Gene Expression Signatures for Prognosis in Gene Expression Signatures for Prognosis in NSCLC, Coupled with Signatures of Oncogenic NSCLC, Coupled with Signatures of Oncogenic

Pathway Deregulation, Provide a Novel Approach Pathway Deregulation, Provide a Novel Approach for Selection of Molecular Targetsfor Selection of Molecular Targets

David H. Harpole, Jr., M.D.David H. Harpole, Jr., M.D.

Professor of SurgeryProfessor of SurgeryDuke University Medical CenterDuke University Medical CenterChief of Cardiothoracic SurgeryChief of Cardiothoracic Surgery

Durham Veterans Affairs Medical CenterDurham Veterans Affairs Medical CenterDirector of the Lung Cancer Prognostic LaboratoryDirector of the Lung Cancer Prognostic Laboratory

The Challenge in Prognosis The Challenge in Prognosis for Individual Patientsfor Individual Patients

Current Tools for PrognosisCurrent Tools for Prognosis• Clinical and histopathologic factors• Single molecular biomarkers• Gene expression profiles

Staging

Improved prognosis

But, the challenge is to provide an individualizedBut, the challenge is to provide an individualizedpatient prognosispatient prognosis

Current Therapy for Clinical Stage I NSCLCCurrent Therapy for Clinical Stage I NSCLC

Stage IA Stage IB, II and IIIAStage IA Stage IB, II and IIIA

Adjuvant Chemotherapy(> 30% relapse)

ObservationObservation(25% relapse)(25% relapse)

Resection Resection

Clinical Stage 1 (45,000 patients in U.S.)Clinical Stage 1 (45,000 patients in U.S.)

What nextWhat next??

Identify Patients atIdentify Patients at Higher Risk?Higher Risk?

Current Therapy for Clinical Stage I NSCLCCurrent Therapy for Clinical Stage I NSCLC

Stage IA Stage IB, II and IIIAStage IA Stage IB, II and IIIA

Adjuvant Chemotherapy(> 30% relapse)

ObservationObservation(25% relapse)(25% relapse)

Resection Resection

Clinical Stage 1 (45,000 patients in U.S.)Clinical Stage 1 (45,000 patients in U.S.)

Develop gene expression profiles Develop gene expression profiles that refine risk predictionthat refine risk prediction

101 Stage I NSCLC101 Stage I NSCLC50% alive > 5yr; 50% dead of Ca < 2.5yr50% alive > 5yr; 50% dead of Ca < 2.5yr50 adenocarcinoma50 adenocarcinoma51 squamous cell carcinoma51 squamous cell carcinoma

AgeAge 6666++9 (range 32-83) years9 (range 32-83) yearsGenderGender 39 female, 62 male39 female, 62 male

Duke Pilot Clinical Stage I NSCLC BankDuke Pilot Clinical Stage I NSCLC Bank

Fresh frozen tissue >50% viable tumorFresh frozen tissue >50% viable tumorRNA quality assessmentRNA quality assessmentGene expression using Affymetrix U133 2.0 plusGene expression using Affymetrix U133 2.0 plus

Alive 5 years Dead of cancer by 2.5 years

Expression Profiles That Predict OutcomeExpression Profiles That Predict Outcome

Tumor Sample (Patients)Tumor Sample (Patients)

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Expression Profiles That Predict OutcomeExpression Profiles That Predict OutcomeP

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Tumor Sample (Patients)Tumor Sample (Patients)

Leave-One-Out-AnalysesLeave-One-Out-Analyses

1

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8

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11

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26 293032 38

39

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81

82

83

84

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88

89

90

92

19

18

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15

12

6

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23

24

25

27

28

31

33

34

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40

44

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63

62

65

68

71

75

76

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78

79

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86

87 91

93

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0.4

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Blue: Alive 5 yrsRed: Cancer death 2.5 yrs

1

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10

11

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1413

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Blue: Alive 5 yrsRed: Cancer death 2.5 yrs

Clinical-Pathology Clinical-Pathology Prediction ModelPrediction Model

Gene ExpressionGene ExpressionPrediction ModelPrediction Model

Accuracy 61%Accuracy 61% Accuracy 94%Accuracy 94%

Predictions for the Individual Patient Predictions for the Individual Patient A Capacity to Adjust Risk AssessmentA Capacity to Adjust Risk Assessment

Re-classify as “high risk”

Stage IA patients

Adjuvant Chemotherapy

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Sample 23

Observe5 yr: 82%

5 yr: 56%

5 yr: 35%

5 yr: 5%

Current Therapy for Clinical Stage I NSCLCCurrent Therapy for Clinical Stage I NSCLC

Stage IA Stage IB, II and IIIAStage IA Stage IB, II and IIIA

Adjuvant ChemotherapyObservationObservation(25% relapse)(25% relapse)

Resection Resection

Second line?Second line?

Survival Survival RelapseRelapse

What is unique in this subset?What is unique in this subset?

Gene Regulatory Signaling Gene Regulatory Signaling Pathways and CancerPathways and Cancer

Ras Myc

E2F

Development of Gene Expression Signatures Development of Gene Expression Signatures to Predict Pathway Deregulation to Predict Pathway Deregulation

Control Ras Control Myc Control E2F Control Src Control -Cat

1.1. Quiescent human mammary epithelial cells infected Quiescent human mammary epithelial cells infected with adenovirus containing either a control insert or with adenovirus containing either a control insert or an activated oncogene of interest.an activated oncogene of interest.

2.2. Each infection is performed multiple times to Each infection is performed multiple times to generate samples for pattern analysis.generate samples for pattern analysis.

3.3. RNA collected for microarray analysis using RNA collected for microarray analysis using Affymetrix U133 Plus 2.0 Array.Affymetrix U133 Plus 2.0 Array.

SSSSSSSASSSSSSSSSSASSASSSSSSSSSASSSSSSSASSSSSAAASAASAAASAAAAAASAAASAAASAAAAAASAAAAAAAAAAAAAAAS

Predicting Pathway Status in NSCLCPredicting Pathway Status in NSCLC Ras Myc E2F Src Ras Myc E2F Src -Cat-Cat

Predict pathway status of NSCLCPredict pathway status of NSCLC

Ras predicts adenocarcinoma

Myc predicts Squamous

(Ras, Src, cat)

(Ras, Myc)

Cluster 1 Cluster 2 Cluster 3 Cluster 4

PatternsPatterns of Pathway Deregulation in NSCLC of Pathway Deregulation in NSCLCHierarchical Clustering Based on Hierarchical Clustering Based on Relative Gene Activation for 5 PathwaysRelative Gene Activation for 5 Pathways

Pathway-Specific Therapeutics: Pathway-Specific Therapeutics:

FTI

SU6656

Src Ras

Prediction of Pathway Status in Breast Cancer Prediction of Pathway Status in Breast Cancer Cell Lines Compared to Sensitivity to TherapeuticsCell Lines Compared to Sensitivity to Therapeutics

p=0.011 p=0.003

Src Ras

Treatment of Early Stage NSCLCTreatment of Early Stage NSCLC Resection with Gene ArrayResection with Gene Array

Stage IAStage IA Stage IB to IIIAStage IB to IIIA

Adjuvant ChemotherapyAdjuvant Chemotherapy

Pathway Specific Drug(s)Pathway Specific Drug(s)

ObserveObserve No RecurrenceNo Recurrence RelapseRelapse

Pathway AnalysisPathway Analysis

Re-classifyRe-classify RiskRisk

ConclusionConclusion

1.1. Development of a predictive model to select stage 1ADevelopment of a predictive model to select stage 1A patients appropriate for adjuvant chemotherapy.patients appropriate for adjuvant chemotherapy.

2.2. Utilization of pathway profiles to guide the use of Utilization of pathway profiles to guide the use of targeted therapeutic agents after relapse from targeted therapeutic agents after relapse from standard chemotherapy.standard chemotherapy.

3.3. Defining an integrated strategy for individualizedDefining an integrated strategy for individualizedtreatment based on molecular characteristics of the treatment based on molecular characteristics of the patient’s tumor.patient’s tumor.

Acknowledgements:Acknowledgements: Duke Lung Cancer Prognostic LaboratoryDuke Lung Cancer Prognostic Laboratory

David Harpole, Jr, M.D., DirectorDavid Harpole, Jr, M.D., DirectorThomas D’Amico, M.D.Thomas D’Amico, M.D.Rebecca Prince Petersen, M.D., M.Sc.Rebecca Prince Petersen, M.D., M.Sc.Mary-Beth Joshi, B.S.Mary-Beth Joshi, B.S.Debbi Conlon, AAS, HT(ASCP)Debbi Conlon, AAS, HT(ASCP)

Duke Center for Applied Genomics and TechnologyDuke Center for Applied Genomics and TechnologyJoseph Nevins, Ph.D., DirectorJoseph Nevins, Ph.D., DirectorAndrea Bild, Ph.D.Andrea Bild, Ph.D.Holly Dressman, Ph.D.Holly Dressman, Ph.D.Anil Potti, M.D.Anil Potti, M.D.

Duke Program in Computational GenomicsDuke Program in Computational GenomicsMike West, Ph.D., Director Mike West, Ph.D., Director Sayan Mukherjee, Ph.D.Sayan Mukherjee, Ph.D.

Haige Chen, B.S., Elena Edelman, B.S.Haige Chen, B.S., Elena Edelman, B.S.

Durham VA Thoracic Oncology LaboratoryDurham VA Thoracic Oncology LaboratoryMichael Kelly, M.D., Ph.D., DirectorMichael Kelly, M.D., Ph.D., DirectorFan Dong, Ph.D.Fan Dong, Ph.D.