Lyn kinase in Basal breast cancers
Ana Porta Cubas
A thesis submitted in fulfilment of the requirements for the Degree in
Master of Science (Research)
University of NSW, Australia Garvan Institute of Medical Research
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ORIGINALITY STATEMENT
‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’
Signed ……………………………………………......
Date ……………………………………………......
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Abstract
Although in the wider community, ‘Breast cancer’ is thought of as a single pathology, it is enormously heterogeneous, to the extent that some consider it to be composed of distinct diseases. Breast cancers can be subdivided by staging, grading and histological subtypes. However, over the last 10 years, research has driven a new classification system based on the expression of different molecular markers. This newer system is able to divide Breast cancers into at least 5 different subgroups each with different incidences, prognoses and available treatments. One subgroup, the ‘Basal breast cancers’ is of particular concern due to its younger age at diagnosis, increased aggressiveness, shorter survival and lack of targeted therapies. Encouragingly, recent work has found that cell lines representing this subtype are sensitive to the multikinase inhibitor ‘Dasatinib’. This suggests that kinase/s targeted by Dasatinib may be overexpressed or deregulated in Basal breast cancers, and may thus represent novel therapeutic targets.
To investigate this hypothesis, mRNA and protein expression of some of the known Dasatinib targets (Src family kinases and Abl family kinases) were compared between cell lines representing Basal breast cancers and the less aggressive Luminal breast cancers. One candidate kinase, Lyn, was found to be significantly overexpressed at the mRNA and protein level in Basal versus Luminal breast cancer cell lines, and exhibited increased activity in the Basal subgroup. Thus Lyn became the focus of further work. To investigate the mechanisms regulating Lyn activation, cell lines were stimulated with various candidate growth factors. HGF stimulated Lyn activity in one cell line suggesting that Lyn might form part of the Met signalling pathway. However, siRNA-mediated Lyn depletion in this cell line did not affect activity or total levels of signalling proteins known to comprise the Met pathway, nor did it affect HGF-
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induced cell scattering. Interestingly, Lyn depletion did affect the morphology of cells upon starvation, with cells becoming more dissociated and fibroblastic in appearance. Consequently, the association of Lyn with the cytoskeleton and its effects on known cytoskeletal regulators needs to be further investigated. Lastly, the translational relevance of these findings was tested by investigating Lyn expression in a large Breast cancer patient cohort by immunohistochemical staining. Lyn was found to be significantly overexpressed in the Basal subgroup of patients and strongly associated with it. In addition, Lyn expression was associated with poor prognosis. Thus, while the functional role of Lyn in Basal breast cancer requires further characterisation, it represents a novel biomarker of the Basal breast cancer subgroup and may represent a therapeutic target in Basal breast cancer cells.
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Acknowledgments
I would like firstly to thank my supervisor Prof. Roger Daly for sharing his extensive knowledge and ideas on all things ‘signalling’, amazing attention to detail, patience and guidance throughout my studentship. I have learnt so much throughout this time!
I would also like to thank Gillian Lehrbach, Sandra O’Toole and of course the one and only Alice Boulghourjian, you are the IHC queen.
We have an amazingly friendly, talented and hard working CST group and I have enjoyed spending time with you guys in and out of the lab. So thanks to Brigid Browne, Tilman Brummer, Dave Croucher, Emmy Fleuren, Maite Herrera Abreu, Falko Hochgräfe, Brian Lee, Ruth Lyons, Kate Patterson, Carolina Ortiz Padilla, Danny Rickwood, Emily Stoddart, Carole Tactacan and of course Luxi Zhang.
Thanks to everyone in the Cancer Department, in particular Labs 1 and 2. I will always remember good times in the gel bay area, and random conversations often ending in fits of teary laughter. Thanks also for not thumping me over the head during my random acts of dancing and singing in the lab- much needed moments of stress release. This has been a tough year for me in more ways than one and of course I appreciate all your emotional support too!
Lastly of course I would like to give a massive thanks to my whole family: Mama, Papa, Fer, Gonza y Lucia- what can I say but a big THANK YOU!! Thanks especially to Mum for encouraging me all the way, and Dad for picking me up from the Lab and train station at ridiculous hours. Thank you to Shellstar, you are indeed a star friend, I couldn’t have asked for a better one, your support throughout this has been amazing. And lastly of course to Leigh.
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Table of Contents
Chapter 1 Introduction…………………………………………………………… 1
1.1 Characteristics of the cancer cell……………………………………………… 1 1.2 Receptor tyrosine kinases……………………………………………………... 2 1.2.1 Met…………………………………………………………………………………. 4 1.2.2 ErbB family……………………………………………………………………….. 6 1.2.3 Kit………………………………………………………………………………….. 7 1.3 Signalling downstream of RTKs……………………………………………… 9 1.4 Src family kinases……………………………………………………………... 12 1.4.1 The unique structure of SFKs………………………………………………….. 12 1.4.2 Regulation of SFK activity……………………………………………………… 13 1.4.3 Signalling pathways and processes mediated by SFKs…………………….. 16 1.4.4 Src and cancer…………………………………………………………………… 17 1.4.5 Lyn-mediated signalling………………………………………………………… 18 1.4.6 Lyn and cancer…………………………………………………………………… 20 1.5 Architecture of the normal breast…………………………………………….. 23 1.6 The heterogeneity of Breast cancer…………………………………………... 24 1.7 Basal Breast cancer…………………………………………………………… 27 1.7.1 Definition of ‘Basalness’……………………………………………………….. 27 1.7.2 Clinical features of Basal breast cancers…………………………………….. 28 1.7.3 The association between Basal breast cancers and BRCA1………………. 29 1.7.4 Additional markers associated with Basal breast cancers…………………. 29 1.7.5 The origins of Basal breast cancers…………………………………………… 30 1.7.6 Therapies for Basal breast cancers…………………………………………… 32 1.7.7 A new promise: Dasatinib sensitivity in Basal breast cancers 33 1.8 Project Aims………………………………………………………………….. 34
Chapter 2 Materials and Methods……………………………………………….. 35
2.1 Maintenance of cell lines…………………………………………………….. 35 2.2 Analysis of mRNA expression in cell lines…………………………………... 36 2.3 Western Blotting……………………………………………………………… 37 2.3.1 Sample preparation……………………………………………………………… 37 2.3.2 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) 37
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and Western blot transfer…………………………………………………………….. 2.3.3 Western blotting and detection………………………………………………… 37 2.3.4 Densitometry and statistical analysis of Western blot bands………………. 40 2.4 Growth factor stimulation…………………………………………………….. 40 2.5 Immunoprecipitation (IP) ……………………………………………………. 41 2.6 siRNA knockdown of Lyn expression………………………………………... 41 2.7 Cell scattering assay………………………………………………………….. 42 2.8 Immunohistochemistry (IHC) ……………………………………………….. 42 2.8.1 Optimisation of Lyn IHC…...…………………………………………………… 42 2.8.2 Final optimised conditions for Lyn staining…………………………………. 42 2.8.3 Lyn staining in test Breast cancer cohort…………………………………….. 43 2.8.4 Lyn staining in a large Breast cancer cohort………………………………… 43 2.8.5 Calculation of H score………………………………………………………….. 44 2.8.6 Analysis of Lyn staining in large cohort……………………………………… 44 2.9 Commonly used buffers and solutions……………………………………….. 45
Chapter 3 Lyn is overexpressed in Basal breast cancer cell lines……………… 46
3.1 Introduction ………………………………………………………………….. 46 3.1.1 Cancer cell lines as a resource for the study of Breast cancer molecular 47 subtypes…………………………………………………………………………………. 3.1.2 Chapter Aims……………………………………………………………………... 48 3.2 Results………………………………………………………………………... 49 3.2.1 Gene expression analysis of Abl SFK members in Breast cancer cell lines……………………………………………………………………………………. 49 3.2.2 Analysis of protein levels of Abl and SFK members in Breast cancer cell lines……………………………………………………………………………………… 52 3.2.3 Analysis of active Lyn levels in Breast cancer cell lines……………………. 59 3.2.4 Association of Lyn expression with Dasatinib sensitivity…………………... 62 3.3 Discussion…………………………………………………………………….. 64
Chapter 4 Regulation and function of Lyn in Basal breast cancer cells………. 68
4.1 Introduction…………………………………………………………………... 68 4.1.1 Experimental strategy…………………………….…………………………….. 69 4.1.2 Chapter Aims…………………………….………………………………………. 69 4.2 Results………………………………………………………………………... 70 4.2.1 Regulation by EGF…………………………….………………………………… 70 4.2.2 Regulation by SCF…………………………….……………………………….... 75 4.2.3 Regulation by HGF…………………………….………………………………... 79 4.2.4 Investigating Lyn’s potential interaction partners………………………….. 84 4.2.5 Investigating Lyn’s role in HGF-induced cell signalling…………………... 86 4.2.6 Investigating Lyn’s functional role in HGF-induced cell scattering……… 93 4.2.7 Lyn knock down and morphology change……………………………………. 97 4.3 Discussion ……………………………………………………………………. 99
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4.3.1 Regulation of Lyn activity by specific RTKs………………………………….. 99 4.3.2 Investigation of Lyn’s role in the Met signalling pathway…………………. 102 4.3.3 Lyn’s role in controlling cell morphology……………………………………. 103
Chapter 5 Lyn expression in Breast cancer patients……………………………. 106
5.1 Introduction…………………………………………………………………... 106 5.1.2 Chapter Aims…………………………….……………………………………….. 107 5.2 Results…………………………………………………………………...…… 108 5.2.1 Lyn antibody optimisation…………………………….………………………... 108 5.2.2 Lyn staining in the normal breast……………………………………………… 112 5.2.3 Lyn expression in different Breast cancer subgroups……………………..... 113 5.2.4 Lyn staining in a large patient cohort and association with survival ……. 115 5.3 Discussion…………………………………………………………………… 118
Chapter 6 General Discussion……………………………………………………. 120
6.1 What is Lyn doing in the cell? New findings on Lyn in Basal breast cancers.. 121 6.2 Using Lyn as a biomarker for Basal breast cancers...... 122 6.3 Is Lyn the target of Dasatinib action?...... 122 6.4 Implications of findings to Basal breast cancer treatment...... 123
References………………………………………………………………………….. 125
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Table of Figures
Chapter 1 Fig 1.1 Signalling downstream of RTKs …………………………………………... 4 Fig 1.2 Structure of SFKs and important residues controlling their activity……….. 12 Fig 1.3 Mechanism of SFK activation……………………………………………… 14 Fig 1.4 Architecture if the normal breast…………………………………………… 23 Fig 1.5 Classification of Breast cancers based upon gene profiling signatures and immunophenotypes………………………………………………………………….. 26 Fig 1.6 The origins of Basal breast cancer………………………………………….. 31
Chapter 3 Fig 3.1 Analysis of mRNA levels of Abl and Src family kinases in Basal and Luminal cell lines…………………………………………………………………… 51 Fig 3.2 Characterisation of Abl protein levels across the cell line panel …………... 54 Fig 3.3 Comparison of Src protein levels in the cell line panel …………...... 55 Fig 3.4 Comparison of Yes protein levels in the cell line panel …………...... 56 Fig 3.5 Comparison of Fyn protein levels in the cell line panel …………...... 57 Fig 3.6 Comparison of Lyn protein levels in the cell line panel …………...... 58 Fig 3.7 Correlation between Lyn mRNA and Lyn protein level …………...... 59 Fig 3.8 Identification of bands corresponding to active Lyn in total cell lysate (TCL) …………...... 60 Fig 3.9 Comparison of active Lyn levels in the cell line panel …………...... 61 Fig 3.10 Correlation between Lyn protein level and Dasatinib sensitivity ………… 63 Fig 3.11 Correlation between EGFR protein level and Dasatinib sensitivity ……… 63 Fig 3.12 A model for high Lyn expression in Normal cell lines based on the hierarchical model proposed by Lim et al., 2009…………………………………… 66
Chapter 4 Fig 4.1 Comparison of EGFR protein levels in a panel of Breast cancer cell lines ... 71 Fig 4.2 The effect of EGF stimulation on Lyn activity …………...... 74 Fig 4.3 Comparison of Kit protein levels in a panel of Breast cancer cell lines……. 76 Fig 4.4 The effect of SCF stimulation on Lyn activity …………...... 78 Fig 4.5 Comparison of Met protein levels in a panel of Breast cancer cell lines….... 80 Fig 4.6 The effect of HGF stimulation on Lyn activity …………...... 83 Fig 4.7 Interaction between Lyn and potential binding partners …………...... 85
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Fig 4.8 Effect of Lyn knock down on HGF-induced cell signalling (100ng/ml) …... 87 Fig 4.9 Densitometric analysis of Western blot data …………...... 89 Fig 4.10 Effect of Lyn knock down on HGF-induced cell signalling (10ng/ml)...... 92 Fig 4.11 Scattering assay of Lyn knock down HCC 70 cells …………...... 95 Fig 4.12 Effect of Lyn knock down on the signalling of HCC 70 cells stimulated with HGF for 24h………………………………………………………………. 97 Fig 4.13 Effect of Lyn knock down on starved HCC 70 cell morphology ………... 98 Fig 4.14 Quantification of cell morphology change upon Lyn knock down ……….. 98
Chapter 5 Fig 5.1 Optimisation of Lyn IHC staining in cell lines…………………………..… 108 Fig 5.2 Lyn expression in normal tissues…………………………………………… 111 Fig 5.3 Lyn staining in the normal mammary gland...……………………………… 113 Fig 5.4 Gradation of Lyn staining intensity used to score Breast cancer cohorts…... 114 Fig 5.5 Lyn staining in a test cohort of Breast cancer samples……………………... 115 Fig 5.6 Lyn staining in a large cohort of Breast cancer samples……………………. 116 Fig 5.7 Association between Lyn expression and survival…………………………. 117
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Abbreviations
ALDH Aldehyde dehydrogenase ALL Acute lymphoblastic leukaemia AML Acute myeloid leukaemia ANOVA One way analysis of variance ATP Adenosine tri-phosphate au Arbitrary units BCR B cell receptor CK Cytokeratin CML Chronic myelogenous leukaemia CSFR Colony stimulating factor receptor ECM Extracellular matrix EGF Epidermal growth factor EGFR Epidermal growth factor receptor EMT Epithelial to mesenchymal transition Epo Erythropoietin ER Estrogen receptor FAK Focal adhesion kinase FGFR Fibroblast growth factor receptor FISH Fluorescence in-situ hybridisation GDP Guanine di-phosphate GIST Gastrointestinal stromal tumour GTP Guanine tri-phosphate h Hour/s HGF Hepatocyte growth factor HMEC Human mammary epithelial cell HR Hazard ratio IDC-NOS Invasive ductal carcinoma, not otherwise specified IGF1R Insulin-like growth factor 1 receptor IHC Immunohistochemistry ILC Invasive lobular carcinoma IP Immunoprecipitation kDa Kilodaltons L12 Lyn targeting siRNA 12 L2 Lyn targeting siRNA 2 M Mock transfection mAb Monoclonal antibody MaSC Mammary stem cell MBD Met-binding domain min Minute/s
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NLB Normal lysis buffer NSLC Non-small cell lung cancer NT Non-targeting (siRNA) p Probability pAb Polyclonal antibody PBS Phosphate buffered saline PDGFR Platelet derived growth factor receptor PR Progesterone receptor PRCC Papillary renal cell carcinoma pSFK Phosphorylated Src family kinase PTB Phosphotyrosine binding domain PTKs Protein tyrosine kinases RIPA Radioimmunoprecipitation buffer rpm Revolutions per minute RTKs Receptor tyrosine kinases SCF Stem cell factor SCLC Small cell lung cancer SDS-PAGE Sodium dodecyl sulfate polyacrylamide gel electrophoresis SEM Standard error of the mean SFKs Src family kinases SMA Smooth muscle actin TBS Tris buffered saline TCL Total cell lysate TDLU Terminal duct lobular unit TKI Tyrosine kinase inhibitor TMA Tissue microarray WB Western blot Y Tyrosine
Publications
Hochgrafe, F., Zhang, L., O'Toole, S.A., Browne, B.C., Pinese, M., Porta Cubas, A., Lehrbach, G.M., Croucher, D.R., Rickwood, D., Boulghourjian, A., Shearer, R., Nair, R., Swarbrick, A., Faratian, D., Mullen, P., Harrison, D.J., Biankin, A.V., Sutherland, R.L., Raftery, M.J., and Daly, R.J. (2010). Tyrosine Phosphorylation Profiling Reveals the Signaling Network Characteristics of Basal Breast Cancer Cells. Cancer Research 70, 9391-9401
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0 Chapter 1: Introduction
CHAPTER ONE
Introduction
1.1 Characteristics of the cancer cell
Cancer is a complex disease. Unlike other pathologies, there is no single gene that causes cancer. Rather, cancer is thought to result from a multi-step process where normal cells accumulate a set of defective genes and proteins over time, to produce a malignant phenotype (Hanahan and Weinberg, 2000). Thus defective genes are said to contribute to cancer rather than cause it outright.
In a simplified manner, these genes can belong to one of three groups: oncogenes, tumour suppressor genes and stability genes (Vogelstein and Kinzler, 2004).
Oncogenes are genes whose upregulation or activating mutations can contribute to cancer pathogenesis. In contrast, tumour suppressor genes are those whose loss or decreased activity through mutation may contribute to cancer. The loss of a tumour suppressor gene can be by deletion or as has been discovered more recently, through epigenetic silencing. The last group, stability genes, are genes whose protein counterparts serve to repair DNA damage which would otherwise result in alterations to oncogenes or tumour suppressor genes.
The above alterations at the gene level contribute to a set of generalised characteristics 1 Chapter 1: Introduction that cancer cells must gain in order to become tumorigenic. These have been termed the ‘Hallmarks of Cancer’ and include: independence from external growth factors, insensitivity to anti-growth signals, evasion of apoptosis or cell death, potential for endless replication, ability to corrupt surrounding stroma, and the ability for increased motility and invasion (Hanahan and Weinberg, 2000).
Importantly, at the heart of each of these capabilities is the deregulation of signalling pathways that would otherwise keep the cell in a ‘normal’ ie non-cancerous state. Indeed throughout a lifetime, signalling needs to be tightly regulated and coordinated to prevent oncogenesis (Blume-Jensen and Hunter, 2001).
1.2 Receptor Tyrosine Kinases
Although phosphotyrosine only represents 0.05 to 0.1% of all phosphoamino acids in the cell, protein tyrosine kinases (PTKs) make up key elements in cell signalling pathways (Weinberg, 2007) and in cancer cells, PTKs make up a large proportion of dominant oncogenes (Blume-Jensen and Hunter, 2001).
The majority of known PTKs are Receptor Tyrosine Kinases (RTKs) and they control key processes such as cell cycle progression, cell migration and metabolism, cell proliferation and survival (Schlessinger, 2000; Blume-Jensen and Hunter, 2001). RTKs are membrane-spanning proteins with intrinsic kinase activity that are able to transduce external stimuli to internal signalling pathways, thus deriving an appropriate cellular response (Olayioye et al., 2000). All RTK monomers have a similar structure, with an extracellular ligand binding domain, a transmembrane region that crosses the membrane once and cytoplasmic kinase and regulatory domains (Schlessinger, 2000; Lemmon and Schlessinger, 2010) (see Fig 1.1).
The activity of RTKs is normally tightly repressed, however upon ligand binding, receptors dimerise, leading to activation of their intracellular kinase domains. This results in autophosphorylation of residues on the receptor that act as binding and activation sites for cytoplasmic signalling proteins. The mechanism whereby
2 Chapter 1: Introduction dimerisation results in kinase activation varies amongst RTKs. However, it is thought that in all RTKs, dimerisation reverses an autoinhibited structural conformation of the receptor, thus allowing the ‘activation loop’ in the kinase domain to be exposed for autophosphorylation (Schlessinger, 2000; Lemmon and Schlessinger, 2010).
Humans have 58 known RTKs, with approximately half being overexpressed or mutated in cancers (Blume-Jensen and Hunter, 2001; Lemmon and Schelssinger, 2010). A description of each of these is outside the scope of this thesis. Thus, the RTKs relevant to this study will be introduced below. For simplicity, a schematic of signalling from one particular receptor, Met is shown (Fig 1.1). Bear in mind however, that many of the proteins and signalling pathways activated by Met are common to most RTKs. Indeed, this allows signalling pathways emanating from different RTKs to ‘cross-talk’.
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Fig 1.1 Signalling downstream of RTKs. Signalling from RTKs normally commences by binding of ligand (in this case HGF) to the extracellular domain of the receptor. Each receptor has unique extracellular domains as depicted for Kit, EGFR and Met. Ligand binding induces receptor dimerisation and activation of the kinase domain though autophosphorylation. Note that for simplicity only a monomer of each receptor is shown. Phosphorylation of the intracellular domains of the receptor create binding sites for specific signalling proteins. These act to relay and amplify signals emanating from the RTK.
1.2.1 Met
Met is a heterodimeric receptor that belongs to the HGFR family along with Ron (Blume-Jensen and Hunter, 2001; Birchmeier et al., 2003). Met is expressed mainly in epithelial and endothelial cells while its ligand, hepatocyte growth factor (HGF) is expressed by cells of the surrounding stroma (Furge et al., 2000; Zhang and Vande
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Woude, 2003; Gao and Woude, 2005). As such, Met is normally activated in a paracrine manner. Met activation induces a variety of cellular responses such as cell proliferation, migration, invasion and angiogenesis (Furge et al., 2000; Zhang and Vande Woude, 2003; Gao and Woude, 2005). The migration and invasion components are encapsulated by two well known HGF-induced effects termed ‘scattering’ and ‘branching morphogenesis’. Scattering involves cell dispersal from a pre-existing colony via loss of cell-cell adhesion and increased motility, while the more complicated branching morphogenesis, which occurs when cells are grown in a 3D matrix, additionally involves extracellular matrix (ECM) degradation, and cell polarisation (Zhang and Vande Woude, 2003). Both of these serve an important role during embryonic development, however these characteristics are though to be hijacked during tumorigenesis, resulting in a phenotypic change termed epithelial to mesenchymal transition (EMT) (Zhang and Vande Woude, 2003). Details of the scattering effect and the underlying signalling pathways will be discussed in detail further on.
As with other RTKs ligand binding results in Met dimerisation, and activation of its C-terminal cytoplasmic kinase domain through phosphorylation of Y1234 and Y1235 on the kinase activation loop (Birchmeier et al., 2003). This leads to phosphorylation of two important tyrosines, Y1349 and Y1356 on the bidentate docking site (Ponzetto et al., 1994). This docking site allows binding of specific downstream effector molecules, thus allowing signal transduction to occur. Mutation of the bidentate docking site has shown that it is essential for all biological functions mediated by Met signalling (Ponzetto et al., 1994; Zhang and Vande Woude, 2003).
The involvement of Met in tumorigenesis was discovered in the 1980’s, with the discovery of the Tpr-Met fusion protein in cells following carcinogenic chemical exposure (Furge et al., 2000; Birchmeier et al., 2003; Zhang and Vande Woude, 2003). Aberration of Met signalling is found in the majority of solid human tumours, and results from Met overexpression or mutation of the receptor, resulting in constitutive activation (see Table 1 and http://www.vai.org/met/). In addition, tumour
5 Chapter 1: Introduction cells are able to produce high levels of HGF, resulting in autocrine activation of Met and independence from the surrounding stroma (Furge et al., 2000; Birchmeier et al., 2003). The expression or mutation of Met in cancers is associated with increased metastasis and poor prognosis (Furge et al., 2000; Zhang and Vande Woude, 2003). This is likely related to its unique role in cell motility and invasion .
1.2.2 ErbB family
The ErbB family contains four closely related RTKs: ErbB1 (EGFR) ErbB2 (Her2/Neu), ErbB3 and ErbB4 (Olayioye et al., 2000; Schlessinger, 2000). EGFR was one of the first mammalian signalling proteins to be characterised and as such has become the prototypic RTK (Prenzel et al., 2000). Epidermal growth factor (EGF) is one of the ligands for EGFR, while a ligand for Her2 has not yet been found (Olayioye et al., 2000; Schlessinger, 2000). As such, Her2 is known as an ‘orphan receptor’ (Lemmon and Schlessinger, 2010). However this is explained by the finding that members of the ErbB family are able to heterodimerise (Olayioye et al., 2000; Schlessinger, 2000). That is, Her2 is able to signal by dimerising to a monomer of another member of the ErbB family. Since each member of the family has distinct cytoplasmic phosphorylation sites, and therefore different cytoplasmic binding partners, heterodimerisation allows a great deal of signal diversity simply through combinatorial mechanisms (Olayioye et al., 2000). Interestingly, signalling by EGFR can not only be stimulated by EGF binding, but also by signalling proteins that are activated following stimulation of other receptors (G-protein coupled, Integrins, Cytokine receptors) in a phenomenon termed ‘signal transactivation’ (Prenzel et al., 2000). Consequently, EGFR signalling can integrate messages from different receptors, resulting in varied but specific cellular responses. Importantly both EGFR and Her2 are overexpressed or mutated in a variety of cancers (Table 1.1). Overexpression of Her 2 is found in 30% of Breast cancers and often reflects amplification of the Her2 gene (Lemmon and Schlessinger, 2010). Interestingly, overexpression due to gene amplification of EGFR is largely restricted to glioblastomas (Lemmon and Schlessinger, 2010). Activating mutations in EGFR are common in non small cell lung cancer (NSCLC). EGFR is overexpressed in
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Breast cancers and this is associated with metastasis and worse prognosis (Olayioye et al., 2000; Weigelt et al., 2005; Lemmon and Schlessinger, 2010). Her2 overexpression can result in homodimerisation or ligand-independent heterodimerisation with EGFR, resulting in inappropriate signalling downstream of this receptor (Olayioye et al., 2000).
1.2.3 Kit
Kit is the receptor for stem cell factor (SCF) and is essential for hematopoiesis, melanogenesis and gametogenesis (Roskoski Jr, 2005a). Like other RTKs Kit becomes activated following the binding of SCF to the extracellular domain of Kit. Kit’s juxtamembrane domain provides a negative regulatory role by binding to the kinase domain to maintain the receptor in an inactive conformation (Lemmon and Schlessinger, 2010). Binding of SCF disrupts this interaction. Kit is often overexpressed and/or mutated in both liquid and solid tumours. For example Kit exhibits over 30 gain of function mutations that render the receptor constitutively active (Blume-Jensen and Hunter, 2001). Tumours commonly exhibiting Kit overexpression and/or mutations are gastrointestinal stromal tumours (GISTs), acute myelogenous leukaemia (AML), mastocytomas and T cell lymphomas (Roskoski Jr, 2005a). In addition, paracrine or autocrine activation of Kit is often found in ovarian and small-cell lung cancers (Roskoski Jr, 2005a). Currently the expression of Kit in tumours is routinely tested to diagnose GISTs and predict responsiveness to the Kit kinase inhibitor Imatinib/Gleevec (Ludwig and Weinstein, 2005)
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Table 1.1: Kinases commonly overexpressed or deregulated in cancers Oncogenic alteration Evidence in Cancer x EGFR v-erbB Avian fibrosarcomas (ErbB1) Overexpression Breast, glioblastoma, ovarian, NSCLC, head and neck, colorectal, pancreatic & other cancers
Mutation NSCLC
Extracellular domain deletions Glioblastoma, lung and breast cancers x Her2 Overexpression Breast, ovarian, gastric, NSCLC & colon cancers (ErbB2) (amplification) x Met Overexpression Breast, cervical, ovarian, colorectal, oesophageal, rhabdomyosarcoma, hematopoietic malignancies & many others.
Activating mutations PRCC, head and neck, liver & kidney cancers x Kit v-kit Feline fibrosacrcomas
Overexpression GISTs, seminomas, SCLC
Activating mutations GISTs, AML, mastocytomas, T cell lymphomas, melanomas x Src v-src Avian sarcomas
Overexpression and/or Breast, ovarian, pancreatic, neuroblastomas, increased kinase activity oesophageal, gastric, lung cancer & melanoma
C-terminal truncation Colon cancer, hepatocellular cancers x Lyn Overexpression AML, CML, colorectal, prostate cancer, glioblastoma & Ewing’s sarcoma x FAK Overexpression and/or Breast, head and neck, thyroid, prostate, cervix, increased kinase activity ovarian & colon cancers
x PI3K Mutations (p85 & p110) Breast, colorectal, stomach, brain ovarian, endometrial, urinary tract cancers
Overexpression (p110) Oesophageal, head and neck, ovarian, cervical, gastric, squamous cell lung cancers x Abl v-abl Murine acute leukaemias
Translocation (BCR-Abl) CML x Akt Overexpression Breast, pancreatic, ovarian, colorectal, head and neck, & hepatocellular cancers
Mutation Breast, colorectal & ovarian cancers x Raf Activating mutations in Melanoma, colorectal, ovarian, sarcoma & thyroid BRaf cancers
Abbreviations: GIST (Gastrointestinal stromal tumours); NSCLC (Non-small cell lung cancer); SCLC (Small cell lung cancer); PRCC (Papillary renal cell carcinoma); AML (acute myeloid leukaemia ); CML (Chronic myelogenous leukaemia). Information from Biscardi et al., 2000; Blume-Jensen and Hunter, 2001; Davies et al., 2002; Birchmeier et al., 2003 Vogelstein and Kinzler, 2004, Yeatman, 2004; McLean et al., 2005; Weinberg, 2007; Dhomen and Marais, 2007; Bennett et al., 2008; Yuan and Cantely, 2008; http://www.vai.org/met/ 8 8 Chapter 1: Introduction
1.3 Signalling downstream of RTK
Signal transduction through transmembrane receptors such as RTKs allows external signals to be transduced to internal signalling pathways to coordinate an appropriate cellular response. But how exactly is this done? As alluded to in the previous section, upon activation, RTKs autophosphorylate specific sites to generate docking sites for cytoplasmic signal transduction proteins. The binding of these downstream effector proteins is dictated by their possession of certain domains while it is the combination of these domains within the protein that ensures specificity of binding (Lemmon and Schlessinger, 2010). The three most common domains are: SH2 domains that recognise and bind phosphotyrosines within a specific sequence context, SH3 domains that typically recognise proline rich sequences, and PH domains that recognise and bind specific phosphoinositides in lipid membranes (Kairouz and Daly, 2000; Schlessinger, 2000).
Grb2 is an adaptor protein that binds to RTKs via its SH2 domain. It links RTKs with the downstream Erk/MAPK cascade, through its recruitment of the Ras guanine nucleotide exchange factor SOS (Kairouz and Daly, 2000; Schlessinger, 2000) (Fig 1.1). The Grb2/SOS complex can also be linked to RTKs through its association with Shc, which binds RTKs through its phosphotyrosine binding (PTB) domain (Schlessinger, 2000). SOS induces Ras to replace GDP with GTP thus activating it. Ras in this active state then binds to and activates the serine/threonine kinase, Raf. Raf (MAPKKK) in turn is able to phosphorylate and activate Mek (MAPKK), which subsequently leads to the activation of Erk (MAPK). Erk then translocates to the nucleus to phosphorylate various transcription factors, thus affecting the transcription of relevant genes. This pathway is involved in regulating a variety of cell responses such as cell cycle control, morphology and migration, and cellular differentiation (Schlessinger, 2000; Gao and Woude, 2005). In addition, the MAPK pathway has been implicated in cell scattering, as pharmacological inhibition or a dominant negative mutant of Mek prevents HGF induced loss of cell-cell contacts and scattering (Potempa and Ridley, 1998; Grotegut et al., 2006).
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In cancer, this signalling cascade is often upregulated through the overexpression of its members such as Erk1/2 and Ras (Bennett et al., 2008). Additionally, activating mutations in Ras (K-ras and N-ras) have been found in ovarian, thyroid, pancreatic, endometrial, colorectal and NSCLC (Vogelstein and Kinzler, 2004). Activating mutations in one of the members of the Raf family (BRaf) have been found in various cancers, in particular melanomas (Davies et al., 2002; Dhomen and Marais, 2007).
Grb2 also has a role in linking the large docking protein Gab1 to RTKs such as EGFR, Met and Insulin receptors (Furge et al., 2000). In the case of Met, Gab1 can also bind directly to the receptor through a unique Met binding domain (MBD) that engages with both Met’s kinase domain and part of the bidentate docking site (Y1349) (Fig 1.1) (Weidner et al., 1996; Schaeper et al., 2000; Lock et al., 2003). Upon ligand stimulation, both Met and Src phosphorylate Gab1 at different tyrosine residues (Chan et al., 2003; Chan et al., 2010). These provide unique binding sites for SH2- containing signalling proteins such as Src, Shp2, PLC-J, Stat3, Crk, Shc and the p85 subunit of PI3K (Furge et al., 2000; Schaeper et al., 2000). The binding of the phosphatase Shp2 to Gab1 is able to stimulate Ras activation (Schaeper et al., 2000; Birchmeier et al., 2003) (Fig 1.1). Thus, in addition to the Grb2/SOS complex, the Erk/MAPK cascade can be stimulated via Shp2. Note that apart from indirect binding through Gab1, Src, PLC-J, PI3K , Shc and Stat3 can also bind directly to Met and other RTKs upon stimulation (Ponzetto et al., 1994; Furge et al., 2000).
Like the Erk/MAPK cascade, the PI3K pathway is stimulated by all RTKs (Schlessinger, 2000). PI3K is a heterodimer that consists of a regulatory and catalytic subunit (Fig 1.1). Stimulation occurs through binding of the p85 subunit to RTKs or proteins such as Gab1. This causes a conformational change in p85 that results in activation of the p110 catalytic subunit. Activation of p110 results in phosphorylation of specific target phosphoinositides in the plasma membrane at the 3’ hydroxyl group, to produce the lipid second messengers PtdIns(3,4,5)P3 and PtdIns(3,4)P2 (Yuan and Cantley, 2008) (Fig 1.1). The serine/threonine kinase Akt is able to bind these second messengers through its PH domain, thus relocalising it to the membrane. Here, Akt can be activated by the phosphorylation of its threonine 308 site by PDK1 and serine
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473 site by TORC2 (Blume-Jensen and Hunter, 2001; Sarbassov et al., 2005). Akt activation leads to phosphorylation of various downstream targets. As such, the PI3K/Akt signalling pathway controls various endpoints such as progression of the cell cycle, regulation of apoptosis and cell survival (Blume-Jensen and Hunter, 2001). PI3K activity can also be stimulated downstream of RTKs through activation of Ras, since Ras binds the PI3K catalytic subunit, thus forming a positive feedback loop for RTK signalling (Blume-Jensen and Hunter, 2001) (Fig 1.1). The PI3K pathway has been implicated in cell scattering, since pharmacological inhibition or the transfection of dominant negative mutants of PI3K resulted in loss of HGF-induced cell junction disassembly (Potempa and Ridley, 1998). The PI3K pathway has central roles in tumorigenesis and is unique in that every member within the pathway is frequently amplified or mutated (Yuan and Cantley, 2008). This pathway can be deregulated by overexpression or mutations of p110 and p85 (Bennett et al., 2008; Yuan and Cantley, 2008) (Table 1.1). Overexpression of the downstream kinase Akt is observed in breast, pancreatic, ovarian, colorectal and hepatocellular cancers (Bennett et al., 2008). Alternatively, Akt can be mutated in its PH domain, allowing its constitutive association with the plasma membrane and therefore activation. This occurs in breast, colorectal and ovarian cancers (Yuan and Cantley, 2008). Aberrant signalling of the PI3K/Akt pathway can also occur through mutation or loss of one of the negative regulators of signalling, PTEN. The tumour suppressor PTEN is a phosphatase that dephosphorylates PtdIns(3,4,5)P3 thus attenuating Akt signalling (Fig 1.1). PTEN inactivating mutations and losses are found in endometrial, colon, skin, prostate, breast, lung cancers and glioblastomas (Bennett et al., 2008; Yuan and Cantley, 2008).
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1.4 Src family kinases
One important group of cytoplasmic signalling proteins that act downstream of a variety of RTKs is the Src family kinases (SFKs). The best-studied and oldest proto- oncogene is c-Src, which was identified following the discovery of its oncogenic viral counterpart (v-Src) in Rous Sarcoma Virus, that was able to cause avian tumours (Martin, 2004). The Src family is comprised of 9 tyrosine kinases (Blk, Fgr, Fyn, Hck, Lck, Lyn, Src, Yes, Yrk), each with a different distribution around the body. Src, Fyn, Yes and Yrk are expressed ubiquitously while others such as Lyn are normally expressed in cells of the immune system, and erythropoietic cells (Parsons and Parsons, 2004).
1.4.1 The unique structure of SFKs
Members of this family have a conserved domain structure, with an N-terminal myristoylation domain that allows membrane tethering, SH3 and SH2 domains, a tyrosine kinase catalytic domain containing the activation loop and a C-terminal regulatory segment (Fig 1.2).
Fig 1.2 Structure of SFKs and important residues controlling their activity. SFKs contain an N-terminal myristoylation site, SH3 and SH2 domains and a kinase domain containing an activation loop with a key tyrosine residue (Y416). Autophosphorylation of this residue results in maximum kinase activity. Intramolecular interactions (dotted arrows) maintain the protein in a closed, autoinhibited conformation. Adapted from Boggon and Eck, 2004.
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Differences amongst the members lie within an N-terminal unique domain of 50-70 residues, and this may mediate different protein-protein interactions, in accordance with the biological role of each SFK member (Ingley, 2008; Boggon and Eck, 2004). The kinase domain of SFKs is bilobal, and in the inactive state, the activation loop is held buried in the cleft between the two lobes, thus preventing inappropriate phosphorylation (Boggon and Eck, 2004). This autoinhibited state is stabilised by intramolecular interactions between the SH3 domain and a polyproline linker between the SH2 and kinase domains, and between the SH2 domain and a phosphorylated C- terminal negative regulatory tyrosine residue Y527 in Src (Xu et al., 1997; Boggon and Eck, 2004) (see Fig 1.2 and 1.3). Note that each SFK member has a different tyrosine residue corresponding to the activation and inactivation tyrosines. For example in Lyn the activation site is Y397 and inactivation site is Y507.
1.4.2 Regulation of SFK activity
The regulation of SFK activity is very similar amongst the members, involving various levels of activation and inactivation (Ingley, 2008). Activation occurs via trans-autophosphorylation of tyrosine 416 (Y416) on the activation loop, following SFK binding to receptors and other target proteins via either its SH2 or SH3 domains (Roskoski Jr, 2005b; Ingley, 2008). This leads to conformational changes in the tertiary structure, allowing activation of the kinase and access of ATP to the active site (Yamaguchi and Hendrickson, 1996; Boggon and Eck, 2004). In this active state, SFKs can catalyse the transfer of the terminal phosphoryl group of ATP to downstream target proteins.
Activation can also occur via the dephosphorylation of the inhibitory Y527 site, thus preventing its interaction with the SH2 domain, and unlocking the autoinhibited conformation (Fig 1.3). This can be carried out by phosphatases including PTPD, PTP1B, SHP1 and SHP2 (Zheng et al., 1992; Bjorge et al., 2000; Roskoski Jr, 2005b). In Lyn, dephosphorylation of Y507 can also be carried out by the CD45 receptor of B cells (Xu et al., 2005).
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Fig 1.3 Mechanism of SFK activation. In the inactive state, SFKs take on an autoinhibited conformation with intramolecular interactions between the SH3 domain and the polyproline linker, and between the SH2 domain and the phosphorylated tyrosine residue Y527 at the C- terminus. Note that in this conformation, the Y416 residue is embedded into the cleft between the two lobes of the kinase domain. Upon activation, Y527 becomes dephosphorylated and the Y416 residue is extruded from the cleft to allow phosphorylation and thus full activation of the SFK. The loss of autoinhibiting intramolecular interactions allows target proteins to interact with the SH2 and SH3 domains of SFKs (depicted as X and Y). Note that for simplicity only the fully inactive and active conformations are depicted and residue numbering refers to Src.
Apart from their own autoinhibition, the activity of SFKs is negatively regulated by a variety of proteins. The cytoplasmic kinase Csk and the related Chk/Ctk protein act negatively on SFK activity by phosphorylating the C-terminal Y527 residue, driving the kinase into the inactive conformation (Okada et al., 1991; Boggon and Eck, 2004; Yeatman, 2004; Fu et al., 2006) (Fig 1.3). In pancreatic cell lines, overexpression of Chk is sufficient to diminish Lyn activity and results in both decreased proliferation and invasion (Fu et al., 2006). Csk must relocate from the cytoplasm to the membrane where SFKs are localised. This is mediated by the adaptor protein Cbp, a transmembrane phosphoprotein that binds and is phosphorylated by both Lyn and Src and acts to recruit Csk (Kawabuchi et al., 2000; Ingley et al., 2006). Recently, Cbp has also been shown to have a role in SFK regulation independently of Csk 14 Chapter 1: Introduction recruitment. Oneyama et al., 2008 demonstrated that Cbp is able to sequester Src and Fyn to lipid rafts where signalling from Src is negatively affected (Oneyama et al., 2008). SFK activity can also be diminished simply by reducing the level of total protein. Cbl, an E3 ubiquitin ligase, can ubiquitinate Src and Lyn and target them for proteosomal degradation (Kyo et al., 2003; Kim et al., 2004). Interestingly, Cbl preferentially ubiquitinates the active form of Src and Lyn, suggesting that Cbl is part of a negative feedback loop affecting SFK signalling (Kyo et al., 2003; Kim et al., 2004). In addition in erythroid cells, Cbp recruits SOCS, which ubiquitinates Lyn and leads to its proteosomal degradation (Ingley et al., 2006). Thus Cbp has a two-step role in negative Lyn regulation, firstly by inactivating it through Csk relocalisation to the plasma membrane and secondly by decreasing total Lyn levels through SOCS recruitment (Ingley et al., 2006).
Finally, recent studies have shown yet another SFK inactivation mechanism: that Y416 on the activation loop can be dephosphorylated both in mouse cells and human colon cancer cells by the phosphatase PTP-BAS/PTPL1 (PTP-BL in mice) (Palmer et al., 2002; Zhang et al., 2009). PTPL1 is recruited to Src via its interaction with RIL, which preferentially recognises active Src (Zhang et al., 2009).
With such a variety of regulatory mechanisms affecting different domains of the SFK protein, it is not surprising that SFKs can have various levels of activity. These are: i) a fully inactive state where the Y416 residue is unphosphorylated, Y527 residue phosphorylated and the intramolecular interactions result in the autoinhibited state, ii) a partially active form where there is a relaxation of intramolecular interactions, but the Y416 remains unphosphorylated and the Y527 residue phosphorylated, and iii) a fully active state where the Y416 residue is phosphorylated but which may or may not be accompanied by a relaxation of the intramolecular interactions (Ingley, 2008).
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1.4.3 Signalling pathways and processes mediated by SFKs
SFKs have pleiotropic effects on the cell, regulating cell growth, proliferation, cell morphology, migration, invasion, and survival (Bromann et al., 2004; Yeatman, 2004). This is because SFKs act to amplify signalling from an extensive array of receptors, often acting as an integrator of signalling from various RTKs. SFKs are known to relay signals from EGFR, Her2, Met, PDGFR, FGFR, IGF1R, Kit and CSFR, G-protein coupled receptors, cytokine receptors, Integrins and Cadherins (Luttrell et al., 1994; Bromann et al., 2004; Parsons and Parsons, 2004; Yeatman, 2004). In the case of EGFR, activation of Src is bi-directional, with Src once becoming activated by EGFR, able to phosphorylate and fully activate the receptor (Ishizawar and Parsons, 2004).
Since SFKs act downstream of many receptors, SFKs also have a vast array of downstream effector molecules. These include among others: FAK, PI3K, PLC-J, STATs, Paxillin, p130Cas and cortactin. One of the best studied Src substrates is Focal Adhesion Kinase (FAK). This kinase, which is activated by Src, affects cell migration by controlling the turnover of focal adhesions. Focal adhesions are dynamic sites that relay information of adhesion of the cell to the ECM via integrin engagement, to internal signalling pathways. FAK becomes autophosphorylated at Y397 which promotes binding to Src. Src can then phosphorylate FAK at sites Y576/577, Y861 and Y925 (Mitra et al., 2005). This results in a dual-activated Src/FAK complex that can phosphorylate Paxillin and p130Cas (Mitra et al., 2005). These proteins, both found at focal adhesions, act to regulate molecules that are integral to cytoskeletal rearrangements. Thus by indirectly regulating cytoskeletal dynamics, Src and FAK are able to alter the motility and migration of cells. FAK is overexpressed in various cancers such as breast, head and neck, thyroid, prostate, cervix and colon cancers (McLean et al., 2005) (Table 1.1). This increase in FAK expression is thought to enhance cell migration, leading to increased metastasis and worse prognosis (McLean et al., 2005).
Another Src substrate is the non-receptor kinase Abl (Plattner et al., 1999). Abl has a
16 Chapter 1: Introduction similar structure to SFKs and takes on a similar autoinhibited conformation despite its lack of a C-terminal inhibitory domain (Boggon and Eck, 2004). Upon EGF or PDGF stimulation SFKs can phosphorylate and activate Abl (Plattner et al., 1999; Srinivasan and Plattner, 2006). Functionally, Abl is thought to regulate SFK-induced mitogenisis by being part of a RTK/SFK/Abl/Myc signalling cascade (Furstoss et al., 2002; Bromann et al., 2004). In chronic myelogenous leukaemia (CML), over 90% of cases are characterised by the presence of the oncogenic Bcr-Abl fusion protein. This results from a reciprocal translocation between chromosomes 9 and 22 [t(9;22)] such that the Abl gene is placed under transcriptional control of the Bcr locus. This results in a constitutively active Abl oncoprotein which results in the activation of the Erk/MAPK and PI3K pathways (Blume-Jensen and Hunter, 2001). In addition, increased activation of Abl in breast cancer cell lines promotes invasion, proliferation, survival, and anchorage-independent growth (Srinivasan and Plattner, 2006; Srinivasan et al., 2008).
1.4.4 Src and cancer
Src is commonly overexpressed or activated in many types of human cancers such as breast, lung, colon, pancreas, ovarian, gastric, oesophageal cancers and neuroblastomas (Ottenhoff-Kalff et al., 1992; Verbeek et al., 1996; Irby and Yeatman, 2000; Yeatman, 2004) (Table 1.1). On its own, Src is only weakly oncogenic, however it has striking effects when combined with perturbations in other oncogenes, in particular RTKs. Thus Src overexpression acts to cooperate and enhance the effects of other oncogenes in the tumorigenic process (Biscardi et al., 2000; Ishizawar and Parsons, 2004). In contrast, the viral form of Src (v-Src) is highly oncogenic and this is due to a loss of the C-terminus negative regulatory region containing the Y527 residue (Boggon and Eck, 2004; Yeatman, 2004). Interestingly, a truncating C- terminus mutation is found in some human colon cancers (Irby et al., 1999). Src has also been shown to have an important role in EMT in cancer cells. Active Src results in loss of cell-cell adhesion, due to re-localisation of E-cadherin, a protein found at cell-cell contacts where it is involved in homo-typic interactions (Avizienyte et al., 2002; Irby and Yeatman, 2002). This effect of Src on E-cadherin dynamics was
17 Chapter 1: Introduction found to be through a mechanism involving FAK (Avizienyte et al., 2002; Irby and Yeatman, 2002). Thus Src overexpression in cancers results in increased motility, firstly by increased activation of FAK and therefore increased focal adhesion turnover, and secondly by loss of cell-cell adhesion through re-localisation of E cadherin away from cell-cell contacts.
Because this thesis focuses on the SFK Lyn, a more detailed description of Lyn will be presented in the next section
1.4.5 Lyn-mediated signalling
The role of Lyn in normal cells has been for the most part studied in hematopoietic cells since cells outside this compartment do not normally express Lyn. Lyn was identified in the 1980’s as a 56 kDa kinase that is expressed in various immune cells and lymphoid tissues of the body (Yamanashi et al., 1987; Yamanashi et al., 1989). There are two isoforms of Lyn, with sizes of 53 kDa and 56 kDa that result from alternative splicing, however no functional differences between the forms are known (Xu et al., 2005).
Lyn is typically activated downstream of several hematopoietic cell receptors including the B cell receptor (BCR), the erythropoietin (Epo) receptor and Kit (Burkhardt et al., 1991; Yamanashi et al., 1991; Tilbrook et al., 2001).
Unlike other SFKs, Lyn acts to potentiate or dampen signalling downstream of receptors depending on the cell context. Its primary role in hematopoietic cells is to attenuate signals downstream of cell surface receptors (Hibbs and Harder, 2006). Evidence for this is that Lyn deficient B cells are hyper-responsive to BCR stimulation, have increased proliferation and exhibit elevated MAPK and Akt activation (Wang et al., 1996; Chan et al., 1997; Xu et al., 2005; Hibbs and Harder, 2006). In addition, Lyn deficient mice develop autoimmune diseases and monocyte/macrophage tumours or ‘myeloid neoplasia’ (Hibbs et al., 1995; Harder et al., 2001; Xu et al., 2005). This negative regulatory role is likely due to its role in
18 Chapter 1: Introduction promoting recruitment of the phosphatases SHP-1, SHIP-1 and SHP-2 to membrane receptors (Harder et al., 2004). Indeed B cells from mice with a Lyn gain of function mutation have constitutive activation of SHP-1 and SHIP-1 (Hibbs et al., 2002).
However Lyn can also act as a positive regulator in cell signalling in erythropoietic cells downstream of the Epo receptor (Boudot et al., 2003; Ingley et al., 2005). In Epo stimulated cells, Lyn binds and activates PLC-J and PI3K (Budot et al., 2003). Lyn is thought to activate PI3K following interaction of Lyn’s SH3 domain and a proline rich segment in the p85 subunit of PI3K (Pleiman et al., 1994). In addition, Lyn deficiency or use of a dominant negative Lyn in mice, results in decreased levels of GATA-1, EKLF and STAT5, transcription factors which are required for the development of mature erythroid cells (Tilbrook et al., 2001; Ingley et al., 2005). Furthermore, the absence of Lyn suppresses erythrocyte differentiation, with mice exhibiting increased numbers of erythroid progenitor cells in the spleen but not in the bone marrow (Tilbrook et al., 2001; Ingley et al., 2005). This suggests that erythrocytes are being produced in the spleen to compensate for defects in erythrocyte production in the bone marrow (Ingley et al., 2005). Similarly, another study demonstrated that Lyn deficient mice exhibited increased myeloid progenitor cells and splenomegaly (Harder et al., 2001). Despite these compensatory mechanisms, older mice still develop anemia (Ingley et al., 2005). In addition, Lyn is activated by GM-CSF, IL-3 and IL-5 receptors and thought to play both positive and negative roles downstream of these receptors (Hibbs and Harder, 2006).
With respect to Kit, Lyn potentiates signalling downstream of this receptor. In leukaemia cell lines and human foetal cells, Lyn binds to Kit’s juxtamembrane region and the complex undergoes bi-directional phosphorylation in response to SCF stimulation (Linnekin et al., 1997). Knock down of Lyn in these cells results in a decrease in SCF-induced proliferation, suggesting that Lyn plays a positive role in SCF signalling (Linnekin et al., 1997). In bone marrow mast cells stimulated with SCF, Lyn deficiency results in reduced JNKs and STAT3 activation and decreased proliferation, indicating a positive signalling role (Shivakrupa and Linnekin, 2005). In contrast, in these same cells, Lyn deficiency resulted in constitutive activation of Akt
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(Shivakrupa and Linnekin, 2005). This suggests that at least in these cells, the absence of Lyn can stimulate one pathway while inactivating another. In another study supporting Lyn as a positive regulator of signalling, myeloma cell lines that overexpressed Lyn, had increased IL-6 induced PI3K and Akt activation, leading to enhanced proliferation (Iqbal et al., 2010).
Thus Lyn has dual roles in both amplifying and inhibiting signals downstream of receptors in immune and erythroid cells and this may be dependant on the cell context and the nature of the stimulation (Xu et al., 2005). However despite this knowledge of Lyn signalling in hematopoietic cells, little is known about Lyn signalling in cells outside this compartment. What has been established is that Lyn has a role in several cancers, involving both hematopoietic and non-hematopoietic cells.
1.4.6 Lyn and cancer
A firmly established role for Lyn has been found in both acute myeloid leukaemias (AML) and chronic myelogenous leukaemias (CML). In AML patient samples, Lyn is the predominant SFK and is found in a highly active state (Roginskaya et al., 1999; Dos Santos et al., 2008). Depletion of Lyn via knock down decreases cell proliferation and growth of leukaemia cell lines (Roginskaya et al., 1999; Dos Santos et al., 2008). In CML, several studies have demonstrated a strong signalling relationship between Lyn and Bcr-Abl. Originally Lyn was shown to bind Bcr-Abl and to be activated downstream of it (Danhauser-Riedl et al., 1996; Wilson et al., 2002). However, recently it has been shown that Lyn can phosphorylate tyrosine residues on the SH2 and SH3 domains of Bcr-Abl and that this is necessary for the transformation of myeloid cells (Meyn et al., 2006). This suggests that Lyn and Bcr-Abl may be trans- activating each other and working in concert to produce a cancerous phenotype. Indeed, when Lyn was knocked down in CML cells with and without Bcr-Abl, proliferation and viability was affected only in those cells with the fusion protein (Ptasznik et al., 2004). Importantly, the presence of Lyn in CML samples is associated with resistance to Gleevec/Imatinib, a tyrosine kinase inhibitor specific for Abl, Kit
20 Chapter 1: Introduction and PDGFR (Donato et al., 2003). CML lines with acquired resistance to Gleevec in vitro, exhibited Lyn overexpression, and Lyn knock down resulted in decreased proliferation and survival, while not affecting Gleevec sensitive lines (Donato et al., 2003). Surprisingly, Gleevec resistant cell lines with increased Lyn expression exhibited decreased Bcr-Abl protein and decreased STAT5 and MAPK phosphorylation (Donato et al., 2003). This is at odds with the previously outlined studies of Lyn in erythrocytes, once again, underlining the complexities of Lyn signalling. Importantly, Lyn overexpression was associated with Gleevec resistance in patient samples, and expression increased as Gleevec treatment progressed in these resistant patients (Donato et al., 2003). This suggests that Gleevec resistance in CML patients may be mediated by overexpression of Lyn as well as through alterations in Bcr-Abl.
In addition to leukaemias, more recent studies have begun to implicate Lyn in solid tumours. In Ewing’s sarcoma, Lyn expression was found in a majority of tumour samples, and Lyn knock down in sarcoma cell lines decreased their invasive properties in vitro and in vivo in a mouse model (Guan et al., 2008). Aberrant expression of Lyn has also been found in prostate cancer. In one study, Lyn expression was found in the majority of primary and metastatic prostate cancer specimens (Goldenberg-Furmanov et al., 2004). Interestingly, Lyn was also found in normal prostate epithelium suggesting it could have a role in normal prostate development, especially since Lyn deficient mice developed abnormal prostates (Goldenberg-Furmanov et al., 2004). Administration of a Lyn specific peptide inhibitor to prostate cancer cell lines resulted in decreased cell proliferation, while administration of the same inhibitor reduced growth of tumour xenografts in mice injected with prostate cancer cells (Goldenberg-Furmanov et al., 2004). A role in promoting prostate cancer cell proliferation is also supported by a study demonstrating decreased proliferation of prostate cancer cells following siRNA- mediated Lyn knock down (Park et al., 2008). In colorectal cancer, Lyn was part of a five-gene signature that was differentially expressed in metastatic versus non- metastatic colorectal cancer cell lines (Hao et al., 2010). Additionally, Lyn expression correlated with lymph node metastasis and decreased overall survival in colorectal
21 Chapter 1: Introduction cancer patients (Hao et al., 2010). This points to a potential role for Lyn in promoting migration and invasion of cancer cells. Finally, Lyn has been implicated in glioblastoma tumours, where it exhibits increased activity when compared with other brain tumours and normal brain samples (Stettner et al., 2005).
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1.5 Architecture of the normal breast
The breast is a complex structure in which milk that is produced by cells in alveoli/lobules, drains into a system of ductules and ducts leading to the nipple (Fig 1.4 A). The lumens of both lobules and ducts are lined by a single layer of cuboidal ‘luminal’ epithelial cells (Fig 1.4 B).
A)
B)
Fig 1.4 Architecture of the normal breast. A) Structure of the normal breast, demonstrating the system of alveoli, ductules and ducts that transport milk to the nipple (Wagner, 2009. http://emedicine.medscape.com/article/1835675-overview). B) Schematic of a terminal duct lobular unit (TDLU) with luminal epithelial cells, basal/myoepithelial cells and surrounding basement membrane which separates the breast epithelial cells from the underlying stroma. Note that basal and myoepithelial cells are shown separately, however these two terms are often used interchangeably (Debnath et al., 2003)
These are surrounded by an intermittent layer of ‘myoepithelial/basal’ cells, which contract to aid the secretion of milk proteins produced by the luminal cells. These basal cells are heterogenous and can be cuboidal or spindle shaped depending on their
23 Chapter 1: Introduction position and the hormone status of the surrounding tissue (Rakha et al., 2006; Da Silva et al., 2007). Encasing these cells is the basement membrane, composed of ECM proteins, which separates the epithelial cells from the underlying ‘stroma’, comprised of adipocytes, fibroblasts and immune cells. Luminal and basal/myoepithelial cells can be distinguished from each other not only by their location but also by their differential expression of specific cytokeratins (CK) as detected by immunohistochemistry (IHC) (Basals express CK 5/6 and Luminals CK 8/18) (Perou et al., 2000).
1.6 The heterogeneity of Breast cancer
Breast cancer is a complex and heterogenous disease, to the extent that some consider it to be comprised of several different diseases, each with different risk factors, pathological features and therapeutic responses that share a common breast origin, usually the terminal duct lobular unit (TDLU) (Sainsbury et al., 2000; Weigelt et al., 2010). Importantly, patients with the same stage, grade and type of cancer can have varied outcomes, underlying the importance of identifying additional molecular markers for patient stratification (Ludwig and Weinstein, 2005).
Traditionally Breast cancer cases have been classified by grading, staging and histological type. Grading is the extent of de-differentiation of a tumour, with more de- differentiation correlating with more aggressiveness. Staging is assessed using the TNM system where T is tumour size and depth, N is lymph node spread and M is presence or absence of metastasis (Ludwig and Weinstein, 2005). This is used to determine treatment and prognosis, with patients possessing larger tumours and metastasis having shorter survival. In addition, Breast cancers can be classified into histological subtypes. The most common type is invasive ductal carcinoma not otherwise specified (IDC-NOS), which accounts for 50-80% of cancer cases. This is a classification based on exclusion since cancers are classified as such when they don’t exhibit any special histological pattern. This is followed by invasive lobular carcinoma (ILC) with a frequency of 5-15% and the remaining 25% are a mix of 17 morphologically distinct ‘special types’, many still not well understood due to their
24 Chapter 1: Introduction relative scarcity (Weigelt et al., 2005; Weigelt et al., 2010). Importantly, in the last decade, in an attempt to further stratify patients, microarray- based transcription profiling on Breast cancer cohorts has derived new subtypes based on their molecular signatures. These molecular ‘portraits’ in part reflect variations in signalling pathways underlying cancer pathogenesis. These 5 molecular subtypes are: Luminal A, Luminal B, Her2, Basal-like and Normal-like (Perou et al., 2000; Sorlie et al., 2001; Sorlie et al., 2003) (see Fig 1.5). The Luminal A and B groups are estrogen receptor (ER) positive and have a gene expression pattern similar to epithelial luminal cells (Perou et al., 2000). Luminal B differs from Luminal A mainly by its decreased expression of ER, and ER-associated genes, and worse survival (Sorlie et al., 2001). The Her2, Basal and Normal-like subgroups are all ER negative. The Her2 group is characterised by Her2 overexpression, while the Normal-like group expresses genes characteristic of basal/myoepithelial cells and adipose tissue (Perou et al., 2000). Recently, it has been suggested that the Normal-like group may merely be an artefact due to normal tissue contamination rather than a true subtype (Weigelt et al., 2010). The classification of 5 groups has also been confirmed at the protein level through IHC on tissue microarrays (TMA) of patient samples, whilst adding additional information on protein expression within subgroups, to generate “immunophenotypes” (Nielsen et al., 2004; Abd El-Rehim et al., 2005) (see Fig 1.5).
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Fig 1.5 Classification of Breast cancers based upon gene profiling signatures and immunophenotypes. Representation of dendrogram generated by Sorlie et al., 2001, subdividing Breast cancers into 5 molecular subgroups depending on gene expression profiling. The molecular characteristics presented are a combination of markers identified through gene profiling and IHC. The incidence for each subgroup, identified solely through gene microarrays, was derived from Sorlie et al., 2003 where the data from 3 independent cohorts were analysed. Thus the incidences presented here may differ from those obtained from different cohorts and using other approaches to define subgroups (eg IHC and Her2 FISH).
The classification of a patient sample into these groups has prognostic significance since Luminal A cancers have the best prognosis, followed by Luminal B, and Basal and Her2 groups having the worst outcome (Sorlie et al., 2003; Abd El-Rehim et al., 2005; Rakha et al., 2006). In addition, classification within these subgroups may drive personalised treatment options since Tamoxifen (ER antagonist) and Trastuzumab/Herceptin (anti-Her2 monoclonal antibody) can be used against cancers of the Luminal and Her2 subgroups respectively (Ludwig and Weinstein, 2005).
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Following the discovery of these molecular subgroups, a pertinent question has now arisen: how do we consolidate our new knowledge of molecular subtypes with the more traditional histological types, and staging classification system? This has recently been addressed by gene expression profiling and protein expression analysis of histological subtypes. IDC-NOS and ILC encompassed all molecular subtypes, while the 17 ‘special types’ had a more homogenous pattern (Abd El-Rehim et al., 2005; Weigelt et al., 2008). Indeed, with one exception, each special type belonged to a single molecular subtype (Weigelt et al., 2008). It should be noted however that even within molecular subtypes, there was a vast difference in gene expression level amongst histological types (Weigelt et al., 2010). The lack of a robust taxonomy based on molecular subtypes may help explain why the molecular classification is not routinely implemented in the clinic (Weigelt and Reis-Filho, 2009). This highlights the need for a single, unified and practical classification system that incorporates elements of grading, staging, histology and molecular subtypes.
1.7 Basal Breast cancer
1.7.1 Definition of ‘Basalness’
Basal breast cancer represents anywhere between 6-37% of Breast cancer cases depending on the populations studied and definitions used to identify Basal breast cancer (Sorlie et al., 2003; Podo et al., 2010). Indeed, there is some controversy as to how Basal breast cancers should be defined. Basal breast cancers are typically called ‘triple-negative’ due to their lack of ER, Progesterone receptor (PR) and Her2 expression (Fig 1.5). However, it is widely acknowledged that the triple-negative phenotype is not sufficient to delineate Basal from non-Basal breast cancers, yet the identity of markers to be used for this is under contention. For example in terms of CKs, Basal breast cancers can be defined by any combinations of the following: CK 5/6, CK 5/17 and CK 14 (Rakha et al., 2008) (Fig 1.5). To this can be added any combination of several other markers such as Caveolin 1 or 2, p63, p53, Smooth muscle actin (SMA), P-cadherin, vimentin, laminin, EGFR and grade III (see Rakha et al., 2008 for a full review on the problem of definition). This lack of a standardised
27 Chapter 1: Introduction criterion for defining Basal breast cancers is not only problematic at the clinical/pathology level, it also makes it difficult to compare the results of different studies. In addition, despite the fact that the terms ‘Basal’ and ‘Myoepithelial’ breast cancer have been used interchangeably to describe triple-negative cancers, these should perhaps be treated separately. One study was able to subdivide triple-negative cancers into ‘Basal’ and ‘Myoepithelial’ cancers, each with distinct markers (CK 5/6 and/or CK 14 vs SMA and/or p63), age at diagnosis, average tumour sizes, and tumour reoccurrence, suggesting that they are in themselves two separate cancer subgroups (Rakha et al., 2006).
1.7.2 Clinical features of Basal breast Cancers
Basal breast cancers have a typically aggressive nature and are associated with a lower age at diagnosis and increased chance of metastasis, particularly to sites associated with mortality such as the brain and lung (Abd El-Rehim et al., 2005; Rakha et al., 2008; Podo et al., 2010). Basal breast cancers are typically of high histological grade, and high mitotic index (Da Silva et al., 2007; Podo et al., 2010). This probably contributes to the reproducibly poor prognosis of Basal breast cancers (Sorlie et al., 2001; Sorlie et al., 2003; Abd El-Rehim et al., 2005; Rakha et al., 2006). Basal breast cancers often develop between routine mammographies, ie are an ‘interval cancer’, which may be explained by the higher proliferative activity and may cause the higher histological grade upon diagnosis (Rakha et al., 2008; Podo et al., 2010). Interestingly, although overall survival is lowest in Basal breast cancers, this appears to be limited to within the first 5 years (Fulford et al., 2007). In fact, after 5 years the prognosis for Basal breast cancers is better than other high-grade IDC-NOS (Fulford et al., 2007). It is essential therefore to identify markers which are able to differentiate patients with ‘bad’ prognosis that are likely to relapse within the first 5 years versus those with ‘good’ prognosis that are able to survive this 5 year interval and subsequently have a better outcome. These findings however must be treated cautiously since this study used CK 14 as the sole marker of Basal breast cancers. This may not be sufficient since Rakha et al., 2006 found that a substantial proportion (40%) of Basal breast cancers were CK 14 negative (Rakha et al., 2006)
28 Chapter 1: Introduction
Histologically the majority of Basal breast cancers are IDC-NOS but they are also represented amongst the special types, in particular adenoid cystic, tubular mixed, acinic cell, medullary, metaplastic, pleomorphic lobular and secretory cancers (Rakha et al., 2006; Weigelt et al., 2008; Weigelt et al., 2010).
1.7.3 The association between Basal breast cancers and BRCA1
BRCA1 is a tumour suppressor that when inherited in a mutated form is associated with the development of pathologically distinct Breast cancers. There are many similarities between Basal breast cancers and Breast cancers that develop in BRCA1 mutation carriers. Firstly, they share a younger age of onset, higher histological grade and poorer outcome than other cancers (Lakhani et al., 2005; Rakha et al., 2008; Podo et al., 2010). At the molecular level BRCA1-associated cancers exhibit a Basal breast cancer phenotype. That is, they lack expression of ER, PR and Her2, exhibit increased p53 and EGFR levels and express CK 14 and CK 5/6 (Lakhani et al., 2002; Foulkes et al., 2003; Lakhani et al., 2005; Rakha et al., 2008; Mavaddat et al., 2010; Podo et al., 2010). In addition, all BRCA1 mutation associated cancers tested by Sorlie et al., 2003 clustered with the Basal subgroup, suggesting they overlap at the gene expression level (Sorlie et al., 2003). Interestingly staining for Basal CKs and lack of ER can predict BRCA1 mutational status (Lakhani et al., 2005). This association between Basal breast cancer and BRCA1 dysfunction is also true of sporadic rather than familial Breast cancers. That is, sporadic Basal breast cancers are associated with a decrease in BRCA1 mRNA levels (Turner et al., 2007). Lastly, a mouse model with loss of BRCA1 and p53 is routinely used to study Basal breast cancers, since the tumours that develop recapitulate many of the characteristics of Basal breast cancers (Weiglet et al., 2010). Despite these facts, the association between BRCA1 mutation/loss and Basal breast cancers is still not understood (Rakha et al., 2008; Podo et al., 2010).
1.7.4 Additional markers associated with Basal breast cancers
In an attempt to tighten the definition of ‘Basalness’ several groups have undertaken
29 Chapter 1: Introduction immunoprofiling studies of Basal breast cancers through IHC and have discovered certain protein markers that are characteristic of Basal breast cancers. The first marker is EGFR. EGFR overexpression was observed in both Basal breast cancer cell lines and in 50-70% of Basal breast cancer cases (Nielsen et al., 2004; Charafe-Jauffret et al., 2006; Kim et al., 2006; Da Silva et al., 2007). Furthermore EGFR expression was associated with worse survival when EGFR staining was analysed in Basal breast cancer tumours (Nielsen et al., 2004). This could potentially be because EGFR expression is associated with a ‘proliferation cluster’ (Jacquemier et al., 2005).
Kit, the second marker is not commonly expressed in Breast cancers, but its levels are highest in Basal breast cancers compared to other subgtypes, and the majority of Kit positive tumours belong to the Basal group (Nielsen et al., 2004; Simon et al., 2004; Kim et al., 2006). Unlike EGFR, Kit expression was not associated with differences in survival in a group of Basal breast cancer patients (Nielsen et al., 2004; Simon et al., 2004).
Finally Met expression is known to be associated with worse survival, tumour progression, and increased metastasis in Breast cancer. However its association specifically with the Basal subgroup has only recently been discovered (Charafe- Jauffret et al., 2006; Garcia et al., 2007; Graveel et al., 2009; Ponzo et al., 2009). In two independent studies, a mouse model harbouring a Met activating mutation resulted in mammary tumours with Basal-like characteristics at the gene and protein level (Graveel et al., 2009; Ponzo, et al., 2009). Additionally, both studies demonstrated that Met expression was highest in Basal breast cancer patient samples, and that high Met expression was associated with worse prognosis (Graveel et al., 2009; Ponzo, et al., 2009).
1.7.5 The origins of Basal breast cancers
The original microarray studies identifying the Basal subgroup at the molecular level brought along with them the idea that Basal breast cancers were derived from basal/myoepithelial cells of the normal breast due to their shared markers, in
30 Chapter 1: Introduction particular the basal CKs (Perou et al., 2000; Da Silva et al., 2007). Recently this idea has been challenged, suggesting that Basal breast cancers may actually arise from luminal rather than basal/myoepithelial progenitor cells (Fig 1.6).
Fig 1.6 The origins of Basal breast cancer. A) Originally Basal and Luminal breast cancers were thought to derive from two separate lineages, comprising the myoepithelial and luminal progenitor cells respectively. B) The current model suggests that both Luminal and Basal breast cancers are derived from a luminal progenitor lineage. (Adapted from Visvader, 2009).
This is backed firstly by findings that normal cells in a location characteristic of luminal epithelial cells in both ducts and TDLUs, express basal/myoepithelial CKs (Da Silva et al., 2007, Rakha et al., 2008). Their scarcity means that they may represent mammary stem cells, now thought to be the source of various Breast cancers, due to their self-renewing capacity. In keeping with this idea, Basal breast cancer lines express the stem cell marker aldehyde dehydrogenase (ALDH), and mammary stem cells express many of the markers associated with Basal breast cancers (Charafe-Jauffret et al., 2009; Visvader, 2009). Secondly, preneoplastic tissue from BRCA1 mutation carriers showed an expanded luminal progenitor population and Basal breast cancers were more similar at the gene expression level to luminal progenitor cells than any other cells of the breast (Lim et al., 2009). As a result, this last study has suggested moving away from the misnomer ‘Basal breast cancer’
31 Chapter 1: Introduction towards ‘Luminal progenitor’ cancers (Lim et al., 2009).
1.7.6 Therapies for Basal breast cancers
Since Basal breast cancers have no/little expression of ER, PR or Her2, the targeted treatments currently available such as Tamoxifen and Trastuzumab/Herceptin are not a therapeutic option. Consequently, there is no specific treatment for Basal breast cancers, and they are treated like any other grade and stage-matched Breast cancer with radiotherapy and generic chemotherapy (Da Silva et al., 2007; Rakha et al., 2008). Although evidence suggests that Basal breast cancers are particularly chemosensitive, the proportion of patients with a complete response to chemotherapy is small, and as such many patients often show relapse after treatment (Rakha et al., 2008; Podo et al., 2010). Treatments which are currently being tested for Basal breast cancers are platinum based drugs such as cisplatin, microtubule inhibitors Plaxitacel and Ixabepilone and DNA damaging agents (Podo et al., 2010). There is also a push for targeted treatment based on the unique markers expressed by Basal breast cancers (EGFR, Met and Kit). Anti-EGFR monoclonal antibodies (Cetuximab/Erbitux, Panitumumab) and tyrosine kinase inhibitor (TKI) agents (Gefitinib/Iressa, Erlotinib) have been used successfully for cancers such as colorectal and head and neck cancers (Cleator et al., 2007; Podo et al., 2010). Surprisingly using single agents against EGFR in Basal breast cancers has not yielded as promising results as expected, with low responses to Cetuximab in Basal breast cancer patient trials and Gefitinib in Basal breast cancer cell lines (Carey et al., 2008; Corkery et al., 2009). However, anti- EGFR treatments may enhance response when used in combination with chemotherapy drugs such as Carboplatin (Carey et al., 2008; Corkery et al., 2009). Alternatively other RTKs could be targeted in combination with EGFR (Cleator et al., 2007). Given the potential importance of Met in Basal Breast cancer development, Met may prove a very useful target. There are many clinical trials currently underway testing anti-Met antibodies and Met kinase inhibitors in various cancers (Gastaldi et al., 2010), and it will only be a matter of time until trials using these on Basal breast cancer patients are undertaken. Kit is also a potential marker which has been successfully targeted in other cancers
32 Chapter 1: Introduction using the Kit and PDGFR inhibitor Imatinib/Gleevec. A small trial of 16 patients with metastatic Breast cancer did not show a positive response to Imatinib (Modi et al., 2005). However, Imatinib is currently undergoing larger clinical trials in Basal breast cancer patients (Cleator et al., 2007). Finally, the molecular and clinical similarities between Basal breast cancers and BRCA1 mutation cancers suggests that treatment strategies for BRCA1 cancers may also be useful for treatment of Basal breast cancers (Cleator et al., 2007; Rakha et al., 2008). Two such treatments are PARP inhibitors, and platinum based chemotherapy (Cleator et al., 2007; Rakha et al., 2008). Both of these act at the level of DNA, with PARP inhibitors preventing PARP’s role in single strand break repair and platinum salts inducing cross-linking of DNA leading to apoptosis of cancer cells.
1.7.7 A new promise: Dasatinib sensitivity in Basal breast cancers
Recent pre-clinical studies have demonstrated that Basal breast cancer cell lines are particularly sensitive to the multikinase inhibitor Dasatinib/Sprycel. Dasatinib was developed in 2004 and shown to be a potent inhibitor of SFKs and Abl family kinases (Lombardo et al., 2004). In addition it also inhibits Kit, PDGFR and EphA2 (Finn, 2008; Araujo and Logothetis, 2010). Dasatinib functions by binding to both the Abl and SFK ATP binding site, thus acting as an ATP competitive inhibitor (Lombardo et al., 2004; Williams et al., 2009). Currently Dasatinib is approved for use in Imatinib refractory CML and Bcr-Abl positive acute lymphoblastic leukaemia (ALL), and pre- clinical trials suggest that Dasatinib may be beneficial for patients with pancreatic, head and neck and lung cancers (Finn, 2008). Finn et al., 2007, demonstrated that cell lines representing Basal breast cancer are sensitive in terms of proliferation to Dasatinib (Finn et al., 2007). In addition Huang et al., 2007 were able to generate a gene expression signature predictive of Dasatinib sensitivity using Breast cancer cell lines (Huang et al., 2007). When this signature was applied to gene expression analysis of primary tumours, it selected for the Basal breast cancer subgroup (Huang et al., 2007). As a result, phase II clinical trials are underway to evaluate the efficiency of Dasatinib treatment in Basal breast cancer patients (Araujo and Logothetis, 2010). One such trial with 43 triple negative breast cancer patients
33 Chapter 1: Introduction showed a promising but modest response to Dasatinib monotherapy (4.7% of cases showed complete or partial response while 26% exhibited stable disease for more than 16 weeks) (Finn et al., 2008). Despite these encouraging results, the identity of protein/s that endow Basal breast cancer cell lines with Dasatinib sensitivity is still unknown (Rakha et al., 2008).
1.8 Project Aims
Given the poor prognosis of Basal breast cancer and the current lack of targeted therapy options for sufferers, there is an obvious need to further investigate this subtype at the molecular level. In particular, the identification of new biomarkers that are characteristic of this cancer may help drive new therapies. The apparent Dasatinib sensitivity of Basal breast cancer gives us a ‘head start’ in identifying crucial kinases responsible for the pathogenesis of Basal breast cancer. This is because in order for a cancer therapy to be effective, it must target molecules within signalling pathways which are aberrantly expressed or regulated and endow the cancer cell with characteristics that drive cancer pathogenesis. In light of this, the main hypothesis of this study was that kinases targeted by Dasatinib are overexpressed, or aberrantly regulated in Basal breast cancers. In order to test this hypothesis the aims of this study were thus to:
x Identify the kinase/s aberrantly expressed in Basal breast cancer cell lines, based on the identity of known Dasatinib targets.
x Investigate the role of these kinases in cell signalling in Basal breast cancer cell lines.
x Determine the translational applications of these findings by investigating the expression of the above kinases in patient tumour samples.
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CHAPTER TWO
Materials and Methods
2.1 Maintenance of cell lines
All cell lines were obtained from the American Type Culture Collection (ATCC), except MCF10A/EcoR cells (a gift from Drs Danielle Lynch and Joan Brugge), MDA-MB-231 and T47D cells (EG and Mason Research Institute) and MCF-7 cells (Michigan Cancer Foundation).
For all experiments, sub-confluent cells between passage numbers 1 to 15 were used. For all cell lines Trypsin/EDTA was used for passaging, while for 184 cells, TrpLE- Express (Gibco) was utilised.
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Table 2.1: Media used for maintenance of cell lines
Cell line Culture Medium (Full) Culture Medium (Starvation) x MCF10A/EcoR DMEM F12, 5% (v/v) Horse serum, DMEM F12, 0.4% (v/v) Horse serum, 20ng/ml human recombinant EGF, 0.5Pg/ml Hydrocortisone, 100ng/ml 0.5Pg/ml Hydrocortisone, 100ng/ml Cholera toxin, 50 U/ml penicillin G and Cholera toxin, 10Pg/ml human 50 Pg/ml streptomycin sulfate insulin, 50 U/ml penicillin G and 50 Pg/ml streptomycin sulfate x 184 HuMEC Basal Serum-Free medium As for MCF10A (Gibco), HuMEC Supplement (Gibco), Bovine Pituitary Extract (Gibco) x BT20 RPMI 1640, 10% (v/v) Fetal Calf RPMI, 0.5% (v/v) Fetal Calf serum, 50 x BT549 serum, 10Pg/ml human insulin, 50 U/ml penicillin G and 50 Pg/ml x MDA-MB-231 U/ml penicillin G and 50 Pg/ml streptomycin sulfate x MDAMB-468 streptomycin sulphate x MDA-MB-157 x HCC 70 RPMI 1640, 10% (v/v) Fetal Calf RPMI 1640, 0.5% (v/v) Fetal Calf x HCC 38 serum, 10mM Hepes, 1mM Na serum, 10mM Hepes, 1mM Na x HCC 1187 Pyruvate, 50 U/ml penicillin G and Pyruvate, 50 U/ml penicillin G and 50 x HCC 1143 50 Pg/ml streptomycin sulfate Pg/ml streptomycin sulfate x HCC 1954
2.2 Analysis of mRNA expression in cell lines
mRNA expression levels of SFKs and Abl family members were derived from Neve et al., 2006. Cell lines were divided into Basal or Luminal groups as designated by the study. Cell lines DU4475, HCC 1008, HCC 1599 were excluded from analysis due to lack of group assignation. For each mRNA, the mean and standard error of the mean (SEM) for the Basal and Luminal group were calculated using the graphics program GraphPad Prism (version 5a, San Diego, USA). For Fyn and Lyn, the mRNA for each cell line was shown (Fig 3.1). The difference in the mean for each mRNA, between the Basal and Luminal groups was calculated using a 2 tailed student’s t-test.
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2.3 Western blotting
2.3.1 Sample Preparation
The majority of samples for Western blotting of the cell line panel in initial experiments (Chapter 3) were derived from a lysate bank maintained at the Cancer department at the Garvan Institute of Medical Research. For subsequent experiments, cell lines were grown to sub-confluency in their appropriate medium as indicated in Table 2.1. For lysate preparation, cells were washed with ice cold phosphate-buffered saline (PBS) and incubated over ice for 15min in either normal lysis buffer (NLB) or Radioimmune precipitation (RIPA) buffer (Table 2.5). Prior to use, NLB or RIPA buffers were supplemented with the protease inhibitors 1mM phenylmethylsulfonyl fluoride, 10Pg/ml aprotinin and 10Pg/ml leupeptin, and the phosphatase inhibitor 0.1mM sodium orthovanadate. After incubation on ice, cells were scraped off, collected and spun at 17,000 xg for 10min at 4qC. The supernatant was collected and protein concentration quantified using the Biorad Protein Assay Reagent (Biorad) according to the manufacturer’s instructions. Lysates were then diluted in NLB and 4x Laemmli buffer to a final concentration of 1 μg/μl, before boiling for 5min.
2.3.2 Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and Western blot transfer
Protein lysates (10-20 Pg protein) were loaded and run on 10% polyacrylamide gels in SDS-PAGE running buffer. Proteins were transferred to 0.45 Pm PVDF Immobilon-P membranes (Millipore) and membranes blocked in 5% (w/v) skim milk in Tris buffered saline (TBS) for 1h.
2.3.3 Western blotting and Detection
Following blocking, membranes were incubated overnight at 4qC with the appropriate antibodies, which were diluted accordingly in TBS/BSA (5% BSA, 0.02% sodium azide, phenol red) (Table 2.2). Following incubation, the membranes were washed a
37 Chapter 2: Materials and Methods minimum of three times for 20min each with either TBS with 0.1% (v/v) Tween-20 (TBS/Tween) or TBS with 0.5% (v/v) Titon-X (TBS/Triton). The membranes were then incubated for 1h at room temperature with the appropriate HRP-conjugated secondary antibodies with agitation (1:5000 in 5% (w/v) skim milk in TBS/Tween). These were either sheep anti-mouse IgG (GE Healthcare), donkey anti-rabbit IgG (GE Healthcare), or donkey anti-sheep IgG (Santa Cruz Biotechnology). Membranes were then washed in either TBS/Tween or TBS/Triton for a minimum of three times for 20min each time. Bound secondary antibodies were visualised using Western Lightning enhanced chemiluminescence (Perkin-Elmer) and exposure to Fuji X-ray film. A “100 plus TM”, All pro-imaging developer (Hicksville, USA), using Fuji fixative and developer solutions was used.
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Table 2.2: Antibodies
Antibody Manufacturer Product # Source Concentration x Abl Calbiochem OP20 Mouse mAb 1:500 x Actin Sigma A5441 Mouse mAb 1:80 000 x Akt Cell signalling 9272 Rabbit pAb 1:1000 x pAkt (S473) Cell signalling 4058 Rabbit pAb 1:1000 x E-Cadherin BD Transduction 610181 Mouse mAb 1:2000 x EGFR Santa Cruz sc-03 Rabbit pAb 1:1000 x pEGFR(Y1086) Cell signalling 2220 Rabbit pAb 1:1000 x Erk 1/2 Cell signalling 9102 Rabbit pAb 1:1000 x pErk 1/2 Cell signalling 9101 Rabbit pAb 1:1000 (T202/Y204) x FAK Santa Cruz sc-1688 Mouse mAb 1:1000 x pFAK (Y397) Invitrogen sc-11765-R Rabbit pAb 1:1000 x pFAK Santa Cruz sc-16563-R Rabbit pAb 1:500 (Y576/577) x Fyn Santa Cruz sc-16 Rabbit pAb 1:2000 x Gab1 (IP) Symansis N/A Sheep pAb See text x Gab1 (WB) Upstate 06-579 Rabbit pAb 1:1000 x pGab1 (Y627) Biosource 44-568G Rabbit pAb 1:1000 x Kit Cell signalling 3308 Mouse mAb 1:1000 x pKit (Y719) Cell signalling 3391 Rabbit pAb 1:1000 x Lyn (IP& IHC) Santa Cruz sc-7274 Mouse mAb 1:1000 (WB) x Lyn (WB) Santa Cruz sc-15 Rabbit pAb 1:1000 x Met Cell signalling 4560 Rabbit pAb 1:1000 x pMet (Y1234/5) Cell signalling 3126 Rabbit pAb 1:500 x pSFK(Y416) Cell signalling 2101 Rabbit pAb 1:1000 x Src Calbiochem OPO7 Mouse mAb 1:1000 x Yes Transduction Y35330 Rabbit pAb 1:125 Laboratories x pY100 Cell signalling 9411 Mouse mAb 1:1000 WB: Western blot IP: Immunoprecipitation IHC: Immunohistochemistry
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2.3.4 Densitometry and statistical analysis of Western blot bands
Densitometry of Western blot bands was undertaken using ImageJ 1.44 software (http://rsb.info.nih.gov/ij/download.html). The intensity of each band was normalised against the corresponding actin intensity. For phospho-proteins, the intensity was further normalised to the corresponding total protein level. In order to compare band intensities from different membranes, common lysates were run on separate gels to generate ‘inter-membrane controls’. The intensity of actin-normalised bands was further normalised to the average intensity of these controls. For growth factor stimulation data, the intensity of bands representing 5 and 15min stimulation was expressed as a fold change from Time 0, the value of which was set to 1. For data analysis, the mean value for groups and the SEM of replicates were calculated using with the graphics program GraphPad Prism (version 5a, San Diego, USA). For statistical analysis a 2-tailed student’s t-test was used. Where appropriate, one way analysis of variance (ANOVA) was undertaken using the graphics program Prism and using a Kruskal-Wallis post-hoc test.
2.4 Growth factor stimulation
For growth factor stimulation experiments, 2x105 cells were plated in 6-well tissue culture plates and allowed to adhere overnight. Cells were then washed twice with PBS and starved overnight in starvation medium (Table 2.1), with the exception of 184 cells that were starved for 6h. Cells were then stimulated with either 100ng/ml recombinant human EGF (R&D Systems), 100ng/ml human cell expressed HGF (Symansis) or 100ng/ml human SCF (Cell Signalling Technology) for 0, 5 or 15min prior to lysis.
40 Chapter 2: Materials and Methods
2.5 Immunoprecipitation (IP)
For immunoprecipitation (IP), lysates prepared in NLB were incubated with the appropriate antibody overnight at 4qC with constant agitation (Lyn IP: 5Pl [1Pg] antibody/200Pg protein; Gab1 IP: 5Pl antibody/250Pg protein) (Table 2.2). Corresponding isotype-matched IgG negative controls were also used at the same concentrations (DAKO). Where appropriate, the volume of IPs was made up to 400Pl with NLB and inhibitors. The following day, 50Pl of a 50% rec-G sepharose bead slurry (Zymed Laboratories) was added to the lysates and allowed to incubate at 4qC with constant agitation for 2h. The beads were then spun at 17 000 xg for 10min at 4qC. The pellets were washed a minimum of three times in 1ml of NLB containing protease inhibitors. Following the final wash, the pellets were resuspended in 25Pl NLB and 6Pl 4xLaemmli buffer. Pellets were then boiled for 10min in a heat block prior to loading in an SDS-PAGE gel.
2.6 siRNA-mediated knockdown of Lyn
Two hundred thousand (2x105) cells were plated in 6-well tissue culture plates and allowed to adhere overnight. Cells were then transfected with the appropriate siRNA (5nM final concentration) (Table 2.3) using Lipofectamine 2000 (Invitrogen), according to the manufacturer’s instructions. The next day, the media was replaced with fresh medium containing antibiotics. Forty-eight hours following transfection, cells were starved overnight for HGF stimulation as outlined previously or starved for 6-9h for video imaging.
Table 2.3: siRNA sequences siRNA Source Sequence x Non-targeting siRNA (NT) Dharmacon “ON-TARGET Plus” sequence unavailable x Lyn 2 siRNA (L2) Qiagen 5’-GAUUGGAGAAGGCUUGUAU-3’ x Lyn 12 siRNA (L12) Qiagen 5’-CGGACGACUUGUCUUUCAA-3’
41 Chapter 2: Materials and Methods
2.7 Cell scattering Assay
Forty-eight hours following transfection, cells were starved for 6-9h and HGF (50ng/ml or 10ng/ml) was added to the appropriate wells. Time lapse imaging of cells was undertaken using a Zeiss inverted bright field microscope (Axiovert 200M) with 10x objective and the Axiovision imaging program (version 4.8). A custom-made heating chamber ensured cells were maintained at 37qC and at 5% CO2 levels during imaging. Images were taken every 15min for a total of 24h and three fields of view were analysed for each well.
2.8 Immunohistochemistry (IHC)
2.8.1 Optimisation of Lyn IHC
For optimisation of Lyn staining for IHC, paraffin imbedded MDA-MB-231 and MDA-MB-157 cells were used as positive controls while BT-474 cells were used as a negative control. A normal body atlas tissue microarray (TMA) consisting of cores derived from various normal tissues was also used to optimise the Lyn antibody by matching staining of tissues to that established in the publicly available Human Protein Atlas (http://www.proteinatlas.org).
2.8.2 Final optimised conditions for Lyn staining
Five Pm sections of each block were cut, mounted on glass slides and baked for 2h at 79qC by Alice Boulghourjian. Slides were de-waxed in xylene for 5min and re- hydrated through immersion in decreasing concentrations of ethanol (100%-70%) for 2min each, followed by a distilled water wash. Antigen retrieval was performed by immersing slides in 1x DAKO Antigen Retrieval Buffer pH6 (S1699, DAKO) and heating in a pressure-cooker for 60sec (Pascal, DAKO). Slides were allowed to cool for 15-20min in a running water bath and IHC was then performed as follows using a DAKO autostainer. Endogenous peroxidase activity was quenched by incubation of slides with 3% hydrogen peroxide for 5min (K4011, DAKO), followed by incubation 42 Chapter 2: Materials and Methods with serum-free protein block (X0909, DAKO) for 10min. Slides were then incubated for 1h at room temperature with Lyn mouse monoclonal antibody (Santa Cruz, sc- 7274) at a concentration of 2Pg/ml. As a negative control, an isotype-matched mouse
IgG (IgG2a; DAKO, X0943) was used at the same concentration. The Envision + mouse system (DAKO, K4001) was then used as a secondary antibody for 30min, followed by development of positive staining with DAB+ chromagen for 10min (DAKO, K3468). Slides were then counter-stained with haematoxylin and dehydrated by immersion in increasing concentrations of ethanol (70-100%) for 2min each. Sections were immersed in xylene prior to coverslipping.
2.8.3 Lyn staining in test Breast cancer cohort
TMAs consisting of 10 cases each of Basal, Her2 ER negative and grade 3 Luminal A IDC-NOS were constructed by Alice Boulghourjian. Molecular markers used to identify cases are listed in Table 2.4. Liver cores were included in each TMA as a positive control (Kupffer cells) while kidney cores were included as a negative control. Staining of the training cohort was undertaken as described above. A normal body atlas TMA was stained concurrently, as was an IgG negative control on the normal body TMA. Results were visualised using a Leica DFC420 camera attached to a Leica DMRB brightfield microscope. Images were recorded using the Leica Application Suite (version 3.6).
2.8.4 Lyn staining in a large Breast cancer cohort
Lyn IHC was performed on a large cohort of 246 IDC-NOS patient samples by Alice Boulghourjian using the optimised staining protocol above. Generation of the cohort and its specific details such as chemotherapy treatment and ethics approval can be found in Millar et al,. 2009 (Millar et al., 2009). Classification of each case according to molecular subtype was previously undertaken using IHC and fluorescence in-situ hybridisation (FISH) for her2 amplification (Table 2.4).
43 Chapter 2: Materials and Methods
Table 2.4: Details of Breast cancer cohort
Molecular Markers used for Number of cases Classification classification (Total: 246) x Basal ER-, PR-, Her2-, 29 (11%) CK5/6 +, and/or EGFR+ x Her2 ER-, PR-, Her2+ 21 (8%) x Luminal A ER+ and/or PR+, 153 (62%) Her2- x Luminal B ER+ and /or PR+, 26 (10%) Her2+ x Unclassified Negative for all 5 17 (6%) markers
2.8.5 Calculation of H score
Analysis of IHC staining was undertaken by designating the percentage of tumour cells positive for staining in each TMA core, and the intensity of this staining using a scale of 0 to 3 (0: no staining, 1+: weak staining, 2+: moderate staining, 3+: strong staining). To quantify staining, an ‘H score’ was calculated for each core by multiplying the % of cells stained positive by the staining intensity.
2.8.6 Analysis of Lyn staining in large cohort
Scoring and analysis of the large cohort for Lyn staining was undertaken by pathologist Dr Sandra O’Toole. ANOVA analysis was used to determine differences in Lyn staining between cancer subtypes. Kaplan-Meier survival curves and Cox proportional hazard ratios (HR) were estimated to obtain risks of Breast cancer specific death. Results were considered significant at the two-sided p<0.05 level. Furthermore, multivariate analysis using standard clinicopathological variables was undertaken to determine the potential use of Lyn as an independent prognostic marker. Statview version 5.0 (Abacus Systems) was used for statistical analysis.
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2.9 Commonly used buffers and solutions
Table 2.5: Buffers and solutions
Solution Composition x Normal Lysis Buffer 50mM HEPES (pH 7.4), 150mM NaCl, 1% (v/v) Triton X- (NLB) 100, 10% (v/v) glycerol, 1.5mM MgCl2, 1mM EGTA, 10mM pyrophosphate, 100mM NaF x Radioimmune 50mM Tris (pH 7.4), 1% (v/v) NP-40, 0.5% (w/v) sodium precipitation assay deoxycholate, 0.1% (w/v) SDS, 137.5mM NaCl, 1% (v/v) Buffer (RIPA) glycerol, 0.5mM EDTA x 4x Laemmli Buffer 175mM Tris (pH 6.8), 29% (v/v) glycerol, 0.04% (v/v) E- mercaptoethanol, 5.8% (w/v) SDS, bromophenol blue x SDS-PAGE Running 25mM Tris (pH 8.3), 190mM glycine, 0.1% (w/v) SDS Buffer x Transfer Buffer 25mM Tris (pH 8.3), 190mM glycine, 20% (v/v) methanol x TBS 50mM Tris (pH 7.4), 150mM NaCl
45 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
CHAPTER THREE
Lyn is overexpressed in Basal breast cancer cell lines
3.1 Introduction
In the past 10 to 20 years, the survival of Breast cancer sufferers has dramatically improved, partly due to the implementation of targeted therapies such as Tamoxifen and Trastuzumab/Herceptin. Despite this, one subgroup of Breast cancers, the Basal breast cancers, still presents a challenge due to its higher histological grade upon presentation, poor prognosis and lack of ER, PR, and Her2 expression, making it unamenable to currently available targeted therapies. There is therefore an obvious need for further research to identify oncoproteins that impart tumorigenic capabilities to Basal breast cancers, and which could potentially be targeted with new pharmaceutical agents. Recent work has discovered that Basal breast cancer cell lines are sensitive to Dasatinib, a multikinase inhibitor against SFKs, Abl, Kit, PDGFR and EphA2 (Finn et al., 2007; Huang et al., 2007). In addition a Dasatinib-sensitive gene expression ‘signature’ was found in Basal breast cancers (Huang et al., 2007). Consequently, a phase II clinical trial using Dasatinib monotherapy in Basal breast cancer patients has shown some promising results (Finn et al., 2008). This sensitivity to Dasatinib could be exploited to identify new kinases targeted by this drug, that are overexpressed or deregulated in Basal breast cancers. The first and simplest way to
46 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines undertake this is to compare the expression of known Dasatinib targets between Breast cancer cell lines representing Basal and non-Basal/Luminal cancers.
3.1.1 Cancer cell lines as a resource for the study of Breast cancer molecular subtypes
Breast cancer cell lines have been used for many years to study cancer pathogenesis, but to what extent do they represent the newly discovered molecular subgroups found in Breast cancers? This has been addressed by a series of studies using transcription profiling of breast cancer cell lines in a similar manner to that utilised for patient cohorts. Neve et al., 2006 carried out gene expression profiling on 51 Breast cancer cell lines and found that as for tumours, cell lines were heterogenous and were able to be grouped into Luminal and Basal-like clusters (Neve et al., 2006). Furthermore, the Basal-like group was subdivided into Basal A and Basal B lines, with the Basal A subgroup clustering more with the molecular signature used by Perou et al., 2000, and Basal B having a more stem-cell like signature, a mesenchymal appearance and increased invasion (Neve et al., 2006). Interestingly, they failed to detect a Her2 group, with Her2 overexpressing lines being distributed amongst the Basal A and Luminal groups (Neve et al., 2006). Another study using 31 Breast cancer cell lines similarly identified two groups, Luminal and Basal, with the Basal group once again being subdivided into two subgroups (Charafe-Jauffret et al., 2006). Interestingly, this study did identify a Her2 overexpressing group of cell lines, which actually clustered with Luminal lines (Charafe-Jauffret et al., 2006). The most recent study by Kao et al., 2009 used 52 cell lines and once again demonstrated the existence of a Luminal group and Basal A and B subgroups with the B subgroup, consistent with Neve et al., 2006, expressing stem-cell/mesenchymal characteristics (Kao et al., 2009). Interestingly, the Basal A lines resembled Basal tumours while Basal B cell lines most closely resembled Her2 tumours (Kao et al., 2009). Thus cell lines are able to recapitulate the molecular characteristics that are found in patient samples while representing an unlimited and easily manipulable resource for the study of Breast cancer.
47 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
3.1.2 Chapter Aims
The aims of this chapter were to characterise the expression of known Dasatinib targets in cell lines representing ‘Normal’, Basal and Luminal Breast cancers. The specific aims were to:
x Analyse gene expression data generated by Neve et al., 2006 to identify potentially overexpressed Abl and SFK members in cell lines representing Basal versus Luminal breast cancers. x Compare protein expression levels of Abl and SFK members in ‘Normal’, Basal and Luminal breast cancer cell lines.
48 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
3.2 Results
3.2.1 Gene expression analysis of Abl and SFK members in Breast cancer cell lines
To identify proteins potentially conferring Dasatinib sensitivity to the Basal subgroup, the Neve et al., 2006 microarray data set was interrogated. Briefly, cell lines were divided into either the Luminal or Basal group as determined by the authors. The mean mRNA level for each member of the Abl and Src family, in the Luminal or Basal group was calculated and a student’s t-test was used to determine any difference in this mean between the two groups. As seen in Fig 3.1, Arg, Yes, Fyn and Lyn mRNA was significantly higher in the Basal group compared to the Luminal group. Lyn in particular demonstrated a striking difference between the two groups (p<0.001). Detailed histograms are provided for Fyn and Lyn to enable comparison with protein data in the next section of this study.
49 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A) ABL ARG
B) SRC BLK
FGR LCK
YES HCK BLK
50 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
FYN
LYN
Fig 3.1 Analysis of mRNA levels of Abl and Src family kinases in Basal and Luminal cell lines. Data from Neve et al., 2006 was used to compare the mRNA level of members of the Abl A) and Src family kinases B) between Basal and Luminal lines. Arg, Yes, Fyn and Lyn mRNA expression was significantly higher in Basal compared to Luminal lines. ‘au’ is arbitrary units. Error bars represent standard error of the mean. * represents p<0.05, *** represents p<0.001.
51 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
3.2.2 Analysis of protein levels of Abl and SFK members in Breast cancer cell lines
Whilst the previous section identified 4 proteins whose mRNA expression was increased in Basal versus Luminal lines, this difference in mRNA must be maintained at the protein level to explain Dasatinib sensitivity, since Dasatinib inhibition works at the protein rather than the mRNA level (section 1.7.7). In order to investigate whether differences in mRNA levels translated to protein levels, cell lysates from a panel of Breast cancer cell lines were blotted for Abl and Src family kinases (Figs 3.2-3.6). Note that Arg was not blotted for due to a lack of a suitable commercially-available antibody.
The cell line panel consisted of ‘Normal’/Immortalised epithelial cell lines, representatives of Basal A and Basal B cancer cell lines and Luminal cancer cell lines. ‘Normal’ cell lines are generated by selection of the epithelial portion of tissue from reduction mammoplasties or non-tumour tissue from Breast cancer patients (see http://hmec.lbl.gov/mindex.html for more information regarding the generation of these lines). Note that 184 and HMEC 219-4 cells are Normal cells with a limited replicative potential while 184A1 and B5 are immortalised derivatives of 184s, while both MCF10A and MCF12A are immortalised lines. For simplicity, all of these lines have been labelled as ‘Normal’ in Fig 3.2-3.6. Also note that MDA-MB-330 was removed from the analysis as it was not identified as belonging to either Basal or Luminal groups by Neve et al., 2006.
From Fig 3.2- 3.6 it is interesting to note that the protein level of some candidates did not correlate with their mRNA level (namely Abl [Fig 3.2], Src [Fig 3.3], Yes [Fig 3.4] and Fyn [Fig 3.5]). In particular, Fyn whose mRNA was significantly higher in the Basal group and therefore of interest, actually had a significantly lower protein level in the Basal group compared to the Luminal group. Specifically, while the cell lines BT20, Hs578-T, MDA-MB-231, HCC 1143 were in the top 5 highest Fyn mRNA expressing lines (Fig 3.1) they expressed very little Fyn protein (note in particular HCC 1143 in Fig 3.5 B).
52 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
In contrast, the differences between Lyn mRNA level between the two groups was maintained at the protein level (Fig 3.1 and 3.6) with Normal and Basal lines exhibiting a significantly higher Lyn protein level than Luminal lines. Indeed only one out of the ten Luminal lines (T47D) exhibited detectable Lyn expression (Fig 3.6 A and B).
53 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.2 Characterisation of Abl protein levels across the cell line panel. A) Lysates from the Breast cancer cell line panel were Western blotted for Abl and actin. B) Densitometry of Abl levels was undertaken and data normalised to actin and inter-membrane control lysates (184B5 and BT20). C) Cell lines were subdivided into 3 groups and their mean Abl level plotted. Luminal lines had significantly higher Abl levels compared to both Basal and Normal lines. Error bars represent standard error of the mean. *** represents p<0.001
54 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.3 Comparison of Src protein levels in the cell line panel. A) Lysates from the Breast cancer cell line panel were blotted for Src and actin. B) Densitometry of Src levels was undertaken and data normalised to actin and inter-membrane control lysates (184B5 and BT20). Note that data for the right-hand WB panel has to be corrected for the weaker signals from the corresponding control lysates (184B5 and BT20). C) Cell lines were subdivided into 3 groups and their mean Src level plotted. Luminal lines had significantly higher Src levels compared to Basal lines. Error bars represent standard error of the mean. * represents p<0.05.
55 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.4 Comparison of Yes protein levels in the cell line panel. A) Lysates from the Breast cancer cell line panel were blotted for Yes and actin. B) Densitometry of Yes levels was undertaken and data normalised to actin and inter-membrane control lysates (184B5 and BT20). C) Cell lines were subdivided into 3 groups and their mean Yes level plotted. Luminal and Basal lines had significantly higher Yes levels compared to Normal lines. Error bars represent standard error of the mean. ** represents p<0.01 and *** p<0.001.
56 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.5 Comparison of Fyn protein levels in the cell line panel. A) Lysates from the Breast cancer cell line panel were blotted for Fyn and actin. B) Densitometry of Fyn levels was undertaken and data normalised to actin and inter-membrane control lysates (MM231, 184B5 and BT20). C) Cell lines were subdivided into 3 groups and their mean Fyn level plotted. Luminal lines had significantly higher Fyn levels compared to both Basal and Normal lines. Error bars represent standard error of the mean. ** represents p<0.01.
57 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.6 Comparison of Lyn protein levels in the cell line panel. A) Lysates from the Breast cancer cell line panel were blotted for Lyn and actin. B) Densitometry of Lyn levels was undertaken and data normalised to actin and inter-membrane control lysates (HCC 70 and MM157). C) Cell lines were subdivided into 3 groups and the mean Lyn level for each plotted. Both Normal and Basal lines had significantly higher Lyn levels compared to Luminal lines. Error bars represent standard error of the mean. *** represents p<0.001.
58 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
To test whether Lyn mRNA and protein level correlated directly in particular cell lines, Lyn protein level was plotted against the corresponding mRNA level in the Basal and Luminal lines used in this study. As can be seen in Fig 3.7, there is a direct and statistically significant correlation between Lyn mRNA and protein level.
Fig 3.7 Correlation between Lyn mRNA and Lyn protein level. Lyn protein levels derived from Fig 3.6 were plotted against the level of Lyn mRNA for cell lines used in this study (derived from Neve et al., 2006). Each red dot represents a single cell line. There was a statistically significant correlation between mRNA and protein level for Lyn.
3.2.3 Analysis of active Lyn levels in Breast cancer cell lines
Next, in order to determine if the activity of Lyn was also increased in the Basal and Normal groups, lysates from the cell line panel were blotted with an active SFK antibody (pSFK), which detects phosphorylation of the tyrosine at the conserved activation loop of SFKs (Y416 in Src) (Fig 1.2 and 1.3). As this antibody detects all active members of the Src family, it was necessary to identify which bands corresponded to active Lyn in cell lysates. Thus, Lyn was immunoprecipitated (IP) from various cell lysates and blotted with pSFK (Fig 3.8). By running a total cell lysate (TCL) alongside the IP, the bottom two bands or ‘doublet’ in the TCL were identified as active Lyn.
59 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
Fig 3.8 Identification of bands corresponding to active Lyn in total cell lysate (TCL). Lyn immunoprecipitates and TCL from various cell lines were Western blotted using an active SFK antibody (pSFK) and a Lyn antibody. Arrows indicate active Lyn bands in TCL, identified by their alignment to the Lyn doublet in the immunoprecipitate. Results from two separate immunoprecipitation experiments are shown
Blotting of the cell line panel with the pSFK antibody (Fig 3.9), while examining the bottom ‘doublet’, demonstrated that Basal lines exhibit a significantly higher level of active Lyn compared to the Luminal lines. Indeed, none of the Luminal lines exhibited active Lyn, including T47D, previously shown to express a low level of Lyn (Fig 3.6B). Regarding differences between the Basal A and B subclassification, there was no significant difference in either total or active Lyn levels between these classes (data not shown).
60 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
A)
B) C)
Fig 3.9 Comparison of active Lyn levels in the cell line panel. A) Lysates from the Breast cancer cell line panel were Western blotted with a pSFK and actin antibodies. Arrows correspond to active Lyn bands. B) The specific activity of Lyn was calculated by dividing the amount of active Lyn by the total Lyn as determined from Fig 3.6. This was Normalised to actin and inter-membrane control lysates (HCC 70 and MM157). C) Cell lines were subdivided into 3 groups and the mean active Lyn level for each group plotted. The Basal lines had significantly higher active Lyn levels compared to Luminal lines. Error bars represent standard error of the mean. ** represents p<0.01.
61 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
3.2.4 Association of Lyn expression with Dasatinib sensitivity
Since Yes and Fyn, which at the mRNA level were higher in the Basal group and therefore of potential interest, subsequently did not show the same pattern at the protein level, these were considered to be unlikely to impart Basal cell lines with the Dasatinib sensitivity observed in the literature. Lyn however did show a concordance of increased total and active protein level in the Basal group and thus appears to be the most promising candidate for Dasatinib sensitivity. Indeed, when Lyn protein level is plotted against Dasatinib sensitivity (data from Huang et al., 2007) for individual cell lines common with this study, there is a direct and statistically significant correlation (Fig 3.10). That is, as the total level of Lyn increases, so does Dasatinib sensitivity. Whether this is simply because Lyn appears to be specific to Basal lines as is also the case for Dasatinib sensitivity (ie Casual vs Causal relationship), cannot be determined from this study. However when EGFR levels (part of the Basal ‘signature’) are plotted against Dasatinib sensitivity, there is no correlation, suggesting that the link between Lyn and Dasatinib sensitivity is not merely due to Lyn’s association with the Basal subtype (Fig 3.11).
62 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
Fig 3.10 Correlation between Lyn protein level and Dasatinib sensitivity. The Lyn protein level in individual cell lines (Fig 3.6) was plotted against their Dasatinib sensitivity as derived from Huang et al., 2007. Each red dot represents a single cell line. There was a statistically significant correlation between Dasatinib sensitivity and Lyn protein levels.
Fig 3.11 Correlation between EGFR protein level and Dasatinib sensitivity. The EGFR protein level in individual cell lines (Fig 4.1) was plotted against their Dasatinib sensitivity as derived from Huang et al., 2007. Each red dot represents a single cell line. There was no statistically significant correlation between Dasatinib sensitivity and EGFR protein levels.
63 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
3.3 Discussion
This study has so far identified a member of the SFKs, Lyn, whose mRNA, protein level and activity are increased in Basal cell lines compared to Luminal cell lines. This was identified by analyses on mRNA data and protein expression and was prompted by the hypothesis that Dasatinib sensitivity in Basal breast cancer cell lines reflects overexpression or deregulation of a Dasatinib target kinase.
Interestingly, for the majority of proteins investigated in this chapter, there was lack of concordance between mRNA and protein levels (see Yes, Fyn, Abl and Src in Fig 3.1 vs Fig 3.3 to 3.5). It is known that post-transcriptional and post-translational regulatory processes exist that can lead to discordance between levels of mRNA and the corresponding encoded protein. For example, translation from mRNA can be prevented by binding to miRNAs, and proteins can be tagged for degradation via ubiquitination. Indeed, a similar phenomenon was found by Neve et al., 2006, when analysis of protein expression was undertaken to confirm differences in mRNA expression between cell lines (Neve et al., 2006). For example, there was discordance between mRNA and protein levels for PTEN (Neve et al., 2006).
In contrast, Lyn showed a positive correlation between mRNA expression and protein level, and this was not only in relation to subgroups as a whole, but also for individual cell lines (Fig 3.7). Surprisingly one cell line, HCC 1500, was the only Basal line with a lack of Lyn protein expression, despite a relatively high Lyn mRNA level (compare Fig 3.1B and 3.6 B). It is interesting to note that unlike the Basal classification assigned to HCC 1500 by Neve et al., 2006 this cell line is designated as Luminal in two separate studies (Charafe-Jauffret et al., 2006; Kao et al., 2009). This suggests that the phenotype of this cell line may be somewhat ambiguous.
From this study, Lyn appears to be a strong candidate imparting Dasatinib sensitivity, based on the fact that firstly, Lyn is overexpressed in the Basal group that is sensitive to Dasatinib (Fig 3.6), secondly that Lyn levels directly correlate with Dasatinib sensitivity in individual cell lines (Fig 3.10) and thirdly that the correlation between
64 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
Lyn levels and Dasatinib sensitivity is not merely due to Lyn’s association with the Basal subtype (Fig 3.11).
It is important to highlight that both Normal and Basal lines demonstrated increased Lyn protein expression and activity. This is consistent with reports showing that Normal lines share gene expression and phenotypic characteristics with Basal cell lines. For example, Normal cells cluster with Basal B cancer cell lines at the gene expression level (Charafe-Jauffret et al., 2006; Kao et al., 2009), and Normal lines express genes found in a ‘Breast Basal epithelial cluster’ (Stingl et al., 1998; Perou et al., 2000; Dontu et al., 2003). Normal cells and Basal cancer cells also share phenotypic characteristics of ER and/or Her2 negativity and expression of Basal CKs (DiRenzo et al., 2002; Debnath et al., 2003). This suggests two possibilities. The first is that Normal and Basal breast cancer cell lines share a common ‘cell of origin’ in the normal breast that is likely to be either a mammary stem cell (MaSC), a common progenitor or a Luminal progenitor cell which also has high Lyn expression (Fig 3.12).
This is substantiated by the findings that MaSCs share many characteristics with Basal cell lines (lack of ER, PR, and Her2 expression, expression of EGFR, CK 5/6) (Lim et al., 2009; Visvader, 2009). In addition, Lyn expression is part of a gene expression signature distinctive of Luminal progenitor cells (Lim et al., 2009). This suggests a model where either stem cells or common/Luminal progenitor cells express a high level of Lyn, which is then subsequently lost during their differentiation to ER positive Luminal cells (see Fig 3.12). If correct this would suggest that Lyn may play a role in the early stages of normal breast development.
65 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines
Fig 3.12 A model for high Lyn expression in Normal and Basal cell lines based on the hierarchical model proposed by Lim et al., 2009. High Lyn expression may be characteristic of mammary stem cells, common progenitor cells and Luminal progenitor cells, and these give rise to Normal cell lines and Basal breast cancer lines which also possess high Lyn expression. Luminal cells can be divided into two groups: ER positive ‘sensor’ cells and ER negative milk-producing ‘secretory’ cells, which produce milk in response to paracrine signalling by the sensor cells (Sleeman et al., 2007). An absence of Lyn in Luminal cell lines, originally derived from Luminal cancers, suggests a loss of Lyn during differentiation of Luminal progenitor cells to ER positive Luminal cells (green arrow). Note that the common progenitor also gives rise to the myoepithelial lineage which is not indicated on the figure. Figure adapted from Visvader, 2009.
The second possibility for the similarities between Normal and Basal cells is that in a manner analogous to convergent evolution, Normal and Basal lines may originate from different progenitor cells, but during passage selection, Normal cell lines take on Basal-like characteristics or vice versa. This is indeed plausible given that Human mammary epithelial cells (HMECs) experience extensive gene expression changes during immortalisation in the laboratory (Li et al., 2007).
Regardless of the basis for phenotypic similarities between Basal and Normal cells,
66 Chapter 3: Lyn is overexpressed in Basal breast cancer cell lines similar levels and activity of Lyn does not necessarily mean the same mechanism of regulation. Indeed, there may be signalling networks to which Lyn belongs, that may be differentially regulated in Basal versus Normal cells. Or indeed Lyn may form part of distinct signalling pathways in Basal versus Normal cells. In other words, a similar level and activity of Lyn may not result in the same functional outcome if networks associated with Lyn are different or differentially regulated in the two groups. For example certain RTKs (eg Kit) are overexpressed in Basal versus Normal lines, and thus may lead to Lyn deregulation in the former cell type. It is therefore crucial to investigate the signalling pathways that Lyn belongs to in Basal breast cancer cells, and this is addressed in the next chapter of this study.
It is also important to bear in mind that Lyn can exhibit different activation states reflecting not only phosphorylation of Y397 but also phosphorylation of Y507 and overall protein conformation (section 1.4.2). Like many other studies, this thesis exclusively used the phosphorylation of Y397 as an indicator of Lyn activity. However, it would be more appropriate to concurrently investigate the phosphorylation state of the Y507 site and overall kinase activity through in vitro kinase assays. This could perhaps reveal differences in Lyn kinase activity between Normal and Basal cell lines not otherwise detected by simply focusing on phosphorylation of the Y397 site.
67 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
CHAPTER FOUR
Regulation and function of Lyn in Basal breast cancer cells
4.1 Introduction
As alluded to in the previous chapter, understanding the elements that regulate Lyn activity is key to discovering its role in the cancer cell. This is particularly important in light of findings that Lyn levels and activity are similar in Normal and Basal breast cancer lines.
Understanding the key proteins regulating Lyn activity allows us firstly to determine whether Lyn is differentially regulated in Normal versus Basal lines, or indeed if it lies in different signalling pathways in these two groups. Secondly, by identifying which pathway Lyn lies in, we may identify an entire pathway that is deregulated in Basal cell lines, allowing us to target additional proteins in the pathway other than Lyn, which may represent better pharmacological targets in terms of response and side effects to the patient. Finally, understanding the elements regulating Lyn activity are likely to shed light on the role of Lyn in the cancer cell.
68 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.1.1 Experimental strategy
As outlined in section 1.4.3, SFKs are regulated by an extensive array of RTKs and other proteins such as Integrins and Cadherins. Investigating Lyn activity in response to stimulation of every receptor would be inefficient. Two factors underpinned our choice to focus on the receptors EGFR, Kit and Met. Firstly, as outlined in section 1.4.3, they are all known to regulate SFK activity. Secondly, they are overexpressed in Basal breast cancer lines.
4.1.2 Chapter Aims
The aim of this chapter was thus to understand the regulatory elements of Lyn activity and to identify its role in the cancer cell. The specific aims were to:
x Identify cell lines suitable for growth factor stimulation through comparison of EGFR, Kit and Met receptor levels in a panel of Breast cancer cell lines. x Identify the signalling pathway/s that Lyn lies in by stimulating the selected cell lines with EGF, SCF and HGF and assaying Lyn activity through phosphorylation of the Y397 site. x Confirm the pathway that Lyn belongs to by identifying Lyn’s interaction partners and effects of Lyn depletion on members of the signalling pathway. x Determine the role of Lyn in Basal breast cancer cells by investigating the functional outcome of Lyn depletion. The signalling pathway encompassing Lyn will inform the experimental systems to use and the likely functional effects on the cell.
69 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.2 Results
4.2.1 Regulation by EGF
To investigate whether EGF stimulates Lyn activity, the cell line panel used in Chapter 3 was blotted for EGFR to determine which cell line would be most suitable for stimulation experiments (Fig 4.1). This was based on the premise that cell lines with moderate to high EGFR levels were most likely to respond to EGF stimulation.
EGFR was significantly higher in the Basal group compared to the Luminal group, with only one Luminal line (SkBr3) exhibiting detectable EGFR levels (Fig 4.1). Several cell lines from this panel with medium and high EGFR expression were selected for use in EGF stimulation experiments (MCF10A, 184, BT549, HCC 70, MDA-MB-231, BT20 and MDA-MB-468). Briefly cells were grown to sub-confluence, starved overnight and stimulated with 100ng/ml EGF for 5 and 15min. An unstimulated control was also included as the 0min time point. Cell lysates were then blotted with the pSFK antibody and the activity of Lyn determined from the intensity of the bottom ‘doublet’ as previously identified (Fig 3.8).
70 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B) C)
Fig 4.1 Comparison of EGFR protein levels in a panel of Breast cancer cell lines. A) Lysates from the cell line panel were Western blotted for EGFR and actin. B) Densitometry of EGFR levels was undertaken and normalised to actin and inter-membrane control lysates (HCC 70 and MM157). C) Densitometry data were grouped into the 3 subtypes and their mean EGFR level plotted. EGFR protein levels in the Basal group were significantly higher than in the Luminal group. Error bars represent the standard error of the mean. * represents p<0.05.
71 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B)
C)
72 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
D)
E)
F)
73 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
G)
H)
Fig 4.2 The effect of EGF stimulation on Lyn activity. Cell lines with varying EGFR expression, as determined from Fig 4.1, were stimulated with EGF for 0, 5 and 15min. Cell lysates were Western blotted as indicated. The level of active Lyn was normalised for total Lyn, and expressed as a fold difference relative to the value at 0min, which was arbitrarily set to 1. Arrows indicate active Lyn bands. Error bars represent standard error of the mean from triplicate experiments. * represents p<0.05, **represents p<0.01.
As can be seen from Fig 4.2 A-E, EGF stimulation of MCF10A, 184, BT549, HCC 70 and MDA-MB-231 cells, did not result in statistically significant changes in Lyn activity. Intriguingly, in BT20 and MDA-MB-468 (Fig 4.2 F and H), the activity of Lyn and other SFKs actually decreased upon EGF stimulation. This was statistically significant in both cell lines. To ensure that this decrease in activity was not artefactual,
74 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells reflecting an association of these proteins with the cytoskeleton, the experiment was repeated in BT20 cells with a more stringent lysis buffer (RIPA buffer) (Fig 4.2 G). The significantly decreased activity of Lyn and SFKs was maintained under these conditions.
Thus, while EGF induced the inactivation of Lyn and other SFKs in two out of seven cell lines, it did not increase the activity of Lyn in any cell line tested.
4.2.2 Regulation by SCF
Since EGF did not stimulate Lyn activity, the effect of SCF was investigated. This was based on findings that the receptor for SCF, Kit, is more commonly expressed in Basal type cancers than other breast cancer subtypes (Nielsen et al., 2004) and that Kit mRNA is highly expressed in Luminal progenitor cells, thought to be the ‘cells of origin’ for Basal breast cancers (Lim et al., 2009). In addition, Kit engages in bi- directional activation with Lyn upon SCF stimulation in leukaemia cell lines and human foetal cells (Linnekin et al., 1997).
In a similar approach to the EGF stimulation experiments, lysates from the cell line panel were first blotted with an anti-Kit antibody (Fig 4.3). Interestingly, only a small proportion of cell lines expressed Kit and these were exclusively in the Basal group. In contrast, the Normal and Luminal groups showed no expression of Kit.
75 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B) C)
Fig 4.3 Comparison of Kit protein levels in a panel of Breast cancer cell lines. A) Lysates from the cell line panel were Western blotted with antibodies against Kit and actin. B) Densitometry of Kit levels was undertaken and data normalised to actin and inter- membrane control lysates (HCC 70 and MM157). C) Densitometry data were grouped into the 3 subtypes and the mean Kit level plotted. Kit expression was significantly different between the 3 groups using ANOVA (p<0.05). Error bars represent the standard error of the mean.
76 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
The cell lines expressing Kit were stimulated with SCF (100ng/ml) in the same manner as for EGF stimulation experiments. As can be seen from Fig 4.4, SCF stimulation did not result in increased Lyn activity in any of the cell lines. Interestingly, the smear-like pattern detected with the anti-Kit antibody in HCC 38 and HCC 1143 lysates, first observed in the panel (Fig 4.3), is also seen in the stimulation experiments, suggesting that this appearance is not due to protein degradation within the panel lysate sample but is rather, a unique characteristic of these two lines. A lack of non-specific protein degradation is also confirmed by the fact that the same lysate, when blotted with other antibodies (eg Lyn, Actin), shows distinct bands, indicating that the effect appears to be Kit-selective.
MDA-MB-157 SCF (min): 0 5 15 pKit
Kit
SCF (min): 0 5 15
pKit
Kit
77 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
SCF (min): 0 5 15
pKit
Kit
SCF (min): 0 5 15 HCC1143 SCF (min): 0 5 15
pKit pKit
Kit Kit
Fig 4.4 The effect of SCF stimulation on Lyn activity. Cell lines expressing Kit (as identified in Fig 4.3) were stimulated with SCF for 0, 5 and 15min. Cell lysates were then Western blotted as indicated. The level of active Lyn was normalised for total Lyn, and expressed as a fold difference relative to the value at 0min, which was arbitrarily set to 1. Error bars represent standard error of the mean from triplicate experiments. Arrows indicate active Lyn bands. Note that in HCC 38, HCC 1187 and HCC 1143, the intensity of the active Lyn bands was not sufficient for accurate densitometry to be performed.
78 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.2.3 Regulation by HGF
Studies have demonstrated that the Met receptor is overexpressed in Basal breast cancers and that this is associated with worse prognosis (Tolgay Ocal et al., 2003; Graveel et al., 2009; Ponzo et al., 2009). In addition, several studies have shown that Src is phosphorylated upon HGF stimulation (Ponzetto et al., 1994; Chen et al., 1998; Rahimi et al., 1998). Thus, the dependence of Lyn activation on HGF stimulation was investigated.
Lysates from the cell line panel were blotted with a Met antibody (Fig 4.5). As for Kit, Luminal cell lines showed no detectable expression of Met. Consequently, both Normal and Basal lines exhibited statistically significant Met overexpression.
79 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B)
C)
Fig 4.5 Comparison of Met protein levels in a panel of Breast cancer cell lines. A) Lysates from the cell line panel were Western blotted for Met and actin. B) Densitometry of Met levels was undertaken and data normalised to actin and inter-membrane control lysates (HCC 70 and MM157). C) Densitometry data were grouped into the 3 subtypes and the mean Met level plotted. Met expression in the Normal and Basal groups was significantly higher than in the Luminal group. Error bars represent the standard error of the mean. ** represents p<0.01 and *** represents p<0.001.
80 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Several Basal cell lines with relatively high Met levels were then selected for HGF stimulation experiments, in the same manner as for EGF and SCF stimulations. As can be seen in Fig 4.6 in the majority of cell lines tested, HGF stimulation did not result in increased Lyn activity. Notice that in HCC 1954 cells, Lyn activity increased in response to HGF stimulation, however this was not statistically significant. In contrast, in HCC 70 cells, Lyn activity did increase upon HGF stimulation. Although this was a modest increase (1.3 fold at 5 min), it was both statistically significant and reproducible in at least six separate experiments. As this was the only cell line to demonstrate activated Lyn upon growth factor stimulation, this cell line became the focus of further experiments.
81 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
82 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Fig 4.6 The effect of HGF stimulation on Lyn activity. Cell lines which express Met (identified in Fig 4.5) were stimulated with HGF for 0, 5 and 15min. Cell lysates were Western blotted as indicated. The level of active Lyn was normalised for total Lyn, and expressed as a fold difference relative to the value at 0min, which was arbitrarily set to 1. Arrows indicate active Lyn bands. In HCC 70 an increase in Lyn activity induced by HGF was statistically significant at 5 min. Error bars represent standard error of the mean from triplicate experiments. * represents p<0.05.
83 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.2.4 Investigating Lyn’s potential interaction partners
One question to arise from the finding of HGF stimulation of Lyn activity in HCC 70 cells is: does Lyn lie in the Met signalling pathway, and if so what are its interaction partners within this pathway?
To address this question, Lyn was immunoprecipitated from starved and HGF- stimulated HCC 70 cells and blotted with a general anti-phosphotyrosine antibody (pY100) (Fig 4.7A). An isotype-matched IgG was also used as a negative control. The strategy behind this was that any tyrosine-phosphorylated protein interacting with Lyn would be detected in the pY100 blot and then identified through a candidate-based approach, according to its size. As can be seen in Fig 4.7 A, there was an absence of bands detected in the pY100 blot specific to the Lyn IP. Note that the faint bands of approximately 160kDa in size are also present in the IgG negative control and therefore do not represent Lyn-specific binding partners.
Src is known to bind the Met receptor directly upon HGF stimulation (Ponzetto et al., 1994; Rahimi et al., 1998; Hui et al., 2009). To determine if this was also true for Lyn, the Lyn and IgG immunoprecipitates were blotted for Met. As can be seen in Fig 4.7 A (bottom panel), Met was not detected in the lanes corresponding to the Lyn IP.
Met signals primarily through the Gab1 docking protein, which has been shown to bind directly to Src (Birchmeier et al., 2003; Chan et al., 2003). In the absence of a detectable interaction between Lyn and Met, we hypothesised that Lyn may be binding to Gab1 instead, in a manner analogous to Src. However, as seen in Fig 4.7A, an interaction between Lyn and Gab1 was not detected. To confirm this, the reverse IP was undertaken. That is, Gab1 was immunoprecipitated and blotted for pY100 and Lyn (Fig 4.7B). Upon HGF stimulation, a single band was observed between the 100 and 150 kDa markers in the pY100 blot. This most likely represents phosphorylated Gab1, in concordance with Met activation following HGF stimulation. However, an interaction between Gab1 and Lyn could not be detected (Fig 4.7B bottom panel).
84 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B)
Fig 4.7 Interaction between Lyn and potential binding partners. A) Lyn was immunoprecipitated from starved HCC 70 cells or cells starved and stimulated with HGF for 5min and IPs Western blotted as indicated. B) Gab1 was immunoprecipitated from starved HCC 70 cells or cells starved and stimulated with HGF for 5min and IPs blotted as shown. Total cell lysates (TCL) were also run as a control. Position of size markers (in kDa) are shown on the left of the panels.
85 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.2.5 Investigating Lyn’s role in HGF-induced cell signalling
Another approach taken to identify Lyn’s potential role in the Met signalling pathway was to deplete Lyn expression in HCC 70 cells through siRNA-mediated knock down and to analyse any effects on potential downstream targets.
Briefly, HCC 70 cells were either left untransfected (M: Mock), transfected with a non-targeting siRNA (NT) or transfected with either of two different Lyn targeting siRNAs; Lyn2 (L2) or Lyn12 (L12). Following 48h of knock down, cells were starved overnight and then stimulated with HGF (100ng/ml) for 5 min. Lysates were blotted for potential down stream targets in accordance with the literature. As seen in Fig 4.8 (top panel), Lyn knock down was successful, with a mean knock down efficiency of 57% for L2 and 79% for L12. This knock down was significant for both L2 and L12 when compared to the M and NT controls (Fig 4.9). As expected, the level of active Lyn was also significantly decreased upon knock down (see pSFK blot and Fig 4.9).
E-cadherin localises to epithelial cell-cell junctions and is frequently downregulated in cancer (Thiery and Sleeman, 2006). In addition, Src overexpression results in a disturbance to E-cadherin regulation, resulting in reduced cell clustering (Avizienyte et al., 2002). Importantly sarcoma cell lines with Lyn knock down exhibited an increase in E-cadherin expression, associated with a loss of invasive properties (Guan et al., 2008). However, Lyn knock down did not affect E-cadherin protein levels (Fig 4.8 and 4.9).
Total and active Met receptor were also examined since Src interacts directly with Met and phosphorylates it on its activation loop (Ponzetto et al., 1994; Rahimi et al., 1998; Hui et al., 2009). Lyn however does not appear to play a role in Met phosphorylation since knock down of Lyn did not affect either phosphorylated or total Met levels in the absence or presence of HGF (Fig 4.8 and 4.9).
86 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
(100ng/ml)
Fig 4.8 Effect of Lyn knock down on HGF-induced cell signalling (100ng/ml). Lyn was knocked down in HCC 70 cells through the transfection of Lyn2 (L2) or Lyn12 (L12) siRNAs. As a control, HCC 70 cells were left untransfected (M) or transfected with a non-targeting siRNA (NT). 48h later, cells were starved and stimulated with HGF (100ng/ml) for 5min. Lysates were generated and Western blotted with the indicated antibodies. The above are representative images from four separate experiments. Arrows indicate active Lyn.
87 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
LYN Active LYN
E-CAD
MET pMET
GAB1 pGAB1
88 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
FAK pFAK (Y397)
pFAK (Y576/577)
AKT
pAKT
ERK pERK
Fig 4.9 Densitometric analysis of Western blot data. Densitometry was undertaken and values normalised to the corresponding actin levels. For phosphoproteins, values were further normalised to the corresponding total levels. Results are presented as fold difference from mock transfected lysates for which the value was arbitrarily set to 1. For the phosphoproteins, only the results from the stimulated lysates are shown (+HGF). Results are from 4 separate experiments. Error bars represent standard error of the mean and *** represents p<0.001. 89 89 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Gab1 was another potential downstream target that was examined. As mentioned previously, Src interacts directly with Gab1 (Chan et al., 2003; Podar et al., 2004). In addition, Src has been shown to regulate Gab1 phosphorylation. Overexpression of Src results in increased Gab1 phosphorylation in response to HGF and conversely, overexpression of a kinase dead mutant or administration of PP1 SFK inhibitor reduces Gab1 phosphorylation (Chan et al., 2003). Importantly Watanabe et al., 2009 demonstrated that cells overexpressing both Gab1 and Lyn exhibited increased Gab1 phosphorylation (Watanabe et al., 2009). Interestingly, the phosphorylation sites that are phosphorylated by Src are different to those phosphorylated by the Met receptor upon HGF stimulation (Chan et al., 2010). Thus, the phosphorylation status of one of these Src-specific sites (Y627) was examined in response to Lyn knock down. As can be seen in Fig 4.8 and Fig 4.9, phosphorylation and total levels of Gab1 remained unchanged upon Lyn knock down.
FAK is one of the most well-known SFK substrates. Upon HGF stimulation, FAK binds Src and as a result becomes activated (Chen et al., 1998). FAK has various phosphorylation sites each with differing roles. Phosphorylation of Y397 is caused by autophosphorylation, which promotes Src binding and allows it to then phosphorylate Y576/577 on FAK’s activation loop, which in turn ensures maximal FAK activation (Mitra et al., 2005). Thus both of these phosphorylation sites were examined upon Lyn knock down. Fig 4.8 and 4.9 show that these phosphorylation sites and total FAK levels were unaffected by Lyn knock down.
The two well-characterised signalling pathways that are activated by Met are the Akt and Erk/MAPK pathways (Birchmeier et al., 2003). In addition, overexpression of a dominant active Lyn in colon cancer cells results in increased Akt activity, and more recently it has been shown that myeloma cells overexpressing wild-type Lyn exhibited increased PI3K and Akt activation (Bates et al., 2001; Iqbal et al., 2010). To determine whether a loss of Lyn would affect these two major pathways, Lyn knock down lysates were blotted for phosphorylated Akt and Erk and their total protein counterparts. As is seen in Fig 4.8 and 4.9, loss of Lyn did not disrupt the signalling from Met to these two pathways.
90 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Since many signalling components downstream of the Met receptor were not affected by Lyn knock down, it appeared that Lyn did not have a significant role in this pathway. However, we hypothesised that the high level of HGF used for stimulation experiments (100ng/ml) may have been hyper-stimulating the receptor, thus ‘masking’ any mild effects that may result from Lyn knock down. Therefore, the above stimulation experiments were repeated with a lower concentration of HGF (10ng/ml). As is seen in Fig 4.10, 10ng/ml HGF is sufficient to stimulate Met signalling pathways, as indicated by the pMet and pGab signals. However, once more, Lyn knock down did not affect the phosphorylated or total level of proteins previously examined.
91 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
(10ng/ml)
Fig 4.10 Effect of Lyn knock down on HGF-induced cell signalling (10ng/ml). Lyn was knocked down in HCC 70 cells through the transfection of Lyn2 (L2) or Lyn12 (L12) siRNA. As a control, HCC 70 cells were left untransfected (M) or transfected with a non-targeting siRNA (NT). 48hrs later, cells were starved and stimulated with HGF (10ng/ml) for 5min. Lysates were generated and Western blotted with the indicated antibodies. Arrows indicate active Lyn.
92 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.2.6. Investigating Lyn’s functional role in HGF-induced cell scattering
So far, this study has demonstrated that Basal breast cancer cells overexpress Lyn, and that at least in one of these cell lines, HCC 70, Lyn activity can be modulated by HGF stimulation. The pertinent question then is what is Lyn’s role in the cancer cell? Is it providing the cell with an additional capability to increase its tumorigenic potential?
HGF is known to illicit a characteristic response termed ‘scattering’. In this, upon HGF stimulation, cells that are growing in colonies lose cell-cell contacts and dissociate from the colony while taking on a more fibroblastic/mesenchymal phenotype (Birchmeier et al., 2003). At the molecular level, the signalling events that underpin this scattering effect are complex, with many proteins and signalling pathways being implicated. However there is extensive evidence that Src has an important role in this response. For example, expression of a dominant negative form of Src in mouse mammary carcinoma cells was seen to inhibit HGF-induced scattering (Rahimi et al., 1998) and two independent studies showed that overexpression of an active form of Src resulted in loss of cell-cell contacts in colon cancer cells (Avizienyte et al., 2002; Irby and Yeatman, 2002). Thus we aimed to investigate if Lyn possessed a similar role to Src, by testing the effect of Lyn knock down on HGF-induced cell scattering.
For the scattering assay, HCC 70 cells were transfected with either non-targeting siRNA (NT) or either of two Lyn siRNAs (L2 and L12) as in previous experiments. Cells were then starved and stimulated with 50ng/ml HGF to induce scattering. An unstimulated set of cells was also used as a negative control. Scattering of cells was monitored by time-lapse microscopy over a 24h period. Fig 4.11 shows two fields of view for each transfection over the first 13h of scattering.
93 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
A)
B)
C)
94 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
D)
E)
F)
95 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Previous Page: Fig 4.11 Scattering assay of Lyn knock down HCC 70 cells. Lyn was knocked down in HCC 70 cells by transfection of Lyn2 (L2) or Lyn12 (L12) siRNAs. As a control, a non-targeting (NT) siRNA transfection was included. Cells were starved and then stimulated with HGF for 24h. During this time, time- lapse microscopy was performed. A) and D) are two separate fields of view for NT. B) and E) are two separate fields of view for L2. C) and F) are two separate fields of view for Lyn12. Note that only 0h, 6h and 13h images are shown. Magnification is 100x.
Upon starvation, HCC 70 cells rounded up into dense clusters of cells that were highly phase-bright (see 0h time point in Fig 4.11). In the continued absence of HGF, cells rounded up further and detached (see –HGF images). In contrast, in the presence of HGF, cells underwent scattering, moving out from the clusters and displaying a fibroblast-like morphology (see +HGF images). Surprisingly, Lyn knock down did not affect cell scattering, as the appearance of cell clusters was very similar between the NT and Lyn knock downs, particularly at the later time points (see 13h time point in Fig 4.11). In addition by ‘stitching’ the frames taken from the time-lapse microscopy into videos, it was observed that the speed of scattering and motility upon HGF stimulation was similar between NT and Lyn knock downs (data not shown).
To confirm that Lyn knock down was maintained throughout the assay, the cells from the scattering assay were lysed and lysates blotted for Lyn (Fig 4.12). Lyn knock down was sustained after 24h of scattering, with a knock down efficiency of 41% for L2 and 77% for L12 with both being significantly decreased from NT (data not shown). HGF stimulation was also sustained throughout the scattering assay, as demonstrated by the increased level of phospho-Met, Gab1, Fak, Akt and Erk (Fig 4.12). However, neither the extent of phosphorylation nor the total levels of these proteins (data not shown) was affected by Lyn knock down. Importantly, all of the proteins blotted for in Fig 4.12 play roles in cell scattering (see sections1.2.1 and 1.3). Thus the absence of any effects of Lyn knock down on the expression or activity of these proteins is consistent with the data from the scattering assay.
96 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Fig 4.12 Effect of Lyn knock down on the signalling of HCC 70 cells stimulated with HGF for 24h. HCC70 cells that had undergone a scattering assay (Fig 4.11) were lysed and lysates Western blotted with the indicated antibodies.
4.2.7 Lyn knock down and morphology change
What was evident from the scattering experiments was that Lyn knock down cells post-starvation exhibited more dispersed colonies, with cells having a more fibroblast morphology (compare 0h time points between NT and the knock downs Fig 4.11; and Fig 4.13). To quantitate this change in morphology, colonies in various fields of view from separate experiments were either designated as having a ‘rounded’ or ‘fibroblastic/spread’ morphology. The results of this quantitation (Fig 4.14) confirm that transfection with both L2 and L12 compared with NT siRNA results in significantly more spread colonies. In addition, this effect is likely a function of the extent of Lyn knock down, since L12, which has a greater level of Lyn knock down, exhibits significantly more dispersed colonies/cells compared to L2 siRNA.
97 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
Fig 4.13 Effect of Lyn knock down on starved HCC 70 cell morphology. HCC 70 cells that had undergone transfection with NT, L2 or L12 siRNAs were starved and images taken. Two examples using a 20x objective and one using a 10x objective are shown from representative experiments.
Fig 4.14 Quantification of cell morphology change upon Lyn knock down. HCC 70 cells that had undergone transfection with NT, L2 or L12 siRNAs were starved and their colony morphology designated as either ‘rounded’ or ‘spread/fibroblast-like’. Colonies fitting into these two groups were quantitated, and the proportion of colonies with the latter characteristic plotted for each knock down. * represents p<0.05 and ***represents p<0.001.
98 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
4.3 Discussion
This chapter aimed to investigate the upstream mechanisms that regulate Lyn activity in Breast cancer cells and potential downstream targets of this activity, with a view to identifying the signalling pathway/s that Lyn is part of. Three RTKs (EGFR, Kit, Met) were designated as potential regulators of Lyn activity, and cell lines suitable for stimulation of these RTKs were identified through Western blotting for these receptors in a cell line panel. Encouragingly, Basal cell lines exhibited significantly higher EGFR, and Met levels compared with Luminal cell lines, in agreement with the literature (Fig 4.1 and 4.5). In addition although a minority of cell lines exhibited Kit expression, this was restricted to the Basal subgroup, and this is also in agreement with the literature as outlined in section 1.7.4 (Fig 4.3).
4.3.1 Regulation of Lyn activity by specific RTKs
Increased Lyn activity in response to RTK stimulation was achieved with HGF in one cell line model, HCC 70. Surprisingly, SCF did not stimulate Lyn activity in any of the cell lines expressing Kit, despite a study demonstrating that in leukaemia cell lines, Lyn activity is stimulated by Kit through Lyn binding to Kit’s juxtamembrane domain (Linnekin et al., 1997). Lyn activation in response to SCF may be a cell- specific effect, such that Breast cancer cell lines do not demonstrate this same phenomenon. This wouldn’t be surprising given Lyn’s contrasting roles in different cell types (section 1.4.5).
Another surprising result was the decreased activity of Lyn and other SFKs in two cell line models, BT20 and MDA-MB-468 upon EGF stimulation. As outlined in section 1.4.2, there could be a variety of mechanisms underlying this effect on Lyn inactivation. For example, it could be due to activation loop dephosphorylation through PTP-BAS/PTPL1 action, or mediated via increased recruitment of Csk or Chk to the plasma membrane by Cbp, leading to Y507 phosphorylation. The latter mechanism is particularly attractive since in erythropoietic cells undergoing Epo stimulation, Csk was able to inactivate Lyn, via Cbp, within a similar time frame
99 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells
(minutes) as observed in this chapter (Ingley et al., 2006). Experimentally, these possibilities could be tested by various methods. Firstly, PTPL1 inactivation of SFKs is thought to involve SFK interaction with the protein Reversion-induced LIM (RIL) (Zhang et al., 2009). Therefore, in BT20 and MDA-MB-468 lysates, the interaction between SFKs and PTPL1 and RIL could be tested by immunoprecipitation of Lyn and other SFKs and Western blotting with PTPL1 and RIL antibodies. Secondly, the subcellular re-localisation of Csk/Chk to the plasma membrane could be monitored via confocal immunofluorescence microscopy following EGF stimulation. It would also be interesting to test the co-localisation of Csk/Chk, Cbp and active SFKs with confocal microscopy. Additionally, upon activation, Lyn phosphorylates Cbp at various tyrosine residues, including Y314 (Ingley et al., 2006). Blotting for phosphorylation of the Y314 site, following EGF stimulation, would indicate whether Lyn is phosphorylating this site, supporting the hypothesis that Lyn is inactivated via a mechanism involving Cbp and Csk/Chk. What is clear however, is that Lyn inactivation is not due to degradation of protein since total levels of Lyn remain constant throughout the experimental time frame (Fig 4.2). It would be interesting to investigate whether this decrease in Lyn activity is maintained over time, or whether it eventually returns to basal levels. A longer time course of EGF stimulation would address this.
It is surprising that Lyn activation upon HGF stimulation was only observed in the HCC 70 cell line. It is not clear why the other cell lines did not respond in the same manner to HGF stimulation. However, it is interesting to note that HCC 70 cells express the highest level of Lyn of all the cell lines (Fig 3.6). Since SFKs are thought to cooperate with RTKs to promote tumorigenesis (Yeatman, 2004), perhaps both Lyn and Met are required to be present at very high levels to ensure a synergistic interaction. Alternatively, as previously mentioned, the sole use of the anti phospho- Y397 antibody may not be a sufficiently informative indicator of Lyn activity. That is, the simultaneous phosphorylation of both the Y397 and the Y507 needs to be investigated to truly determine Lyn activity (see section 1.4.2). In addition, Lyn may be partially active without phosphorylation of the Y397 site (Ingley, 2008). Thus it is possible that in the cell lines other than HCC 70, HGF is indeed stimulating Lyn, but
100 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells that due to our use of only the Y397 antibody, this increase in Lyn activity is not being detected. Two ways to test this are to undertake Western blotting with a phosphospecific antibody that specifically recognizes the negative regulatory C- terminal phosphorylation site of Lyn and to perform in vitro kinase assays following immunoprecipitation of Lyn. An alternative explanation for the lack of Lyn activation in the majority of cell lines is that these may have a different complement or contrasting levels of SFK regulators to HCC 70, as outlined in section 1.4.2, and these may maintain Lyn in a more inactive conformation, even in the face of potential stimulation by HGF. This could be tested by blotting for levels or activity of SFK regulators (eg PTPD, PTP1B, SHP1, SHP2, PTPL1, Csk/Chk and Cbp) in these cell lines.
This thesis focused on RTKs that would likely affect Lyn activity. As mentioned previously, SFKs are also known to be activated downstream of Integrins and Cadherins. It may well be that Lyn is more strongly activated by these than the RTKs tested here. This could be investigated by various approaches. Firstly, the activity of Lyn could be determined during cell attachment and spreading, by plating trypsinised cells on surfaces coated with different ECM components such as fibronectin, laminin and collagens. Adhesion to these substrates would result in integrin engagement and signalling downstream, potentially to Lyn. Additionally, Lyn regulation by Cadherins could be tested by cell-cell adhesion assays where cells are switched from media containing low calcium to media containing high calcium concentrations, which are necessary for E-cadherin homotypic interactions (see Avizienyte et al., 2002; Playford et al., 2008). In this assay, cells could be lysed at various time points after the ‘calcium switch’, thus allowing for investigation of Lyn activity during the progression of intercellular contact formation. Lastly, Lyn activity in response to IL-6 could be tested since IL-6 has been shown to activate Lyn, and Basal breast cancers exhibit elevated IL-6 mRNA levels (Hallek et al., 1997; Sansone et al., 2007). Interestingly IL-6 stimulation is thought to drive the tumorigenic conversion of mammary stem cells (Sansone et al., 2007; Schafer and Brugge, 2007). Thus, it is feasible that although both Normal and Luminal progenitor cells express Lyn (Chapter 3), it is only when cells gain the ability to produce and secrete IL-6 that cells become
101 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells tumorigenic.
4.3.2 Investigation of Lyn’s role in the Met signalling pathway
In order to investigate Lyn’s role in the Met signalling pathway, potential binding partners in this pathway were investigated through immunoprecipitation experiments (Fig 4.7). The interaction of Lyn with candidate interaction partners (Gab1 and Met) and any unknown tyrosine phosphorylated proteins was tested, but the results were negative. This does not mean however that binding partners do not exist, since protein-protein interactions are likely to underlie the demonstrated activation of Lyn by Met stimulation. It may be that the technique employed was not sensitive enough for detecting transient interactions characteristic of cell signalling networks. Indeed, many proteins demonstrate ‘kiss and run’ interactions where proteins interact for a very short period before disengaging once again. In many studies focusing on protein- protein interactions, the candidate proteins are often overexpressed, increasing the likelihood of their interaction. Thus one approach to detect Lyn’s interaction partners would be to co-overexpress Lyn and various candidate proteins known to form part of the Met signalling pathway, and to repeat the immunoprecipitation experiments. Alternatively, Mass spectrometry (MS) could be used to identify proteins that co-IP with Lyn. This involves separating the IP complex by SDS-PAGE and digesting the associated proteins with trypsin. Tryptic peptides are then identified through MS and database searches. Another approach to detect Lyn’s interaction partners would be to use chemical cross-linkers to stabilise transient protein-protein interactions. For example, disuccinimidyl suberate can be used to ‘lock’ protein-protein interactions in that state.
Another approach taken to identify Lyn’s signalling role was to investigate the effect of Lyn depletion on proteins known to lie in the Met signalling pathway (Fig 4.8- 4.10). Lyn depletion did not affect the total levels or activity of any of the proteins tested. Neither did Lyn depletion affect cell scattering, a well-characterised effect resulting from HGF stimulation. This is surprising as Lyn has been associated with migration and invasion. For example knock down of Lyn in sarcoma lines negatively
102 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells affects invasion and this is associated with increased expression of E-cadherin (Guan et al., 2008). In addition Lyn forms part of a 5-gene signature associated with metastasis in colon cancer (Hao et al., 2010).
There are many reasons why Lyn depletion may not have altered Met signalling or Met associated functional outcomes. Firstly, although knock down of Lyn averaged 79% for L12 siRNA and 57% for L2 siRNA, perhaps the remaining Lyn was sufficient to sustain effective signalling downstream of Met. Secondly, there may be sufficient redundancy in the SFKs such that in the absence of one member, there is an increase in total protein level or activity of another member to compensate for the original loss of Lyn. Redundancy in SFKs is well-known and indeed this is thought to be the reason underlying the milder than expected phenotypes of mice where a single SFK member has been deleted (Varmus and Lowell, 1994; Lowell and Soriano, 1996; Taylor and Shalloway, 1996; Klinghoffer et al., 1999). Western blotting of Lyn knock down lysates for increased levels or activity of other SFK members would test this hypothesis. In addition, there could be redundancy in the Met signalling pathway itself, as is quite common in signalling networks (Schlessinger, 2000). Consequently the effect of Lyn depletion on Lyn’s downstream targets could be compensated by other capable kinases so that the functional outcomes of Met signalling are maintained in the absence of Lyn.
4.3.3 Lyn’s role in controlling cell morphology
What was evident upon Lyn depletion, and subsequent starvation, was that colonies of Lyn knock down cells exhibited a more dispersed morphology, with cells taking on fibroblast characteristics. This effect was more pronounced in L12 knock down cells, consistent with its increased knock down efficiency. This morphological effect could be further tested by using additional Lyn-targeting siRNAs and by complementing Lyn knock down cells with siRNA-resistant Lyn constructs, for example mouse Lyn. This would test for off-target effects upon Lyn knock down.
As mentioned previously, cell-cell adhesion and thus cell clustering is partly
103 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells controlled by E-cadherin levels, and loss of E-cadherin is a characteristic of invasive cancers (Thiery and Sleeman, 2006). It was surprising therefore that E-cadherin levels were maintained upon Lyn knock down (Fig 4.8, 4.10 and 4.12). It must be remembered however that it’s also the localisation and phosphorylation of the E- cadherin/E-catenin complex that regulates cell-cell adhesion function. Cells with decreased cell adhesion function exhibit internalisation of E-cadherin away from the plasma membrane. In addition c- and v-Src overexpression leads to phosphorylation of both E-cadherin and E-catenin, leading to loss of cell-cell adhesion (Behrens et al., 1993; Roura et al., 1999). Thus both the localisation and activation of E-cadherin/E- catenin in Lyn knock down cells could be investigated via confocal microscopy and Western blotting, respectively.
The unexpected effect of Lyn depletion on cell morphology suggests that Lyn may be playing a distinct role to that of Src. Indeed it is the overexpression or increased activation of Src that usually results in a fibroblast–like morphology (Behrens, et al., 1993; Avizienyte et al., 2002). This suggests that at least in HCC 70 cells, Lyn is normally acting to enhance cell-cell adhesion, and thus represents, in contrast to Src, a SFK that negatively regulates tumour progression. This is consistent with the ability of Lyn to undertake varying negative regulatory roles (Section 1.4.5).
Apart from Lyn’s effects on cell-cell junctions, the altered morphology in Lyn knock down cells suggests alterations to the underlying cytoskeleton. Interestingly, changes in Lyn and Cbp levels and their interaction have been shown to play a role in the morphology of COS7 cells (Ingley, 2009). For example, expression of active Lyn results in filopodia formation while overexpression of Cbp or an inactive form of Lyn results in lamellipodia formation (Ingley, 2009). While the exact mechanism behind the morphological changes observed in this thesis and by Ingley, 2009 is unknown, there are many proteins that are known to regulate actin dynamics and thus cell shape. For example, key GTPases that control cell shape and spreading are Rho, Rac and Cdc42 (Hall, 1998). Thus changes in the activity of these GTPases could be assessed in Lyn knock down HCC 70 cells, to determine whether this is the mechanism whereby Lyn affects cell shape. Lyn has also been shown to bind and phosphorylate
104 Chapter 4: Regulation and function of Lyn in Basal breast cancer cells the F-actin binding protein Annexin A2 (Rescher et al., 2004; Matsuda et al., 2006). Consequently, investigating the phosphorylation status of Annexin A2 in HCC 70 knock down cells would prove a fruitful line of investigation. Additionally, the effects of Lyn on cell morphology may affect invasion, since cells with a fibroblast-like morphology exhibit increased invasiveness. This could be investigated by conducting Matrigel invasion assays using Lyn knock down HCC 70 cells.
105 Chapter 5: Lyn expression in Breast cancer patients
CHAPTER FIVE
Lyn expression in Breast cancer patients
5.1 Introduction
One of the main objectives of cancer research is to translate findings gained from an in vitro setting to new approaches related to prevention, diagnosis and treatment of patients. The discovery of biomarkers and targeted therapies against these often occurs simultaneously (Ludwig and Weinstein, 2005). That is, the discovery of a biomarker for specific cancer subgroups drives research aimed at developing therapies against that biomarker. In the opposite manner, successful therapy against a particular cancer can drive research into the specific target of that therapy. The latter was the approach taken by this research project, identifying that the potential Dasatinib target Lyn, is strongly associated with Basal breast cancer lines. Thus, it was important to determine if Lyn expression was also elevated in primary Basal breast cancers and thus could potentially be used as a biomarker. New biomarkers, able to identify or predict the outcome of Basal breast cancers are required, given that there are currently no specific guidelines for diagnosis or treatment of these cancers (Rakha et al., 2008).
106 Chapter 5: Lyn expression in Breast cancer patients
5.1.2 Chapter Aims
Thus, the aim of this chapter was to determine whether Lyn expression was characteristic of primary Basal breast cancers. The specific aims of this chapter were to: x Optimise an antibody against Lyn for use in IHC on archival breast cancer specimens x Determine the pattern of Lyn expression in the normal mammary gland using this optimised protocol x Characterise differences in Lyn expression amongst Breast cancer subgroups
107 Chapter 5: Lyn expression in Breast cancer patients
5.2 Results
5.2.1 Lyn antibody optimisation
To determine whether the tight association of Lyn with the Basal subtype observed in Breast cancer cell lines was also characteristic of primary breast cancers, a Lyn antibody was optimised for use in IHC.
Firstly, IHC conditions were adjusted so that paraffin-embedded cell lines that were shown to be have high Lyn levels by Western Blotting (MDA-MB-231 and MDA- MB-157) (see Fig 3.6) exhibited intense staining while those demonstrated to have undetectable Lyn levels (BT-474) exhibited little/no staining (Fig 5.1). Encouragingly Lyn staining was most pronounced at the cell periphery, consistent with its interaction with the plasma membrane.
MDA-MB-157 MDA-MB-231 BT-474
Fig 5.1 Optimisation of Lyn IHC staining in cell lines. Cell lines with known high Lyn protein levels (MDA-MB-157 and MDA-MB-231) and undetectable Lyn protein (BT-474) were used to optimise staining with the Lyn antibody. Scale bar represents 50Pm.
108 Chapter 5: Lyn expression in Breast cancer patients
Lyn staining was also optimised by staining of normal human tissues, to match its known tissue distribution as detailed in the publicly available Human Protein Atlas (http://www.proteinatlas.org). Tissues and cells which were highly positive for Lyn staining were the tonsil, spleen, Kupffer cells of the liver (specialised macrophages), macrophages in the lung, glandular cells of the pancreas and lymphoid tissue of the appendix (Fig 5.2A). This staining is consistent with Lyn’s expression in cells of the immune system. Importantly, IHC of these same tissues with an isotype-matched control IgG antibody did not stain the tissues, indicating that the positive staining was specific to the Lyn antibody (Fig 5.2A). Tissues which were negative for Lyn staining were the brain, kidney, smooth muscle, skeletal muscle and testis (Fig 5.2B) and this is consistent with the distribution found in the Human Protein Atlas.
109 Chapter 5: Lyn expression in Breast cancer patients Lyn IgG
A. Tonsil
Spleen
Liver
Appendix
Lung
Pancreas
110 Chapter 5: Lyn expression in Breast cancer patients Lyn B.
Kidney
Brain
Smooth Muscle
Skeletal muscle
Testis
Fig 5.2 Lyn expression in normal tissues. The known distribution of Lyn expression in various normal tissues was used to optimise the Lyn antibody. A) Tissues positive for Lyn staining. These tissues were also stained with a mouse IgG as a negative control. B) Tissues negative for Lyn staining. Scale bar represents 50Pm.
111 Chapter 5: Lyn expression in Breast cancer patients
5.2.2 Lyn staining in the normal breast
Until recently, it has been assumed that Basal breast cancers are derived from basal/myoepithelial cells in the normal breast due to shared protein markers such as CK 5, vimentin, and lack of ER and PR expression (Rahka et al., 2008). Recently however, Lim et al., 2009 have suggested that Basal breast cancers may be derived from Luminal rather than Basal progenitor cells (Fig 1.6).
In order to investigate which cells in the normal breast expressed Lyn, the optimised Lyn antibody was used to stain tissue derived from reduction mammoplasties (Fig 5.3). Unexpectedly, Lyn was detected in both Luminal and Basal/myoepithelial cells, with the majority of staining found in the apical membrane of Luminal cells (see arrows in Fig 5.3 and Fig 1.4 for reference). Basal/ myoepithelial cells are found below Luminal cells and can be recognised by their ‘vacuolated’ appearance (red arrow in Fig 5.3). In any one field of view, 0-20% of Basal/myoepithelial cells exhibited Lyn positivity compared to a much larger proportion, 40-70%, of Luminal cells. Stromal staining varied ranging from no staining/weak staining to strong positive staining in macrophages.
112 Chapter 5: Lyn expression in Breast cancer patients
Fig 5.3 Lyn staining in the normal mammary gland. Staining for Lyn was undertaken in normal breast tissue taken from healthy subjects undergoing reduction mammoplasties. Black arrows indicate examples of Luminal cell staining while the red arrow (B) is an example of Basal/myoepithelial cell staining. Note that scale bars in A, B and C represent 25Pm while that in D represents 50Pm.
5.2.3 Lyn expression in different Breast cancer subgroups
Prior to the use of the optimised Lyn antibody on a large Breast cancer patient cohort, Lyn staining was undertaken in a test cohort consisting of 10 Basal cases, 10 Her2 cases and 10 Luminal A cases, all of which represented grade 3 IDC-NOS. Scoring was undertaken by assigning samples a score of 1+, 2+ or 3+ to represent increasing staining intensity (see Fig 5.4).
113 Chapter 5: Lyn expression in Breast cancer patients
A B
C D
Fig 5.4 Gradation of Lyn staining intensity used to score Breast cancer cohorts. A) No staining. B) 1+ intensity. C) 2+ intensity. D) 3+ intensity. Scale bar represents 50Pm.
In addition, the percentage of cells stained positive in a sample was scored, and multiplied by the staining intensity to generate a Lyn ‘H score’. As seen in Fig 5.5, Basal cases had both the highest proportion of positive cases (7/10) and the highest mean Lyn H score. In the Her2 group, only 3/10 cases were positive for Lyn staining, however, the H score from one case was particularly high (see Fig 5.5) thus explaining the lack of significant difference in Lyn staining between the Basal and Her2 groups. In the luminal group, only 2/10 cases exhibited Lyn staining, and unlike the Her2 group, these had low H scores, thus resulting in a significant difference in Lyn staining between the Basal and Luminal groups. Interestingly, Lyn staining was found not only at the membrane but also throughout the cytoplasm (Fig 5.4)
114 Chapter 5: Lyn expression in Breast cancer patients
Fig 5.5 Lyn staining in a test cohort of Breast cancer samples. The gradation system in Fig 5.4 was used to score Lyn staining in 10 Basal, 10 Her2 ER negative and 10 Grade 3 Luminal A cancers. An H score was calculated by multiplying the percentage of cells in a sample stained positive and the intensity of this staining. There was a significant difference in Lyn staining between Basal and Luminal A cancers. Horizontal red bars represent mean H score for each group and error bars represent standard error of the mean. * represents p<0.05.
5.2.4 Lyn staining in a large patient cohort and association with survival
In light of the encouraging results in the patient test cohort, staining of Lyn was undertaken in a larger cohort of 246 patients with IDC-NOS (See Materials and Methods for cohort details). The staining was carried out by Miss Alice Boulghourjian and scoring and statistical analysis was undertaken by pathologist Dr Sandra O’Toole. As seen in Fig 5.6, Lyn staining in the larger cohort exhibited a similar pattern to the smaller cohort, with the Basal group having the highest Lyn H score followed by the Her2 group and the Luminal Groups having little or no staining. The increased Lyn staining in the Basal group was statistically significant compared to all other groups using ANOVA. In addition, high Lyn expression was strongly associated with the Basal group as tested by F2 analysis (p<0.0001).
115 Chapter 5: Lyn expression in Breast cancer patients
120
100
80
60 ***
40 *** H score Lyn 20 *** *** 0
Her2 Basal
Luminal A Luminal B Luminal
Unclassified Fig 5.6 Lyn staining in a large cohort of Breast cancer samples. The gradation system in Fig 5.4 was used to score Lyn staining in a cohort of 246 patients with IDC-NOS. Patients were divided into molecular subtypes and their mean Lyn H scores plotted. Error bars represent standard error of the mean. *** represents p<0.0001for comparison of a particular breast cancer subgroup with the Basal subgroup.
In order to investigate whether Lyn expression affected patient survival, a Kaplan- Meier survival analysis was performed for high Lyn expression (H score > or equal to 14) and low Lyn expression (H score < 14). High Lyn expression was associated with decreased Breast cancer-specific survival (Fig 5.7) (HR=1.88, p=0.04).
To extend this further, multivariate analysis was performed using standard clinicopathological variables. However in a resolved multivariate model, high expression of Lyn was not independently prognostic.
116 Chapter 5: Lyn expression in Breast cancer patients
Fig 5.7 Association between Lyn expression and survival. The patients in the large cohort were divided into two groups; those with high Lyn staining and those with low Lyn staining (the cut off was arbitrarily set at an H score of 14). The cumulative survival for these groups was plotted against time in months. High Lyn staining was associated with a worse survival than low Lyn staining.
117 Chapter 5: Lyn expression in Breast cancer patients
5.3 Discussion
This chapter aimed to determine if the elevated expression of Lyn observed in Basal breast cancer cell lines was also characteristic of primary Basal breast cancers. Prior to this, the Lyn antibody for use in IHC was optimised by staining of Breast cancer cell lines and normal human tissues. Positive staining of lymphoid tissue and specialised cells such as macrophages and Kupffer cells is in agreement with Lyn’s role in hematopoiesis as outlined in section 1.4.5.
Lyn staining was also undertaken in normal breast tissue derived from reduction mammoplasties. Interestingly, the majority of Lyn staining was found in the apical membrane of Luminal rather than Basal/myoepithelial cells. Around the body, cells surrounding a lumen have a typically polarized morphology, with highly specialised apical surfaces in order to effectuate the efficient absorption or secretion of substances into the lumen. During lactation, it is the Luminal cells that produce milk, which is secreted to the lumen with the help of the underlying Basal/myoepithelial cells (Fig 1.4). Interestingly, the non-lactating breast, as would be the case for reduction mammoplasties used in this study, also secretes a variety of substances and proteins such as concentrated milk components, EGF, Her2, prolactin, progesterone and IgA (Yap et al., 1981; Gann et al., 1997; Lang and Kuerer, 2007). It is thus tempting to speculate that Lyn may be localising to particular structures or molecular complexes associated with the secretion of these products. This could be tested by determining the co-localisation of Lyn with milk components and other proteins characteristic of the secretory pathway such as specific SNARE proteins by confocal microscopy. Interestingly, immunofluorescence microscopy of cell lines derived from the skin, brain and bone show that Lyn localises not only to the plasma membrane but also to the Golgi and distinct cytoplasmic vesicles, consistent with a role of Lyn in trafficking/secretory pathways (www.proteinatlas.org). In addition, Luminal cells of the normal breast are thought to be divided into two groups: ER positive ‘sensor’ cells and ER negative milk-producing ‘sensory’ cells which produce milk in response to paracrine signalling by the sensor cells (Fig 3.12) (Sleeman et al., 2007). It is encouraging to note that Lyn was part of a ‘network hub’ associated with ER negative
118 Chapter 5: Lyn expression in Breast cancer patients
Luminal cells isolated from virgin mice (Kendrick et al., 2008). This lends support to the hypothesis that Lyn may be expressed in ER negative, milk-producing cells. However, this requires further investigation, for example by co-staining of normal mammary glands with Lyn and ER antibodies.
Importantly, Lyn staining of the patient cohort demonstrated a significantly higher level of Lyn expression in the Basal breast cancer subgroup, compared with the other molecular groups. Thus, the association of Lyn with the Basal subtype is found both at the level of Breast cancer cell lines and patient samples. Unlike the localisation of Lyn in normal tissues, Lyn in tumour samples was localised not only at the membrane but also throughout the cytoplasm. This was previously observed in AML samples, and raises the question of whether Lyn may have different roles depending on its localisation (Dos Santos et al., 2008). Significantly, high Lyn expression was associated with shorter survival. This agrees with a study demonstrating an association between Lyn expression and decreased survival in colorectal cancer (Hao et al., 2010). In contrast, a recent study found no association between Lyn expression and survival of Breast cancer patients (Elsberger et al., 2010). However, the localisation of Lyn in that study was predominantly in the nucleus and cytoplasm, with only a minority of staining (5%) at the cell membrane (Elsberger et al., 2010). The use of an alternative antibody and a different Breast cancer cohort may explain why these data contrast with those presented in this thesis. Despite high Lyn expression correlating with decreased survival, it was not an independent prognostic factor. That is, its association with worse survival may be because it simply selects for Basal breast cancer, which is a naturally more aggressive cancer than other subtypes. In light of this, an improved study would be to undertake Lyn staining in a cohort of solely Basal breast cancers. In this way, Lyn’s association with the Basal subtype would be negated and stratification based uniquely on Lyn levels could be undertaken. Unfortunately, given the low incidence of Basal breast cancers compared to Luminal cases (11% in this study), a larger cohort than that used here may be needed in order to give the study significant power. Additionally the use of a combination of markers (vimentin, p63, caveolins) including Lyn may lead improved stratification of Basal breast cancers according to patient outcome.
119 Chapter 6: General Discussion
CHAPTER SIX
General Discussion
While diagnosis and treatment of Breast cancer as a whole has vastly improved in recent times, one particular subgroup, the Basal breast cancers has continued to lag behind this positive trend. This subgroup still presents challenges due to its aggressive nature, poor prognosis and importantly, lack of targeted treatment. This latter problem is likely due to a poor understanding of the signalling pathways characteristic of this subtype (Rakha et al., 2008). Interestingly, Breast cancer lines corresponding to Basal breast cancers are sensitive to Dasatinib, a multikinase inhibitor, and a gene expression signature predictive of Dasatinib sensitivity selects for Basal breast cancers (Finn et al., 2007; Huang et al., 2007). In light of these findings, this thesis hypothesised that kinases targeted by Dasatinib were overexpressed or deregulated in Basal breast cancers and aimed to identify these kinases.
To address this aim, Chapter 3 investigated differential mRNA and protein expression of known Dasatinib targets between cell lines representing Normal breast, Basal and Luminal breast cancers. Striking differences in one candidate, Lyn, were detected at both the mRNA and protein level, and as such prompted us to focus on this kinase for further work. Consequently, Chapter 4 addressed the regulation of Lyn activity and functional outcomes of this kinase in the cell, in order to understand the signalling
120 Chapter 6: General Discussion pathways that Lyn belongs to. Lyn activity was found to be positively regulated in one cell line by HGF, suggesting that Lyn forms part of the Met signalling pathway in this cell line. However, Lyn depletion was shown to have no effect on the activity or total levels of proteins downstream of Met, nor to have an effect on HGF-mediated cell scattering. In contrast, a role for Lyn in controlling cell morphology was apparent, however the mechanism underlying this morphological change requires further investigation as outlined in section 4.3.3. Lastly, the translational application of the findings in Chapters 3 and 4 were addressed in Chapter 5 through investigation of Lyn expression in a Breast cancer cohort using IHC. Importantly, Lyn expression was not only significantly higher in Basal breast cancers, but also strongly associated with this subgroup. Preliminary data on the localisation of Lyn in normal breast tissue demonstrated a potential role for Lyn in secretion, however this needs to be further confirmed and investigated through experiments as outlined in section 5.3.
6.1 What is Lyn doing in the cell? New findings on Lyn in Basal breast cancers
During the production of this thesis, two studies on Lyn in Basal breast cancer cell lines were published which shed light on Lyn’s potential role in the cell. The first, from our own laboratory, used global phosphoprotein profiling through MS in Basal versus Luminal cell lines and found that in addition to increased phosphorylation of Lyn in Basal breast cancer cell lines, there was also increased phosphorylation of EphA2, Met, FAK and p130Cas, suggesting a role for Lyn in cytoskeletal regulation (Hochgrafe et al., 2010). Furthermore Lyn knock down in various Basal breast cancer cell lines decreased cell invasion through Matrigel (Hochgrafe et al., 2010). This role for Lyn in promoting migration and invasion was also confirmed by the second study, Choi et al., 2010, using a similar invasion assay with Lyn knock down in Basal breast cancer cells (Choi et al., 2010). Furthermore, Lyn was found to be part of an ‘EMT signature’ that was characteristic of Basal breast cancer cells that exhibited a mesenchymal morphology (Choi et al., 2010). Both these studies suggest that the presence of Lyn in the Basal breast cancer cell may promote tumorigenesis by affecting cell morphology to enhance cell invasion. In contrast, results presented in Chapter 4 suggest that Lyn loss rather than gain is
121 Chapter 6: General Discussion associated with a more fibroblastic morphology during starvation conditions. Although it is not clear why this discrepancy exists, it could be another example of Lyn playing varying roles depending on different contexts and in different cell lines. Indeed our MS profiling indicates that the signalling network present in HCC 70 cells differs from other Basal breast cancer lines in that tyrosine phosphorylation of certain proteins (eg focal adhesion components) is less pronounced (Hochgrafe et al., 2010).
6.2 Using Lyn as a biomarker for Basal breast cancers
Lyn’s significant overexpression in Basal breast cancers and its strong association with this subtype (Chapter 5) was also confirmed by Choi et al., 2010 (Choi et al., 2010). This lends credence to the use of Lyn as an additional marker for the definition and diagnosis of Basal breast cancers. However, it would still need to be demonstrated whether the addition of Lyn would further refine the current ‘Basal breast signature’ (Fig 1.5). That is, would it add any new information regarding diagnosis of Basal breast cancers, their survival or predicted responses to certain treatments? This could be answered by the study suggested in Section 5.3 where Lyn staining in a uniquely Basal breast cancer cohort is undertaken. This angle of future work is certainly worthwhile given that Lyn expression is variable even amongst the Basal subgroup (Fig 5.5), and that differential levels of Lyn within this group may be associated with different prognoses and response to treatments.
6.3 Is Lyn the target of Dasatinib action?
One of the questions arising from the results in Chapter 3 was: is Lyn the target of Dasatinib action? Indirect evidence in section 3.2.4 suggested that this may be the case. In agreement with this idea, studies in prostate cancer cell lines and the Basal breast cancer cell line BT549 show that treatment with Dasatinib reduces the activity of Lyn as evidenced by decreased phosphorylation of the Y397 residue (Park et al., 2008; Choi et al., 2010). A decrease in Lyn phosphorylation was also seen in ovarian cell lines upon exposure to Dasatinib (Konecny et al., 2009). Additionally, Lyn expression is one of the molecular markers associated with Dasatinib sensitivity in
122 Chapter 6: General Discussion both prostate and ovarian cancer cell lines (Wang et al., 2007; Konecny et al., 2009). However, it would be presumptuous to assume that out of the known Dastinib targets (SFKs, Abl family kinases Kit, PDGFR and EphA2) Lyn is the only target of Dasatinib action. This is especially relevant in light of our group’s finding that EphA2 exhibits increased activation/phosphorylation in Basal breast cancer cell lines, and that Dasatinib treatment of pancreatic and ovarian cell lines decreases EphA2 activation (Chang et al., 2008; Konecny et al., 2009; Hochgrafe et al., 2010). Additionally it has been suggested that the effect of Dasatinib in Basal breast cancer cell lines may be due to its targeting of Kit (Dizdar et al., 2008). While the protein expression of EphA2, Arg and PDGFR was not tested in this thesis, results shown in Fig 4.3 indicate that Dasatinib does not target Kit in Basal breat cancer cell lines. This is because there does not appear to be a concordance between cell lines expressing Kit and their Dasatinib sensitivity as determined by Huang et al., 2007 (Huang et al., 2007). For example the Dasatinib sensitive lines Hs578T, MDA-MB-231, BT20, HCC 1937 and HCC 1954 do not express Kit at all (Fig 4.3), suggesting that at least in these cell lines Dasatinib is targeting another kinase/s.
Further experiments investigating the change in phosphorylation/activation of EphA2, PDGFR and Arg in response to Dasatinib treatment of Basal breast cancer cells would test whether Dasatinib is indeed targeting these kinases in addition to Lyn. Additionally, it would be interesting to determine how knock down of Lyn alone or in combination with the above candidate targets affects the Dasatinib sensitivity of Basal breast cancer cell lines.
6.4 Implications of findings to Basal breast cancer treatment
This study’s discovery of Lyn overexpression in Basal breast cancer cell lines and primary Basal breast cancers, its potential incorporation into the Met signalling pathway and the overexpression of Met in the Basal subgroup, supports clinical trials targeting SFKs and/or Met in Basal breast cancers. Currently, in addition to larger trials using Dasatinib on Basal breast cancers, trials involving other SFK-targeting agents, for example AZD0530 (Saracatinib) and SKI-606 (Bosutinib), are underway
123 Chapter 6: General Discussion for Breast cancers (Jallal et al., 2007; Kim et al., 2009) (ww.asco.org; www.clinicaltrials.gov). Further trials of the latter two inhibitors, specifically on Basal breast cancer patients, would also be valuable. Met signalling has been targeted in clinical trials by agents that either compete with HGF, are monoclonal antibodies against Met or HGF (eg MetMAb, AMG102), or are tyrosine kinase inhibitors targeting Met activation (eg ARQ197, MP470) (Eder et al., 2009; Gastaldi et al., 2010). MP470, has undergone phase 1b trials in ‘triple-negative’ Breast cancers and recruitment is currently underway for a trial combining MetMAb with Paclitaxel and Bevacizumab in patients with metastatic Basal breast cancers (Gastaldi et al., 2010) (www.clinicaltrials.gov). Lastly, due to kinase co-activation it is acknowledged that Src combination therapies may be more effective against cancers than monotherapies (Finn, 2008; Hochgrafe et al., 2010). For example, gastric cancer cell lines that were resistant to Dasatinib due to Met activation, became sensitive when treated with a combination of both Dasatinib and the Met inhibitor PHA-665752 (Okamoto et al., 2010). Consequently, pending further investigations into Lyn’s role in the Met signalling pathway, targeting both Lyn and Met in combination may produce better outcomes than Lyn or Met monotherapy in the treatment of Basal breast cancers.
124 References
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