1. Abstracts #338 and 339 Jordan Berlin, MD Ingram Professor of Cancer Research

2. Our goal is to improve the outcomes for those we can help while minimizing toxic exposure for those for whom our treatments provide no benefit

3. What do we know about stage II colon cancer? It is a heterogeneous stage with an overall good prognosis Fluoropyrimidine adjuvant therapy provides benefit to ~3-4% of unselected patients –FOLFOX does not appear to improve outcomes for low risk patients –At time of last publication, FOLFOX did not statistically improve survival of high risk stage II patients The HR was 0.72, NS and more people were alive with relapse in the 5FU arm at time of publication ASCO guidelines state that discussion of adjuvant therapy in stage II should be done

4. Clinicopathologic markers for high risk Consider treatment if presence of 1 or more of the following based on ASCO guidelines 4 –T4 stage –Poorly differentiated histology –Bowel obstruction or perforation –<12 Lymph nodes resected –Lympho-vascular invasion –Perineural invasion –Close margins –Elevated preoperative CEA Also, we have Mismatch Repair (MMR) or MSI –This is prognostic for improved when MMR is deficient –This also appears predictive of 5FU effect in stage II 4. Benson AB et al. J Clin Oncol 2004.

5. Strategies to improve risk/benefit in stage II Further refine the population at risk –Theoretically these patients have the best chance for benefit –Eg T4 and or at least 2 high risk features –Other prognostic markers Define the populations who will benefit from chemotherapy –Predictive markers

6. Oncotype DX: Primary Analysis: Recurrence Score Predicts Recurrence Risk in Stage II & III Colon Cancer Patients in NSABP C-07 (n=892) Solid: 5FU Dashed: 5FU+Ox Stage III C Stage III A/B Stage II With similar relative benefit of oxaliplatin added to adjuvant 5FU across the range of RS, absolute benefit of oxaliplatin increases with increasing RS, most apparently in stage II and stage IIIA/B patients p<0.001 Solid: 5FU Dashed: 5FU+Ox O’Connell, ASCO 2012

7. Oncotype DX: 5-year Recurrence Risk in 5FU treated arm Cox Regression Analysis (n=892) Stage IIStage IIIA/BStage IIIC RS Group*% pts Average Risk 95% CI % pts Average Risk 95% CI % pts Average Risk 95% CI Low39%9% (6-13%)41%21% (16-26%)33%40% (32-48%) Intermediate36%13% (8-17%)34%29% (24-34%)37%51% (43-59%) High25%18% (12-25%)25%38% (30-46%)30%64% (55-74%) * Pre-specified RS Groups: Low (RS<30), Intermediate (30≤RS<41), High (RS≥41). Recurrence risk is significantly higher in High vs. Low RS group: HR = 2.11, p<0.001 O’Connell, ASCO 2012

8. Contribution of RS Beyond Clinical and Pathologic Covariates Pre-specified Multivariate Analysis (n=892) VariableValueHRHR 95% CIP value Stage <0.001 (by nodal status)Stage III A/B vs. II0.97(0.55,1.71) Stage III C vs. II2.07(1.16,3.68) TreatmentFU+Oxali vs. FU0.82(0.64,1.06)0.12 MMRMMR-D vs. MMR-P0.27(0.12,0.62)<0.001 T-stage T4 st II & T3-T4 st III vs. T3 st II & T1-T2 st III 3.04(1.84,5.02)<0.001 Nodes examined<12 vs. ≥121.51(1.17,1.95)0.002 Tumor gradeHigh vs. Low1.36(1.02,1.82)0.041 RSper 25 units1.57(1.19,2.08)0.001 RS is significantly associated with risk of recurrence after controlling for effects of T and N stage, MMR status, number of nodes examined, grade and treatment

9. Contribution of RS Beyond Clinical and Pathologic Covariates Pre-specified Multivariate Analysis (n=892) VariableValueHRHR 95% CIP value Stage <0.001 (by nodal status)Stage III A/B vs. II0.97(0.55,1.71) Stage III C vs. II2.07(1.16,3.68) TreatmentFU+Oxali vs. FU0.82(0.64,1.06)0.12 MMRMMR-D vs. MMR-P0.27(0.12,0.62)<0.001 T-stage T4 st II & T3-T4 st III vs. T3 st II & T1-T2 st III 3.04(1.84,5.02)<0.001 Nodes examined<12 vs. ≥121.51(1.17,1.95)0.002 Tumor gradeHigh vs. Low1.36(1.02,1.82)0.041 RSper 25 units1.57(1.19,2.08)0.001 RS is significantly associated with risk of recurrence after controlling for effects of T and N stage, MMR status, number of nodes examined, grade and treatment

10. Kaplan-Meier plot of time to cancer-related death in the independent validation set. Kennedy R D et al. JCO 2011;29:4620-4626 ©2011 by American Society of Clinical Oncology Gene signature analysis This prognosis was independent of known clinicopathologic factors

11. Data from QUASAR study using 13 cancer-related genes : Kaplan-Meier estimates of 3-year recurrence in surgery-alone patients by risk group. Gray R G et al. JCO 2011;29:4611-4619 ©2011 by American Society of Clinical Oncology QUASAR prognosis stratification

12. Gray R G et al. JCO 2011;29:4611-4619 ©2011 by American Society of Clinical Oncology Risk groups did not predict for chemotherapy benefit

13. Use of Adjuvant Chemotherapy & Outcomes in Stage II Colon Cancer with vs. without Poor Prognostic Features Aalok Kumar, Hagen Kennecke, Howard Lim, Daniel Renouf, Ryan Woods, Caroline Speers, and Winson Cheung Department of Medical Oncology British Columbia Cancer Agency

14. What was the study? An exploratory analysis of prospectively collected data in the British Columbia Cancer Agency Gastro-Intestinal Cancers Outcomes Unit (GICOU) –10 year period of analysis –1,697 patients divided into high vs low risk based on ASCO guidelines Note: these guidelines are well-considered and literature based. Although 9 years old, they are still relevant 73% high risk and 27% low risk

15. Some baseline lessons –More patients in the high risk category (29%) received adjuvant therapy than in the low risk category (13%) –Patients ≥ 70 years of age were less likely to receive adjuvant chemotherapy –Perforation and T stage appeared to play the largest roles in selecting patients for adjuvant chemotherapy CEA also played a role

16. Some issues to note The performance status –This may have been measured early, but PS = 3 were treated (surprising) and comprised a huge population Perforation/obstruction –Seemed like a high proportion of patients presented with these findings Adjuvant therapy –We don’t actually know what the choice of adjuvant therapy was for the patients

17. Putting this into perspective Low risk group –Nothing about adjuvant chemotherapy looked good –Univariate 3 year relapse survival, 5 year disease specific survival and 5 year overall survival were similar with or without adjuvant chemotherapy –Multivariate Just made chemo look worse in this setting. 3 year RFS, 5 year DSS were worse with adjuvant chemo Note: The selection of low risk patient for chemo may have included some key factors not assessed that put them at higher risk for recurrence or death

18. High Risk Group Very confusing Univariate outcomes –3 year RFS and 5 year DSS were identical with or without adjuvant chemotherapy –5 year OS was improved with adjuvant chemo

19. Does adjuvant chemotherapy heal what is ailing you? The univariate analysis seems to suggest that while chemotherapy does not impact death from colorectal cancers, it does affect overall survival. –This is counterintuitive suggesting that chemotherapy reduced deaths from other causes –The multivariate outcomes analysis sheds more light

20. High Risk Outcomes Multivariate outcomes analysis showed that the high risk group does appear to benefit from adjuvant chemotehrapy (HR = 0.67 for OS) –However benefits vary T4 seems to have more benefit than T3 Multiple high risk features predicts for more benefit from adjuvant chemotherapy Single high risk feature T3 patients derive uncertain benefit

21. What is the bottom line? This seems to confirm that stage II patients overall have a good prognosis, but –There are subsets with better and worse prognoses –Clinicopathologic parameters can separate these groups to an extent –But we don’t truly know who benefits from chemo That group is small –And to benefit these patients we need to expose a large number of these patients to the risks

22. Is there a best way to use the data? We could use prognosis to segregate –Low risk individuals based on clinicopathologic criteria Don’t appear to benefit from chemotherapy and may even be harmed based on this data –Very high risk (T4, multiple high risk features) Appear to benefit from chemotherapy and maybe have the best chance to derive benefit –Intermediate risk (T3, one high risk feature) These are still the most complex, and maybe they would derive the most benefit from one of these recurrence risk panels

23. Summary However, we still risk exposing many patients who have no chance of benefit to adjuvant chemotherapy –While there are many gene signatures out there, these need to be validated prospectively Ideally we need predictive markers—these markers would ideally completely separate those who benefit from adjvant chemotherapy from those who don’t –Currently, our only predictive marker appears to be MMR –ECOG 5202 may shed more light on prognostic groups

24. Summary II The abstract presented did not assess mismatch repair (MMR) phenotype (ie microsatellite instability/stability) –While mismatch repair biology and its impact on treatment and prognosis is still emerging –Mismatch repair deficiency correlates with a better prognosis in stage II colon cancer The pathology often reads as poorly differentiated –In stage II colon cancer MMR deficiency predicts for lack of benefit from fluoropyrimidines

25. A Molecular Profile of Colorectal Cancer to Guide Prognosis and Therapy after Resection of Primary or Metastatic Disease Joshua M. Uronis, Ph.D. Hsu Laboratory Duke Institute for Genome Sciences and Policy Duke Cancer Institute Duke University

26. What did they do? They took microarray data from 850 primary CRC samples Looked at patterns of pathway activation/deregulation for 19 oncogenic pathways –6 subrgroups came out of this Then evaluated 133 metastatic CRC samples –6 subgroups again identified

27. What they learned Their 6 subgroups had different recurrence free survival –The same group did the worst in both resected primary and resected liver metastases –These subgroups could not be differentiated by any specific oncogene mutations –But these subgroups had potential to have differential sensitivity to targeted agents.

28. Next step: prove the differential sensitivity to targeted agents--predictive They have now developed 50 explant models from patients into mice –These appear to maintain their characteristic features over multiple generations They can place these explants into the 6 subgroups –Example given was from subgroups expected to be resistant and expected to be sensitive to mTOR inhibitors based on their pathway activation analysis

29. Rad001 Control Predicted Sensitive Predicted Resistant Control Rad001 Predicting Drug Responses

30. What have we learned? This is early and needs further testing However, they can potentially use the mouse model to identify targets to “hit” and agents that are effective for each subgroup –This may allow us to find more active agents for each subgroup –The authors hope to use this to find adjuvant therapies

31. Are there advantages over other model systems? This model would not require growing every patient’s tumor in vivo, –It identifies putative drugs for subgroups –Analysis of a patient’s tumor specimen could lump them into one of the 6 subgroups –And if clinical trials prove what their preclinical models show, Each subgroup will be treated differently, but with potentially more active agents

32. Conclusions Both abstracts add to our knowledge in colorectal cancer –Kumar, et al showed that our clinicopathologic prognosis models in stage II need more modification However, there are groups that can potentially be designated to treatment or no treatment based on these factors alone –Uronis, et al have developed a model that may help us to subdivide CRC patients and treat them (personalized) based on their subgroup However, this needs further preclinical testing and clinical trial validation This provides an alternative to genomic analysis which may identify mutations, but we don’t know if this means pathway dependence/activation/dysregulation