Background

1. Prognostic and predictive blood-based biomarkers in patients with advanced pancreatic cancer: Results from CALGB 80303 A.B. Nixon1, H. Pang1,2, M. Starr1, D. Hollis1,2, P.N. Friedman3, M.M. Bertagnolli4, H.L. Kindler3, R.M. Goldberg5, A.P. Venook6, H.I. Hurwitz1 1Duke University Medical Center; 2CALGB Statistical Office; 3University of Chicago Cancer Research Center; 4Brigham and Women's Hospital, Boston; 5University of North Carolina, Chapel Hill; 6University of California, San Francisco

2. Background • Pancreatic cancer is the 4th leading cause of cancer-related death in the United States1. • Bevacizumab (Genentech/Roche) is a humanized monoclonal antibody against vascular endothelial growth factor A (VEGF A) and has been shown to provide clinical benefit in a number of tumor types including colon, glioblastoma, renal, and non-small cell lung cancer2. • CALGB 80303 was a randomized Phase III Trial of gemcitabine plus bevacizumab versus gemcitabine plus placebo in patients with advanced pancreatic cancer. • In this study, no clinical benefit was observed from the addition of bevacizumab3. • Blood-based biomarker profiling to identify potential prognostic/predictive factors has been supported by several recent studies4-7. • 1. Jemal A, et al. CA Cancer J Clin 2010; 60: 277-300. • Bevacizumab (Avastin) resource center: http://www.avastin.com • Kindler, HL, et al. JCO. 2010; 28:3617-22. • Nixon, AB, et al. JCO. 2010; 28: (suppl; abstr e21009). • Miles, DW, et al. San Antonio Breast Cancer Symposium: December 8-12, 2010. • Hanrahan EO, et al. JCO. 2010; 28: 193-201 • Tran, HT, et al. JCO. 2011; 29 (suppl 7; abstr 334).

3. Objectives • To correlate baseline values of multiple plasma-based angiogenic factors with the primary clinical outcome (Overall Survival) of CALGB 80303 • Identify univariate and multivariate prognostic markers • Predict outcome independent of treatment group • Identify univariate and multivariate predictive markers • Predict outcome that is dependent upon treatment • Identify patterns of correlation among analytes

4. Methods I • Clinical Study • Patients with advanced or metastatic pancreatic adenocarcinoma were randomized to gemcitabine (1000 mg/m2) ± bevacizumab (10mg/kg) • Treatment was continued until progression, adverse event, or withdraw of consent • Median overall survival • Gemcitabine + placebo - 5.9 months (95% CI: 5.1,6.9) • Gemcitabine + bevacizumab - 5.8 months (95% CI: 4.9,6.6) • Sample Collection and Handling • EDTA plasma, citrated plasma, serum, urine were processed at individual sites. • Samples were frozen and shipped for centralized storage at the CALGB Pathology Coordinating Office (PCO).

5. Methods II • Laboratory Methods • Samples were shipped from the PCO, thawed one time, precipitate removed, aliquoted, and re-frozen until assayed. • Every sample tested was exposed to one freeze/thaw cycle. • Laboratory personnel were blinded throughout study. • Assays were performed in triplicate. • Samples were evaluated using a multiplex ELISA platform (SearchLight system, Aushon Biosciences). • Statistical Methods • Univariate Cox proportional hazards regression models were used for both prognostic and predictive biomarker identification. • Multivariate Cox models were developed with leave-one-out cross validation (LOOCV). • Bivariate models were used to identify two-analyte predictive models. • Spearman correlations were calculated across all pairs of analytes.

6. SA-HRP SA-HRP SA-HRP SA-HRP SA-HRP SA-HRP B B B B B B B B B B B B B B B B SA-HRP SA-HRP SA-HRP SA-HRP SA-HRP SA-HRP SearchLight Array Protocol Specific proteins are distinguished by their unique spotted position within the well. Spotted Capture Ab’s Add Sample &Stds Add Detection Ab’s Add SA-HRP Reagent Add Substrate Image Plate The brightness of the spotted feature, captured by the cooled CCD camera, is indicative of the expression of the protein. The feature’s signal is analyzed using the PROarray Analyst Software to determine the amount of protein in each unknown sample. -Figure adapted with permission from Aushon Biosciences (Billerica, MA)

7. Panel of Angiome Analytes Standard ELISAs were run for IGF-1 and TGFbRIII

8. Assay Performance Limits of Detection Coefficients of Variation

9. Patient Characteristics

10. Correlation of Analytes at Baseline Cluster Dendrogram

11. Correlation of Analytes at Baseline Cluster Dendrogram

12. Correlation of Analytes at Baseline Cluster Dendrogram

13. Prognostic Marker Analysis

14. UnivariatePrognosticMarkers Gemcitabine + Placebo (OS) * significant in both gemcitabine + placebo & gemcitabine + bevacizumab cohorts # from Cox regression using continuous analyte values

15. UnivariatePrognosticMarkers Gemcitabine + Placebo (OS) * significant in both gemcitabine + placebo & gemcitabine + bevacizumab cohorts # from Cox regression using continuous analyte values

16. UnivariatePrognosticMarkers for Gemcitabine + Bevacizumab (OS) * significant in both gemcitabine + placebo & gemcitabine + bevacizumab cohorts # from Cox regression using continuous analyte values

17. UnivariatePrognosticMarkers for Gemcitabine + Bevacizumab (OS) * significant in both gemcitabine + placebo & gemcitabine + bevacizumab cohorts # from Cox regression using continuous analyte values

18. Multivariate Prognostic Markers Gem + Placebo & Gem + Bevacizumab Model for GP: IGFBP-1, CRP, PDGF-AA, PAI-1 tot, PEDF Model for GB: IGFBP-1, IL-6, PDGF-AA, PDGF-BB, TSP-2 Multivariate prognostic models for OS were developed using a leave-one-out cross validated Cox Proportional Hazard model. GP: gemcitabine + placebo GB: gemcitabine + bevacizumab N: events cens: number censored Survival expressed in months

19. Multivariate Prognostic Models Gemcitabine + Placebo Gemcitabine + Bevacizumab Median OS (months) 7.2 3.6 Median OS (months) 7.3 3.3 Low Risk High Risk Low Risk High Risk 95% CI (5.8,8.6) (2.6,4.6) 95% CI (5.8,9.7) (2.6,4.7) HR 2.0 HR 2.1 (IGFBP-1, CRP, PDGF-AA, PAI-1 tot, PEDF) (IGFBP-1, IL-6, PDGF-AA, PDGF-BB, TSP-2)

20. Predictive Marker Analysis

21. UnivariatePredictiveMarkers #p-values are uncorrected and represent log-rank tests comparing the two treatment arms

22. UnivariatePredictiveMarkers Favors Bev Favors Placebo Ang-2 <median >median SDF-1b <median >median <Q1 VEGF-D >Q1 1.0 1.2 1.4 1.6 1.8 0.4 0.6 0.8 Hazard Ratio

23. BivariatePredictiveMarkers Gem + Placebo Gem + Bev Gem + Placebo Gem + Bev SDF-1b>med OPN <med SDF-1b<med Ang-2 <med #p-values are uncorrected and represent log-rank tests comparing the two treatment arms

24. Conclusions • Angiome analyses were technically robust • Multiple factors with strong prognostic importance were identified • Factors were similar in the gemcitabine + placebo &gemcitabine + bev group • Several markers predicted for potential benefit or lack of benefit from bevacizumab • Results need confirmation before being applied to clinical practice • Inclusion of multi-analyte angiome analyses in other trials is warranted

25. Acknowledgments • We thank all the patients, and their families and caregivers, who participated in the parent protocol and in the correlative science sub-study • We thank the investigators and their study teams • We thank the members of the Pathology Coordinating Office at CALGB • We thank all the members of the Duke/CALGB Molecular Reference Lab and the team at Aushon Biosciences for assay development • This study was sponsored by a grant from the CALGB foundation