Gene Expression Signatures for Prognosis in NSCLC, Coupled with Signatures of Oncogenic Pathway Deregulation, Provide a Novel Approach for Selection of Molecular Targets David H. Harpole, Jr., M.D. Professor of Surgery Duke University Medical Center Chief of Cardiothoracic Surgery Durham Veterans Affairs Medical Center Director of the Lung Cancer Prognostic Laboratory

The Challenge in Prognosis for Individual Patients Current Tools for Prognosis Clinical and histopathologic factors Single molecular biomarkers Gene expression profiles Staging Improved prognosis But, the challenge is to provide an individualized patient prognosis

Current Therapy for Clinical Stage I NSCLC Stage IA Stage IB, II and IIIA Adjuvant Chemotherapy (> 30% relapse)Observation (25% relapse) Resection Clinical Stage 1 (45,000 patients in U.S.) What next ? Identify Patients at Higher Risk? Higher Risk?

Current Therapy for Clinical Stage I NSCLC Stage IA Stage IB, II and IIIA Adjuvant Chemotherapy (> 30% relapse)Observation (25% relapse) Resection Clinical Stage 1 (45,000 patients in U.S.) Develop gene expression profiles that refine risk prediction

101 Stage I NSCLC 50% alive > 5yr; 50% dead of Ca 5yr; 50% dead of Ca < 2.5yr 50 adenocarcinoma 51 squamous cell carcinoma Age66+9 (range 32-83) years Gender39 female, 62 male Duke Pilot Clinical Stage I NSCLC Bank Fresh frozen tissue >50% viable tumor RNA quality assessment Gene expression using Affymetrix U plus

Alive 5 years Dead of cancer by 2.5 years Expression Profiles That Predict Outcome Tumor Sample (Patients) Genes

Expression Profiles That Predict Outcome Probability of Disease-Free Survival Tumor Sample (Patients) Leave-One-Out-Analyses Blue: Alive 5 yrs Red: Cancer death 2.5 yrs Blue: Alive 5 yrs Red: Cancer death 2.5 yrs Clinical-Pathology Prediction Model Gene Expression Prediction Model Accuracy 61% Accuracy 94%

Predictions for the Individual Patient A Capacity to Adjust Risk Assessment Re-classify as “high risk” Stage IA patients Adjuvant Chemotherapy Probability of Disease-Free Survival Months Sample Probability of Disease-Free Survival Months Sample Probability of Disease-Free Survival Months Sample Probability of Disease-Free Survival Months Sample 23 Observe 5 yr: 82% 5 yr: 56% 5 yr: 35% 5 yr: 5%

Current Therapy for Clinical Stage I NSCLC Stage IA Stage IB, II and IIIA Adjuvant Chemotherapy Observation (25% relapse) Resection Second line? Survival Relapse What is unique in this subset?

Gene Regulatory Signaling Pathways and Cancer Ras Myc E2F

Development of Gene Expression Signatures to Predict Pathway Deregulation Control Ras Control Myc Control E2F Control Src Control  -Cat 1.Quiescent human mammary epithelial cells infected with adenovirus containing either a control insert or an activated oncogene of interest. 2.Each infection is performed multiple times to generate samples for pattern analysis. 3.RNA collected for microarray analysis using Affymetrix U133 Plus 2.0 Array.

SSSSSSSASSSSSSSSSSASSASSSSSSSSSASSSSSSSASSSSSAAASAASAAASAAAAAASAAASAAASAAAAAASAAAAAAAAAAAAAAAS Predicting Pathway Status in NSCLC Ras Myc E2F Src  -Cat Predict pathway status of NSCLC Ras predicts adenocarcinoma Myc predicts Squamous

(Ras, Src,  cat) (Ras, Myc) Cluster 1 Cluster 2Cluster 3 Cluster 4 Patterns of Pathway Deregulation in NSCLC Hierarchical Clustering Based on Relative Gene Activation for 5 Pathways

Pathway-Specific Therapeutics: FTI SU6656 Src Ras

Prediction of Pathway Status in Breast Cancer Cell Lines Compared to Sensitivity to Therapeutics p=0.011 p=0.003 Src Ras

Treatment of Early Stage NSCLC Resection with Gene Array Stage IA Stage IB to IIIA Stage IA Stage IB to IIIA Adjuvant Chemotherapy Pathway Specific Drug(s) Observe No Recurrence Relapse Pathway Analysis Re-classify Risk Risk

Conclusion 1.Development of a predictive model to select stage 1A patients appropriate for adjuvant chemotherapy. 2.Utilization of pathway profiles to guide the use of targeted therapeutic agents after relapse from standard chemotherapy. 3.Defining an integrated strategy for individualized treatment based on molecular characteristics of the patient’s tumor.

Acknowledgements: Duke Lung Cancer Prognostic Laboratory David Harpole, Jr, M.D., Director Thomas D’Amico, M.D. Rebecca Prince Petersen, M.D., M.Sc. Mary-Beth Joshi, B.S. Debbi Conlon, AAS, HT(ASCP) Duke Center for Applied Genomics and Technology Joseph Nevins, Ph.D., Director Andrea Bild, Ph.D. Holly Dressman, Ph.D. Anil Potti, M.D. Duke Program in Computational Genomics Mike West, Ph.D., Director Sayan Mukherjee, Ph.D. Haige Chen, B.S., Elena Edelman, B.S. Haige Chen, B.S., Elena Edelman, B.S. Durham VA Thoracic Oncology Laboratory Michael Kelly, M.D., Ph.D., Director Fan Dong, Ph.D.