vesicles in suspension in a video. The hydrodynamic diameter is determined using the Stokes-Einstein relationship. The zeta potential of the vesicles is measured through their movement in an applied electric field. The samples were measured with ZetaView® PMX 110 V3.0 from Particle Metrix using the software ZetaView 8.4.2. The instrument can detect the size range of 40nm – 800nm. Before each measurement, the instrument was calibrated for cell quality and auto-alignment of laser and microscope using 100nm standard polystyrene beads (at ratio 1:125000) supplied by the company. The parameters were pre-set during measurement in the automated software with the shutter speed of 70 and frame rate of 30 frames per second (fps). For determining the size distribution, each sample was measured at 11 different positions (5 cycles) with the removal of any outlier positions (at least 8 positions were used in each measurement).

For zeta potential, the samples were measured at 11 different positions. The samples were diluted in 0.1% PBS in case of cell-derived EVs and 0.1% HEPES in case of patients-derived EVs. 1mL of the diluted sample was injected into the cell with the help of a syringe. The dilution of the sample was appropriately adjusted according to the Particle v/s Sensitivity graph to maintain the sensitivity of measurements at 85-90 for all the samples. Only in case of shortage of sample sensitivity up to 95 was used. The cell was rinsed with double distilled water and 0.1% buffer between individual readings. The post-acquisition parameters were set to a minimum brightness of 20, max brightness of 255, a minimum size of 5 pixels and maximum size of 1000 pixels. In case of patients-derived EVs, following parameters were used: min brightness = 20, min size = 5 pixels and may size = 200 pixels. The percentage of vesicles under 30 nm, 30-150 nm, and 150-300 nm were also determined. The dilution factor was used for calculating the absolute number of vesicles and the concentration of EVs from the yield with Microsoft excel sheet 2010 (Microsoft Corp., Seattle, WA, USA). The zeta potential was auto- generated by the ZetaView8.4.2 software.

4.2.19 DLS

In order to determine the hydrodynamic size distribution of vesicles, dynamic light scattering method was employed. Since the size limit of detection for NTA was 40nm-800nm, the presence of bigger vesicles like oncosomes and apoptotic bodies could be seen with the help of DLS. 10µl of the sample (EV5, EV12 or EV120) was measured using a NANO-flex 180° particle size

Materials & Methods analyzer (Particle Metrix, Meerbusch, Germany) according to manufacturer’s guidelines. The system detects size distribution in the range of 300nm -10µm. It is based on heterodyne 180° backscattering principle. The viscosity and refractive index were set according to the buffer in which the EVs were resuspended. In case of cellular derived EVs, PBS was used while for patients-derived EVs HEPES buffer was used. The measurements were analyzed using the software- MicrotracFLEX 11.0.0.5.

4.2.20 Protein quantification

The protein concentration of the cell lysates was determined using the PierceTM BCA Protein Assay kit (Thermo ScientificTM) according to the manufacturer’s guidelines. It’s based on bicinchoninic acid (BCA) for colorimetric detection and quantification of protein. Briefly, the BSA standards (provided with the kit) were prepared according to the manufacturer guidelines. In a 96-well plate, 1µl of the standards and sample were added. 100µl of working reagent mixture was added to the wells (Reagents A&B in the ratio 50:1). The plate was incubated at 37°C for 40min. The absorbance was measured at 562nm with the help of TECAN microplate reader. A standard curve was generated with the standard readings using Microsoft excel sheet 2010 (Microsoft Corp., Seattle, WA, USA). The standard curve equation was used to determine the protein concentration in µg/mL.

The exosomal protein concentration was determined using Micro BCATM Protein Assay kit (Thermo Scientific) according to the manufacturer’s guidelines. It is based on the same principle as discussed above but has been optimized to measure low concentrations (0.5 to 20 micrograms/mL). The lower detection limit of the BCA kit can is 5 µg/mL. For measuring, 150 µl of standards were taken in a 96-well plate. 150 µl of samples dilution (1 µl sample + 149 µl PBS/HEPES) was added followed by 150 µl of working reagent (Reagents A, B & C in the ratio 25:24:1). The plate was incubated at 37°C for 2 h and absorbance was measured at 562 nm with TECAN microplate reader.

4.2.21 Proteomics (LC-MS/MS) For proteomics analysis, EVs were isolated and dried pellets were stored at -80. The mass spectrometry was performed at Mass Spectrometry Core Facility in collaboration with Prof. Dr. Stefan Tenzer (University Medical Centre, Mainz). Briefly, the EV pellets were resuspended in RapiGest 0.1% (in TEAB 0.1 M, pH 8) and volumes were adjusted to 100µl. The protein samples were reduced with TCEP (tris(2-carboxyethyl) phosphine) to a final concentration of 10 mM. The samples were then alkylated with iodoacetamide at room temperature for 60min in the

Materials & Methods dark to a concentration of 40 mM. The samples were further digested with trypsin (ratio of 1:25 w/w), overnight at 37°C. Thereafter, the samples were labeled with 10-plex Tandem mas Tags (TMT). 5% Hydroxylamine (V/V) was used for TMT quenching. Trifluoroacetic acid solution (pH < 2) was used to cleave RapiGest by incubating at 37°C for 45 min. It was centrifuged at 14 000 rpm for 10 min and the supernatant was dried under vacuum. The samples were resuspended in 5% CAN/0.1% FA and desalted with C18 micro spin columns. Then, the peptides were separated by off-gel electrophoresis and solubilized with 5% ACN/0.1% formic acid for mass spectrometry analysis. The analysis with the raw data obtained by mass spectrometry was kindly performed by Prof. Dr. Andreas Keller (Saarland University; University Hospital, Saarbrücken).

4.2.22 HER2 ELISA

The HER2 levels in sera and sera derived EVs from 10 breast cancer patients were determined using the ‘Human ErbB2/Her2 Quantikine ELISA’ Kit (R&D Systems) according to the manufacturer’s guidelines. Briefly, the ELISA standards provided with the kit and 50µl of samples were loaded onto the 96-well microplate pre-coated with monoclonal antibody specific to human ErbB2 or HER2. Sera were diluted to 1:9.5 with HEPES buffer. The unbound substances were washed away with a wash buffer provided with the kit and an enzyme-linked polyclonal antibody specific to HER2 was added to the wells. After the washing step, the substrate solution was added leading to color development. After 30 min, the color development was stopped, and the optical density was measured at 450 nm using TECAN microplate reader. The standard curve was generated with the ELISA standards using Microsoft excel sheet 2010 (Microsoft Corp., Seattle, WA, USA). The concentration was calculated corresponding to the concentration of 21x107 EVs/mL for cell-derived EVs and to the amount of 10 µl of the patients derived sera. This experiment was performed by our lab technician Ms. Maren Voglstätter.

4.2.23 WGM Laser measurements

The EVs from the breast cancer cell lines and patients’ sera were isolated and characterized in our lab. The detection of exosomal HER2 by WGM lasers was performed by Mr. Sentayehu Wondimu (Lab of Prof. Dr. Christian Koos, Karlsruhe Institute of Technology). Briefly, for measurements with EVs derived from breast cancer cells, 15 µl of the sample was diluted with 1X PBS up to 1mL for measurements. Additionally, a pilot study with EVs derived from 10 breast cancer patients was made. 1X HEPES buffer was used for dilution in case of patients’ derived EVs.

Materials & Methods

4.2.24 Statistical Analysis

All the data were analyzed, and figures were generated using Prism 5.0 (GraphPad, USA) software unless stated otherwise. To calculate the significance of the data, unpaired two-tailed Student’s t-test was performed. A p-value of <0.05 was considered significant (*: p< 0.05, **: p< 0.01, ***: p< 0.001; ****: p< 0.0001). The graphs for densitometry analysis of western blot bands were generated using Microsoft excel sheet 2010 (Microsoft Corp., Seattle, WA, USA). The immunoblot images were compiled together using Photoshop CS6 (Adobe, USA). The data figures for measurements with WGM lasers were kindly provided by Mr. Sentayehu Wondimu (Lab of Prof. Dr. Christian Koos, Karlsruhe Institute of Technology).

Results

5 Results

5.1 Investigating the role of Tspan8 in tumor progression in breast cancer cells

5.1.1 Establishment of Tspan8 overexpressing breast cancer cell models

To investigate the role of Tspan8 protein in tumor progression in breast cancer, breast cancer cells overexpressing plasmid containing Tspan8 encoding gene were generated. Two breast cancer cell lines, MDA-MB-231 and BT-549 representing triple-negative subtype were chosen (Table 3). MCF7 representing luminal A subtype was also included in the study (Fig. 14). MDA- MB-361 cells which represent luminal B subtype and express Tspan8 protein endogenously was used as a control. The cells were stably transfected with the pcDNA3.1 plasmid containing TM4SF3 gene (gene encoding Tspan8 protein) using Lipofectamine LTX Plus. The cells were kept in selection medium (culture medium + hygromycin) for about 2 weeks and the population of cells a expressing high amount of Tspan8 protein was sorted and examined for Tspan8 expression.

MDA-MB-231 BT-549 (Triple-negative subtype) (Triple-negative subtype)

MCF7 MDA-MB-361 (Luminal A subtype) (Luminal B subtype)

Figure 14: Phase contrast images of the breast cancer cells chosen for the study (200X magnification). Scale bar = 20µm.

Results

Table 3: Characteristics of the breast cancers cells chosen for study

MDA-MB-231 BT-549 MCF7 MDA-MB-361

Molecular Triple-negative Triple-negative Luminal A Luminal B Classification (Claudin low) (Claudin low)

Tumor Type Adenocarcinoma Invasive ductal Adenocarcinoma Adenocarcinoma carcinoma

Source Metastasis, Primary tumor Metastasis, Metastasis, Brain Pleural Effusion Pleural Effusion

Phenotype Mesenchymal Mesenchymal Epithelial Epithelial (Post-EMT) (Post-EMT)

Receptor ER-, PR- ER-, PR- ER+, PR+/-, ER+, PR+/-

Expression HER2low, EGFR+, HER2-, EGFR+ HER2 low, EGFRlow HER2+, EGFR+

Tspan8 TSPAN8-/low Tspan8- Tspan8- Tspan8+ Expression

The total protein expression levels of Tspan8 were examined in cell extracts with western blotting. As can be seen in figure 15, Tspan8 is highly expressed in transfected cells and no expression is seen in parental, non- transfected cells except MDA-MB-231 cells when compared to their wild-type counterparts. However, the parental MDA-MB-231 cells show some amount of Tspan8 expression. MDA-MB-361 cells were used as positive control. The expression of Tspan8 on the cell surface was further validated by flow cytometry. 85% of the 231-Tspan8 cells expressed Tspan8 as opposed to 12% of the parental cells. While BT-549 cells were negative, 73% of BT-Tspan8 cells were found to be Tspan8 positive. Similarly, MCF7-Tspan8 cells showed 99% ectopic expression as opposed to none in parental cells. The high expression of Tspan8 assures the feasibility of the cell models generated to investigate the impact of Tspan8 in breast cancer progression.

Results

A. B.

86% Count 30% 45%

Tspan8 Control MDA-MB-231 231-pcDNA3 231-Tspan8 MDA-MB-361

73% Count

Tspan8 Control BT-pcDNA3 BT-549 BT-Tspan8 MDA-MB-361

99% Count

Tspan8 Control MCF7-pcDNA3 MCF7

MCF7-Tspan8 MDA-MB-361 Figure 15: Validation of Tspan8 expression in established Tspan8 overexpressing breast cancer cell models by (A) Western Blotting, 40µg of total cell lysate was loaded and GAPDH was used as loading control. (B) Flow Cytometry, to check the surface expression of Tspan8 in the cells.

Results

MDA-MB-231 231-Tspan8

BT-549 BT-Tspan8

MCF7 MCF7-Tspan8

Figure16: Phase contrast images (200X magnification) of breast cancer cells and their Tspan8 overexpressing counterparts. Scale Bar: 20µm.

Additionally, the morphology of the cells was studied under the microscope. Interestingly, the Tspan8 cells showed a change in morphology, especially the triple-negative breast cancer cells (Fig. 16). 231-Tspan8 cells showed a slight difference from the parental in that the Tspan8 cells had more population of cells with mesenchymal-like appearance at low confluency. BT-Tspan8 cells showed a prominent and distinct change in morphology. The cells exhibited epithelial like features such as increased cell-cell adhesion when compared to the parental cells. BT-Tspan8

Results cells also grew in colonies, unlike their parental counterpart. However, MCF7-Tspan8 cells did not show any alteration in morphology. This interesting observation raises the question of whether Tspan8 plays any role in EMT or partial EMT processes or not, which needs to be further examined.

Figure 17: Effect of Tspan8 on expression of E-Cadherin family proteins by western blotting. Cell lysates from empty vector (pcDNA3.1) transfected cells were used as controls. GAPDH was used as loading control for SDS-PAGE.

Tspan8 has been shown to regulate mesenchymal-epithelial transition (MET) in rat breast cancer MTPa cells from the previous findings of the lab (manuscript submitted, Journal of Pathology). Tspan8 was seen to upregulate the expression E-Cadherin in MTPa cells. Since the effect of Tspan8 was seen in the morphology of BT-549 cells (Fig. 16), where the Tspan8 cells showed strong cell–cell attachment and grew in clusters, contrary to the parental counterparts. Therefore, it was investigated whether Tspan8 potentially affects the expression of the classical cadherins which are involved in cell-cell adhesion at adherens junctions and cell signaling in human breast cancer cells. The preliminary results showed that, in 231-Tspan8 cells, there was no difference in the expression of E-Cadherin, p120-Catenin and β-Catenin (Fig. 17). N-Cadherin expression was seen to be upregulated. Interestingly, in BT-Tspan8 cells, N-Cadherin and p120- Catenin were downregulated. Also, no change was observed in β-Catenin expression. In MCF7- Tspan8 also, the downregulation of N-Cadherin was observed. However, no change in expression of p120-Catenin was observed. Since there was no effect seen on the regulation of E- Cadherin expression, the role of Tspan8 in MET was not explored further.

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5.1.2 Tspan8 regulates the expression of other tetraspanins breast cancer cells

Tetraspanins work in close association with each other forming a functional unit called Tetraspanins enriched domain (TEM) (Hemler, 2005; Yáñez-Mó et al., 2009). Hence, the expression of Tetraspanins CD63, CD9 and CD151 was determined by immunoblotting in parental and Tspan8 overexpressing breast cancer cells generated for the study (Fig. 18). CD63 was found to be highly upregulated in Tspan8 overexpressing MDA-MB-231 cells. The upregulation of CD63 was further confirmed by flow cytometry analysis (Fig. 19). While surface expression of CD9 seemed to be downregulated in 231-Tspan8 cells when analyzed by flow cytometry (Fig. 19). In BT-Tspan8 cells also, CD63 expression was seen to be upregulated both by western blotting (Fig. 18) and flow cytometry (Fig. 19). There was no difference in the expression of CD9 and CD151 in parental and BT-Tspan8 cells. On the other hand, when surface expression was analyzed by flow cytometry, CD9 was seen to be downregulated. In MCF7- Tspan8 cells, no significant difference was observed in the expression of CD63, CD9, and CD81 when examined by immunoblotting. However, the surface expression of CD63 was slightly upregulated and CD9 was slightly downregulated when analyzed by flow cytometry when compared to the parental cells. There was no effect on the expression of CD151 observed.

Figure 18(A): Tspan8 regulates expression of tetraspanins in breast cancer cells as shown by western blotting. 40 µg of protein lysate was loaded onto the SDS-PAGE gel. The gel was transferred to PVDF membrane and immunoblotted with CD63, CD151, CD9 and Tspan8 antibodies. GAPDH was used as loading control. Data is representative of 2 independent experiments.

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Taken together, Tspan8 regulates the expression of tetraspanins in breast cancer cells. CD63 was observed to be upregulated, especially in triple negative breast cancer cells. While it downregulates surface expression of CD9 protein in both triple negative and Luminal A breast cancer cells.

Figure 18(B): Densitometric Analysis of tetraspanins expression in parental and Tspan8 overexpressing cells. The bands were quantified using 1-D analysis software Quantity One 4.6.6 from BIORAD. GAPDH was used as endogenous control. The values have been normalized against GAPDH. Each biological replicate is independently represented. The data unit is density or intensity per mm2 (INT/mm2).

Results Neg.Ctrl MDA-MB-361 MFI: 15292 CD63 MFI: 60409 CD9 MFI: 14387 CD81 MFI: 22519 Tspan8

Count 97% 98% 97% 86%

Neg.Ctrl MDA-MB-231 231-Tspan8

MFI: 6037 CD63 MFI: 20409 CD9 MFI: 14278 CD81 MFI: 366 Tspan8 MFI: 9476 MFI: 9625 MFI: 3965 MFI: 2268

91% 97% 95% 7%

Count 97% 96% 96% 53%

Fluorescence intensity

Neg.Ctrl BT-549 BT-Tspan8

MFI: 10190 CD63 MFI: 11999 CD9 MFI:5228 CD81 MFI:0 Tspan8 MFI: 16986 MFI: 6450 MFI: 6409 MFI: 2275

96% 93% 91%

94% 87% 94% 51%

Fluorescence intensity

Neg.Ctrl MCF7 MCF7-Tspan8

MFI: 9462 CD63 MFI: 112606 CD9 MFI: 14181 CD81 MFI: 8 Tspan8 MFI: 18128 MFI: 89985 MFI: 149463 MFI: 49935

97%

97% 97% Count 98% 98% 97% 97%

Fluorescence intensity

Figure 19: Tspan8 regulates the expression of tetraspanins. The expression of tetraspanins on the surface of breast cancer cells was examined by Flow Cytometry. The figure is representative of 2 biological replicates in case of MDA-MB-361 and BT-549 Tspan8+/- cells and 2 technical replicates in case of MDA-MB-231Tspan8+/- and MCF7Tspan8+/- cells.

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5.1.3 Examining the effect of ectopic expression of Tspan8 on cell proliferation and adhesion properties of the breast cancer cells

To characterize the impact of Tspan8 on cell growth in breast cancer cells, cell proliferation activity was evaluated in the established system. The cells were seeded in a 6 well-plate and were grown for 3 days. Each day, the number of cells was counted after trypsinization with the help of an automated cell counter. Parental cells and mock cells (parental cells transfected with empty plasmid vector) were taken as negative controls in the experiments. Tspan8 showed significant increase in proliferation of 231-Tspan8 cells on day2 (p=0.0232) and day3 (p=0.0063) (Fig. 20A). In contrast, it tended to significantly decrease the proliferation in BT- Tspan8 cells on day2 (p=0.0002) and day3 (p< 0.0001). While MCF7-Tspan8 cells did show less proliferation on day 3 as compared to the parental cells but the effect was not significant. The cell proliferative activity was further confirmed by WST-1 Assay. It helps in determining the metabolic activity of the cells and is an indirect measure of cell proliferation. The assay corroborated the previous findings (Fig. 20B), 231-Tspan8 showed an increase in metabolic activity with a significant increase on day3 (p=0.0248). BT-Tspan8 showed a significant decrease in proliferation on day3 (p=0.0214). While MCF7Tspan8 cells, also showed significantly decreased activity on day3 (p=0.0210). Thus, Tspan8 significantly affects cell growth in breast cancer and has a varying effect in different cell types.

Next, to see the effect of Tspan8 on attachment to different components of the extracellular matrix, adhesion assay was performed with the established breast cancer cells. The cells were seeded onto a 96-well plate coated with Collagen I, Collagen IV, fibronectin, laminin, and BME. After 1h of seeding, the attachment of cells to the ECM components was quantified by the colorimetric method (10% Crystal Violet). Tspan8 did not show any significant effect on cell adhesion with collagen I&IV, fibronectin and laminin in MDA-MB-231 cells (Fig. 21). 231- Tspan8 cells showed significantly higher attachment to BME (p=0.0359) than the parental cells. On the other hand, BT-Tspan8 cells showed significantly less attachment to Collagen IV (p=0.0047) and BME (p=0.0077) as compared to the parental cells. BT-Tspan8 adhered to fibronectin significantly more than the parental cells (p=0.0414). Further, Tspan8 did not have a significant effect on cell adhesion properties of MCF7 cells.

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A. B.

MDA-MB-231 MDA-MB-231 231-pcDNA3

231-pcDNA3

231-Tspan8 231-Tspan8

Cell count

Absorbance

Days Days

BT-549 BT-549 BT-pcDNA3 BT-pcDNA3 BT-Tspan8 BT-Tspan8

Cell Cell count

Absorbance

Days Days

MCF7 MCF7-pcDNA3 MCF7

MCF7-Tspan8

MCF7-pcDNA3 MCF7-Tspan8

count

Cell Cell

Absorbance

Days Days

Figure 20: Effect of Tspan8 on proliferation and metabolic activity in breast cancer cells determined by (A) cell count; data is representative of at least five replicates and further confirmed by (B) WST1 Assay; data is representative of 3 independent experiments performed in at least 10 technical replicates. (p < 0.05).

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MDA-MB-231 231-Tspan8

Absorbance

Extracellular Matrix

BT-549 BT-Tspan8

Absorbance

Extracellular Matrix MCF7 MCF7-Tspan8

Absorbance

Extracellular Matrix

Figure 21: Adhesion Assay was performed in 96 well plates. Wells were coated with extracellular matrices: collagen I (Coll I), collagen II (Coll II), fibronectin, laminin and basement membrane extract (BME) or Matrigel. Uncoated cells and cells coated with only 1%BSA/PBS were used as negative controls. 1h after seeding the cells, the cells were fixed with 4%PFA and stained with 10% crystal violet. The adherent cells were measured at OD595 on ELISA reader. Data is representative of three technical replicates. (p < 0.05) Data for BME is representative of 2 independent experiment. The experiments were performed at 3 time points: 30 min, 1h and 1.5h.; data shown here is only for time point 1h.

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5.1.4 Tspan8 regulates expression of integrins in breast cancer cells

Integrins are family of cell surface adhesion receptors that mediate the attachment of the cells to the extracellular matrix (ECM) and cell-cell interactions (Desgrosellier and Cheresh, 2010). They trigger cell signaling pathways and are involved in tumor progression (Hamidi and Ivaska, 2018). As described earlier, 231-Tspan8 cells showed significantly higher attachment to BME while BT-Tspan8 showed significantly lower attachment to BME when compared to their parental counterparts (Fig, 21). Since this showed that Tspan8 had some effect on cell adhesion properties, prominently in triple negative type breast cancer cells, the expression of integrins was checked by immunoblotting with the cell lysates.

Table 4: Integrins expression in Tspan8 +/- cells as detected by immunoblotting.

Cell Line α1 α2 α3 α4 α6 αV β1 β4 β5

MDA-MB-361 ++ ++ - - - + ++ - ++

MDA-MB-231 ++ ++ + - ++ + +++ ++ ++

231-Tspan8 ++ ++ ++ - ++ + ++++ + + ↑ ↑ ↓ ↓

BT-549 + - + ++ - + ++ - +

BT-Tspan8 + - + +++ - + +++ - - ↑ ↑ ↓

MCF7 + + + - - + + + +

MCF7-Tspan8 + + + - - + + + - ↓

(-): Negative, (+): weak positive, (++): moderate positive, (+++) strong positive, (↑) upregulation, (↓): downregulation.

The expression of integrins α1, α2, α3, α4, α6, αV, β1, β4, and β5 was profiled by immunoblotting in wild-type and Tspan8 overexpressing breast cancer cells (Fig. 22). It was observed that there was no difference in the expression of α1, α2 and α6 integrins between parental and transfectants in all the cell lines. α3 integrin was seen to be upregulated in 231- Tspan8 cells as compared to wild-type MDA-MB-231 cells. α4 integrin was also seen to be upregulated in BT-Tspan8 cells. β1 integrin was seen to be significantly upregulated in 231-

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Tspan8 and BT-Tspan8 cells. On the other hand, β4 was seen to be downregulated in 231- Tspan8 and MCF7-Tspan8 cells. β5 integrin was downregulated in all three Tspan8 overexpressing cells. Overall, Tspan8 does regulate the expression of integrins in breast cancer cells at total proteins level (Table 4) and their interaction may have a certain impact on the cellular processes in tumor progression which needs to be further investigated.

Figure 22(A): Tspan8 regulates integrin expression in breast cancer cells. 40µg of cell lysates were analysed by western blotting for different integrins. GAPDH was used as loading control for SDS-PAGE. Cells transfected with empty vector pcNA3.1 were also used as negative controls.

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Figure 22(B): Densitometric analysis of the integrin western blots in 231 vs 231-Tspan8 system. GAPDH was used as endogenous control. The values have been normalized against GAPDH. Each biological replicate is independently represented.

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Figure 22(C): Densitometric analysis of western blots in BT-549 vs BT-Tspan8 system. GAPDH was used as endogenous control. The values have been normalized against GAPDH. Each biological replicate is independently represented. The integrins α6 and β4 were not quantified as BT-549 and BT-Tspan8 cells don't express them.

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Figure 22(D): Densitometric analysis of western blots in MCF7 vs MCF7-Tspan8 system. GAPDH was used as endogenous control. The values have been normalized against GAPDH. Each biological replicate is independently represented. The integrins α4 and α6 were not quantified as MCF7and MCF7-Tspan8 cells don't express them.

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5.1.5 Effect of Tspan8 on the expression of tetraspanins and integrins under hypoxic environments

Hypoxia has been known to affect ECM remodeling and integrin expression in tumors (Jean et al., 2011). Certain integrins have been found to have increased expression under hypoxia. For example, the expression of angiogenesis-associated integrins, αV, β1, β3, and β5 was upregulated under hypoxia (1%) in human microvascular endothelial cells (HMEC-1) (Befani and Liakos, 2017). Integrin α5 and β1 were seen upregulated under hypoxia in breast cancer and correlated with HIF gene signature (Ju et al., 2017). So far, the role of Tspan8 in breast cancer cells under stress environment has not been studied before. So, we wanted to see if Tspan8 affects integrin expression in breast cancer cells under hypoxia and starvation. For this, we carried out immunoblotting with parental and Tspan8 overexpressing cell lysates obtained under normoxia, starvation (48 h treatment with medium without FBS), hypoxia (1% O2 treatment for 48h) and hypoxia + starvation conditions for 48h.

In 231-Tspan8 model (Fig. 23A), it was observed that there was no difference between normoxia and other conditions in both parental and Tspan8+ cells in expression of other tetraspanins like CD63, CD151, and CD9. Interestingly, however, it was observed that MDA-MB- 231 cells expressed higher amount of Tspan8 in ‘hypoxic + starvation’ conditions as opposed to normoxic conditions. But no additive effect of this Tspan8 was seen in the expression of other tetraspanins. Similarly, in case of expression of integrins not much difference was seen. In normoxia, the band for α3 integrin was seen a little above the regular band as compared to parental cells which may be due to dephosphorylation in absence of FBS in the culture medium. Previous studies have shown that FBS promotes phosphorylation and activation of the ERK1/2 pathway, the PI3K/PKB pathway (Ley et al., 2003). But this needs to be further confirmed. There was a downregulation of α6 protein in starvation and hypoxia condition, this effect was again reverted in hypoxia and starvation conditions. In the case of β1 integrin, we observed a slight shift in the band in starvation and hypoxia + starvation conditions which again may be due to dephosphorylation and needs to be further examined. Most interestingly, downregulation of β4 integrin under normoxic conditions was seen to be reverted in starvation and starvation+hypoxia condition. This supports the previous reports that breast cancer epithelial cells increase the β4 integrin expression under starvation (Muranen et al., 2017). In BT-Tspan8 model (Fig. 23B), no difference of expression observed in tetraspanins under different conditions. αV expression was weak to none. β5 expression was downregulated in hypoxia and starvation conditions but the expression reverted with the dual treatment of cells with both hypoxia and starvation. Again, as seen with 231-Tspan8 model, a shift in the band for

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β1 integrin was observed which may be due to dephosphorylation. In MCF7-Tspan8 model (Fig.23C), no significant observations were made.

Overall, no additive effect of Tspan8 expression was observed on the expression of tetraspanins and integrins under starvation, hypoxia, and starvation + hypoxia conditions. Except that β4 integrin expression is reverted in 231-Tspan8 cells under starvation and starvation+hypoxia conditions.

(i)

(ii)

Figure 23(A): Effect of Tspan8 under hypoxic conditions in MDA-MB-231 cells detected by immunoblotting on (i) Expression of tetraspanins. (ii) Expression of integrins. GAPDH was used as loading control. Cells were cultured under 1% hypoxia conditions and cells were harvested after 48h.

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(i)

(ii)

Figure 23(B): Effect of Tspan8 under hypoxic conditions in BT-549 cells detected by immunoblotting on (i) Expression of tetraspanins. (ii) Expression of integrins. GAPDH was used as loading control.

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(i)

(ii)

Figure 23(C): Effect of Tspan8 under hypoxic conditions in MCF7 cells detected by immunoblotting on (i) Expression of tetraspanins. (ii) Expression of integrins. GAPDH was used as loading control.

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5.2 Analysis of extracellular vesicles obtained from breast cancer cells cultured in 2D and 3D environments

Tspan8 has been reported to regulate the generation, delivery and content of the extracellular vesicles (EVs) in pancreatic and colon cancer (Heiler et al., 2016; Lu et al., 2017). Also, we observed that Tspan8 regulates expression of tetraspanins such as CD63 and CD9 (Fig. 18 &19) which not only play a significant part in modulating tumor microenvironment (Lu et al., 2017) but are also established exosomal markers. Also, the impact of Tspan8 on EVs derived from breast cancer cells has not been studied so far. Hence, EVs were isolated from established breast cancer cells and characterized. First, EVs from three different fractions obtained during differential centrifugation method were analyzed. Additionally, an agarose-based microwell array was optimized for culturing of breast cancer cells and to obtain EVs in the 3D environment. Subsequently, the methods used for characterization of EVs included nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), dynamic light scattering (DLS) and bead-assisted flow cytometry. To study the impact of Tspan8 on the protein content of EVs, preliminary study with proteomics analysis of EVs derived from 2D culture was performed.

5.2.1 Characterization of different subpopulations of EVs

It has been already reported that heterogeneity is seen in the size, origin and proteomic profile of extracellular vesicles (EVs) released by cells (Willms et al., 2016). To find out the most suitable EV fraction for our study, three EV fractions were characterized and analyzed. Since MDA-MB-361 cells endogenously express Tspan8, the vesicles were isolated from this cell line. The three EV fractions obtained during various steps of differential centrifugation were EV5 (obtained at 5000g), EV12 (obtained at 12000g) and EV120 (obtained at 120 000g). The TEM images revealed the intact vesicles of different size in all 3 fractions (Fig. 24A). To examine the size distribution of the vesicles, dynamic light scattering was performed with detection range 3nm – 10µm. It was observed that EV5 consisted of mode size in the range of 1- 8µm (Fig. 24C). While EV12 had 2 populations in the range of 100 – 400 nm and 600nm – 2µm. EV120 fraction consisted majority of the population in the range of 50 nm - 20nm and a smaller % of bigger vesicles were found (upto 800nm). NTA analysis of the EVs revealed the mode size of 142 nm (Fig. 24B).

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The EVs coupled with latex beads were further examined for surface expression of known exosomal markers like CD63, CD9, and CD81 by flow cytometry (Kowal et al., 2016). The absence of these markers was seen in EV5 (Fig 24D). While EV12 was positive for CD63, it was weakly positive for CD9 and CD81. EV120 was enriched in all the three exosomal markers. On further analyzing Tspan8 in these fractions, a similar trend was observed. EV5 did not show any Tspan8 expression, EV12 was 16% positive and EV120 was 76% positive for Tspan8. Since EV120 showed higher expression of Tspan8 and was positive for all 3 exosomal markers, it was used for all the further experiments.

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A. B. EV5 EV12 EV120/EVs 9 EV120 (8x 10 /ml) 0.8

142

0.6 9

0.4

0.2 EVs/ml x 10 x EVs/ml

0 10 100 1000 Diameter (nm)

C. D. Neg.Ctrl CD63 CD9 CD81 Tspan8

Count EV5

Fluorescence intensity

Neg.Ctrl CD63 CD9 CD81 Tspan8

Count 16% EV12

Fluorescence intensity

Neg.Ctrl CD63 CD9 CD81 Tspan8

76% Count

EV120 EVs /

Fluorescence intensity

Figure 24: Characterization of EVs isolated from MDA-MB-361 cells during differential centrifugation at 5000g (EV5), 12000g (EV12) and 120 000g (EV120) by size distribution and flow cytometry. A. TEM images of the the three fractions. B. Hydrodynamic size distribution of vesicles in different fractions analyzed using DLS C. Detection of Tspan8 and exosomal markers CD63, CD9 & CD81 in vesicles coupled to latex beads by flow cytometry. Scale Bar: 200nm. Neg:ctrl=negative control..EVs coupled to beads and stained with only secondary antibody.

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5.2.2 Optimizing 3D cell culture method of breast cancer cells using agarose microwell array for EVs isolation

In order to study the impact of Tspan8 on EVs content and their release in the 3D environment, an agarose-based microwell array or matrix was employed (Thomsen et al., 2017). The array consists of 950 microwells which allow the long-term culture of the cell aggregates and spheroids. The system was adapted for isolation of EVs from the breast cancer and Tspan8 overexpressing cells in 3D environment.

Firstly, the seeding cell density was determined in order to obtain spheroids sustainable for long term culturing. MDA-MB-231, 231-Tspan8, MCF7 and MCF7-Tspan8 cells were seeded at densities 100, 400, and 1000 cells per microwell i.e. 9.5 x104, 38 x 104, 95 x 104 cells per microwell array respectively. The growth of the cell aggregates was monitored for 7 days with images being taken every alternate day. On day1, the cell aggregates were loose and formed compact spheroid like structures by day 7 (Fig. 25). Also, with 1000 cells per microwell, more compact spheroids were formed about 250-300nm in size (Table 5): -

Table 5: Size of spheroids microspheres v/s cell density seeded.

Density Size of the spheroid/cell aggregates on day7 (µm)

(cells per microwell) MDA-MB-231 231-Tspan8 MCF7 MCF7-Tspan8

100 200 250-300 100-150 150

400 250 300 200 200

1000 250-300 300 250 250

Secondly, FBS supplementation was optimized. As the cells grow in close proximity, the growth supplements and nutrients are provided by the surrounding cells and don’t require a high amount of FBS for growth (Knight and Przyborski, 2015). Hence, 2.5% FBS was used for culturing as opposed to 10% FBS used in 2D cell culture. To prevent contaminating our EVs preparation with bovine vesicles, vesicles depleted FBS was used for culturing. Additionally, it was observed that when cells were seeded in medium without FBS, several cells were seen afloat in the medium outside the agarose matrix (not shown in the figure). Therefore, the cells were seeded in medium containing FBS and the FBS was withdrawn a day after. The cell aggregates were maintained in serum-free medium for 6 days. It was observed the use of FBS

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enhanced the growth of spheroids, prominently seen in MDA-MB-231 and 231-Tspan8 cells (Fig. 26).

MDA-MB-231 231-Tspan8

Day 1 Day 3 Day 5 Day 7 Day 1 Day 3 Day 5 Day 7

100 cells cells 100

per microwell per

400 cells cells 400 per microwell per

1000 cells cells 1000

per microwell per

MCF7 MCF7-Tspan8 Day 1 Day 3 Day 5 Day 7 Day 1 Day 3 Day 5 Day 7

100 cells cells 100

per microwell per

400 cells cells 400

per microwell per

1000 cells cells 1000 per microwell per

Figure 25: Optimization of 3D cell culture method using agarose based microwell array (CAMA) or agarose matrix. Representative microscope images of spheroids obtained with different cell seeding numbers imaged every alternative day after seeding until day7. Different cell densities were tested for seeding onto the agarose matrix for MDA-MB-231, MDA-MB-231Tspan8, MCF7 and MCF7-Tspan8 cells. Seeding 1000 cells/microwell or at a density of 1x 105 /ml led to the formation of larger spheroids. Scale bar:50µm.

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MDA-MB-231 231-Tspan8 Day 1 Day 3 Day 5 Day 7 Day 1 Day 3 Day 5 Day 7

FBS

FBS Withdrawal FBS

No FBS FBS No

MCF7 MCF7-Tspan8 Day 1 Day 3 Day 5 Day 7 Day 1 Day 3 Day 5 Day 7

FBS

FBS Withdrawal FBS

No FBS FBS No

Figure 26: Optimization of FBS supplementation in 3D cell culture method using agarose microwell array (CAMA). Cells were cultured in the CAMA for 7 days under 3 conditions, i) with FBS ii) FBS withdrawal a day after seeding and iii) without FBS. Scale bar:50µm.

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Day 1 Day 3 Day 5 Day 7

MDA-MB-361 2000 cells per microwell

MDA-MB-231 1500 cells per microwell

231-Tspan8 1500 cells per microwell

BT-549 1500 cells per microwell

BT-Tspan8 1500 cells per microwell

MCF7 2000 cells per microwell

MCF7-Tspan8 2000 cells per microwell

Figure 27: Representative images of cell aggregates/spheroids cultured in agarose microwell array for a week to isolate EVs from the established breast cancer cell models. Seeding cell density is shown in the left panel. Scale bar:50µm.

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Day 1 Day 3 Day 5 Day 7

MDA-MB-231

231-Tspan8

MCF7

MCF7-Tspan8

Figure 28: Immunohistochemistry with spheroids of breast cancer cells. The spheroids were fixed on Day 7, embedded in paraffin and stained with haematoxylin and eosin. MCF7 and MCF7-Tspan8 spheroids showed void like formations. Scale bar: 50µm. This experiment was performed by Ms. Hannah Füllgraf (Lab of Dr. med. Peter Bronsert, Institute for Clinical Pathology, Medical Centre, University of Freiburg).

For isolating EVs, the cell seeding density per matrix was increased to obtain a higher number of EVs. Cell seeding density for MDA-MB-231 & 23-Tspan8, and BT-549 & BT-Tspan8 cells was kept at 1500 cells per microwell or 14.25 x 105 cells per matrix. While MCF7Tspan8+/- and MDA-MB-361 cells were seeded at 2000 cells/microwell or 19 x 105 cells per matrix. The growth of the spheroids was monitored and imaged every alternate day for up to 7 days (Fig. 27). The cells formed spheroids up to 250µm in size. Further, the H&E staining with spheroids obtained from MDA-MB-231 & 231-Tspan8, and MCF7 & MCF7-Tspan8 cells was performed by Ms. Hannah Füllgraf (Lab of Dr. med. Peter Bronsert, Institute for Clinical Pathology, Medical

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Centre, University of Freiburg). It was observed that the spheroids did not form lumen implying that the cells stopped proliferating on reaching certain cell density within the spheroid (Thomsen et al., 2017). However, MCF7 and MCF7-Tspan8 showed some void like structures (Fig. 28).

5.2.3 Effect of Tspan8 on proliferation and invasion properties in the 3D environment

Since Tspan8 influenced proliferation properties in a 2D environment, the growth of the cells was also characterized in the 3D environment. The cells were cultured in agarose microwell array for 7 days. On a day after seeding, the medium was changed to FBS free medium. On day 7 the cells were harvested from the spheroids using trypsinization and stained with trypan blue. The cells were counted using an automated cell counter. We observed a similar pattern in the 3D environment as seen in 2D environment. 231-Tspan8 cells proliferated significantly higher than their parental counterpart (p=0.0043). Tspan8 reduced the proliferation in BT-549 cells significantly (p=0.0402) and had no effect in MCF7 cells (Fig. 29).

For the tumor cells to metastasize, cells must first cross the barrier of the underlying basement membrane which separates the cells from the stroma. The basement membrane is a thin layer of extracellular matrix lining the epithelial and endothelial cells; which consists of proteins like collagen, fibronectin, and laminin. Hence, to further investigate the role of Tspan8 in breast cancer progression, invasion assay performed with the cells. A customized Invasion matrix (InMx) was developed for the experiment and kindly provided by Dr. Andreas Thomsen (Department of Radiology, Medical Centre, University of Freiburg). Cells were seeded onto the InMx and cells were layered with Collagen or basement membrane extract (BME or Matrigel) a day after seeding. Images were taken on day 1, day 2 and day 3 to examine if the cells can invade the extracellular matrix. Since the proliferation activity was high in case of 231-Tspan8 and parental cells, the cells were found to be dead on day 3. Therefore, only day 1 and day 2 images are shown for 231-Tspan8 and parental cells. With collagen, there was no significant difference found in parental and 231-Tspan8 cells (Fig. 30A). Even though the number of invading cells seemed slightly more in parental cells on day 2, the distance invaded was the same for both the cells. With BME, 231-Tspan8 cells showed significantly enhanced invasive activity on both day 1 (p=0.0484) and day 2 (p=0.0051) when compared to the parental cells (Fig. 30B). On the contrary, BT-549 cells were seen to not invade but digest the BME around them during the span of 3 days (Fig. 30C). BT-Tspan8 cells abrogated the effect shown by the parental counterpart.

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While no difference of activity was seen against Collagen in both the cells. MCF7 and Tspan8 overexpressing MCF7 cells showed no activity against either Collagen or BME (Fig. 30D).

MDAMB231 231-Tspan8

BT-549 BT-Tspan8

MCF7 M7-Tspan8

Figure 29: Cell proliferation in 3D environment. (A) MDA-MB-231 vs 231-Tspan8 (B) BT-549 vs BT-Tspan8. (C) MCF7 vs MCF7-Tspan8. Cells were seeded for EVs isolation in CAMA placed in 6 well plates. Cells were treated with starvation medium on day 2 after seeding. On day7, cells were harvested and counted after trypsinization using automated cell counter. The cell supernatant was used to isolate EVs. Data is representative of at least 3 independent experiments.

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A. MDA-MB-231 231-Tspan8 Ctrl Collagen BME Ctrl Collagen BME

Day1

Day2

Scale Bar= 100µm

B. MDA-MB-231

231-Tspan8

Invaded distance Invaded

Day Figure 30: (A) Invasion Assay performed with MDA-MB-231 and 231-Tspan8 cells. Tspan8 cells seem to have enhanced invasive capacity. Ctrl: - Control, no ECM added, BME: - basement membrane extract. (B) The distance migrated by cells on BME was quantified. The distance between the bottom of the well was measured using software Zen2.3 lite. Day 0 measurements were subtracted from the Day 1 and Day2 measurements of the respective wells. Data is representative of at least five wells quantified.

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BT-549 BT-Tspan8

Ctrl Collagen BME Ctrl Collagen BME

Day1

Day2

Scale Bar= 100µm

Figure 30(C): Invasion Assay performed with BT-549 and BT-Tspan8 cells. BT-Tspan8 cells are seen to digest the basement membrane extract while parental cells donot have any effect. Ctrl: - Control, no ECM added, BME: - basement membrane extract. Scale Bar= 100µm.

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MCF-7 MCF7-Tspan8

Ctrl Collagen BME Ctrl Collagen BME

Day1

Day2

Scale Bar= 100µm

Figure 30(D): Invasion Assay performed with MCF7 and MCF7-Tspan8 cells. No difference was observed between parental and MCF7-Tspan8 cells. Ctrl: - Control, no ECM added, BME: - basement membrane extract. Scale Bar= 100µm.

5.2.4 Characterization of EVs isolated from breast cancer cells in 2D and 3D environments

The EVs isolated from parental and established breast cancer cells were characterized using TEM, NTA, microBCA and flow cytometry. The EVs were isolated from cells cultured under normoxia (21% Oxygen) and hypoxia (1% Oxygen). The EVs from 2D cell culture were isolated from five 14.5 cm dishes while 3D EVs were isolated from 1 agarose microwell array for comparison by TEM, NTA analysis and microBCA. For bead-assisted flow cytometry, multiple dishes (up to 30) and microwell arrays (6-12) were used for EVs isolation. And the expression markers were tested on 1 x 109 EVs per test marker.

The objective of examining the EVs in the 3D environment was first to see a) the difference in physical characteristics of EVs b) the difference in expression of EVs markers, in 2D and 3D

Results environments. And secondly, to see if the hypoxic environment has any effect on the physical characteristics of EVs released by the breast cancer cells.

5.2.4.1 Transmission electron microscopy First, the integrity of the EVs isolated was verified using Transmission electron microscopy. The EVs were negatively stained with uranyl acetate and fixed on Copper grids. The grids were imaged at the Institute for Anatomy and Cell Biology, Faculty of Medicine, Albert-Ludwigs University Freiburg (Core facility, Microscopy and Image Analysis Platform). As can be seen in figure 31, the typical cup-shaped, spherical and shrunken structures of EVs are visible in all the EVs preparations both in 2D and 3D environments. Similar observations were made with EVs derived from cells under hypoxia (Fig. 32). It is now known that the EVs are spherical in structure and the shrunken appearance in the images occurs due to the artifact created during fixation step of sample preparation for TEM (György et al., 2011).

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Figure 31: Transmission electron miscrocopic images illustrating typical cup shaped and shrunken structures of EVs isolated in normoxic conditions. Scale Bar: 100nm

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Figure 32: EVs isolated from cells under hypoxic conditions were checked for their integrity using Transmission electron miscrocopy. Intact and cup shaped structures can be seen in the images. Scale Bar: 100nm.

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5.2.4.2 Nanoparticle Tracking Analysis

i) EVs released per cell count Next, we wanted to compare the number of EVs released in 2D and 3D environments under normoxic and hypoxic conditions. For this, the number of EVs were quantified using NTA and a total number of EVs isolated were normalized to the number of cells releasing these EVs. The cells were counted after the supernatant had been taken for EVs isolation using the trypan blue exclusion method with the help of an automated cell counter. Under normoxic conditions we observed, barring MDA-MB-361 and 231-Tspan8 all the cells had significantly higher EVs/cell count in 3D when compared to 2D (Fig. 33) (p<0.05) 231-Tspan8 cells also showed slightly higher count but it was not statistically significant. Under hypoxic conditions also, we observed a similar trend wherein 3D environment all the cells had statistically significant higher EVs/cell count except MDA-MB-231 cells which had no significant difference. Interestingly, we observed that all the Tspan8 positive cells showed significantly higher EVs/cell count in hypoxic 3D environment consistently. This indicates that Tspan8 facilitates EVs release under tumor microenvironment. This also corroborates the previous findings of the lab in rat breast cancer model that Tspan8 enhances EVs release (manuscript under revision, Journal of Pathology).

i) Size distribution Nanoparticle tracking analysis was done to determine the size distribution in the isolated EVs and to quantify the number of EVs in the preparations. First, three sub-populations of EVs of sizes <50 nm, 50-250 nm, and >250 nm were analyzed in EVs preparations from each cell line (Fig. 34). It was seen that MDA-MB-361 cells in 3D environment released a significantly higher number of smaller vesicles (<50 nm) both in normoxic (p=0.0004) and hypoxic (p=0.0166) environments. And had significantly lower amount of >250 nm sized vesicles under normoxia (p=0.0006) and hypoxia (p=0.0212) Similarly, MDA-MB-231 and 231-Tspan8 cells also had slightly higher % of >50nm vesicles in 3D but the difference was not statistically significant both in normoxic and hypoxic conditions. In the case of BT-549 and BT-Tspan8 EVs, under normoxia the EV% for EVs < 50nm was slightly higher in 3D but the difference was not statistically significant. However, both cell lines released significantly higher % of smaller vesicles in 3D under hypoxic conditions (BT549, p=0.0003; BT-Tspan8 0.0122). The EV % for vesicles >250nm was also significantly lower in 3D environment (BT-549, p=0.0005; BT-Tspan8 p=0.0123). In MCF7 derived EVs, there was no difference seen between 2D and 3D environments in both normoxic and hypoxic conditions. In MCF7-Tspan8 derived EVs, <50nm % EVs was significantly higher (p= 0.0195) in 3D as compared to 2D in normoxic conditions. The same difference was

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observed under hypoxia, but the difference was not statistically significant. Overall, the cells tend to release smaller vesicles (<50 nm in size) in the 3D environment.

MDA-MB-231 A. MDA-MB-361 B. 231-Tspan8

EVs released cell per EVsreleased EVs released cell per EVsreleased

Normoxia Hypoxia Normoxia Hypoxia

C. D.

BT-549 MCF7 BT-Tspan8 MCF7-Tspan8

EVs released cell per EVsreleased EVs released cell per EVsreleased

Normoxia Hypoxia Normoxia Hypoxia

Figure 33: EVs released per cell count in 2D & 3D environments under normoxic & hypoxic conditions. The exsomes were quantified using Nanoparticle Tracking Analysis. Cell number was determined after staining with Trypan Blue using automated cell counter. (A) EVs from MDA-MB-361 cells (B)MDA-MB-231 vs 231-Tspan8 EVs (C)BT-549 vs BT-Tspan8 EVs. (D)MCF7 vs MCF7-Tspan8 EVs.

Results EVs <50nm EVs 50-250 nm Normoxia EVs >250 nm ***

***

MDA-MB-361 MDA-MB-231 231-Tspan8

** *

BT-549 BT-Tspan8 MCF7 MCF7-Tspan8

Hypoxia EVs <50nm EVs 50-250 nm EVs >250 nm *

MDA-MB-361 MDA-MB-231 231-Tspan8

*** * *

* ***

BT-549 BT-Tspan8 MCF7 MCF7-Tspan8

Figure 34: Size distribution analysis of EVs measured by NTA. Each data is representative of at least 3 independent isolations; except BTT 2D Normoxia, MCF7 2D hypoxia where third value was excluded because the zeta potential of the preparation was lower in magnitude than 15mV.

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Normoxia

MDA-MB-361 MDA-MB-231 231-Tspan8

BT-549 BT-Tspan8 MCF7 MCF7-Tspan8

Hypoxia

MDA-MB-361 MDA-MB-231 231-Tspan8

BT-549 BT-Tspan8 MCF7 MCF7-Tspan8

Figure 35: Size distribution analysis of EVs measured by NTA. Each data is representative of at least 3 independent isolations; except BTT 2D Normoxia, MCF7 2D hypoxia where third value was excluded because the zeta potential of the preparation was lower in magnitude than 15mV.

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ii) Mode Size Further, looking at the mode size of the EVs, the observations are consistent with the size distribution analysis. As can be seen in Table 6, the mode size for all the breast cancer 2D derived EVs lied in the range from 100 nm -132nm both in normoxia and hypoxia. In 3D however, the size range was 68nm – 132nm for overall breast cancer EVs. Looking at the dot plot for mode size (Fig. 35), we observed significantly smaller vesicles in MDA-MB-361 derived EVs when compared to 2D EVs both in normoxia (p=0.0073) and hypoxia (p=0.0079). While the 2D EVs were in the range 121-122nm, 3D EVs had the size range of 68-70nm (Table 6). For MDA-MB-231 and 231-Tspan EVs, we did not see any significant difference in mode size between 2D and 3D (size range = 122 – 132nm) in both normoxia and hypoxia. In case of BT- 549 and BT-Tspan8, not much difference was seen in 2D vs 3D under normoxia. However, under hypoxia, we saw significantly smaller vesicles in 3D as compared to 2D EVs both in BT-549 (p=0.0010) and BT-Tspan8 (p=0.0203). The 2D EVs were in size range 118-126 nm while 3D EVs were in the 80-92 nm size range (Table 6). The MCF7 and MCF7-Tspan8 EVs did not show any significant difference between 2D and 3D EVs mode size. The only significant difference observed was between MCF7 3D and MCF7-Tspan8 3D EVs under normoxia. MCF7Tspan8 3D EVs being significantly smaller than those derived from parental cells (p=0.0182). This further corroborates the previous finding that the cells tend to release smaller vesicle in 3D environment.

iii) Zeta Potential The stability of the EVs was further validated by determining their zeta potential. Zeta potential informs about the stability of the vesicles in the buffer. The surface charge density distributed around the vesicles in the buffer causes the potential difference and hence the zeta potential. It should not be close to zero so that the electrostatic repulsion between the vesicles is enough to prevent aggregation and instability. A lower zeta potential indicates aggregation or flocculation. Ideally, the zeta potential should be higher in magnitude than 30 (Beit-Yannai et al., 2018). The zeta potential measured was negative for all the EV samples and EVs with zeta potential lower in magnitude than 15 were not included in the analysis. Table 6 shows the median zeta potential of at least 3 isolations (except BT-Tspan8 2D normoxia, MCF7 2D hypoxia and 231 3D hypoxia where one replicate was excluded as zeta potential was lower in magnitude than 15). As can be seen in Table 6, under normoxic 2D conditions the zeta potential was in the range -26.15 to - 34.41 mV in all the breast cancer cells. The 3D EVs showed slightly better stability with the range of -30.49 to -37.15 mV. While in hypoxic 2D condition, the zeta potential varied from - 21.53 to -31.61mV. In 3D hypoxic condition, the range was -25.95 to -37.34 mV. In general, 3D

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EVs showed better stability as compared to 2D EVs. However, no significant difference was seen in zeta potential of EVs between normoxic and hypoxic conditions.

5.2.4.3 Protein concentration The protein content of the EVs was measured using Micro BCA kit. The BSA standards provided by the kit were used to calculate the protein concentration of the EVs. All the measurements were done with at least 3 independent isolations. Taking all the breast cancer cells derived EVs together; under 2D normoxic conditions the protein concentration was found to be 0.45- 1.74µg/ml (Table 6). While in 3D environment it varied from 0.33 – 1.37µg/ml. Under hypoxia, 2D EVs had protein concentration in the range 0.97 – 1.38 µg/ml while 3D EVs had 0.67- 1.15µg/ml.

Table 6: EVs analysis in normoxic and hypoxic conditions.

Normoxia 2D 3D Zeta Protein Protein Cell Line EVs released Mode Size EVs released Mode Size Potential Concentration Zeta Potential Concentration per cell (nm) per cell (nm) (mV) (µg/ml) (mV) (µg/ml) MDA-MB-361 417 121.4 -32.13 ± 0.67 1.62 111 70.1 -32.74 ± 1.04 0.66 MDA-MB-231 113 131.5 -28.74 ± 0.81 0.77 905 121.3 -30.84 ± 1.24 0.50 231-Tspan8 211 121.7 -32.56 ± 0.66 1.11 299 125.8 -31.10 ± 0.99 0.67 BT-549 193 103.1 -26.15 ± 1.47 1.29 3351 100.2 -30.49 ± 1.6 0.81 BT-Tspan8 39 100.2 -28.41 ± 1.02 0.45 3242 102.2 -37.15 ± 1.04 1.37 MCF7 248 119.2 -32.95 ± 0.38 0.51 2283 131.7 -33.97 ± 0.96 0.33 MCF7-Tspan8 633 119.4 -34.41 ± 1.16 1.74 925 117.8 -32.94 ± 1.09 0.92

Hypoxia 2D 3D Zeta Protein Protein Cell Line EVs released Mode Size EVs released Mode Size Potential Concentration Zeta Potential Concentration per cell (nm) per cell (nm) (mV) (µg/ml) (mV) (µg/ml) MDA-MB-361 153 122.4 -21.53 ± 0.81 1.24 214 66.7 -28.99 ± 0.68 0.67 MDA-MB-231 731 121 -28.09 ± 0.78 1.16 663 122.5 -32.86 ± 0.86 0.94 231-Tspan8 178 121.2 -26.33 ± 0.36 1.09 4010 125.1 -32.14 ± 0.88 0.82 BT-549 150 125.9 -28.56 ± 0.40 0.97 2719 92.4 -37.34 ± 1.75 1.15 BT-Tspan8 124 118.4 -30.35 ± 0.78 1.38 5129 79.9 -29.59 ± 1.12 0.94 MCF7 353 127.3 -31.61 ± 1.86 1.16 1204 126.9 -28.59 ± 0.88 0.93 MCF7-Tspan8 467 125.3 -28.78 ± 0.87 1.10 3528 120.8 -25.95 ± 0.53 0.81

Note: EVs concentration shows minimum and maximum values recorded. Peak size: - the average of at least 3 independent values. Zeta Potential: - median of at least 3 independent values. (Note: at least 3independent values were taken for analysis except BT-Tspan8 2D (normoxia), MDA-MB-231 3D (hypoxia), MCF7 3D(hypoxia) where one replicate was excluded from the analysis because it had zeta potential lower in magnitude than 15mV). Protein concentration: - Average of at least 3 independent isolations.

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5.2.4.4 Bead-assisted flow cytometry for detection of exosomal markers

Further, we wanted to examine the expression of classical EVs markers CD63, CD9, and CD81 (Kowal et al., 2016) in EVs from the breast cancer cells in 2D and 3D environments. Due to their nano-size, polydispersity and low refractive index, bead-assisted flow cytometry was employed for the detection of the markers. This method has been previously described and proved to be a reliable method for the analysis of EVs (Suárez et al., 2017).

As we can see in figure 36, the MDA-MB-361 EVs were positive for all the exosomal markers CD63, CD9, and CD81 for both 2D and 3D EVs. Though, 3D EVs showed a comparatively lower amount of expression for all three markers which are evident from the mean fluorescence intensity (MFI) data (Fig. 36A, lower panel). A similar observation was made with Tspan8 expression.

231-Tspan8 cells showed significantly lower expression of CD9 and CD81 tetraspanins as compared to their parental counterparts (Fig. 36B, left panel). Though Tpsan8 was not recruited to the EVs derived from 231-Tspan8 cells, we observed Tspan8 did regulate the expression pattern of CD9 and CD81 in EVs obtained under both 2D and 3D environments. A similar trend was observed with EVs as observed with the cells, CD9 and CD81 levels were lower in 231- Tspan8 EVs as compared to MDA-MB-231 EVs.

BT-Tspan8 cells showed a significantly high expression of CD63 as compared to parental cells (Fig. 36A, left panel) corroborating the finding from immunoblotting results (Fig. 18). CD9 was also significantly lowered in BT-Tspan8 cells as compared to the BT-549 cells. A similar trend of CD63 and CD9 expression was observed with BT-Tspan8 & BT-549 EVs derived from respective cells under both 2D and 3D environments (Fig 36A, right panel). All EVs were positive for CD63, CD9, and CD81. However, 3D culture derived EVs showed lower levels of expression as compared to the 2D culture derived EVs.

There was a significant difference in expression of CD63, CD9 and CD81 in MCF7 and MCF7- Tspan8 cells (Fig. 37B, left panel). And like MDA-MB-361, BT-549, and BT-Tspan8 derived EVs, EVs from MCF7 & MCF7-Tspan8 cells in 3D culture had reduced mean fluorescence intensity when compared to EVs from 2D cultured cells. In general, the expression of CD63, CD9 and CD81 markers was reduced in EVs derived from 3D cultures as compared to 2D culture derived EVs (except that of CD63 and CD9 expression in MDA-MB-231 and 231-Tspan8 derived EVs). It is to be noted that all the results shown for EVs are from single measurements for each tested marker due to the tedious method of EVs isolation and scarcity of samples.

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A. CELLS EVs

B. CELLS EVs

Figure 36: The expression pattern of tetraspanins CD63, CD9, CD81 and Tspan8 by flow cytometry in A) MDA-MB-361 cells (left) and EVs from them under 2D and 3D environments (right). The figure is representative of 2 biological replicates for Cells. B) MDA-MB-231 & 231- Tspan8 cells (left) and EVs derived from them under 2D and 3D environments (right). The figure is representative of 2 technical replicates for cells. The figure shows percentage of cells tested positive (upper panel) and mean fluorescence intensity measured (lower panel). The EVs (1x 109 per sample) were first coated with 4µm latex beads prior to flow cytometric measurements. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of sample. CD63, CD9 and CD81 are well reported exosomal markers.

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B. CELLS EVs

Figure 37: The expression pattern of tetraspanins CD63, CD9, CD81 and Tspan8 by flow cytometry in A) BT-549 & BT-Tspan8 cells (left) and extracellular vesicle EVs from them under 2D and 3D environments (right). The figure is representative of 2 biological replicates for ‘Cells‘. B) MDA-MB-231 & 231-Tspan8 cells (left) and EVs derived from them under 2D and 3D environments. The figure is representative of 2 technical replicates for ‘Cells‘. The figure shows percentage of cells tested positive (upper panel) and mean fluorescence intesnisty measured (lower panel). The EVs (1x 109 per sample) were first coated with 4µm latex beads prior to flow cytometric measurements. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of samples. CD63, CD9 and CD81 are well reported exosomal markers.

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5.2.4.5 Examining recruitment of integrins to the breast cancer cell EVs using bead-assisted flow cytometry

Next, we wanted to determine the integrins that are recruited to the EVs derived from breast cancer cells. Additionally, we wanted to see if Tspan8 can regulate integrin (ITG) content in EVs. It was observed that integrins α1, α2, β1, β5, and α3β1 were present on the surface of MDA-MB- 361 derived EVs. They also had a weak expression of α3 integrin (Fig. 38A).

Interestingly, the expression of α3, αV, β1, and α3β1 integrins was seen to be significantly upregulated in 231-Tspan8 cells as compared to the parental cells (Fig. 38B, left panel). This corroborates the previous findings with immunoblotting (Fig. 22). A similar difference of expression pattern was observed with the EVs derived from the respective cells; 231-Tspan8 EVs showing higher expression of α3, αV, β1, and α3β1 as compared to MDA-MB-231 EVs.

BT-Tspan8 cells also showed significant upregulation of β1 integrin when compared to the parental cells (Fig. 39A). Similar observations were made with immunoblotting too (Fig. 22). BT-Tspan8 cells also had significantly higher levels of β5 integrin surface expression when compared to BT-549 cells, although β5 integrin expression was seen to be downregulated in BT- Tspan8 cells with immunoblotting (Fig. 22). Further, BT-Tspan8 derived EVs also showed higher expression of α3, α3β1, and β1 integrins when compared to BT-549 derived EVs (Fig.39A, right panel).

MCF7-Tspan8 cells showed significant downregulation in expression of αV, β5 and αVβ3 integrins (Fig. 39B, left panel) when compared to MCF7 cells. MCF7-Tspan8 derived EVs did not show much difference in expression. α3β1 integrin expression was seen lowered in MCF7Tspan8 cells as compared to the MCF7 derived EVs (Fig. 39B, right panel). Overall, Tspan8 regulates the expression of α3, αV, β1 and α3β1 integrins in EVs, especially those derived from triple-negative breast cancer cells.

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B. CELLS EVs

Figure 38: The surface expression pattern of integrins in A) MDA-MB361 cells and EVs. B) MDA- MB-231 & 231-Tspan8 cells (left) and EVs derived from them under 2D environment (right). The figure is representative of 2 technical replicates for ‘Cells‘. The EVs (1x 109 per sample) were first coated with 4µm latex beads prior to flow cytometric measurements. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of samples.

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A. CELLS EVs

B. CELLS EVs

Figure 39: The expression pattern of integrins in A) BT-549 & BT-Tspan8 cells and EVs derived from them under 2D environment (right). B) MCF7 & MCF7-Tspan8 cells (left) and EVs derived from them under 2D environment (right). The figure is representative of 2 technical replicates for ‘Cells‘. The EVs (1x 109 per sample) were first coated with 4µm latex beads prior to flow cytometric measurements. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of samples.

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5.2.4.6 Proteomic analysis of EVs derived from MDA-MB-231 and 231-Tspan8 cells under 2D environment

To study the impact of Tspan8 on EV content, proteomics analysis of EVs isolated from MDA- MB- 231 and 231-Tspan8 cells under 2D environment was performed using mass spectroscopy. In total 1602 proteins were identified in EVs derived from MDA-MB-231 and 231-Tspan8 under 2D environment. To stress on the differences in expression profiles of EVs from MDA-MB-231 and 231-Tspan8 cells, supervised clustering analysis was performed for 20 proteins with the highest significance (Figure 40). We observed 231-Tspan8-EVs showed upregulation of 9/20 proteins and downregulation of 11/20 proteins when compared to the protein profile of MDA- MB-231-EVs. Small GTPases like Rab10 and Rac2 were found to be upregulated in 231-Tspan8 derived EVs as compared to MDA-MB-231EVs. Rab proteins play a significant role in EV biogenesis. Rac2 has been identified among other proteins including Rab6A as a predictive biomarker for relapse after adjuvant chemotherapy in triple-negative breast cancer patients (Chiva C etal., 2017). We observed downregulation of components of eIF-3 protein (eIF-3c & eIF-3e) which are involved in initiation of protein synthesis. This preliminary data suggests that Tspan8 affects the content of EVs in MDA-MB-231 cells and needs to be further investigated in detail.

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231-Tspan8 MDA-MB-231

Figure 40: Proteomic analysis of EVs derived from MDA-MB-231 and 231-Tspan8 cells under 2D environment. The figure shows heatmap and dendrogram of the 20 most significantly different proteins between MDA-MB-231-EVs and 231-Tspan8-EVs (Z-score normalized expression values). Data shows 3 technical and 3 biological replicates. The analysis was done in collaboration with Dr. Stefan Tenzer, University Medical Centre, Mainz, Germany; & Prof. Dr. Andreas Keller, Saarland University; University Hospital, Saarbrücken, Germany

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5.3 EVs bound HER2 as a putative diagnostic marker in breast cancer patients

HER2 is overexpressed in 20 -30% of breast cancers (Jackson et al., 2013) and is the target for Trastuzumab-based therapy. Since EVs cargo (RNA, miRNA, DNA, and proteins) can give information on the state of their originating tumor cells, it can be used to diagnose and monitor the progression of the disease. Exosomal HER2 has been shown to regulate the sensitivity of tumor cells to Trastuzumab therapy (Ciravolo et al., 2012). Hence, we wanted to examine the detection of HER2 in EVs derived from breast cancer cells and sera obtained from breast cancer patients. This part of the thesis was done in collaboration with the group of Prof. Dr. Christian Koos, Institute of Photonics and Quantum Electronics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany. For the detection of EVs, Whisper Gallery Mode lasers were developed by Mr. Sentayehu Fentene Wondimu (KIT). All the EVs were isolated and characterized in our laboratory. The techniques used for characterization were electron microscopy, NTA, DLS, and western blotting while experiments for detection of HER2 in EVs were performed at KIT using WGM lasers.

The tumor cells release HER2 via EVs or by shedding the extracellular domain of HER2 as a soluble form in the blood. HER2 has three domains, extracellular domain (ECD) present on the surface of the cell, transmembrane domain spanning the plasma membrane and a cytoplasmic domain. In the routine testing, HER2 can be detected via immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) techniques in tissues. The ECD of HER2 can also be detected in serum by ELISA which can be an indicator of the HER2 positive cells metastasizing to a new site. We hypothesize that detection of EVs bound HER2 can give more information regarding origin cells and can contribute to better diagnosis and prognosis of the disease.

5.3.1 HER2 is recruited to EVs derived from breast cancer cells

Three cell lines were chosen for the study: - MDA-MB-361, MDA-MB-231, and BT-549. MDA-MB- 361 cells are Luminal B type which and strong HER2 expression. While MDA-MB-231 are triple negative cells, they show weak expression of HER2 in immunoblots. Hence, a third cell line BT- 549 was chosen which do not express HER2 at all. The EVs from these three cell lines were isolated and tested for the presence of HER2 and other EV markers such as CD9, TSG101 and HSP70 by immunoblotting. GAPDH was used as the control. For comparison, cell lysates from the three cell lines were also taken for immunoblotting. HER2 expression in the EVs seemed to replicate the expression pattern of their respective cell lines. MDA-MB-361 derived EVs had strong HER2 expression, MDA-MB-231 had very weak expression and BT-549 cells were

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Figure 41: HER2 expression in breast cancer cells and EVs derived from their respective cell lines as detected by western blotting. 20 ug of cell lysates and 4 ug of EVs were loaded onto SDS- PAGE gel and adjusted with respect to GAPDH. The antibody used recognizes extracellular domain of the HER2 protein

5.3.2 Detection limit of EVs by western blotting in breast cancer cells

Further, we wanted to determine the minimum amount of EV protein required to detect HER2 by immunoblotting. EVs derived from MDA-MB-361 were taken according to their protein concentration in decreasing amounts from 4µg up to 0.06 µg. HER2 could be detected up to 0.05 µg of protein and weakly detected at the concentration 0.25 µg (Fig. 42).

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MDA-MB-361 EVs (µg)

Figure 42: Detection limit of HER2 protein in MDA-MB-361 derived EVs by western blotting at decreasing concentration of exosomal protein (µg) as determined by BCA. Corresponding number of EVs in decreasing amount (x107 ):- 3.2, 1.6, 0.8, 0.4, 0.2, 0.1, 0.05.

5.3.3 HER2 selectivity as a target

To validate that the WGM lasers target the HER2 in EVs specifically, recombinant HER2 was used for comparison. The functionality of the recombinant HER2 was cross-checked via immunoblotting with antibodies used in the biological and WGM laser measurement experiments (Fig. 43A&B). We observed strong equivalent shifts for MDA-MB-361 (strongly positive) and MDA-MB-231 (weakly positive) EVs (Fig. 43C). Even though MDA-MB-231 EVs showed weak HER2 expression via immunoblotting, equal shifts may be due to the high sensitivity of the WGM lasers towards HER2 positive EVs. The negative controls BT-549 EVs and WGM lasers functionalized with isotype control showed a very weak response. Recombinant HER2 also showed a weak response confirming that the devices responded specifically to EV- HER2.

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A. Detection of HER2 by antibody used in biological experiments (#4290, Cell Signalling Technologies)

B. Detection of HER2 by antibody used in WGMlaser experiments (#BMS120, biotinylated, eBioscience)

Figure 43: WGMLasers were functionalized to specifically target HER2 in EVs. (A) Validation of funtionality of the recombinant human HER2 protein by immunoblotting using antibodies used in biological experiments (HER2Antibody: #4290, Cell Signallinging Technologies) and (B) WGMlaser experiments, (HER2Antibody: #BMS120BT, eBioscience, biotinylated). Breast cancer cell lysates were used as controls. (C) Detection of HER2 in EVs derived from BT-549, MDA-MB- 231 and MDA-MB-361 cells by using WGMlasers. recombinant HER2 protein was used as control. This experiment (Fig.C) was performed and the figure was generated by Mr. Sentayehu Wondimu (Karlsruhe Institute of Technology, Karlsruhe) for his doctoral work.

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5.3.4 Comparing EV5, EV12 and EV120 derived from breast cancer patients

As observed earlier, EV120 fraction is seen to be enriched in proteins. As proof of principle for EVs derived from breast cancer patients’ sera, we tested the HER2 expression in EV5, EV12 and EV120 obtained during differential centrifugation using WGM lasers. First, HER2 expression was checked in EV5, EV12, and EV120 derived from MDA-MB-361 cells using bead assisted flowcytometry. As expected, EV120 was enriched in exosomal markers CD63 and CD9 as compared to EV5 and EV120. Also, EV5 and EV12 were negative for HER2 expression. On the other hand, we saw 23% positive events with EV120/EVs (Fig. 44A). The total amount of protein as detected by BCA kit was also seen to be significantly increased in EV120 fraction as compared to EV5 and EV12 (Fig. 44C). As EV120 was enriched in HER2 in protein, EV120 was used for all the further experiments. Hence, the term EVs used for all the further experiments refers to EV120.

5.3.5 Characterization of EVs (EV120 fractions) derived from breast cancer patients

EV120 fractions were isolated from breast cancer patients’ sera and were characterized using TEM and NTA. The EVs isolated from serum are usually difficult to image as they can be masked by the presence of albumin protein. Hence, the EVs were diluted in HEPES buffer for imaging (1:50 dilution in HEPES buffer). The spherical structure of EVs was observed (Supplementary figure 9A). The EVs mode size was found to be in the range of 123 – 138 nm (Supplementary figure 9B). Ten breast cancer samples were taken for the study (Table 7). It is to be noted that this HER2 status was determined on primary diagnosis and provided by the University Medical Centre University of hospital Freiburg at the time of the collection of the sample. The HER2 status was determined by Immunohistochemistry of the tissues obtained from the patients. HER2 score above 2 is considered positive and below 2 is considered negative. Also, HER2 status of the patient can change over time during treatment and course of the disease. The Table 7 shows the serum HER2 concentration to be in the range of 0.31 – 0.62 pg/µl as determined by ELISA kit. On the other hand, exosomal HER2 was in the range of 0.06-0.12 pg/µl. The total protein concentration of the EVs was very high as compared to those obtained from the breast cancer cells used in the study in the range of 80 -133 µg/ml. The concentration for EVs from sera was 260 – 1400 x 109 EVs/ml which is very high when compared to EVs obtained from the cells which had the concentration of 1-80 x 109 EVs/ml. The zeta potential was seen to be unexpectedly lower in magnitude in the range of -3.82 ± 0.59 to -6.95 ± 0.48 mV when compared to that of EVs derived from cells (-21.53 to -34.41 mV). This may be due to a very high concentration of the serum derived EVs which may have resulted in flocculation.

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A. EV 5 EV 12 EV 120

Neg.Ctrl CD63 CD9

CD81

Count

Neg.Ctrl HER2

23%

Fluorescence intensity

B. EV 5 EV 12 EV 120

Figure 44: Comparitive analysis of 3 EV fractions from breast cancer patients ‘sera obtained during differential centrifugation at 5000g (EV5), 12000g (EV12) and 120 000g (EV120) (A) Detection of HER2 and exosomal markers CD63, CD9 and CD81 in EVs derived from MDA-MB- 361 cells coupled using beads-assisted flow cytometry. 14µl of EV5, EV12 and EV120 were coated onto 4µm latex beads. (B) Size distribution of extracellular vesicles (EVs) isolated from breast cancer patient‘s serum using Dynamic light scattering technique. Data depicts 1 patient sample (C) Total protein concentration in the three fractions obtained from patients ‘sera. Data represents 10 patient samples used in the study.

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Table 7: Characterization of EVs derived from patients’ sera

Total Serum EVs total HER2 status EV HER2 EVs Number of Zeta HER2 protein Mode Size S.No. on primary Metastasis conc. concentration EVs from Potential conc. concentration (nm) diagnosis (x10 9 / ml) 1ml of (mV) (pg/µl) (pg/µl) µg/ml serum

negative 1 negative (lymph nodes) 0.47 0.06 132.87 1400 261.8 -4.78 ± 1.17 123.9

2 positive negative 0.31 0.12 85.13 180 37.44 -4.35 ± 0.60 123.1

negative 3 positive (lymph nodes) 0.34 0.16 89.98 260 54.08 -1.48 ± 0.55 138.1

negative 4 positive (lymph nodes) 0.41 0.07 103.40 280 43.68 -6.95 ± 0.48 131.6

negative 5 negative (lymph nodes) 0.42 0.03 77.56 650 101.4 -3.82 ± 0.59 126.4

negative 6 negative (lymph nodes) 0.54 0.03 127.49 1100 171.6 -5.59 ± 0.71 128.4

7 negative negative 0.57 0.10 93.05 360 56.16 -6.45 ± 0.77 131.5

8 negative negative 0.62 0.10 79.96 270 42.12 -4.79 ± 0.54 137.9

9 negative negative 0.48 0.06 99.14 850 132.6 -4.84 ± 0.43 132.4

10 positive negative 0.42 0,08 102.88 270 42.12 -6.15 ± 0.79 133.2

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5.3.6 EV-HER2 levels correlate well with HER2 score as determined by immunohistochemistry

The HER2 levels in EVs derived from breast cancer patients’ sera were compared with those of sera and free-circulating fractions from the respective patients. Interestingly, the HER2 levels of EVs correlated better than those of the sera obtained from patients (Fig. 45). This indicates that EVs can potentially determine the HER2 status of breast cancer patients without the traditional invasive methods.

2.5 Serum-HER2 fc-HER2

1.5

µ 1 2+ 0 0 0 0 1+

HER2pg/ 3+ 3+ 0.5 3+ 3+

0.2 EV-HER2 3+ 0.16 3+

0.12 0 0

l µ 3+ 0.08 2+ 3+ 1+ 0 0 HER2pg/ 0.04

-0.04

Figure 45: EV-HER2 levels correlated well with HER2 score as determined by immunohistochemistry. HER2 levels (pg/µl) in sera, free-circulating fractions (top) and EVs (bottom) were detrmined using Her2 Quantikine ELISA kit (R&D). HER2 scores were provided by PD Dr. Erbes Thalia (Department of Obstetrics and Gynecology, Medical Centre, University of Freiburg). The number on top of the bar represents HER2 score. 10 breast cancer patient samples were used in the study.

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5.3.7 Whispering-gallery mode lasers for detection of EVs

First, the sensitivity of the WGM lasers measured against varying concentrations of EVs (Fig. 46A). The concentration of 21x 107 EV/mL showed maximum detection. The sensitivity of the device was determined to be 1.4 pm/107 EV/mL and the detection limit ~ 107 exosomes/mL. Next, the EVs derived from the three cell lines: MDA-MB-361, MDA-MB-231, and BT-549 were tested with sensors functionalized with CD63 and HER2 antibodies using identical concentration of EVs (21 x 107). The highest shifts were measured for anti-CD63 functionalized sensors, while the lowest shifts were measured for those functionalized with anti-CD9 (Fig. 46B). A stronger shift was observed with MDA-MB-231 EVs for CD63 and HER2 when compared with BT-549 EVs. The spectral shift for HER2 in MDA-MB-231 and MDA-MB-361 derived EVs were equally strong even though MDA-MB-231 has a weak expression of HER2. This could be due to a high sensitivity of WGM lasers and needs further fine-tuning to distinguish samples with different HER2 levels. For detection of HER2 in patient-derived EVs, 15 µl of sample was taken and the volume was made up to 1ml to fill up the flow chamber. A specific number of EVs was not taken to emulate an actual diagnostic test. Isotype antibody was used as negative control. On measuring HER2 content in breast cancer patients with WGM lasers, we observed a correlation between HER2 content measured from ELISA kit and that from WGM laser measurements (Fig. 46C). Further, no correlation was observed between the number of EVs and spectral shift (Fig. 46D).

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A. 21.0 x 107 EVs/mL 14.0 x 107 EVs/mL

10.5 x 107 EVs/mL 7.5 x 107 EVs/mL 1.4 x 107 EVs/mL 0.7 x 107 EVs/mL 0.0 x 107 EVs/mL

(pm) shift Spectral Spectral shift (pm) shift Spectral

7 Time (minutes) Concentration (10 EVs /mL) B.

EVs 361

- MB

- MDA

1 2 3 4 5 6 7 8 9 10 D.

Spectral shift (pm) shift Spectral

9 Nr. Of EVs 10 /mL

Figure 46: Application of WGM lasers specifically targeting EVs (A) Binding kinetics (left) and sensitivity curves of EVs at varying concentrations (B) Detection of HER2 in MDA-MB-361, MDA-MB-231 and BT-549 cells-derived EVs. breast cancer cell lines. Additionally, sensors functionalized with exosomal markers CD63 and CD9 were tested with the concentration of 21 x 107 EVs (C) Correlation between HER2 content as measured by WGM lasers and ELISA in breast cancer patients-derived EVs. (D) Correlation between spectral shift measured by WGM lasers and number of EVs. 10 breast cancer patient samples were used in the study. All the experiments were performed and the figures were generated by Mr. Sentayehu Wondimu (Karlsruhe Institute of Technology, Karlsruhe) for his doctoral work.

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6 Discussion

6.1 Tspan8 has diverse effects on cell morphology, proliferation, invasion and adhesion properties in triple-negative breast cancer cells

Tspan8 has been associated with poor prognosis and tumor progression in colorectal, gastric, esophageal, pancreatic, ovarian and liver cancers, and melanoma (Richardson et al., 2011; Zöller, 2009). There have been no studies on the impact of Tspan8 in breast cancer. Hence, in the present study, one of our aims was to address the impact of Tspan8 on cell behavior in breast cancer cells. Previous findings from our lab show that Tspan8 is overexpressed in the primary tumor and metastatic sites in breast cancer patients (manuscript submitted, Journal of Pathology). In our established cell model, we observed Tspan8 had some significant effect on cell proliferation and cell invasion in triple-negative breast cancer cells (MDA-MB-231 and BT- 549). However, in Luminal A type MCF7 cells, the effect was not significant. The study shows that Tspan8 has potentially some impact on the behavior of breast cancer cells and further investigation is needed for a thorough understanding of its function in breast cancer.

We also observed that Tspan8 has varied effects within triple-negative subtype breast cancer cells. Tspan8 seems to significantly increase the proliferative and metabolic activities in MDA- MB-231 cells. On the other hand, it reduces proliferative and metabolic activities in BT-549 cells. Tspan8 has been demonstrated to increase cell proliferation and cell migration in gastric cancer cells (Wei et al., 2015).

The diverse behavior was also observed with invasive properties in the cells against BME (Matrigel) in the 3D environment. 231-Tspan8 cells showed enhanced invasive capacity when compared to the wild-type. While BT-Tspan8 cells seem to abrogate the effect of BME digestion shown by the parental cells. However, Tspan8 overexpression had no effect in luminal A type MCF7. Thus, in MDA-MB-231 cells Tspan8 shows pro-metastatic potential while in BT-549 cells Tspan8 shows anti-metastatic activity. Previous studies have shown the association of Tspan8 with an invasive phenotype in ovarian cancer (Park et al., 2016), hepatocellular cancer (Akiel et al., 2016) and melanoma (Berthier-Vergnes et al., 2011).

A recent study by Park et al (Park et al., 2016) showed that on knocking down Tspan8 expression in SKOV3 ovarian cancer cells, the cells showed a reduction in invasion capacity. A similar phenomenon was observed on using a blocking antibody against the large extracellular loop (LEL) of Tspan8. The same antibody was able to reduce metastasis in in vivo model too. Tspan8 was also found to be the mediator of invasion in melanoma cells in a study conducted by

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O Berthier-Vergnes O. et al (Berthier-Vergnes et al., 2011). They performed an oligonucleotide array with 2 subpopulations of a melanoma cell line, one being invasive and other being non- invasive. Tspan8 was overexpressed at mRNA and protein levels in the invasive clones from the melanoma cells. Its occurrence was found in melanoma cells at primary and metastasis sites but not in epidermal cells in healthy skin. On silencing the Tspan8 gene, reduction in invasion from the tumor spheroids against BME was observed. But no effect on cell proliferation and survival was seen. In another study (Akiel et al., 2016), Tspan8 was seen to contribute to the oncogene AEG-1 (Astrocyte elevated gene) induced invasion and migration in hepatocellular carcinoma (HCC) cells. On knocking down Tspan8, cells showed reduced invasion and migration without affecting cellular proliferation. It also annulled the AEG-1 induced primary tumor and intrahepatic metastasis in an orthotopic xenograft model. Also, co-culturing the knockdown cells with HUVEC cells (human umbilical vein endothelial cells), the cells inhibited tube formation implicating the role of Tspan8 in angiogenesis and reduction in primary tumor size.

Further, the 231-Tspan8 cells show more attachment to the basement membrane extract (BME) while BT-Tspan8 cells show less attachment to the BME when compared to their parental counterparts. It is known that the tumor cells interact with ECM interchangeably. They may strengthen the interaction with ECM in order to invade and metastasize or they may weaken the interaction in order to relocate from the primary site (Dajee et al., 2003). Thus, it can be speculated that Tspan8 may act differently at the primary tumor site and metastatic sites in breast cancer.

Additionally, Tspan8 may play some role in partial MET as observed in the distinct morphological changes in BT-Tspan8 cells. The Tspan8 overexpressing cells showed stronger cell-cell attachment and grew in colonies, unlike the wild-type cells. Interestingly, we see a downregulation of N-Cadherin expression in the BT-Tpsan8 cells. N-Cadherin is a well- established marker for ongoing EMT and is known to promote stem cell-like properties in breast cancer (Qian et al., 2014). The previous findings from lab, also demonstrated Tspan8 to promote mesenchymal to epithelial transition in rat breast adenocarcinoma cell model (MTPa cells) (manuscript submitted, Journal of Pathology). A recent study also highlighted the association of Tspan8 with adult mammary stem cells (Fu et al., 2017). It showed that a subset of quiescent mammary stem cells (Lgr5+Tspan8hi) expressed Tspan8 at higher levels and exhibited a transcriptome very similar to that of claudin-low tumor cells. It had high repopulation frequency. Such cells remain dormant for a long time but can be activated by ovarian hormones and have the possibility of accumulating genetic errors over time. Thus, Tspan8 has been associated with the mesenchymal phenotype and promotes MET in rat breast

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Discussion cancer but the extent of Tspan8 contribution to MET in human breast cancer needs to be further examined intensively.

To sum up, Tspan8 regulates physiological processes like proliferation, adhesion and invasion in triple-negative breast cancer cells MDA-MB-231 and BT-549. Thus, it affects cell growth in triple-negative breast cancer cells, but the effect is varying. It does not regulate these processes significantly in Luminal A type MCF7 cells. We can also speculate that such diverse actions of Tspan8 may contribute to heterogeneity observed in breast cancer (Polyak, 2011) or the diverse effect is seen due to the heterogeneity of breast cancer. It may be speculated that different integrin profiles of the cells, MDA-MB-231 {α6β4 (+), α4 (-)} & BT-549 {α4β1(+), α6β4 (-)} can play a role in affecting interactions within the TEM (tetraspanin enriched domain), thereby regulating downstream effector molecules in signaling pathways. This needs to be experimentally examined what are the interacting partners of Tspan8 in breast cancer and how this interaction may affect its function in breast cancer. Further, to confirm the effect of Tspan8 in breast cancer cells, viewed in the present study Tspan8 knockdown experiments need to be performed.

6.2 Tspan8 regulates expression of integrins in breast cancer cells and in EVs derived from them

6.2.1 Tspan8 regulates expression of integrins, especially α3β1 in triple-negative breast cancer cells Integrins belong to a family of cell surface receptors which mediate adhesion to extracellular matrix (ECM) and immunoglobulin superfamily molecules. They help in connecting the matrix to the cell’s cytoskeleton. There are about 24 distinct integrin heterodimers formed by the combination of 18α and 8β subunits. It has already been reported that integrins expression varies in breast cancer contributing to varying phenotype and hence, heterogeneity (Taherian et al., 2011). Their role in cell proliferation and metastasis is also well reported (Brakebusch et al., 2002; Detchokul et al., 2014). For example, β1 integrin interacts with various proteins that activate signaling pathways involved in various processes in breast cancer such as epithelial to mesenchymal transition (EMT) (Bhowmick et al., 2001), metastasis and angiogenesis (Imanishi et al., 2007; Jahangiri et al., 2014; Schlaepfer and Hunter, 1997). Its expression is linked to poor survival in invasive breast cancer (dos Santos et al., 2012; Yao et al., 2007). Several downstream signaling pathways of β1 such as FAK PI3K ERK/MAPK which coordinate through receptor tyrosine kinases (RTKs) intersect each other dynamically to control the physiological events of tumor progression (Castelló-Cros et al., 2009; Miranti and Brugge, 2002; Mitchell et al., 2010). It

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Discussion has also been studied to regulate cell proliferation in MDA-MB-231 cells (Hou et al., 2016). Tspan8 upregulates the expression of β1 integrin in MDA-MB-231 and BT-549 cells. We also observed downregulation of β4 integrin in 231-Tspan8 cells. This inverse relation of β1 and β4 integrins have been described earlier (Hou et al., 2016). The loss of β4 integrin expression has also been observed in prostate carcinoma (Cress et al., 1995). We do observe higher cell proliferation, adhesion, and invasion in 231-Tspan8 cells. But it needs to be experimentally confirmed if β1 is the causative agent or not. On the other hand, we see the opposite effect on BT-Tspan8 cells: reduction in proliferation. Further, it needs to be validated if Tspan8 interacts directly with the integrins molecules or has an indirect effect on the breast cancer cells through regulating integrins expression.

Tspan8 overexpression also led to an upregulated expression of α3β1 integrin in MDA-MB-231 cells. In BT-549 cells, we see an enhanced expression of α3β1 integrin at the surface level. α3β1 is known to play a significant role in invasion, metastasis and tumor progression in breast cancer. It is also overexpressed in different types of metastatic tumors. The previous study has shown the overexpression of α3β1 in breast cancer metastases as compared to the primary tumors. Further, on inhibiting the activity of α3 in MDA-MB-231 cells using a function-blocking anti-α3 antibody, invasion and migration were curtailed. It also inhibited MMP -9 gelatinase activity as demonstrated by zymography (Morini et al., 2000). It was also demonstrated to promote invasive and migratory capacities in MDA-MB-231 cells and nude mice model (Mitchell et al., 2010). In a recent study, it was revealed that the silencing of α3 expression by 70% in 4T1 murine mammary cells led to impaired proliferation and reduction in adhesion to laminin isoform (LM-332) (Zhou et al., 2014). Tspan8 is known to interact with α3β1 integrin in pancreatic adenocarcinoma (Zöller, 2009) It needs to be examined whether Tspan8 associates with α3β1 integrin in breast cancer or not.

In α4β1+ BT-549 cells, we see upregulation of α4β1 on Tspan8 overexpression; we also see significantly increased adhesion to fibronectin. MDA-MB-231 and MCF7 cells do not express α4 integrin and we do not see any effect on Tspan8 overexpression. α4β1 is a known receptor for fibronectin, a component of the extracellular matrix (Wu et al., 1995). Its role in breast cancer is not very well defined when compared with the studies of α3β1 integrin. A study by Barbara Garmy-Susini et al showed α4β1 to be a biomarker of tumor-draining lymph node lymphatic endothelium in patients with ductal breast carcinoma (Garmy-Susini et al., 2013). On activation by PI3Kα and VEGF-C, α4β1 promoted adhesion of metastatic tumor cells via VCAM-1. It helped in creating the metastatic niche and expansion of lymphatic endothelium in lymph nodes. Lymph node metastasis is associated with poor prognosis.

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In conclusion, it could be that β1 integrin is contributing to the enhanced proliferation and metabolic activities of MDA-MB-231 cells. We also see upregulation of α3β1 integrin in 231- Tspan8 cells and BT-Tspan8 cells. α3β1 integrin, a known associating partner of Tspan8, positively contributes to tumor progression as described earlier. Whether it associates with Tspan8 in breast cancer needs to be experimentally examined. The varied effects of Tspan8 in MDA-MB-231 and BT-549 cells could be attributed to interplay of different Tspan8 interacting partners in both the cells. For example, the association of Tspan8 and α6β4 integrin results in enhanced tumor motility in pancreatic and colon cancer (Yue et al., 2013). MDA-MB-231 cells are α6β4+ while BT-549 is not. This calls for an investigation into which partners Tspan8 is associating with in triple-negative breast cancer cells.

6.2.2 Tspan8 regulates integrins content in EVs Our results show that integrins are recruited to the surface of the EVs and Tspan8 regulates the integrins content in them. The integrins α1, α2, β1, β5, and α3β1 were found on the surface of breast cancer EVs at different levels. α3β1 integrin was upregulated in both 231-Tspan8 and BT-Tspan8 EVs. β1 subunit was also upregulated in 231-Tspan8, BT-Tspan8 and MCF7- Tspan8EVs. Various studies have shown that extracellular vesicles including EVs can communicate to neighboring tumor cells, contribute to primary tumor microenvironment (Becker et al., 2016) and establish a pre-metastatic niche through their cargo (Peinado et al., 2011). Recently, Hoshine et al demonstrated that exosomal integrins were able to direct the tumor cells to metastasize to specific organs in breast cancer (Hoshino et al., 2015). The EVs were uptaken by the target cells and created a pre-metastatic niche. Further proteomic profiling of the brain-, lung- and liver-tropic EVs revealed a strong correlation between the exosomal integrins and site of metastasis. The exosomal integrins α6β1 and α6β4 were related to lung metastasis while αVβ5 integrin was connected to liver metastasis. Therefore, exosomal integrins can act as a significant biomarker to predict future metastatic site in cancer. In another study, αVβ3 integrin was transferred from pancreatic tumor cells to non-tumorigenic and cancer cells via EVs (Singh et al., 2016). The recipient cells showed phenotypical changes such as an increase in cell adhesion and migration. In a similar study, EVs were seen to have a distinct ‘exosomal protein signature’ which could predict a high risk for metastasis to non-specific distant sites in melanoma patients (Peinado et al., 2012). Thus, through regulating integrin content in EVs, Tspan8 can also alter the tumor microenvironment, especially in triple-negative breast cancer subtype. However, the underlying mechanisms need to be further investigated.

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6.3 Tspan8 enhances release of EVs in breast cancer

Hypoxia forms a ubiquitous feature of tumor microenvironment (TME) during later stages of cancer (Ruan et al., 2009). The hypoxic tumors are also related to aggressive phenotypes and poor prognosis (Vaupel and Mayer, 2007). Due to the lack of oxygen supply to cancer and stromal cells, cancer cells adapt to the hypoxic TME via hypoxia-inducible factors (HIF). These are transcription factors which trigger cell signaling pathways leading to various outcomes like angiogenesis, tumor progression and metastasis. They also show less susceptibility to chemotherapy and radiation therapy (Yang et al., 2013). Under normoxic (21% O2) conditions the HIF factors are degraded by O2-, iron- and 2-oxoglutarate dependent prolyl hydroxylases. However, in hypoxia, these hydroxylases are inhibited which allows HIF factors to bind to their target genes and activate their transcription (Elvidge et al., 2006). Recent studies have shown the role of EVs and extracellular vesicles in promoting angiogenesis and metastasis under hypoxic tumor microenvironment (Park et al., 2010; Svensson et al., 2011).

In one study it was observed that breast cancer cells MCF7, MDA-MB-231 and SKBR3 released a significantly high number of EVs under moderate (1%) and severe (0.1%) hypoxic conditions when cultured in a hypoxic glovebox. The silencing of HIF-1α prevented this effect (King et al., 2012). Additionally, the hypoxic exosomal fraction contained enhanced levels of hypoxically regulated mir-210.

In our study, the EVs were derived under moderate hypoxic condition (1% O2). While we do see similar findings with MDA-MB-231 cells releasing a significantly higher number of EVs under hypoxia as compared to normoxia, MCF7 cells released only slightly more in hypoxic 2D conditions (insignificant). Moreover, under 3D hypoxic conditions, we do not see much difference between normoxic and hypoxic conditions with MDA-MB-231 cells. MCF7 cells released a slightly lower number of EVs in 3D hypoxic conditions. MDA-MB-361 cells released a significantly lower number of EVs under 2D hypoxia environment. However, all the Tspan8 expressing cells including MDA-MB-361 (endogenous Tspan8 expression) released a significantly higher number of EVs under 3D hypoxic environment when compared to the normoxia conditions. Additionally, the Tspan8 overexpressing cells release significantly more EVs under 3D hypoxic conditions, when compared to the parental cells in the same conditions. 231-Tspan8 cells showed a 6-fold increase in EVs/cell released count, BT-Tspan8 had ~2-fold while MCF7-Tsan8 had ~3-fold increase when compared to their wild-type counterparts. This implies that Tspan8 facilitates EVs release under hypoxic tumor microenvironment contributing

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Discussion to cell-cell communication and tumor progression. This further corroborates the previous findings of the lab in rat breast cancer model that Tspan8 enhances EVs release (manuscript submitted, Journal of Pathology).

It is interesting to note that Tspan8 EVs have been reported to play a role in activation of endothelial cells in rat adenocarcinoma (AS) cell model (Nazarenko et al., 2010). Endothelial cells (EC) could uptake EVs bearing Tspan8-CD49d complex. The cells showed enhanced proliferation and migration after uptake. Tspan8 was also able to regulate various angiogenesis- related factors in ECs such as CXCL5, MIF, chemokine receptor CCR1, VEGF and VEGFR2. MIF is known to boost angiogenic growth factor expression by stabilizing hypoxia-inducible factor-1α (Rendon et al., 2009).

Breast cancer cells in 3D environment tend to release smaller vesicles.

NTA analysis of the exosomal fraction revealed that most of the vesicles lied in the range of 50 - 250nm (~70 – 90 %) under 2D environment. There was also found a subpopulation of size <50nm in the range of ~3 – 16%. While the 3D derived EVs had slightly higher % of <50nm size subpopulation in the range of ~10-40 %. The 50-250nm sized 3D EVs were 52 – 87 % of the total population. Especially, MDA-MB-361 cells released a significantly higher amount of EVs of size <50 nm in 3D environment, both in normoxia and hypoxia when compared to the 2D derived EVs. These findings are consistent with previous work of the lab and collaboration group who found that gastric cancer cells in 3D environment released significantly smaller vesicles when compared to the 2D derived vesicles (Advanced Science, manuscript accepted). This indicates that the cells in 3D environment may tend to release smaller vesicles when compared to those cultured in 2D conditions.

6.4 EVs bound HER2 can be a potential biomarker in diagnosis and prognosis of breast cancer

EVs are present in body fluids such as blood, urine, breast milk, cerebrospinal fluid and saliva (Abels and Breakefield, 2016; Kalluri, 2016). The cargo they carry including DNA, microRNAs, and proteins, mimic the molecular profile of the cells of their origin (Stremersch et al., 2016). Hence, EVs as a means of liquid biopsy have a great potential for early detection of cancer, monitoring disease progression and therapy response. Various studies have implicated the significance of EVs in breast cancer diagnostics and even drug delivery potential (Green et al., 2015). miRNAs have been found to be enriched in EVs from metastatic breast cancer cells such

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Discussion as MDA-MB-231 over those from normal cells and non-metastatic cells (MCF7) (Melo et al., 2014). Amongst the miRNAs reported in breast cancers are miR-195 (Melo et al., 2014), miR-21 (Asaga et al., 2011) and miR-484/191 (Hu et al., 2012). Several clinical trials have been or are being conducted with EVs in various cancers including breast cancer (Conlan et al., 2017).

They also present an advantage over traditional fine needle aspiration (FNA) biopsies as they’re non-invasive. FNAs provide small sample size and don’t depict the heterogenic nature of breast cancer at primary and metastatic sites (Esposito et al., 2016). Further, to inspect the therapeutic treatment of the patient over time, it is not possible to do FNAs repeatedly and especially, immediately after surgery (Feller and Lewitzky, 2016). Liquid biopsies with biofluids, on the other hand, are minimally invasive and cost-effective alternative to conventional biopsies. They can help not only in early detection of cancer but also in monitoring disease progression and treatment surveillance. There are also serum HER2 ECD tests available which are not widely used at clinical level (Lam et al., 2012). This test was developed when it was found that several HER2 negative patients (as determined by traditional methods) responded well to HER2 targeted therapy. The HER2 ECD is detected using ELISA which need improvement in terms of specificity and sensitivity of the assay. Interestingly, using ELISA-based detection, we observed EV-HER2 levels correlated with the HER2 score of the breast cancer patients better than the serum-HER2 levels, highlighting the potential of EVs as diagnostic/prognostic tools.

The EVs also pose advantages over circulating tumor cells (CTC) and other extracellular vesicles such as microvesicles. Because EVs are comparatively homogeneous in their physical attributes and can be identified by specific markers (Halvaei et al., 2017). Moreover, the CTCs do not have well-established isolation and characterization techniques (Alix-Panabières and Pantel, 2013).

However, the current methods for isolation and characterization of EVs still need standardization for clinical application (Halvaei et al., 2017). Ultracentrifugation is the widely used technique for EVs isolation, but it doesn’t give a pure fraction of EVs. It is cost-effective but very time consuming (Greening et al., 2015). Therefore, a novel technique is required for high- throughput detection of EVs at the clinical level.

We see that WGM laser developed by Mr. Sentayehu Wondimu (in collaboration with Prof. Dr. Christian Kroos, Karlsruhe Institute of Technology), were able to specifically detect EV-HER2 in both cells and breast cancer patients’ sera. It recognized only EV-HER2 and not recombinant HER2 protein proving its specificity. It required only 15µl of the EVs sample (from breast cancer patients) for detection of HER2 in breast cancer patient samples. The only drawback was: it could not distinguish HER2 expression between strongly positive MDA-MB-361 EVs and weakly positive MDA-MB-231 EVs. Conclusively, WGM lasers hold a great promise for detection of EVs

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in clinical applications. Their major advantage is low starting volume of sample, high sensitivity and cost efficiency. However, it still needs to be fine-tuned for distinguishing low and high levels of exosomal HER2 in serum.

6.5 Conclusion and Future outlook

To sum up, Tspan8 significantly regulates physiological processes like cell behaviour, proliferation and adhesion in triple-negative breast cancer cells MDA-MB-231 and BT-549 (Fig. 45). It does not regulate these processes significantly in Luminal A type MCF7 cells. And the effect seen is varying which could be attributed to heterogeneity in breast cancer. Previous findings of lab have shown that Tspan8 promotes mesenchymal to epithelial transition in rat breast cancer. We observed BT-Tspan8 cells showed epithelial like morphology and downregulation of N-Cadherin expression. This suggests Tspan8 may have some role in partial MET in BT-549 cells which needs to be further examined.

Enhanced EVs release Enhanced EVs release especially under 3D under 3D hypoxic hypoxic conditions conditions

Tspan8 regulates EV content: Tspan8 regulates EV content: • Upregulation of α3β1 • Upregulation of α3β1 expression expression • Downregulation of CD9, • Upregulation of CD63 CD81 expression expression

• Increased proliferation • Reduced proliferation • Increased attachment to BME • Decreased attachment to BME • Upregulation of intergins αV, α3β1 • Upregulation of integrins α4β1 expression expression • Upregulation CD63 expression • Upregulation of tetraspanins CD63 expression

Cells MDA-MB-231 BT-549 Triple-Negative Breast Cancer EVs

Figure 47: Diverse effects of Tspan8 in triple negative breast cancer. Tspan8 also boosts EV release in breast cancer cells. But how the EVs impact the recipient cells needs to be further examined.

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We also discovered that Tspan8 regulates the expression of integrins in breast cancer cells. It upregulates the expression of αV & α3β1 integrins in MDA-MB-231 and α4β1integrins in BT- 549 cells. It also increases the expression of α3β1in EVs derived from both the cell lines. Thus, Tspan8 modulates the content of EVs derived from breast cancer cells. Tspan8 is also known to interact with α3β1integrin impacting cell motility in pancreatic adenocarcinoma. But we do not know if Tspan8 is interacting with integrins directly or indirectly in breast cancer cells which needs to be further investigated. Furthermore, Tspan8 boosted the release of EVs in breast cancer cells. But it needs to be investigated whether the EVs bring about phenotypic changes in the recipient cells and affect their functions. This study shows potential function of Tspan8 in breast cancer and warrants an intensive investigation on function of Tspan8 in breast cancer.

Lastly, we found that EV-HER2 levels correlated better with the HER2 score as compared to serum-HER2 levels, indicating diagnostic and prognostic potential of EVs. Further, for detection of EV-HER2, a novel tool was employed. WGM lasers were able to detect EVs bound HER2 exclusively. However, the sensitivity was so high that it couldn’t distinguish between high and low levels of HER2 in EVs derived from MDA-MB-361 cells (strong HER2 expression) and MDA- MB-231 (weak HER2 expression). The detection of HER2 in EVs from breast cancer patients’ sera using WGM lasers correlated well with the HER2 levels determined by ELISA method. Thus, WGM lasers pose to be potential tool in label free detection of EVs and as a novel approach in liquid biopsy.

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Appendix: Supplementary figures

7 Appendix

7.1 Supplementary figures MDA-MB-361 Neg.Ctrl MDA-MB-361 MFI: 15292 CD63 MFI: 60409 CD9 MFI: 14387 CD81 MFI: 14167 Tspan8

CELLS Count 97% 98% 97% 83%

Fluorescence intensity

Neg.Ctrl MDA-MB-361

MFI: 3748 MFI: 10 451 MFI: 3867 MFI: 381 CD63 CD9 CD81 Tspan8

2D 2D EVs Count 99% 99% 99% 83%

Fluorescence intensity

Neg.Ctrl MDA-MB-361

MFI: 1075 MFI: 961 CD9 MFI: 1710 CD81 MFI: 134 Tspan8 CD63

3D 3D EVs

Count 94% 93% 94% 24%

Fluorescence intensity Supplementary figure1: Characterization of EVs isolated from MDA-MB-361 in 2D and 3D environments using Flow Cytometry. EVs were coated onto 4µm latex beads, incubated with appropriate antibodies and measured with BD-AccuriTM C6 Flow Cytometer. Beads incubated with only secondary antibody were used as negative control. Upper lane shows expression of CD63, CD9, CD81 and Tspan8 detected on the surface of MDA-MB-361 cells for comparison. The figure is representative of 2 biological replicates for cells and for EVs the figure is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

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Appendix: Supplementary figures

MDA-MB-231 vs 231-Tspan8

Neg.Ctrl MDA-MB-231 231-Tspan8

MFI: 6037 CD63 MFI: 20409 CD9 MFI: 14278 CD81 MFI: ´525 Tspan8 MFI: 9476 MFI: 9625 MFI: 3965 MFI: 2072

91% 97% 95% 20%

CELLS Count 97% 96% 96% 82%

Fluorescence intensity Neg.Ctrl MDA-MB-231 231-Tspan8

MFI: 412 CD63 MFI: 149 CD9 MFI: 466 CD81 MFI: 19 Tspan8

MFI: 248 MFI: 73 MFI: 136 MFI: 24

57% 68% 30% 0%

Count 2D 2D EVs 44% 11% 25% 0%

Fluorescence intensity

Neg.Ctrl MDA-MB-231 231-Tspan8

MFI: 353 CD63 MFI: 268 CD9 MFI: 120 CD81 MFI: 30 Tspan8 MFI: 368 MFI: 178 MFI: 70 MFI: 45

66% 54% 21% 0% Count 3D 3D EVs 67% 36% 8% 0%

Fluorescence intensity

Supplementary figure2: Characterization of EVs isolated from MDA-MB-231 and 231-Tspan8 cells in 2D and 3D environments using Flow Cytometry. EVs were coated onto 4µm latex beads, incubated with appropriate antibodies and measured with BD-AccuriTM C6 Flow Cytometer. EVs coated beads incubated with only secondary antibody were used as negative control. Upper lane shows expression of CD63, CD9, CD81 and Tspan8 detected on the surface of MDA-MB-231 and 231-Tspan8 cells for comparison. The figure is representative of 2 technical replicates for cells and for EVs the figure is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

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Appendix: Supplementary figures

BT-549 vs BT-Tspan8

Neg.Ctrl BT-549 BT-Tspan8 MFI: 10190 CD63 MFI: 11999 CD9 MFI:5228 CD81 MFI:0 Tspan8 MFI: 16986 MFI: 6450 MFI: 6409 MFI: 2275

96% 93% 91% 0% CELLS Count 94% 87% 94% 51%

Fluorescence intensity

Neg.Ctrl BT-549 BT-Tspan8

MFI: 1808 CD63 MFI: 489 CD9 MFI: 1395 CD81 MFI: 11 Tspan8 MFI: 2805 MFI: 311 MFI: 1203 MFI: 10

76% 96% 0%

99% Count 2D 2D EVs 99% 60% 96% 0%

Fluorescence intensity

Neg.Ctrl BT-549 BT-Tspan8

MFI: 905 CD63 MFI: 69 CD9 MFI: 185 CD81 MFI: 11 Tspan8 MFI: 1228 MFI: 28 MFI: 172 MFI: 10

89% 9% 34% 0%

3D 3D EVs Count 96% 4% 35% 0%

Fluorescence intensity Supplementary figure 3: Characterization of EVs isolated from BT-549 and BT-Tspan8 cells in 2D and 3D environments using Flow Cytometry. EVs were coated onto 4µm latex beads, incubated with appropriate antibodies and measured with BD-AccuriTM C6 Flow Cytometer. EVs coated beads incubated with only secondary antibody were used as negative control. Upper lane shows expression of CD63, CD9, CD81 and Tspan8 detected on the surface of BT-549 and BT-Tspan8 cells for comparison. The figure is representative of 2 biological replicates for cells and for EVs the figure is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

130

Appendix: Supplementary figures

MCF7 vs MCF7-Tspan8

Neg.Ctrl MCF7 MCF7-Tspan8

MFI: 9462 CD63 MFI: 89985 CD9 MFI: 14181 CD81 MFI: 8 Tspan8 MFI: 18128 MFI: 112606 MFI: 19463 MFI: 46386

97% 97% 97% 0%

Count CELLS 98% 98% 97% 96%

Fluorescence intensity

Neg.Ctrl MCF7 MCF7-Tspan8

MFI: 1313 CD63 MFI: 2729 CD9 MFI: 2631 CD81 MFI: 69 Tspan8 MFI: 1581 MFI: 3029 MFI: 3268 MFI: 2305

95% 97% 96%

Count 2D 2D EVs 97% 97% 97% 97%

Fluorescence intensity Neg.Ctrl MCF7 MCF7-Tspan8

MFI: 263 CD63 MFI: 926 CD9 MFI: 322 CD81 MFI: 33 Tspan8 MFI: 291 MFI: 936 MFI: 534 MFI: 1223

45% 95% 60%

Count 3D 3D EVs 43% 94% 83% 92%

Fluorescence intensity

Supplementary figure 4: Characterization of EVs isolated from BT-549 and BT-Tspan8 cells in 2D and 3D environments using Flow Cytometry. EVs were coated onto 4µm latex beads, incubated with appropriate antibodies and measured with BD-AccuriTM C6 Flow Cytometer. EVs coated beads incubated with only secondary antibody were used as negative control. Upper lane shows expression of CD63, CD9, CD81 and Tspan8 detected on the surface of MCF7 and MCF7-Tspan8 cells for comparison. The figure is representative of 2 technical replicates for cells and for EVs the figure is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

131

Appendix: Supplementary figures

MDA-MB-361

IgG control MDA-MB-361

MFI: 777 ITG-α1 MFI: 23618 ITGα2 MFI: 35168 ITGα3 MFI: 2088 ITGαV

32% 98% 96% 55%

Counts CELLS MFI: 37327 ITGβ1 MFI: 7531 ITGβ5 MFI: 31414 ITGα3β1 MFI: 0 ITGαVβ3

96% 98% 96% 0%

Fluorescence intensity IgG control MDA-MB-361

MFI: 0 ITGα1 MFI: 1304 ITGα2 MFI: 382 ITGα3 MFI: 38 ITGαV

0% 98% 34% 0%

Counts

EVs (2D) EVs MFI: 1858 ITGβ1 MFI: 376 ITGβ5 MFI: 8412 ITGα3β1 MFI: 0 ITGαVβ3

94% 64% 96% 0%

Fluorescence intensity Supplementary figure 5: Examining recruitment of integrins to the MDA-MB-361 derived EVs via beads-assisted flow cytometry as described earlier. The expression of integrin in cells is shown in upper panel for comparison while the lower panel shows it in Evs. The figure is representative of 2 technical replicates for ‘Cells‘. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

132

Appendix: Supplementary figures

MDA-MB-231 vs 231-Tspan8 IgG control MDA-MB-231 MDA-MB-231 IgG control 231-Tpan8 231-Tspan8 MFI: 6717 MFI: 10236 MFI: 39211 ITGα1 ITGα2 ITGα3 MFI: 2541 ITGαV MFI: 7383 MFI: 8345 MFI: 152728 MFI: 8769

35% 95% 97% 92%

95% 96% 91% 92%

Counts

CELLS MFI: 71428 ITGβ1 MFI: 4923 ITGβ5 MFI: 21031 ITGα3β1 MFI: 2341 ITGαVβ3 MFI: 103444 MFI: 5765 MFI: 51139 MFI: 1621

90% 94% 90% 87% 94% 97% 92% 89%

Fluorescence intensity

IgG control MDA-MB-231 MDA-MB-231 IgG control 231-Tspan8 231-Tspan8

MFI: 0 ITGα1 MFI: 596 ITGα2 MFI: 115 ITGα3 MFI: 132 ITGαV MFI: 0 MFI: 534 MFI: 220 MFI: 215

counts

11% 0% 8% 78% 0% 79% 18% 16%

Counts EVs (2D) EVs MFI: 295 ITGβ1 MFI: 85 ITGβ5 MFI: 2379 ITGα3β1 MFI: 80 ITGαVβ3 MFI: 483 MFI: 99 MFI: 4539 MFI: 8

34% 5% 95% 3% 94% 51% 10% 0%

Fluorescence intensity

Supplementary figure 6: Examining recruitment of integrins to the MDA-MB-231Tspan8+/- cells derived EVs via beads-assisted flow cytometry as described earlier. The expression of integrin in cells is shown in upper panel for comparison while the lower panel shows it in Evs. The figure is representative of 2 technical replicates for ‘Cells‘. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

133

Appendix: Supplementary figures

BT-549 vs BT-Tspan8

IgG control BT-549 BT-549 IgG control BT-Tspan8 BT-Tspan8 MFI: 42 MFI: 52825 MFI: 3273 ITGα1 ITGα2 ITGα3 MFI: 2094 ITGαV MFI: 1248 MFI: 50 MFI: 66216 MFI: 1822

0% 6% 95%

0% 0%

20% 94% 4%

Counts CELLS MFI: 4038 MFI: 65505 MFI: 58696 ITGβ1 ITGβ5 ITGα3β1 MFI: 3866 ITGαVβ3 MFI: 70326 MFI: 5468 MFI: 67230 MFI: 3674

95% 65% 96% 70% 83% 94% 94% 67%

Fluorescence intensity

IgG control BT-549 BT-549

IgG control BT-Tspan8 BT-Tspan8

MFI: 0 ITGα1 MFI: 8 ITGα2 MFI: 18 ITGα3 MFI: 28 ITGαV MFI: 0 MFI: 7 MFI: 214 MFI: 64

0% 0% 0% 0%

0% 0% 17% 0%

Counts MFI: 829 MFI: 0 EVs (2D) EVs MFI: 89 ITGβ1 MFI: 33 ITGβ5 ITGα3β1 ITGαVβ3 MFI: 399 MFI: 30 FL2MFI:- H2066 FL4MFI:-H 10

4% 0% 50% 0% 35% 0% 87% 0%

Fluorescence intensity Supplementary figure 7: Examining recruitment of integrins to the BT-549 Tspan8+/- cells derived EVs via beads-assisted flow cytometry as described earlier. The expression of integrin in cells is shown in upper panel for comparison while the lower panel shows it in Evs. The figure is representative of 2 technical replicates for ‘Cells‘. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

134

Appendix: Supplementary figures

MCF7 vs MCF7-Tspan8 IgG control MCF7 MCF7 IgG control MCF7-Tspan8 MCF7-Tspan8 MFI: 997 MFI: 15585 ITGα1 ITGα2 MFI: 16298 ITGα3 MFI: 2662 ITGαV MFI: 0 MFI: 13442 MFI: 13065 MFI: 1178

8% 90% 96% 68%68% 0% 93% 97% 8%

Counts CELLS MFI: 8795 MFI: 15306 ITGβ1 ITGβ5 MFI: 16756 ITGα3β1 MFI: 2938 ITGαVβ3 MFI: 6512 MFI: 11495 MFI: 15407 MFI: 0

94% 90% 97% 89% 95% 93% 97% 13%

Fluorescence intensity

IgG control MCF7 MCF7 IgG control MCF7-Tspan8 MCF7-Tspan8

MFI: 7 MFI: 0 ITGα1 MFI: 348 ITGα2 ITGα3 MFI: 33 ITGαV MFI: 58 MFI: 117 MFI: 0 MFI: 405

0% 59% 0% 0% 0% 0%

0% 68%

Counts EVs (2D) EVs MFI: 0 MFI: 37 ITGβ1 MFI: 235 ITGβ5 MFI: 1245 ITGα3β1 ITGαVβ3 MFI: 203 MFI: 257 MFI: 1051 MFI: 15

0% 41% 81% 0%

5% 49% 71% 0%

Fluorescence intensity Supplementary figure 8: Examining recruitment of integrins to the MCF7Tspan8+/- cells derived EVs via beads-assisted flow cytometry as described earlier. The expression of integrin in cells is shown in upper panel for comparison while the lower panel shows it in Evs. The figure is representative of 2 technical replicates for ‘Cells‘. Data for EVs is representative of single experiment due to tedious method of EVs isolation and scarcity of sample.

135

Appendix: Supplementary figures

Supplementary figure 9: (A) TEM image of EVs isolated from breast cancer patients‘s serum. Scale Bar = 200nm. (B) NTA Analysis of the EVs derived from breast cancer patients‘sera depicting the mode size in nm.

136

Appendix: References

7.2 References

Abels, E.R., and Breakefield, X.O. (2016). Introduction to Extracellular Vesicles: Biogenesis, RNA Cargo Selection, Content, Release, and Uptake. Cell. Mol. Neurobiol. 36, 301–312.

Akiel, M.A., Santhekadur, P.K., Mendoza, R.G., Siddiq, A., Fisher, P.B., and Sarkar, D. (2016). Tetraspanin 8 (TSPAN8) mediates AEG-1-induced invasion and metastasis in hepatocellular carcinoma (HCC) cells. FEBS Lett. 590, 2700–2708.

Alix-Panabières, C., and Pantel, K. (2013). Circulating tumor cells: liquid biopsy of cancer. Clin. Chem. 59, 110–118.

Andreu, Z., and Yáñez-Mó, M. (2014). Tetraspanins in Extracellular Vesicle Formation and Function. Front. Immunol. 5.

Arteaga, C.L., Sliwkowski, M.X., Osborne, C.K., Perez, E.A., Puglisi, F., and Gianni, L. (2011). Treatment of HER2-positive breast cancer: current status and future perspectives. Nat. Rev. Clin. Oncol. 9, 16–32.

Asaga, S., Kuo, C., Nguyen, T., Terpenning, M., Giuliano, A.E., and Hoon, D.S.B. (2011). Direct Serum Assay for MicroRNA-21 Concentrations in Early and Advanced Breast Cancer. Clin. Chem. 57, 84–91.

Azmi, A.S., Bao, B., and Sarkar, F.H. (2013). Exosomes in Cancer Development, Metastasis and Drug Resistance: A Comprehensive Review. Cancer Metastasis Rev. 32.

Baaske, M.D., Foreman, M.R., and Vollmer, F. (2014). Single-molecule nucleic acid interactions monitored on a label-free microcavity biosensor platform. Nat. Nanotechnol. 9, 933–939.

Baietti, M.F., Zhang, Z., Mortier, E., Melchior, A., Degeest, G., Geeraerts, A., Ivarsson, Y., Depoortere, F., Coomans, C., Vermeiren, E., et al. (2012). Syndecan-syntenin-ALIX regulates the biogenesis of exosomes. Nat. Cell Biol. 14, 677–685.

Balaj, L., Lessard, R., Dai, L., Cho, Y.-J., Pomeroy, S.L., Breakefield, X.O., and Skog, J. (2011). Tumour microvesicles contain retrotransposon elements and amplified oncogene sequences. Nat. Commun. 2, 180. van Balkom, B.W.M., de Jong, O.G., Smits, M., Brummelman, J., den Ouden, K., de Bree, P.M., van Eijndhoven, M.A.J., Pegtel, D.M., Stoorvogel, W., Würdinger, T., et al. (2013). Endothelial cells require miR-214 to secrete exosomes that suppress senescence and induce angiogenesis in human and mouse endothelial cells. Blood 121, 3997–4006, S1-15.

Becker, A., Thakur, B.K., Weiss, J.M., Kim, H.S., Peinado, H., and Lyden, D. (2016). Extracellular vesicles in cancer: cell-to-cell mediators of metastasis. Cancer Cell 30, 836–848.

Befani, C., and Liakos, P. (2017). Hypoxia upregulates integrin gene expression in microvascular endothelial cells and promotes their migration and capillary-like tube formation. Cell Biol. Int. 41, 769–778.

Beit-Yannai, E., Tabak, S., and Stamer, W.D. (2018). Physical exosome:exosome interactions. J. Cell. Mol. Med. 22, 2001–2006.

137

Appendix: References

Belli, C., Trapani, D., Viale, G., D’Amico, P., Duso, B.A., Della Vigna, P., Orsi, F., and Curigliano, G. (2018). Targeting the microenvironment in solid tumors. Cancer Treat. Rev. 65, 22–32.

Berthier-Vergnes, O., El Kharbili, M., de la Fouchardière, A., Pointecouteau, T., Verrando, P., Wierinckx, A., Lachuer, J., Le Naour, F., and Lamartine, J. (2011). Gene expression profiles of human melanoma cells with different invasive potential reveal TSPAN8 as a novel mediator of invasion. Br. J. Cancer 104, 155–165.

Bhowmick, N.A., Zent, R., Ghiassi, M., McDonnell, M., and Moses, H.L. (2001). Integrin β1 Signaling Is Necessary for Transforming Growth Factor-β Activation of p38MAPK and Epithelial Plasticity. J. Biol. Chem. 276, 46707–46713.

Biasutto, L., Chiechi, A., Couch, R., Liotta, L.A., and Espina, V. (2013). Retinal pigment epithelium (RPE) exosomes contain signaling phosphoproteins affected by oxidative stress. Exp. Cell Res. 319, 2113–2123.

Bienstock, R.J., and Barrett, J.C. (2001). KAI1, a prostate metastasis suppressor: prediction of solvated structure and interactions with binding partners; integrins, cadherins, and cell-surface receptor proteins. Mol. Carcinog. 32, 139–153.

Bonifacino, J.S., and Traub, L.M. (2003). Signals for sorting of transmembrane proteins to endosomes and lysosomes. Annu. Rev. Biochem. 72, 395–447.

Brakebusch, C., Bouvard, D., Stanchi, F., Sakai, T., and Fässler, R. (2002). Integrins in invasive growth. J. Clin. Invest. 109, 999–1006.

Castelló-Cros, R., Khan, D.R., Simons, J., Valianou, M., and Cukierman, E. (2009). Staged stromal extracellular 3D matrices differentially regulate breast cancer cell responses through PI3K and beta1-integrins. BMC Cancer 9, 94.

Charrin, S., le Naour, F., Silvie, O., Milhiet, P.-E., Boucheix, C., and Rubinstein, E. (2009). Lateral organization of membrane proteins: tetraspanins spin their web. Biochem. J. 420, 133–154.

Cho, N. (2016). Molecular subtypes and imaging phenotypes of breast cancer. Ultrason. Seoul Korea 35, 281–288.

Chow, A., Zhou, W., Liu, L., Fong, M.Y., Champer, J., Van Haute, D., Chin, A.R., Ren, X., Gugiu, B.G., Meng, Z., et al. (2014). Macrophage immunomodulation by breast cancer-derived exosomes requires Toll-like receptor 2-mediated activation of NF-κB. Sci. Rep. 4.

Ciravolo, V., Huber, V., Ghedini, G.C., Venturelli, E., Bianchi, F., Campiglio, M., Morelli, D., Villa, A., Della Mina, P., Menard, S., et al. (2012). Potential role of HER2-overexpressing exosomes in countering trastuzumab-based therapy. J. Cell. Physiol. 227, 658–667.

Citri, A., and Yarden, Y. (2006). EGF-ERBB signalling: towards the systems level. Nat. Rev. Mol. Cell Biol. 7, 505–516.

Colombo, M., Moita, C., van Niel, G., Kowal, J., Vigneron, J., Benaroch, P., Manel, N., Moita, L.F., Théry, C., and Raposo, G. (2013). Analysis of ESCRT functions in exosome biogenesis, composition and secretion highlights the heterogeneity of extracellular vesicles. J. Cell Sci. 126, 5553–5565.

Colombo, M., Raposo, G., and Théry, C. (2014). Biogenesis, secretion, and intercellular interactions of exosomes and other extracellular vesicles. Annu. Rev. Cell Dev. Biol. 30, 255–289.

138

Appendix: References

Conlan, R.S., Pisano, S., Oliveira, M.I., Ferrari, M., and Mendes Pinto, I. (2017). Exosomes as Reconfigurable Therapeutic Systems. Trends Mol. Med. 23, 636–650.

Costa-Silva, B., Aiello, N.M., Ocean, A.J., Singh, S., Zhang, H., Thakur, B.K., Becker, A., Hoshino, A., Mark, M.T., Molina, H., et al. (2015). Pancreatic cancer exosomes initiate pre-metastatic niche formation in the liver. Nat. Cell Biol. 17, 816–826.

Crescitelli, R., Lässer, C., Szabó, T.G., Kittel, A., Eldh, M., Dianzani, I., Buzás, E.I., and Lötvall, J. (2013). Distinct RNA profiles in subpopulations of extracellular vesicles: apoptotic bodies, microvesicles and exosomes. J. Extracell. Vesicles 2.

Cress, A.E., Rabinovitz, I., Zhu, W., and Nagle, R.B. (1995). The alpha 6 beta 1 and alpha 6 beta 4 integrins in human prostate cancer progression. Cancer Metastasis Rev. 14, 219–228.

Dajee, M., Lazarov, M., Zhang, J.Y., Cai, T., Green, C.L., Russell, A.J., Marinkovich, M.P., Tao, S., Lin, Q., Kubo, Y., et al. (2003). NF-kappaB blockade and oncogenic Ras trigger invasive human epidermal neoplasia. Nature 421, 639–643.

De, P., Hasmann, M., and Leyland-Jones, B. (2013). Molecular determinants of trastuzumab efficacy: What is their clinical relevance? Cancer Treat. Rev. 39, 925–934.

De Toro, J., Herschlik, L., Waldner, C., and Mongini, C. (2015). Emerging roles of exosomes in normal and pathological conditions: new insights for diagnosis and therapeutic applications. Front. Immunol. 6, 203.

Desgrosellier, J.S., and Cheresh, D.A. (2010). Integrins in cancer: biological implications and therapeutic opportunities. Nat. Rev. Cancer 10, 9–22.

Desrochers, L.M., Antonyak, M.A., and Cerione, R.A. (2016). Extracellular Vesicles: Satellites of Information Transfer in Cancer and Stem Cell Biology. Dev. Cell 37, 301–309.

Detchokul, S., Williams, E.D., Parker, M.W., and Frauman, A.G. (2014). Tetraspanins as regulators of the tumour microenvironment: implications for metastasis and therapeutic strategies. Br. J. Pharmacol. 171, 5462–5490.

Drake, C.G., Jaffee, E., and Pardoll, D.M. (2006). Mechanisms of immune evasion by tumors. Adv. Immunol. 90, 51–81.

Eichelser, C., Stückrath, I., Müller, V., Milde-Langosch, K., Wikman, H., Pantel, K., and Schwarzenbach, H. (2014). Increased serum levels of circulating exosomal microRNA-373 in receptor-negative breast cancer patients. Oncotarget 5, 9650–9663.

EL Andaloussi, S., Mäger, I., Breakefield, X.O., and Wood, M.J.A. (2013). Extracellular vesicles: biology and emerging therapeutic opportunities. Nat. Rev. Drug Discov. 12, 347–357.

Eldh, M., Ekström, K., Valadi, H., Sjöstrand, M., Olsson, B., Jernås, M., and Lötvall, J. (2010). Exosomes communicate protective messages during oxidative stress; possible role of exosomal shuttle RNA. PloS One 5, e15353.

Elvidge, G.P., Glenny, L., Appelhoff, R.J., Ratcliffe, P.J., Ragoussis, J., and Gleadle, J.M. (2006). Concordant regulation of gene expression by hypoxia and 2-oxoglutarate-dependent dioxygenase inhibition: the role of HIF-1alpha, HIF-2alpha, and other pathways. J. Biol. Chem. 281, 15215–15226.

139

Appendix: References

Escola, J.M., Kleijmeer, M.J., Stoorvogel, W., Griffith, J.M., Yoshie, O., and Geuze, H.J. (1998). Selective enrichment of tetraspan proteins on the internal vesicles of multivesicular endosomes and on exosomes secreted by human B-lymphocytes. J. Biol. Chem. 273, 20121–20127.

Esposito, A., Criscitiello, C., Locatelli, M., Milano, M., and Curigliano, G. (2016). Liquid biopsies for solid tumors: Understanding tumor heterogeneity and real time monitoring of early resistance to targeted therapies. Pharmacol. Ther. 157, 120–124.

Fan, X., White, I.M., Shopova, S.I., Zhu, H., Suter, J.D., and Sun, Y. (2008). Sensitive optical biosensors for unlabeled targets: a review. Anal. Chim. Acta 620, 8–26.

Feller, S.M., and Lewitzky, M. (2016). Hunting for the ultimate liquid cancer biopsy - let the TEP dance begin. Cell Commun. Signal. CCS 14.

Fu, N.Y., Rios, A.C., Pal, B., Law, C.W., Jamieson, P., Liu, R., Vaillant, F., Jackling, F., Liu, K.H., Smyth, G.K., et al. (2017). Identification of quiescent and spatially restricted mammary stem cells that are hormone responsive. Nat. Cell Biol. 19, 164–176.

Gao, L., Wang, L., Dai, T., Jin, K., Zhang, Z., Wang, S., Xie, F., Fang, P., Yang, B., Huang, H., et al. (2018). Tumor-derived exosomes antagonize innate antiviral immunity. Nat. Immunol. 19, 233– 245.

Garcia-Contreras, M., Brooks, R.W., Boccuzzi, L., Robbins, P.D., and Ricordi, C. (2017). Exosomes as biomarkers and therapeutic tools for type 1 diabetes mellitus. Eur. Rev. Med. Pharmacol. Sci. 21, 2940–2956.

Garmy-Susini, B., Avraamides, C.J., Desgrosellier, J.S., Schmid, M.C., Foubert, P., Ellies, L.G., Lowy, A.M., Blair, S.L., Vandenberg, S.R., Datnow, B., et al. (2013). PI3Kα activates integrin α4β1 to establish a metastatic niche in lymph nodes. Proc. Natl. Acad. Sci. U. S. A. 110, 9042–9047.

Géminard, C., De Gassart, A., Blanc, L., and Vidal, M. (2004). Degradation of AP2 during reticulocyte maturation enhances binding of hsc70 and Alix to a common site on TFR for sorting into exosomes. Traffic Cph. Den. 5, 181–193.

Gesierich, S., Berezovskiy, I., Ryschich, E., and Zöller, M. (2006). Systemic induction of the angiogenesis switch by the tetraspanin D6.1A/CO-029. Cancer Res. 66, 7083–7094.

Ghossoub, R., Lembo, F., Rubio, A., Gaillard, C.B., Bouchet, J., Vitale, N., Slavík, J., Machala, M., and Zimmermann, P. (2014). Syntenin-ALIX exosome biogenesis and budding into multivesicular bodies are controlled by ARF6 and PLD2. Nat. Commun. 5, 3477.

Gohring, J.T., Dale, P.S., and Fan, X. (2010). Detection of HER2 breast cancer biomarker using the opto-fluidic ring resonator biosensor. Sens. Actuators B Chem. 146, 226–230.

Gratkowski, H., Lear, J.D., and DeGrado, W.F. (2001). Polar side chains drive the association of model transmembrane peptides. Proc. Natl. Acad. Sci. U. S. A. 98, 880–885.

Greco, C., Bralet, M.-P., Ailane, N., Dubart-Kupperschmitt, A., Rubinstein, E., Le Naour, F., and Boucheix, C. (2010). E-cadherin/p120-catenin and tetraspanin Co-029 cooperate for cell motility control in human colon carcinoma. Cancer Res. 70, 7674–7683.

Green, T.M., Alpaugh, M.L., Barsky, S.H., Rappa, G., and Lorico, A. (2015). Breast Cancer-Derived Extracellular Vesicles: Characterization and Contribution to the Metastatic Phenotype. BioMed Res. Int. 2015, 634865.

140

Appendix: References

Greening, D.W., Xu, R., Ji, H., Tauro, B.J., and Simpson, R.J. (2015). A protocol for exosome isolation and characterization: evaluation of ultracentrifugation, density-gradient separation, and immunoaffinity capture methods. Methods Mol. Biol. Clifton NJ 1295, 179–209.

Guo, Q., Xia, B., Zhang, F., Richardson, M.M., Li, M., Zhang, J.S., Chen, F., and Zhang, X.A. (2012). Tetraspanin CO-029 Inhibits Colorectal Cancer Cell Movement by Deregulating Cell-Matrix and Cell-Cell Adhesions. PLOS ONE 7, e38464.

György, B., Szabó, T.G., Pásztói, M., Pál, Z., Misják, P., Aradi, B., László, V., Pállinger, É., Pap, E., Kittel, Á., et al. (2011). Membrane vesicles, current state-of-the-art: emerging role of extracellular vesicles. Cell. Mol. Life Sci. 68, 2667–2688.

György, B., Hung, M.E., Breakefield, X.O., and Leonard, J.N. (2015). Therapeutic Applications of Extracellular Vesicles: Clinical Promise and Open Questions. Annu. Rev. Pharmacol. Toxicol. 55, 439–464.

Halvaei, S., Daryani, S., Eslami-S, Z., Samadi, T., Jafarbeik-Iravani, N., Bakhshayesh, T.O., Majidzadeh-A, K., and Esmaeili, R. (2017). Exosomes in Cancer Liquid Biopsy: A Focus on Breast Cancer. Mol. Ther. Nucleic Acids 10, 131–141.

Hamidi, H., and Ivaska, J. (2018). Every step of the way: integrins in cancer progression and metastasis. Nat. Rev. Cancer 18, 533–548.

Hawari, F.I., Rouhani, F.N., Cui, X., Yu, Z.-X., Buckley, C., Kaler, M., and Levine, S.J. (2004). Release of full-length 55-kDa TNF receptor 1 in exosome-like vesicles: a mechanism for generation of soluble cytokine receptors. Proc. Natl. Acad. Sci. U. S. A. 101, 1297–1302.

He, L., Özdemir, Ş.K., Zhu, J., Kim, W., and Yang, L. (2011). Detecting single viruses and nanoparticles using whispering gallery microlasers. Nat. Nanotechnol. 6, 428–432.

Hedlund, M., Nagaeva, O., Kargl, D., Baranov, V., and Mincheva-Nilsson, L. (2011). Thermal- and oxidative stress causes enhanced release of NKG2D ligand-bearing immunosuppressive exosomes in leukemia/lymphoma T and B cells. PloS One 6, e16899.

Heiler, S., Wang, Z., and Zöller, M. (2016). Pancreatic cancer stem cell markers and exosomes - the incentive push. World J. Gastroenterol. 22, 5971–6007.

Hemler, M.E. (2005). Tetraspanin functions and associated microdomains. Nat. Rev. Mol. Cell Biol. 6, 801–811.

Hemler, M.E. (2014). Tetraspanin proteins promote multiple cancer stages. Nat. Rev. Cancer 14, 49–60.

Henne, W.M., Buchkovich, N.J., and Emr, S.D. (2011). The ESCRT pathway. Dev. Cell 21, 77–91.

Hervent, A.-S., and De Keulenaer, G.W. (2012). Molecular mechanisms of cardiotoxicity induced by ErbB receptor inhibitor cancer therapeutics. Int. J. Mol. Sci. 13, 12268–12286.

Hessvik, N.P., and Llorente, A. (2018). Current knowledge on exosome biogenesis and release. Cell. Mol. Life Sci. 75, 193–208.

Higginbotham, J.N., Demory Beckler, M., Gephart, J.D., Franklin, J.L., Bogatcheva, G., Kremers, G.- J., Piston, D.W., Ayers, G.D., McConnell, R.E., Tyska, M.J., et al. (2011). Amphiregulin exosomes increase cancer cell invasion. Curr. Biol. CB 21, 779–786.

141

Appendix: References

Hood, J.L., San, R.S., and Wickline, S.A. (2011). Exosomes released by melanoma cells prepare sentinel lymph nodes for tumor metastasis. Cancer Res. 71, 3792–3801.

Hoshino, A., Costa-Silva, B., Shen, T.-L., Rodrigues, G., Hashimoto, A., Mark, M.T., Molina, H., Kohsaka, S., Di Giannatale, A., Ceder, S., et al. (2015a). Tumour exosome integrins determine organotropic metastasis. Nature 527, 329–335.

Hoshino, A., Costa-Silva, B., Shen, T.-L., Rodrigues, G., Hashimoto, A., Tesic Mark, M., Molina, H., Kohsaka, S., Di Giannatale, A., Ceder, S., et al. (2015b). Tumour exosome integrins determine organotropic metastasis. Nature 527, 329–335.

Hou, S., Isaji, T., Hang, Q., Im, S., Fukuda, T., and Gu, J. (2016). Distinct effects of β1 integrin on cell proliferation and cellular signaling in MDA-MB-231 breast cancer cells. Sci. Rep. 6, 18430.

Hu, Z., Dong, J., Wang, L.-E., Ma, H., Liu, J., Zhao, Y., Tang, J., Chen, X., Dai, J., Wei, Q., et al. (2012). Serum microRNA profiling and breast cancer risk: the use of miR-484/191 as endogenous controls. Carcinogenesis 33, 828–834.

Huang, S., Yuan, S., Dong, M., Su, J., Yu, C., Shen, Y., Xie, X., Yu, Y., Yu, X., Chen, S., et al. (2005). The phylogenetic analysis of tetraspanins projects the evolution of cell-cell interactions from unicellular to multicellular organisms. Genomics 86, 674–684.

Im, H., Shao, H., Park, Y.I., Peterson, V.M., Castro, C.M., Weissleder, R., and Lee, H. (2014). Label- free detection and molecular profiling of exosomes with a nano-plasmonic sensor. Nat. Biotechnol. 32, 490–495.

Im, H., Lee, K., Weissleder, R., Lee, H., and Castro, C.M. (2017). Novel nanosensing technologies for exosome detection and profiling. Lab. Chip 17, 2892–2898.

Imanishi, Y., Hu, B., Jarzynka, M.J., Guo, P., Elishaev, E., Bar-Joseph, I., and Cheng, S.-Y. (2007). Angiopoietin-2 stimulates breast cancer metastasis through the alpha(5)beta(1) integrin- mediated pathway. Cancer Res. 67, 4254–4263.

Jackson, C., Browell, D., Gautrey, H., and Tyson-Capper, A. (2013). Clinical Significance of HER-2 Splice Variants in Breast Cancer Progression and Drug Resistance. Int. J. Cell Biol. 2013.

Jahangiri, A., Aghi, M.K., and Carbonell, W.S. (2014). β1 Integrin: Critical Path to Antiangiogenic Therapy Resistance and Beyond. Cancer Res. 74, 3–7.

Jang, J.-Y., Lee, J.-K., Jeon, Y.-K., and Kim, C.-W. (2013). Exosome derived from epigallocatechin gallate treated breast cancer cells suppresses tumor growth by inhibiting tumor-associated macrophage infiltration and M2 polarization. BMC Cancer 13, 421.

Jean, C., Gravelle, P., Fournie, J.-J., and Laurent, G. (2011). Influence of stress on extracellular matrix and integrin biology. Oncogene 30, 2697–2706. de Jong, O.G., Verhaar, M.C., Chen, Y., Vader, P., Gremmels, H., Posthuma, G., Schiffelers, R.M., Gucek, M., and van Balkom, B.W.M. (2012). Cellular stress conditions are reflected in the protein and RNA content of endothelial cell-derived exosomes. J. Extracell. Vesicles 1.

Ju, J.A., Godet, I., Ye, I.C., Byun, J., Jayatilaka, H., Lee, S.J., Xiang, L., Samanta, D., Lee, M.H., Wu, P.-H., et al. (2017). Hypoxia Selectively Enhances Integrin Receptor Expression to Promote Metastasis. Mol. Cancer Res. MCR 15, 723–734.

142

Appendix: References

Kajimoto, T., Okada, T., Miya, S., Zhang, L., and Nakamura, S. (2013). Ongoing activation of sphingosine 1-phosphate receptors mediates maturation of exosomal multivesicular endosomes. Nat. Commun. 4, 2712.

Kalluri, R. (2016). The biology and function of exosomes in cancer. J. Clin. Invest. 126, 1208– 1215.

Kalra, H., Simpson, R.J., Ji, H., Aikawa, E., Altevogt, P., Askenase, P., Bond, V.C., Borràs, F.E., Breakefield, X., Budnik, V., et al. (2012). Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation. PLoS Biol. 10, e1001450.

Kanetaka, K., Sakamoto, M., Yamamoto, Y., Yamasaki, S., Lanza, F., Kanematsu, T., and Hirohashi, S. (2001). Overexpression of tetraspanin CO-029 in hepatocellular carcinoma. J. Hepatol. 35, 637–642.

Kaplan, R.N., Riba, R.D., Zacharoulis, S., Bramley, A.H., Vincent, L., Costa, C., MacDonald, D.D., Jin, D.K., Shido, K., Kerns, S.A., et al. (2005). VEGFR1-positive haematopoietic bone marrow progenitors initiate the pre-metastatic niche. Nature 438, 820–827.

Keller, S., Sanderson, M.P., Stoeck, A., and Altevogt, P. (2006). Exosomes: from biogenesis and secretion to biological function. Immunol. Lett. 107, 102–108.

Khan, S., Bennit, H.F., Turay, D., Perez, M., Mirshahidi, S., Yuan, Y., and Wall, N.R. (2014). Early diagnostic value of survivin and its alternative splice variants in breast cancer. BMC Cancer 14, 176.

Kharaziha, P., Ceder, S., Li, Q., and Panaretakis, T. (2012). Tumor cell-derived exosomes: a message in a bottle. Biochim. Biophys. Acta 1826, 103–111.

Kilic, T., Valinhas, A.T.D.S., Wall, I., Renaud, P., and Carrara, S. (2018). Label-free detection of hypoxia-induced extracellular vesicle secretion from MCF-7 cells. Sci. Rep. 8, 9402.

Kilpinen, L., Impola, U., Sankkila, L., Ritamo, I., Aatonen, M., Kilpinen, S., Tuimala, J., Valmu, L., Levijoki, J., Finckenberg, P., et al. (2013). Extracellular membrane vesicles from umbilical cord blood-derived MSC protect against ischemic acute kidney injury, a feature that is lost after inflammatory conditioning. J. Extracell. Vesicles 2.

Kim, D.-K., Lee, J., Kim, S.R., Choi, D.-S., Yoon, Y.J., Kim, J.H., Go, G., Nhung, D., Hong, K., Jang, S.C., et al. (2015a). EVpedia: a community web portal for extracellular vesicles research. Bioinformatics 31, 933–939.

Kim, T.-K., Park, C.S., Jeoung, M.H., Lee, W.R., Go, N.K., Choi, J.R., Lee, T.S., Shim, H., and Lee, S. (2015b). Generation of a human antibody that inhibits TSPAN8-mediated invasion of metastatic colorectal cancer cells. Biochem. Biophys. Res. Commun. 468, 774–780.

Kim, Y.-S., Ahn, J.-S., Kim, S., Kim, H.-J., Kim, S.-H., and Kang, J.-S. (2018). The potential theragnostic (diagnostic+therapeutic) application of exosomes in diverse biomedical fields. Korean J. Physiol. Pharmacol. Off. J. Korean Physiol. Soc. Korean Soc. Pharmacol. 22, 113–125.

King, H.W., Michael, M.Z., and Gleadle, J.M. (2012). Hypoxic enhancement of exosome release by breast cancer cells. BMC Cancer 12, 421.

Kitai, Y., Kawasaki, T., Sueyoshi, T., Kobiyama, K., Ishii, K.J., Zou, J., Akira, S., Matsuda, T., and Kawai, T. (2017). DNA-Containing Exosomes Derived from Cancer Cells Treated with Topotecan

143

Appendix: References

Activate a STING-Dependent Pathway and Reinforce Antitumor Immunity. J. Immunol. Baltim. Md 1950 198, 1649–1659.

Knight, E., and Przyborski, S. (2015). Advances in 3D cell culture technologies enabling tissue‐ like structures to be created in vitro. J. Anat. 227, 746–756.

Kogure, T., Lin, W.-L., Yan, I.K., Braconi, C., and Patel, T. (2011). Intercellular nanovesicle- mediated microRNA transfer: a mechanism of environmental modulation of hepatocellular cancer cell growth. Hepatol. Baltim. Md 54, 1237–1248.

Kowal, J., Tkach, M., and Théry, C. (2014). Biogenesis and secretion of exosomes. Curr. Opin. Cell Biol. 29, 116–125.

Kowal, J., Arras, G., Colombo, M., Jouve, M., Morath, J.P., Primdal-Bengtson, B., Dingli, F., Loew, D., Tkach, M., and Théry, C. (2016). Proteomic comparison defines novel markers to characterize heterogeneous populations of extracellular vesicle subtypes. Proc. Natl. Acad. Sci. U. S. A. 113, E968–E977.

Krop, I.E. (2013). Targeted therapies: HER2-positive breast cancer-sifting through many good options. Nat. Rev. Clin. Oncol. 10, 312–313.

Kucharzewska, P., and Belting, M. (2013). Emerging roles of extracellular vesicles in the adaptive response of tumour cells to microenvironmental stress. J. Extracell. Vesicles 2.

Kucharzewska, P., Christianson, H.C., Welch, J.E., Svensson, K.J., Fredlund, E., Ringnér, M., Mörgelin, M., Bourseau-Guilmain, E., Bengzon, J., and Belting, M. (2013). Exosomes reflect the hypoxic status of glioma cells and mediate hypoxia-dependent activation of vascular cells during tumor development. Proc. Natl. Acad. Sci. U. S. A. 110, 7312–7317.

Lam, L., McAndrew, N., Yee, M., Fu, T., Tchou, J.C., and Zhang, H. (2012). Challenges in the clinical utility of the serum test for HER2 ECD. Biochim. Biophys. Acta 1826, 199–208.

Laulagnier, K., Grand, D., Dujardin, A., Hamdi, S., Vincent-Schneider, H., Lankar, D., Salles, J.-P., Bonnerot, C., Perret, B., and Record, M. (2004). PLD2 is enriched on exosomes and its activity is correlated to the release of exosomes. FEBS Lett. 572, 11–14.

Lee, Y., El Andaloussi, S., and Wood, M.J.A. (2012). Exosomes and microvesicles: extracellular vesicles for genetic information transfer and gene therapy. Hum. Mol. Genet. 21, R125-134.

Ley, R., Balmanno, K., Hadfield, K., Weston, C., and Cook, S.J. (2003). Activation of the ERK1/2 Signaling Pathway Promotes Phosphorylation and Proteasome-dependent Degradation of the BH3-only Protein, Bim. J. Biol. Chem. 278, 18811–18816.

Lim, P.K., Bliss, S.A., Patel, S.A., Taborga, M., Dave, M.A., Gregory, L.A., Greco, S.J., Bryan, M., Patel, P.S., and Rameshwar, P. (2011). Gap junction-mediated import of microRNA from bone marrow stromal cells can elicit cell cycle quiescence in breast cancer cells. Cancer Res. 71, 1550–1560.

Llorente, A., Skotland, T., Sylvänne, T., Kauhanen, D., Róg, T., Orłowski, A., Vattulainen, I., Ekroos, K., and Sandvig, K. (2013). Molecular lipidomics of exosomes released by PC-3 prostate cancer cells. Biochim. Biophys. Acta 1831, 1302–1309.

Lötvall, J., Hill, A.F., Hochberg, F., Buzás, E.I., Di Vizio, D., Gardiner, C., Gho, Y.S., Kurochkin, I.V., Mathivanan, S., Quesenberry, P., et al. (2014). Minimal experimental requirements for definition of extracellular vesicles and their functions: a position statement from the International Society for Extracellular Vesicles. J. Extracell. Vesicles 3.

144

Appendix: References

Lowry, M.C., Gallagher, W.M., and O’Driscoll, L. (2015). The Role of Exosomes in Breast Cancer. Clin. Chem. 61, 1457–1465.

Lu, J., Li, J., Liu, S., Wang, T., Ianni, A., Bober, E., Braun, T., Xiang, R., and Yue, S. (2017). Exosomal tetraspanins mediate cancer metastasis by altering host microenvironment. Oncotarget 8, 62803–62815.

Luga, V., Zhang, L., Viloria-Petit, A.M., Ogunjimi, A.A., Inanlou, M.R., Chiu, E., Buchanan, M., Hosein, A.N., Basik, M., and Wrana, J.L. (2012). Exosomes mediate stromal mobilization of autocrine Wnt-PCP signaling in breast cancer cell migration. Cell 151, 1542–1556.

Madison, M.N., and Okeoma, C.M. (2015). Exosomes: Implications in HIV-1 Pathogenesis. Viruses 7, 4093–4118.

Martinez, V.G., O’Neill, S., Salimu, J., Breslin, S., Clayton, A., Crown, J., and O’Driscoll, L. (2017). Resistance to HER2-targeted anti-cancer drugs is associated with immune evasion in cancer cells and their derived extracellular vesicles. Oncoimmunology 6, e1362530.

Masciopinto, F., Campagnoli, S., Abrignani, S., Uematsu, Y., and Pileri, P. (2001). The small extracellular loop of CD81 is necessary for optimal surface expression of the large loop, a putative HCV receptor. Virus Res. 80, 1–10.

Mathivanan, S., Ji, H., and Simpson, R.J. (2010). Exosomes: extracellular organelles important in intercellular communication. J. Proteomics 73, 1907–1920.

Matsuo, H., Chevallier, J., Mayran, N., Le Blanc, I., Ferguson, C., Fauré, J., Blanc, N.S., Matile, S., Dubochet, J., Sadoul, R., et al. (2004). Role of LBPA and Alix in multivesicular liposome formation and endosome organization. Science 303, 531–534.

McCullough, J., Fisher, R.D., Whitby, F.G., Sundquist, W.I., and Hill, C.P. (2008). ALIX-CHMP4 interactions in the human ESCRT pathway. Proc. Natl. Acad. Sci. U. S. A. 105, 7687–7691.

McGough, I.J., and Vincent, J.-P. (2016). Exosomes in developmental signalling. Dev. Camb. Engl. 143, 2482–2493.

Melo, S.A., Sugimoto, H., O’Connell, J.T., Kato, N., Villanueva, A., Vidal, A., Qiu, L., Vitkin, E., Perelman, L.T., Melo, C.A., et al. (2014a). Cancer Exosomes Perform Cell-Independent MicroRNA Biogenesis and Promote Tumorigenesis. Cancer Cell 26, 707–721.

Melo, S.A., Sugimoto, H., O’Connell, J.T., Kato, N., Villanueva, A., Vidal, A., Qiu, L., Vitkin, E., Perelman, L.T., Melo, C.A., et al. (2014b). Cancer exosomes perform cell-independent microRNA biogenesis and promote tumorigenesis. Cancer Cell 26, 707–721.

Miranti, C.K., and Brugge, J.S. (2002). Sensing the environment: a historical perspective on integrin signal transduction. Nat. Cell Biol. 4, E83-90.

Mitchell, K., Svenson, K.B., Longmate, W.M., Gkirtzimanaki, K., Sadej, R., Wang, X., Zhao, J., Eliopoulos, A.G., Berditchevski, F., and Dipersio, C.M. (2010). Suppression of integrin alpha3beta1 in breast cancer cells reduces cyclooxygenase-2 gene expression and inhibits tumorigenesis, invasion, and cross-talk to endothelial cells. Cancer Res. 70, 6359–6367.

Mittelbrunn, M., and Sánchez-Madrid, F. (2012). Intercellular communication: diverse structures for exchange of genetic information. Nat. Rev. Mol. Cell Biol. 13, 328–335.

145

Appendix: References

Montecalvo, A., Larregina, A.T., Shufesky, W.J., Stolz, D.B., Sullivan, M.L.G., Karlsson, J.M., Baty, C.J., Gibson, G.A., Erdos, G., Wang, Z., et al. (2012). Mechanism of transfer of functional microRNAs between mouse dendritic cells via exosomes. Blood 119, 756–766.

Morini, M., Mottolese, M., Ferrari, N., Ghiorzo, F., Buglioni, S., Mortarini, R., Noonan, D.M., Natali, P.G., and Albini, A. (2000). The alpha 3 beta 1 integrin is associated with mammary carcinoma cell metastasis, invasion, and gelatinase B (MMP-9) activity. Int. J. Cancer 87, 336–342.

Muranen, T., Iwanicki, M.P., Curry, N.L., Hwang, J., DuBois, C.D., Coloff, J.L., Hitchcock, D.S., Clish, C.B., Brugge, J.S., and Kalaany, N.Y. (2017). Starved epithelial cells uptake extracellular matrix for survival. Nat. Commun. 8, 13989.

Nazarenko, I., Rana, S., Baumann, A., McAlear, J., Hellwig, A., Trendelenburg, M., Lochnit, G., Preissner, K.T., and Zöller, M. (2010). Cell surface tetraspanin Tspan8 contributes to molecular pathways of exosome-induced endothelial cell activation. Cancer Res. 70, 1668–1678. van Niel, G., Charrin, S., Simoes, S., Romao, M., Rochin, L., Saftig, P., Marks, M.S., Rubinstein, E., and Raposo, G. (2011). The tetraspanin CD63 regulates ESCRT-independent and -dependent endosomal sorting during melanogenesis. Dev. Cell 21, 708–721. van Niel, G., D’Angelo, G., and Raposo, G. (2018). Shedding light on the cell biology of extracellular vesicles. Nat. Rev. Mol. Cell Biol. 19, 213–228.

Nuzzo, P.V., Rubagotti, A., Argellati, F., Di Meglio, A., Zanardi, E., Zinoli, L., Comite, P., Mussap, M., and Boccardo, F. (2015). Prognostic Value of Preoperative Serum Levels of Periostin (PN) in Early Breast Cancer (BCa). Int. J. Mol. Sci. 16, 17181–17192.

O’Brien, K., Rani, S., Corcoran, C., Wallace, R., Hughes, L., Friel, A.M., McDonnell, S., Crown, J., Radomski, M.W., and O’Driscoll, L. (2013). Exosomes from triple-negative breast cancer cells can transfer phenotypic traits representing their cells of origin to secondary cells. Eur. J. Cancer Oxf. Engl. 1990 49, 1845–1859.

Pakravan, K., Babashah, S., Sadeghizadeh, M., Mowla, S.J., Mossahebi-Mohammadi, M., Ataei, F., Dana, N., and Javan, M. (2017). MicroRNA-100 shuttled by mesenchymal stem cell-derived exosomes suppresses in vitro angiogenesis through modulating the mTOR/HIF-1α/VEGF signaling axis in breast cancer cells. Cell. Oncol. Dordr. 40, 457–470.

Palma, J., Yaddanapudi, S.C., Pigati, L., Havens, M.A., Jeong, S., Weiner, G.A., Weimer, K.M.E., Stern, B., Hastings, M.L., and Duelli, D.M. (2012). MicroRNAs are exported from malignant cells in customized particles. Nucleic Acids Res. 40, 9125–9138.

Park, C.S., Kim, T.-K., Kim, H.G., Kim, Y.-J., Jeoung, M.H., Lee, W.R., Go, N.K., Heo, K., and Lee, S. (2016). Therapeutic targeting of tetraspanin8 in epithelial ovarian cancer invasion and metastasis. Oncogene 35, 4540–4548.

Park, J.E., Tan, H.S., Datta, A., Lai, R.C., Zhang, H., Meng, W., Lim, S.K., and Sze, S.K. (2010). Hypoxic tumor cell modulates its microenvironment to enhance angiogenic and metastatic potential by secretion of proteins and exosomes. Mol. Cell. Proteomics MCP 9, 1085–1099.

Parolini, I., Federici, C., Raggi, C., Lugini, L., Palleschi, S., De Milito, A., Coscia, C., Iessi, E., Logozzi, M., Molinari, A., et al. (2009). Microenvironmental pH is a key factor for exosome traffic in tumor cells. J. Biol. Chem. 284, 34211–34222.

Peinado, H., Lavotshkin, S., and Lyden, D. (2011). The secreted factors responsible for pre- metastatic niche formation: old sayings and new thoughts. Semin. Cancer Biol. 21, 139–146.

146

Appendix: References

Peinado, H., Alečković, M., Lavotshkin, S., Matei, I., Costa-Silva, B., Moreno-Bueno, G., Hergueta- Redondo, M., Williams, C., García-Santos, G., Nitadori-Hoshino, A., et al. (2012a). Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat. Med. 18, 883–891.

Peinado, H., Alečković, M., Lavotshkin, S., Matei, I., Costa-Silva, B., Moreno-Bueno, G., Hergueta- Redondo, M., Williams, C., García-Santos, G., Ghajar, C., et al. (2012b). Melanoma exosomes educate bone marrow progenitor cells toward a pro-metastatic phenotype through MET. Nat. Med. 18, 883–891.

Perez-Hernandez, D., Gutiérrez-Vázquez, C., Jorge, I., López-Martín, S., Ursa, A., Sánchez-Madrid, F., Vázquez, J., and Yáñez-Mó, M. (2013). The intracellular interactome of tetraspanin-enriched microdomains reveals their function as sorting machineries toward exosomes. J. Biol. Chem. 288, 11649–11661.

Pols, M.S., and Klumperman, J. (2009). Trafficking and function of the tetraspanin CD63. Exp. Cell Res. 315, 1584–1592.

Polyak, K. (2011). Heterogeneity in breast cancer. J. Clin. Invest. 121, 3786–3788.

Prat, A., and Perou, C.M. (2011). Deconstructing the molecular portraits of breast cancer. Mol. Oncol. 5, 5–23.

Psaila, B., and Lyden, D. (2009). The metastatic niche: adapting the foreign soil. Nat. Rev. Cancer 9, 285–293.

Qian, X., Anzovino, A., Kim, S., Suyama, K., Yao, J., Hulit, J., Agiostratidou, G., Chandiramani, N., McDaid, H.M., Nagi, C., et al. (2014). N-cadherin/FGFR promotes metastasis through epithelial- to-mesenchymal transition and stem/progenitor cell-like properties. Oncogene 33, 3411–3421.

Rana, S., Claas, C., Kretz, C.C., Nazarenko, I., and Zoeller, M. (2011). Activation-induced internalization differs for the tetraspanins CD9 and Tspan8: Impact on tumor cell motility. Int. J. Biochem. Cell Biol. 43, 106–119.

Raposo, G., Nijman, H.W., Stoorvogel, W., Liejendekker, R., Harding, C.V., Melief, C.J., and Geuze, H.J. (1996). B lymphocytes secrete antigen-presenting vesicles. J. Exp. Med. 183, 1161–1172.

Ratajczak, J., Miekus, K., Kucia, M., Zhang, J., Reca, R., Dvorak, P., and Ratajczak, M.Z. (2006). Embryonic stem cell-derived microvesicles reprogram hematopoietic progenitors: evidence for horizontal transfer of mRNA and protein delivery. Leukemia 20, 847–856.

Reis-Filho, J.S., and Pusztai, L. (2011). Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet Lond. Engl. 378, 1812–1823.

Rendon, B.E., Willer, S.S., Zundel, W., and Mitchell, R.A. (2009). Mechanisms of macrophage migration inhibitory factor (MIF)-dependent tumor microenvironmental adaptation. Exp. Mol. Pathol. 86, 180–185.

Richardson, M.M., Jennings, L.K., and Zhang, X.A. (2011). Tetraspanins and tumor progression. Clin. Exp. Metastasis 28, 261–270.

Ruan, K., Song, G., and Ouyang, G. (2009). Role of hypoxia in the hallmarks of human cancer. J. Cell. Biochem. 107, 1053–1062.

147

Appendix: References

Rubin, I., and Yarden, Y. (2001). The basic biology of HER2. Ann. Oncol. Off. J. Eur. Soc. Med. Oncol. 12 Suppl 1, S3-8. dos Santos, P.B., Zanetti, J.S., Ribeiro-Silva, A., and Beltrão, E.I.C. (2012). Beta 1 integrin predicts survival in breast cancer: a clinicopathological and immunohistochemical study. Diagn. Pathol. 7, 104.

Sasso, M., Bianchi, F., Ciravolo, V., Tagliabue, E., and Campiglio, M. (2011). HER2 splice variants and their relevance in breast cancer. J. Nucleic Acids Investig. 2, 9.

Sceneay, J., Smyth, M.J., and Möller, A. (2013). The pre-metastatic niche: finding common ground. Cancer Metastasis Rev. 32, 449–464.

Scheler, O., Kindt, J.T., Qavi, A.J., Kaplinski, L., Glynn, B., Barry, T., Kurg, A., and Bailey, R.C. (2012). Label-free, multiplexed detection of bacterial tmRNA using silicon photonic microring resonators. Biosens. Bioelectron. 36, 56–61.

Schlaepfer, D.D., and Hunter, T. (1997). Focal adhesion kinase overexpression enhances ras- dependent integrin signaling to ERK2/mitogen-activated protein kinase through interactions with and activation of c-Src. J. Biol. Chem. 272, 13189–13195.

Segura, E., Nicco, C., Lombard, B., Véron, P., Raposo, G., Batteux, F., Amigorena, S., and Théry, C. (2005). ICAM-1 on exosomes from mature dendritic cells is critical for efficient naive T-cell priming. Blood 106, 216–223.

Shao, R., Bao, S., Bai, X., Blanchette, C., Anderson, R.M., Dang, T., Gishizky, M.L., Marks, J.R., and Wang, X.-F. (2004). Acquired expression of periostin by human breast cancers promotes tumor angiogenesis through up-regulation of vascular endothelial growth factor receptor 2 expression. Mol. Cell. Biol. 24, 3992–4003.

Simons, M., and Raposo, G. (2009). Exosomes--vesicular carriers for intercellular communication. Curr. Opin. Cell Biol. 21, 575–581.

Simpson, R.J., Jensen, S.S., and Lim, J.W.E. (2008). Proteomic profiling of exosomes: current perspectives. Proteomics 8, 4083–4099.

Singh, A., Fedele, C., Lu, H., Nevalainen, M.T., Keen, J.H., and Languino, L.R. (2016). Exosome- mediated Transfer of αvβ3 Integrin from Tumorigenic to Nontumorigenic Cells Promotes a Migratory Phenotype. Mol. Cancer Res. MCR 14, 1136–1146.

Steinbichler, T.B., Dudás, J., Riechelmann, H., and Skvortsova, I.-I. (2017). The role of exosomes in cancer metastasis. Semin. Cancer Biol. 44, 170–181.

Stipp, C.S., Kolesnikova, T.V., and Hemler, M.E. (2003). Functional domains in tetraspanin proteins. Trends Biochem. Sci. 28, 106–112.

Strauss, K., Goebel, C., Runz, H., Möbius, W., Weiss, S., Feussner, I., Simons, M., and Schneider, A. (2010). Exosome secretion ameliorates lysosomal storage of cholesterol in Niemann-Pick type C disease. J. Biol. Chem. 285, 26279–26288.

Stremersch, S., De Smedt, S.C., and Raemdonck, K. (2016). Therapeutic and diagnostic applications of extracellular vesicles. J. Controlled Release 244, 167–183.

Stuffers, S., Sem Wegner, C., Stenmark, H., and Brech, A. (2009). Multivesicular endosome biogenesis in the absence of ESCRTs. Traffic Cph. Den. 10, 925–937.

148

Appendix: References

Su, J., Goldberg, A.F., and Stoltz, B.M. (2016). Label-free detection of single nanoparticles and biological molecules using microtoroid optical resonators. Light Sci. Appl. 5, e16001.

Suárez, H., Gámez-Valero, A., Reyes, R., López-Martín, S., Rodríguez, M.J., Carrascosa, J.L., Cabañas, C., Borràs, F.E., and Yáñez-Mó, M. (2017). A bead-assisted flow cytometry method for the semi-quantitative analysis of Extracellular Vesicles. Sci. Rep. 7.

Sun, Y., and Liu, J. (2014). Potential of cancer cell-derived exosomes in clinical application: a review of recent research advances. Clin. Ther. 36, 863–872.

Sun, W., Luo, J.-D., Jiang, H., and Duan, D.D. (2018). Tumor exosomes: a double-edged sword in cancer therapy. Acta Pharmacol. Sin. 39, 534–541.

Sundararajan, V., Sarkar, F.H., and Ramasamy, T.S. (2018). The versatile role of exosomes in cancer progression: diagnostic and therapeutic implications. Cell. Oncol. Dordr. 41, 223–252.

Svensson, K.J., Kucharzewska, P., Christianson, H.C., Sköld, S., Löfstedt, T., Johansson, M.C., Mörgelin, M., Bengzon, J., Ruf, W., and Belting, M. (2011). Hypoxia triggers a proangiogenic pathway involving cancer cell microvesicles and PAR-2-mediated heparin-binding EGF signaling in endothelial cells. Proc. Natl. Acad. Sci. U. S. A. 108, 13147–13152.

Szala, S., Kasai, Y., Steplewski, Z., Rodeck, U., Koprowski, H., and Linnenbach, A.J. (1990). Molecular cloning of cDNA for the human tumor-associated antigen CO-029 and identification of related transmembrane antigens. Proc. Natl. Acad. Sci. U. S. A. 87, 6833–6837.

Taherian, A., Li, X., Liu, Y., and Haas, T.A. (2011). Differences in integrin expression and signaling within human breast cancer cells. BMC Cancer 11, 293.

Tan, S.S., Yin, Y., Lee, T., Lai, R.C., Yeo, R.W.Y., Zhang, B., Choo, A., and Lim, S.K. (2013). Therapeutic MSC exosomes are derived from lipid raft microdomains in the plasma membrane. J. Extracell. Vesicles 2.

Théry, C., Boussac, M., Véron, P., Ricciardi-Castagnoli, P., Raposo, G., Garin, J., and Amigorena, S. (2001). Proteomic analysis of dendritic cell-derived exosomes: a secreted subcellular compartment distinct from apoptotic vesicles. J. Immunol. Baltim. Md 1950 166, 7309–7318.

Théry, C., Duban, L., Segura, E., Véron, P., Lantz, O., and Amigorena, S. (2002). Indirect activation of naïve CD4+ T cells by dendritic cell-derived exosomes. Nat. Immunol. 3, 1156–1162.

Théry, C., Amigorena, S., Raposo, G., and Clayton, A. (2006). Isolation and characterization of exosomes from cell culture supernatants and biological fluids. Curr. Protoc. Cell Biol. Editor. Board Juan Bonifacino Al Chapter 3, Unit 3.22.

Thomsen, A.R., Aldrian, C., Bronsert, P., Thomann, Y., Nanko, N., Melin, N., Rücker, G., Follo, M., Grosu, A.L., Niedermann, G., et al. (2017). A deep conical agarose microwell array for adhesion independent three-dimensional cell culture and dynamic volume measurement. Lab. Chip 18, 179–189.

Tickner, J.A., Urquhart, A.J., Stephenson, S.-A., Richard, D.J., and O’Byrne, K.J. (2014). Functions and therapeutic roles of exosomes in cancer. Front. Oncol. 4, 127.

Todorova, D., Simoncini, S., Lacroix, R., Sabatier, F., and Dignat-George, F. (2017). Extracellular Vesicles in Angiogenesis. Circ. Res. 120, 1658–1673.

149

Appendix: References

Trajkovic, K., Hsu, C., Chiantia, S., Rajendran, L., Wenzel, D., Wieland, F., Schwille, P., Brügger, B., and Simons, M. (2008). Ceramide triggers budding of exosome vesicles into multivesicular endosomes. Science 319, 1244–1247.

Umezu, T., Ohyashiki, K., Kuroda, M., and Ohyashiki, J.H. (2013). Leukemia cell to endothelial cell communication via exosomal miRNAs. Oncogene 32, 2747–2755.

Valadi, H., Ekström, K., Bossios, A., Sjöstrand, M., Lee, J.J., and Lötvall, J.O. (2007). Exosome- mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 9, 654–659.

Vardaki, I., Ceder, S., Rutishauser, D., Baltatzis, G., Foukakis, T., and Panaretakis, T. (2016). Periostin is identified as a putative metastatic marker in breast cancer-derived exosomes. Oncotarget 7, 74966–74978.

Vaupel, P., and Mayer, A. (2007). Hypoxia in cancer: significance and impact on clinical outcome. Cancer Metastasis Rev. 26, 225–239.

Wang, J., Willumsen, N., Zheng, Q., Xue, Y., Karsdal, M.A., and Bay-Jensen, A.C. (2013). Bringing cancer serological diagnosis to a new level: focusing on HER2, protein ectodomain shedding and neoepitope technology. Future Oncol. Lond. Engl. 9, 35–44.

Wang, K., Zhang, S., Weber, J., Baxter, D., and Galas, D.J. (2010). Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res. 38, 7248–7259.

Washburn, A.L., Gunn, L.C., and Bailey, R.C. (2009). Label-free quantitation of a cancer biomarker in complex media using silicon photonic microring resonators. Anal. Chem. 81, 9499– 9506.

Wei, L., Li, Y., and Suo, Z. (2015). TSPAN8 promotes gastric cancer growth and metastasis via ERK MAPK pathway. Int. J. Clin. Exp. Med. 8, 8599–8607.

WEIDLE, H.U., BIRZELE, F., KOLLMORGEN, G., and RÜGER, R. (2016). The Multiple Roles of Exosomes in Metastasis. Cancer Genomics Proteomics 14, 1–16.

Weigelt, B., Mackay, A., A’hern, R., Natrajan, R., Tan, D.S.P., Dowsett, M., Ashworth, A., and Reis- Filho, J.S. (2010). Breast cancer molecular profiling with single sample predictors: a retrospective analysis. Lancet Oncol. 11, 339–349.

Whitehead, C.A., Luwor, R.B., Morokoff, A.P., Kaye, A.H., and Stylli, S.S. (2017). Cancer exosomes in cerebrospinal fluid. Transl. Cancer Res. 6, S1352–S1370.

Willms, E., Johansson, H.J., Mäger, I., Lee, Y., Blomberg, K.E.M., Sadik, M., Alaarg, A., Smith, C.I.E., Lehtiö, J., Andaloussi, S.E., et al. (2016). Cells release subpopulations of exosomes with distinct molecular and biological properties. Sci. Rep. 6, 22519.

Wondimu, S.F., von der Ecken, S., Ahrens, R., Freude, W., Guber, A.E., and Koos, C. (2017). Integration of digital microfluidics with whispering-gallery mode sensors for label-free detection of biomolecules. Lab. Chip 17, 1740–1748.

Wondimu, S.F., Hippler, M., Hussal, C., Hofmann, A., Krämmer, S., Lahann, J., Kalt, H., Freude, W., and Koos, C. (2018). Robust label-free biosensing using microdisk laser arrays with on-chip references. Opt. Express 26, 3161–3173.

150

Appendix: References

Wu, C., Fields, A.J., Kapteijn, B.A., and McDonald, J.A. (1995). The role of alpha 4 beta 1 integrin in cell motility and fibronectin matrix assembly. J. Cell Sci. 108 ( Pt 2), 821–829.

Wubbolts, R., Leckie, R.S., Veenhuizen, P.T.M., Schwarzmann, G., Möbius, W., Hoernschemeyer, J., Slot, J.-W., Geuze, H.J., and Stoorvogel, W. (2003). Proteomic and biochemical analyses of human B cell-derived exosomes. Potential implications for their function and multivesicular body formation. J. Biol. Chem. 278, 10963–10972.

Xu, R., Rai, A., Chen, M., Suwakulsiri, W., Greening, D.W., and Simpson, R.J. (2018). Extracellular vesicles in cancer - implications for future improvements in cancer care. Nat. Rev. Clin. Oncol. 15, 617–638.

Yáñez-Mó, M., Barreiro, O., Gordon-Alonso, M., Sala-Valdés, M., and Sánchez-Madrid, F. (2009). Tetraspanin-enriched microdomains: a functional unit in cell plasma membranes. Trends Cell Biol. 19, 434–446.

Yáñez-Mó, M., Gutiérrez-López, M.D., and Cabañas, C. (2011). Functional interplay between tetraspanins and proteases. Cell. Mol. Life Sci. CMLS 68, 3323–3335.

Yang, Y., Sun, M., Wang, L., and Jiao, B. (2013). HIFs, angiogenesis, and cancer. J. Cell. Biochem. 114, 967–974.

Yao, E.S., Zhang, H., Chen, Y.-Y., Lee, B., Chew, K., Moore, D., and Park, C. (2007). Increased beta1 integrin is associated with decreased survival in invasive breast cancer. Cancer Res. 67, 659– 664.

Yarden, Y., and Pines, G. (2012). The ERBB network: at last, cancer therapy meets systems biology. Nat. Rev. Cancer 12, 553–563.

Yersal, O., and Barutca, S. (2014). Biological subtypes of breast cancer: Prognostic and therapeutic implications. World J. Clin. Oncol. 5, 412–424.

Yu, S., Liu, C., Su, K., Wang, J., Liu, Y., Zhang, L., Li, C., Cong, Y., Kimberly, R., Grizzle, W.E., et al. (2007). Tumor exosomes inhibit differentiation of bone marrow dendritic cells. J. Immunol. Baltim. Md 1950 178, 6867–6875.

Yue, S., Mu, W., and Zöller, M. (2013). Tspan8 and CD151 promote metastasis by distinct mechanisms. Eur. J. Cancer Oxf. Engl. 1990 49, 2934–2948.

Yue, S., Mu, W., Erb, U., and Zöller, M. (2015). The tetraspanins CD151 and Tspan8 are essential exosome components for the crosstalk between cancer initiating cells and their surrounding. Oncotarget 6, 2366–2384.

Zhou, B., Gibson-Corley, K.N., Herndon, M.E., Sun, Y., Gustafson-Wagner, E., Teoh-Fitzgerald, M., Domann, F.E., Henry, M.D., and Stipp, C.S. (2014a). Integrin α3β1 Can Function to Promote Spontaneous Metastasis and Lung Colonization of Invasive Breast Carcinoma. Mol. Cancer Res. 12, 143–154.

Zhou, W., Fong, M.Y., Min, Y., Somlo, G., Liu, L., Palomares, M.R., Yu, Y., Chow, A., O’Connor, S.T.F., Chin, A.R., et al. (2014b). Cancer-secreted miR-105 destroys vascular endothelial barriers to promote metastasis. Cancer Cell 25, 501–515.

Zhou, Z., Ran, Y.-L., Hu, H., Pan, J., Li, Z.-F., Chen, L.-Z., Sun, L.-C., Peng, L., Zhao, X.-L., Yu, L., et al. (2008). TM4SF3 promotes esophageal carcinoma metastasis via upregulating ADAM12m expression. Clin. Exp. Metastasis 25, 537–548.

151

Appendix: References

Zhu, L., Wang, K., Cui, J., Liu, H., Bu, X., Ma, H., Wang, W., Gong, H., Lausted, C., Hood, L., et al. (2014). Label-free quantitative detection of tumor-derived exosomes through surface plasmon resonance imaging. Anal. Chem. 86, 8857–8864.

Zöller, M. (2006). Gastrointestinal tumors: metastasis and tetraspanins. Z. Gastroenterol. 44, 573–586.

Zöller, M. (2009). Tetraspanins: push and pull in suppressing and promoting metastasis. Nat. Rev. Cancer 9, 40–55.

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Appendix: List of Abbreviations

7.3 List of Abbreviations

ADAM a disintegrin and metalloprotease ADAM12m disintegrin and metalloproteinase domain-containing protein 12 ALIX ALG-2interacting protein X AREG amphiregulin ARRDC1 arrestin domain-containing protein-1 BCC breast cancer cells BMDCs bone-marrow derived hematopoietic progenitor cells BSA/PBS bovine serum albumin/phosphate buffered saline CAFs carcinoma associated fibroblasts CAMA (CAMA) or agarose matrix conical agarose microwell array CD cluster of differentiation CK5/6, CK14 cytokeratins 5/6, cytokeratins 14 ECD extracellular domain ECL extracellular loop EGFR epidermal growth factor receptor EMT epithelial to mesenchymal transition EpCAM epithelial cell adhesion molecule ER estrogen receptor ESCRT endosomal sorting complex required for transport EVs extracellular vesicles EV5 EVs isolated at 5000 g during differential centrifugation method of EVs isolation EV12 EVs isolated at 12 000 g during differential centrifugation method of EVs isolation EV120 (small EVs) EVs isolated at 120 000 g during differential centrifugation method of EVs isolation EV-HER2 HER2 bound to EVs FBS fetal bovine serum fps frames per second HCC hepatocellular carcinoma HER2 human epidermal growth factor receptor 2 HIF hypoxia-inducible factor ICAM-1 Intercellular Adhesion Molecule 1 ICD intracellular domain ICL intracellular loop IgSF Immunoglobulin superfamily ILVs Interluminalvesicles InMx Invasion Matrix Ki-67 cell proliferation marker LEL large extracellular loop lncRNA long non-coding RNAs MET mesenchymal to epithelial transition MHC major histocompatibility complex mi RNA micro RNA

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Appendix: List of Abbreviations

MMP2 matrix metalloproteinases 2 MSCs mesenchymal stem cells MV microvesicles MVB multivesicular bodies MVEs multivesicular endosomes N-SMase neutral sphingomyelinase PLD phospholipase D enzyme PLP proteolipid protein PR progesterone receptor Rab ‘Ras-related in brain’ protein Rac2 Ras-related C3 botulinum toxin substrate 2 RTK receptor tyrosine kinases S1P sphingosine-1-phosphate and SEL small extracellular loop SNARE soluble NSF Attachment protein receptor STAM signal transducing adaptor molecule TAK1 transforming growth factor-beta activated kinase TAMs tumor-associated macrophages TEM tetraspanin-enriched microdomain TME tumor microenvironment TNBC triple-negative breast cancer TNF receptor tumor necrosis factor receptor tRNAs, transfer RNAs TSG101 tumor susceptibility gene 101

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Acknowledgments

First and foremost, I would like to express my sincere gratitude to my supervisor Dr. Irina Nazarenko for giving me this opportunity to work in the subject of my interest and helping me to conduct this research. Her guidance allowed me to become a better independent researcher. Her insightful comments and constructive criticism helped me in shaping my thesis. I am deeply grateful to my faculty supervisor Prof. Dr. Annegret Wilde for her support and kind words of encouragement, especially during uncertain times.

My heartfelt thanks go to all my laboratory colleagues for the motivation and fruitful discussions in the lab. I would like to extend my special thanks to our ‘NTA’ expert Mrs. Tanja Gainey-Schleicher for helping me with the invasion experiments and keeping the NTA machine functional throughout my work. I would like to thank Mrs. Maren Voglstätter for performing the HER2 detection by ELISA experiments. I would like to thank the latest members to join our lab, Liliia Paniushkina and Elena Grueso Navarro for all the useful discussions, motivation and making the lab lively again. I would like to thank Ms. Carla Römmelt for finishing the flow cytometry experiments with EVs. I would also like to thank my old colleagues Amal Mahmoud, Andrea Groß and Farah Hossein.

I am also indebted to my other colleagues from the University Medical Centre, Freiburg who helped me in my experiments. I would like to give my sincere thanks to Dr. Marie Follo for her patience and guidance with the confocal microscopy. I would like to thank Ms. Hannah Fühllgraf for performing the H&E staining with the 3D spheroids. I would like to thank Ms. Sigrun Nestel for kindly providing the electron microscopy images of the EVs. I would also like to offer my special thanks to Mr. Sentayehu Wondimu from Karlsruhe Institute of Technology for performing all the experiments with WGM lasers for detection of EVs.

Finally, I owe my deepest gratitude to my family for their patience, constant support and encouragement through all the highs and lows. I would also like to take this opportunity to remember my late father who departed this life during my thesis work. He got my first research journal to read during summer holidays and forever inspired me with his diligence and resilience. I would also like to thank all my friends in Freiburg who helped me survive especially during the time of uncertainties.

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