Mace L. Rothenberg, M.D.

1. Developing a Patient-Tailored Strategy to Stratify and Treat Patients with Pancreatic Cancer Mace L. Rothenberg, M.D. Professor of Medicine Ingram Professor of Cancer Research

2. Conflict of Interest Disclosure Consultant or Advisory Role Stock Ownership

3. Patient-Tailored Strategy for Pancreatic Cancer Treatment How Are These Presentations Related? • Development of a more robust preclinical model • Pharmacogenomics • Randomized Phase II clinical trial • Patterns of gene expression and amplification • Each looks at a different, and complementary approach to the development of improved therapy • Tumor genetics, host genomics, effect of therapy in the mouse, effect of therapy in the patient

4. Patient-Tailored Strategy for Pancreatic Cancer Treatment A prospective validation of a direct tumor xenograft model in pancreatic ductal adenocarcinoma (PDA) Antonio Jimeno, Ignacio Garrido-Laguna, Maria Uson-Olaso, Dante Trusty, Ralph Hruban, Richard Schulick, John Cameron, Anirban Maitra, and Manuel Hidalgo Abstract #4500

5. Direct Tumor Xenograft Model in Pancreatic Cancer What Did They Do and Why is it Important? • Obtained fresh pancreatic ductal carcinoma from the operating room which were then implanted directly into nude mice • This maintains the genetic integrity of the original tumor over multiple passages • Evaluated response of tumor explants to drugs to determine if this model has promise in predicting • clinical response to the agent in the patient • potential activity of investigational drugs against pancreatic cancer

6. Direct Tumor Xenograft Model in Pancreatic Cancer Key Findings • Explantation and propagation of human pancreatic ductal adenocarcinomas in nude mice is feasible • In a limited number of patients, information from this model was be used to select treatment upon relapse • In 4 patients, there was concordance between ex vivo sensitivity and clinical response • Concordance between ex vivo sensitivity and gene expression patterns of tumor cell lines known to be sensitive to gemcitabine and docetaxel

7. Direct Tumor Xenograft Model in Pancreatic Cancer Limitations and Challenges • Only 20% of all consenting patients yielded successful xenografts • Model lacks certain elements that may be critical in determining treatment response • Immune system • Tumor-stromal-vascular interactions • Very few drugs have demonstrated greater activity than the standard agent used to treat this disease: gemcitabine • Clinical “successes” consisted of 2 pts with SD x 4 and 6 months • Can PD at 100 days be considered a success? • Poor correlation between ex vivo sensitivity and PFS with adjuvant gemcitabine: R2 = .08

8. Patient-Tailored Strategy for Pancreatic Cancer Treatment Significant Effect of Single Nucleotide Polymorphisms of Gemcitabine Metabolic Genes on Pancreatic Cancer Survival and Drug Toxicity Milind Javle, Taro Okazaki, Robert Wolff, Chris Crane, Linus Ho, Gauri Varadhachary, Douglas B. Evans, James L. Abbruzzese, Donghui Li. Abstract #4501

9. Correlation of Gemcitabine Metabolic SNPs and Clinical Outcome What Did They Do and Why is it Important? • Identified SNPs in gemcitabine-associated cellular transporter, anabolic and catabolic genes • Correlated SNP patterns (haplotypes) with therapeutic outcomes as well as patterns and severity of toxicity • This approach has the potential to identify patients at increased risk of harm as well as patients more likely to benefit from treatment

10. Correlation of Gemcitabine Metabolic SNPs and Clinical Outcome Key Findings • Grade 3-4  ANC was increased 2-3 fold for those patients with CT or TT genotype at position 111 in the cytidine deaminase gene or the CC/CT genotype in position 1205 in the deoxycytidine kinase gene. • Longer survival was observed in those patients with the same genotype in the cytidine deaminase gene or with an AA or AG genotype in position 42 of the ribonucleotide reductase M1 gene. • Two models, one comprised of selected haplotypes of metabolic genes and the other with selected haplotypes of transporter genes, were associated different patterns of survival

11. Correlation of Gemcitabine Metabolic SNPs and Clinical Outcome Limitations and Challenges • Data generated from a relatively small dataset (120 patients) • Combined data from 2 clinical trials with different chemotherapy regimens • Gemcitabine alone (54 patients) • Gemcitabine + cisplatin (66 patients) • All patients received radiation in addition to chemotherapy • Was the predictive value of these SNPs still seen in multivariate analysis that included known clinical prognostic variables such as tumor grade, baseline CA19-9, + or - margin or LN?

12. Correlation of Gemcitabine Metabolic SNPs and Clinical Outcome Conclusions • These findings should be validated in an independent cohort involving a larger number of patients • Agree (this has been the downfall of prior efforts) • But does the model need further refinement first? • Prospectively select SNPs based on biological pathways (for gemcitabine and/or other drugs) • Agree Results are encouraging but more work needs to be done before it is likely to have a major clinical impact on patient management

13. Patient-Tailored Strategy for Pancreatic Cancer Treatment Final analysis of a randomized phase II trial of bevacizumab and gemcitabine plus cetuximab or erlotinib in patients with advanced pancreatic cancer Hedy Lee Kindler, Tara Gangadhar, Theodore Karrison, Howard Hochster, Malcolm Moore, Kenneth Micetich, Weijing Sun, Daniel Catenacci, Walter M Stadler, and Everett E Vokes for the University of Chicago Phase II Consortium Abstract #4502

14. Addition of EGFR Inhibitors to Gemcitabine + Bevacizumab in Advanced Pancreatic Cancer What Did They Do and Why is it Important? • Tried to build upon exciting early clinical data for gemcitabine + VEGF inhibitor (bevacizumab) and gemcitabine + EGFR inhibitor (erlotinib or cetuximab) • Promising preclinical data on the effect of simultaneous inhibition of multiple growth factor signaling pathways • Randomized Phase II design could help identify if either of these regimens should be taken to Phase III

15. Addition of EGFR Inhibitors to Gemcitabine + Bevacizumab in Advanced Pancreatic Cancer Key Findings • Primary endpoint of RR ≥ 35% for either arm was not met • Is RR the best indicator of activity in this disease? • Both regimens were relatively well tolerated • Patients with better PS and locally advanced disease did best • Appearance of rash during treatment correlated with  PFS for GBE but not for GBC group • Early HTN may predict for response but not for PFS or OS • Neither VEGF nor soluble VEGFR2 correlated with clinical outcome

16. Addition of EGFR Inhibitors to Gemcitabine + Bevacizumab in Advanced Pancreatic Cancer Issues to Consider • Interference between VEGF inhibitor and EGFR inhibitor • biologically • pharmacologically • Presence of KRAS mutation in >90% of all pancreatic cancers • Likely to be a cause of resistance to EGFR inhibitors in CRC and NSCLC • But why was NCIC PA.3 study positive? (Death HR = 0.81, p = .028 for combination of gemcitabine + erlotinib). • Is there a definable subset of patients with PDAC who benefit from an EGFR inhibitor?

17. Addition of EGFR Inhibitors to Gemcitabine + Bevacizumab in Advanced Pancreatic Cancer Two Alternative Interpretation of Results Trial was a Failure Trial was a Success The trial didn’t meet protocol-specified endpoint for success With >90% of PDAC carrying KRAS mutations, the lack of benefit is not surprising In mCRC, the addition of an EGFR inhibitor to chemotherapy and bevacizumab doesn’t improve outcomes Requirement of a RR ≥35% was unrealistic RR is not a reliable indicator of efficacy in PDAC OS = 7-8 months compares favorably with gemcitabine (6 mo), gem + erlotinib (6.4 mo), gem + bevacizumab (5.7 mo), and gem + cetuximab (6.4 mo)

18. Patient-Tailored Strategy for Pancreatic Cancer Treatment Whole Genome Expression Analysis of Pancreatic Adenocarcinoma Predicts Prognosis after Surgery Eric A. Collisson, R. Mori, A.C. Hoffmann, G.E. Kim, R.Hajnal, P.V. Danenberg, J. Cooc, K.D. Danenberg, M.A. Tempero Abstract #4503

19. Whole Genome Expression Analysis in PDAC What Did They Do and Why is it Important? • Tried to identify a genetic signature that distinguished long from short survivors following Whipple resection of PDAC • Performed this on gross and microdissected tumor • Also evaluated tumors for differences in gene copy number

20. Whole Genome Expression Analysis in PDAC Key Findings • Expression pattern of PDAC differs dramatically from NL pancreas • Gene expression profile differences identified from microdissected specimens were not maintained when gross tumor was analyzed • Only 3 of >700 genes overlapped suggesting dilutional effect of stroma and acini • Two genetic loci appear to be amplified disproportionately in patients with PDAC and short survival: 6p21.1-21.3 and 8p12 - 8p11.23 (LETM2)

21. Whole Genome Expression Analysis in PDAC Issues to Consider • Was the genetic signature an independent predictive or prognostic tool after taking known clinical factors into account? • Although adjuvant therapy is the only statistically significant difference, there are notable imbalances in gender, and histologic grade. • The short surviving patients survived a median of 3 months; why so short? • Does this analysis include death from any cause or just disease-specific death? • Will either model hold up in a larger, independent cohort? • What are the biological consequences of the overexpressed or amplified genes?