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)
Ge
ne
sG
en
es
Expression Profiles That Predict OutcomeExpression Profiles That Predict OutcomeP
rob
abili
ty o
f D
ise
ase
-Fre
e S
urv
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rob
abili
ty o
f D
ise
ase
-Fre
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iva
l
Tumor Sample (Patients)Tumor Sample (Patients)
Leave-One-Out-AnalysesLeave-One-Out-Analyses
1
2
3
4
7
8
9
10
11
13
14
1617
21
22
26 293032 38
39
42
43
49
53
54 58
59
61
64
66
6769
70
72
73
74
81
82
83
84
85
88
89
90
92
19
18
20
15
12
6
5
23
24
25
27
28
31
33
34
35
373641
40
44
45
46
47
48
505251
55
5657 60
63
62
65
68
71
75
76
77
78
79
80
86
87 91
93
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 80 90 100
Blue: Alive 5 yrsRed: Cancer death 2.5 yrs
1
234
5
6
7
8
9
10
11
12
1413
1516
17
1819
20
21
22
242325
26
27
28
]'?29 32
31
33
3436
37
38
39
4140
4342
44
45 484746
49
51505235 565557
58
59
60
61
62
63
53
54
64
65
6766
68
7069
71
727374
767980
91
92
78
7577
84
85
8793
90828183 8988
860
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 10 20 30 40 50 60 70 80 90 100
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
0.1
.2.3
.4.5
.6.7
.8.9
1
Pro
babi
lity
of D
isea
se-F
ree
Sur
viva
l
0 10 20 30 40 50Months
Sample 30
0.1
.2.3
.4.5
.6.7
.8.9
1
Pro
babi
lity
of D
isea
se-F
ree
Sur
viva
l
0 10 20 30 40 50Months
Sample 54
0.1
.2.3
.4.5
.6.7
.8.9
1
Pro
babi
lity
of D
isea
se-F
ree
Sur
viva
l
0 10 20 30 40 50Months
Sample 27
0.1
.2.3
.4.5
.6.7
.8.9
1
Pro
babi
lity
of D
isea
se-F
ree
Sur
viva
l
0 10 20 30 40 50Months
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.