Are we making any progress in the molecular taxonomy of CRC?

1. Are we making any progress in the molecular taxonomy of CRC? Marcello Maugeri-Saccà, MD, PhD Istituto Nazionale Tumori “Regina Elena”

2. Various ways of classifying CRC:first-generation classifier

3. Second-generation classifier: genome instability-based classification chromosomal instable Chromosomal gains and losses and structural rearrangements: LOH at APC, TP53, SMAD4, etc Defective MMR due to MLH1methylation or germlinemutations in MMR genes microsatellite instable CpG island methylator phenotype Transcriptional silencing of tumor suppressor and DNA repair genes (MLH1, BRAF-mut.) Simons et al., Ann Oncol 2013

4. Thirdgeneration classifiers:Gene expressionprofiling-basedclassification A B C: mixed classification

5. Cell of origin-based classification: salient characteristics of the six CRC subtypes and correlation with colon-crypt location and Wnt signaling two published gene expression data sets (core data sets, n = 445) Sadanandam et al., Nat Med 2013

6. Cell of origin-based classification: transit-amplifying subtype was a heterogeneous collection of samples with variable expression of stem cell and Wnt-target genes. Sadanandam et al., Nat Med 2013

7. CRC subtypes and treatment response Khambata-Ford dataset (n:110, cetuxmonotherapy)

8. Prognosis-derived classification AMC-AJCCII-90 dataset, stage II De Sousa E Melo, Nat Med 2013

9. The prognosis-based classifiers are represented in cell lines and xenografts

10. Prognosis-derived classification: subtype-specific molecular alterations and reproducibility in the clinical setting CCS1 (CIN 18q loss, 20q gain ) CCS2 (MSI/CIMP+/Braf-mutCCS3 (heterogenous) TMA-based miniclassifier using IHC for four gene products (FRMD6, ZEB1, HTR2B and CDX2) selected on the basis of validated reliable staining and high differential expression between CCS1 and CCS3 in multiple data sets

11. Different published prognostic signatures show very limited overlap

12. Gene sets specific for serrated- or FAP-associated adenomas (APC germlinemutation/tumor development via the CIN pathway)showed association with CCS3 or CCS1-CIN tumors

13. Thirdgeneration classifiers:Gene expressionprofiling-basedclassification- celloforigin+genomicinstability + prognosis - 416 patients with stage II–III Marisa L, Plos Med 2013

14. Summary of the main characteristics of the six subtypes. Marisa L, Plos Med 2013

17. filtering biologically relevant mutations MutSig: Frequency Paradigm Shift: Data integration (mutations, gene expression profile, etc) MEMo: Mutual exclusivity module analysis

18. MutSig: Frequency (False positive) threshold is chosen to control for the False Discovery Rate (FDR), and genes exceeding this threshold are reported as significantly mutated.

19. Paradigm Shift: Data integration NFE2L2 /KEAP1 pathway

20. MEMo: Mutual exclusivity module analysis

21. The Cancer Genome Atlas (TCGA)

22. Diversity and frequency of genetic changes leading toderegulation of signalling pathways in CRC

23. DruggablevsUndruggable Module 1 Module 2 Module 3 Module 4

24. The origin of cancer heterogeneity Maugeri-Saccà M. TargetedTherapies in Oncology (in press)

25. Biomarker frequencies of discordance between primary tumours and metastases Bedard et al., Nature 2013

26. Variable Clonal Repopulation Dynamics in CRC The progeny of single CRC cells was followed by carrying out clonal tracking experiments through lentiviralintegration site mapping by Southern blotting Clonal behavior (proliferative potential and tumor re-generation potency is classified across multiple recipients • nongenetic mechanisms of heterogeneity • Oxa treatment altered repopulation dynamics Persistent clones Short-term clones Transient clones Fluctuating Clones Resting clones Kreso et al., Science 2013

27. Take-home message • Novel prognostic classifiers need validation and head-to-head comparisons • Think beyond superstar pathways: back to the lab to set up novel strategies for targeting the untargetable • Investigations of tumor heterogeneity in the clinical setting (e.g. single cell analisys)

29. Prognosis-based classification and therapy efficacy

30. Poor-prognosis CRC develops from serrated precursor lesions

32. Common Cancer Stem Cell Gene Variants Predict Colon Cancer Recurrence Gerger et al., CCR 2011

33. Circulating Cancer Stem-Like Cells and Prognosis in Patients With Dukes’ Stage B and C Colorectal Cancer (Training Set) Iinuma et al., JCO 2011

34. Circulating Cancer Stem-Like Cells and Prognosis in Patients With Dukes’ Stage B and C Colorectal Cancer (Validation Set)

35. CD133 expression and the prognosis of colorectal cancer Flowchart of selection of studies for inclusion in meta-analysis CD133 expression and 5-year OS rate CD133 expression and 5-year DFS rate Chenet al., PlosOne 2013

36. CSCs and chemoresistance A-B-C: CSC-intrinsicmechanisms D: CSC-extrinsicmechanisms Maugeri-Saccà M .et al. Clin Cancer Res 2011

37. Drugging the undruggable Chan et al., Nature ReviewsDrugDiscovery 2011

38. The principle of high-throughput loss-of-function genetic screens for biomarker-driven clinical trials Maugeri-Saccà M., et al. Current Pharmaceutical Design. In press

39. Unresponsiveness of colon cancer to BRAF(V600E)inhibition through feedback activation of EGFR Prahallad et al., Nature 2012

40. CRC subtypes and treatment response

41. Variable Clonal Repopulation Dynamics Influence Chemotherapy Response proportion of clone types generated by reinjecting Ctrl and OX-treated tumors Post-chemo enriched population