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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 Clinical Stage 1 (45,000 patients in U.S.) Resection Stage IA Stage IB, II and IIIA Identify Patients at Higher Risk? Observation (25% relapse) Adjuvant Chemotherapy (> 30% relapse) What next?
Current Therapy for Clinical Stage I NSCLC Clinical Stage 1 (45,000 patients in U.S.) Resection Stage IA Stage IB, II and IIIA Observation (25% relapse) Adjuvant Chemotherapy (> 30% relapse) Develop gene expression profiles that refine risk prediction
Duke Pilot Clinical Stage I NSCLC Bank 101 Stage I NSCLC 50% alive > 5yr; 50% dead of Ca < 2.5yr 50 adenocarcinoma 51 squamous cell carcinoma Age 66+9 (range 32-83) years Gender 39 female, 62 male Fresh frozen tissue >50% viable tumor RNA quality assessment Gene expression using Affymetrix U133 2.0 plus
Expression Profiles That Predict Outcome Dead of cancer by 2.5 years Alive 5 years Genes Tumor Sample (Patients)
Expression Profiles That Predict Outcome Gene Expression Prediction Model Clinical-Pathology Prediction Model Blue: Alive 5 yrs Red: Cancer death 2.5 yrs Blue: Alive 5 yrs Red: Cancer death 2.5 yrs Probability of Disease-Free Survival Accuracy 61% Accuracy 94% Tumor Sample (Patients) Leave-One-Out-Analyses
Predictions for the Individual Patient A Capacity to Adjust Risk Assessment Sample 30 1 .9 .8 .7 .6 .5 .4 Probability of Disease-Free Survival .3 .2 .1 0 0 10 20 30 40 50 Months Sample 54 1 .9 .8 .7 .6 .5 .4 .3 Probability of Disease-Free Survival .2 .1 0 0 10 20 30 40 50 Months Sample 27 1 .9 .8 .7 .6 .5 .4 .3 Probability of Disease-Free Survival .2 .1 0 0 10 20 30 40 50 Months Sample 23 1 .9 .8 .7 .6 .5 .4 .3 Probability of Disease-Free Survival .2 .1 0 0 10 20 30 40 50 Months Stage IA patients 5 yr: 82% Observe 5 yr: 56% 5 yr: 35% Re-classify as “high risk” 5 yr: 5% Adjuvant Chemotherapy
Current Therapy for Clinical Stage I NSCLC Resection Stage IA Stage IB, II and IIIA Observation (25% relapse) Adjuvant Chemotherapy Survival Relapse What is unique in this subset? Second line?
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 b-Cat • 1. Quiescent human mammary epithelial cells infected with adenovirus containing either a control insert or an activated oncogene of interest. • Each infection is performed multiple times to generate samples for pattern analysis. • RNA collected for microarray analysis using Affymetrix U133 Plus 2.0 Array.
Predicting Pathway Status in NSCLC Ras Myc E2F Src b-Cat Predict pathway status of NSCLC Ras predicts adenocarcinoma SSSSSSSASSSSSSSSSSASSASSSSSSSSSASSSSSSSASSSSSAAASAASAAASAAAAAASAAASAAASAAAAAASAAAAAAAAAAAAAAAS Myc predicts Squamous
Hierarchical Clustering Based on Relative Gene Activation for 5 Pathways Patterns of Pathway Deregulation in NSCLC Cluster 4 Cluster 2 Cluster 3 Cluster 1 (Ras, Src, b-cat) (Ras, Myc)
Pathway-Specific Therapeutics: Ras Src FTI SU6656
Prediction of Pathway Status in Breast Cancer Cell Lines Compared to Sensitivity to Therapeutics Ras Src p=0.003 p=0.011
Treatment of Early Stage NSCLC Resection with Gene Array Stage IAStage IB to IIIA Re-classify Risk Adjuvant Chemotherapy Observe No Recurrence Relapse Pathway Analysis Pathway Specific Drug(s)
Conclusion • Development of a predictive model to select stage 1Apatients appropriate for adjuvant chemotherapy. • Utilization of pathway profiles to guide the use of targeted therapeutic agents after relapse from standard chemotherapy. • 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. Durham VA Thoracic Oncology Laboratory Michael Kelly, M.D., Ph.D., Director Fan Dong, Ph.D.