EMT ☤ MET CRC

1. EMT ☤ METCRC MET overexpression as a hallmark of the epithelial-mesenchymal transition (EMT) phenotype in colorectal cancer K. Raghav, W. Wang, G.C. Manyam, B.M. Broom, C. Eng, M.J. Overman, S. Kopetz The University of Texas M D Anderson Cancer Center, Houston TX

2. Disclosures • No relevant relationships to disclose.

3. Learning Objectives • Recognize epithelial-mesenchymal transition (EMT) as a principal molecular subtype in colorectal cancers. • Identify MET protein overexpression as a key clinical biomarker of EMT physiology in colorectal cancers.

4. Overview • Introduction • Epithelial-mesenchymal transition (EMT) • Challenges & Research question • MET/HGF Axis • Study • Objective • Methodology • Results • Conclusions • Future

5. Overview • Introduction • Epithelial-mesenchymal transition (EMT) • Challenges & Research question • MET/HGF Axis • Study • Objective • Methodology • Results • Conclusions • Future

6. EMT & Normal cells • Epithelial phenotype ► Mesenchymal phenotype • Embryogenesis & Development Weinberg RA et al. J Clin Invest. Jun 2009

7. EMT & Tumors • EMT ‘mesenchymal’ phenotype: • Migratory capacity: Invasion & Metastasis • Linked to chemo-resistance (oxaliplatin and 5FU) Thiery JP. Nature Reviews Cancer. Jun 2002 ; Yang AD et al. Clin Cancer Res. Jul 2006

8. Gene Signatures identify EMT • Gene signatures: • EMT ‘mesenchymal’ subtype • Distinct biology Cheng WY et al. PLoS One. Apr 2012 ; Loboda A et al. Med Genomics. Jan 2011

9. EMT foretells Poor prognosis • EMT molecular classification is prognostic • EMT or mesenchymal-subtype: Worse Prognosis • Epithelial-Subtype: Better Prognosis EMT - Low EMT Score EMT + High EMT Score Figure 1 Figure 2 Shioiri M et al. Br J Cancer. Jun 2006 ; Loboda A et al. Med Genomics. Jan 2011

10. Challenges in Defining EMT Phenotype in Clinic • EMT Gene Signature: • Extensive ongoing efforts • Hard to implement in clinic • Limited availability • Protein Biomarker: • More practical • Readily available Epigenetic Modulation A B C Genes Post Translational Modification A B C Proteins Protein Processing Tumor Weigelt B et al. Ann Oncol. Sep 2012

11. Research Question • Possibility of using a clinical biomarker, to reflect EMT biology to recognize EMT “mesenchymal” subtype as identified by EMT gene signatures ? • Possible marker: MET • MET is motogenic: + Cell mobility & invasiveness • First EMT cell lines transformed using MET activation. • Common signaling pathways with EMT • Optimized assays & integrated as a biomarker Thiery JP. Nature Reviews Cancer. Jun 2002

12. MET/HGF Axis • MET/HGF Axis: • Receptor: MET • Ligand: HGF/SF • Regulates • Gene expression • Cytoskeleton • Aberrancy: • Tumor Proliferation, Survival, Invasion, Migration Raghav K & Eng C. Colorectal Cancer Aug 2012

13. Overview • Introduction • Epithelial-mesenchymal transition (EMT) • Challenges & Research question • MET/HGF Axis • Study • Objective • Methodology • Results • Conclusions • Future

14. Study Objective • To identify association between MET protein expression and gene/protein expression of EMT markers and EMT gene signatures in human colorectal cancers.

15. Study Methodology • Data collection: • The Cancer Genome Atlas (TCGA) Data • The cBio Cancer Genomics Portal • Data type (Untreated primary): • Gene expression: mRNA Expression • RNA Sequencing • Protein levels (MET, SLUG, ERCC1): • Reverse phase protein array RPPA

16. Study Methodology • Tumors classified as per MET protein levels: • MET High/Overexpressed: Protein in top quartile • MET Low: Protein level < 3rd Quartile • 58 genes associated with EMT phenotypes evaluated: • Unsupervised: ≥ 2 EMT signatures (N = 41) • Loboda, Taube, Salazar & Cheng EMT profiles • Nominated: Common EMT markers (N = 17) Salazar R et al. J Clin Oncol. Jan 2011 ; Cheng WY et al. PLoS One. Apr 2012 ; Taube JH et al. Proc Natl Acad Sci U S A. Aug 2010

17. Study Methodology • Statistical methods: • Non-parametric Spearman rank correlation • Mann-Whitney unpaired two-sample U test • Regression tree method • Kaplan-Meier estimates • P < 0.05: Statistically significant • All tests were two-sided

18. Baseline Characteristics • Protein & Gene expression data (N = 139) • Median age at diagnosis: 71 yrs. (35-90 yrs.) • Stage Distribution: • Anatomy:

19. MET overexpression: A Distinct Subset • MET protein expression is right skewed • Top quartile represents distinct subset Study Sample (N = 139) Right Skewed Protein (Z-score) • Poor correlation with MET gene expression (r = 0.16)

20. High MET portends poor survival

21. High MET portends poor survival Hazard Ratio: 2.92 (P = 0.003) MET Low MET-High MET High MET-Low

22. Clinicopathological Associations • MET protein expression: • Not associated with any clinical-pathological variables including stage • Colon > Rectum P = 0.008 P < 0.0001

23. Protein-Protein Associations

24. MET & SLUG Protein • SLUG encoded by SLUG/SNAI2 gene • Zinc finger protein transcription factor • Represses E-cadherin transcription  EMT r = 0.63 P < 0.0001 P < 0.0001

25. MET & ERCC1 Protein • DNA nucleotide excision repair protein • Negative predictive marker for platinum therapy • SNAIL upregulates ERCC1 expression • ERCC1 protein correlates with MET expression (r = 0.6) • Higher ERCC1 in MET overexpressed (P < 0.001) P < 0.001

26. Protein-Gene Associations

27. Results : EMT Markers VIM P = 0.011 ZEB2 P = 0.005 ZEB1 P = 0.010 AXL P = 0.005 MET-High MET-Low

28. EMT signatures correlate well • EMT gene signature scores: • Cheng vs. Salazar (r = 0.8) • Salazar vs. Taube (r = 0.6) • Taube vs. Cheng (r = 0.7) P < 0.001 P < 0.001 P < 0.001 Salazar R et al. J Clin Oncol. Jan 2011 ; Cheng WY et al. PLoS One. Apr 2012 ; Taube JH et al. Proc Natl Acad Sci U S A. Aug 2010

29. EMT gene scores & MET • EMT meta gene score: • MET overexpression group vs. MET normal group Cheng (P = 0.016) Salazar (P = 0.017) Taube (P = 0.029)

30. Conclusions • MET protein expression • Highest quartile represents a distinct subset • Not correlate with MET mRNA expression • Higher in colon than in rectal cancers • Higher expression of SLUG transcription factor • Higher ERCC1 protein levels • Increased gene expression of EMT markers • Higher EMT gene signature scores

31. Take Home Message • MET protein expression can potentially be used as a clinical biomarker representative of the EMT “mesenchymal” phenotype in CRC.

32. Overview • Introduction • Epithelial-mesenchymal transition (EMT) • Problem at hand & Research question • MET/HGF Axis • Study • Objective • Methodology • Results • Conclusions • Future

33. Future • Validation of these results on an independent dataset is currently being performed. • Evaluation of IHC in assessing MET protein expression is underway. • MET can be used as a clinical bio-marker for patient selection for trials targeting EMT. • Unique approach for biomarker search

34. Proposed Paradigm for Pursuitof Biomarkers Conventional Strategy Target based biomarkers Drug Biomarker Trial Taxonomy based biomarkers Proposed Strategy A Tumor Biology Biomarker Genomic Profiling Trial B Drug C

35. Acknowledgement Co-Investigators Wenting Wang, Ph.D. Ganiraju C Manyam, Ph.D. Bradley M Broom, Ph.D. Cathy Eng, M.D., FACP Michael J. Overman, M.D. Scott Kopetz, M.D., Ph.D., FACP Collaborators Dr. Amin Hesham, M.D., M.Sc. Dr. David S. Hong, M.D. Kopetz Lab Team Dr. Ali Kazmi, M.D. Dr. Arvind Dasari, M.D. Maria Pia Morelli, M.D., Ph.D. Shweta Aggarwal, M.D. Feng Tian, Ph.D. Zhi-Qin Jiang, M.D., Ph.D. NCI TCGA initiative Collaborators