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Transcript of Lecture III: Interpreting genomic information for clinical care Richard L. Haspel, MD, PhD Karen L....
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Lecture III: Interpreting genomic information for
clinical careRichard L. Haspel, MD, PhD
Karen L. Kaul, MD, PhD
Henry M. Rinder, MD, PhD
TRiG Curriculum: Lecture 3 1March 2012
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Coming to a clinic near you…
2TRiG Curriculum: Lecture 3March 2012
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Why Pathologists? We have access, we know testing
PersonalizedRisk Prediction,MedicationDosing,Diagnosis/Prognosis
Physician sendssample toPathology (blood/tissue)
Pathologists
Access to patient’s genome
Just anotherlaboratory test
3TRiG Curriculum: Lecture 3March 2012
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What we could test for? Same Stuff
• Somatic analysis– Tumor genomics
• Diagnosis/Prognosis• Response to treatment
– May change/ evolve/require repeat testing
• Laboratory testing– Microbiology– Pre-natal testing
http://www.bcm.edu/breastcenter/pathology/index.cfm?pmid=11149
4TRiG Curriculum: Lecture 3March 2012
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What we could test for? Something New
• Risk prediction– Pathologists
involved in preventive medicine• Predict risk of
disease• Predict drug
response (pharmacogenomics)
• Germline– Heritable genomic
targets– Does not change
during lifetime
Just anotherlaboratory test
5TRiG Curriculum: Lecture 3March 2012
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What we will cover today:
• Review current and future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
6TRiG Curriculum: Lecture 3March 2012
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Diagnosis/Prognosis Timeline: Cancer
• Single gene– HER2
• Multi-gene assays– Breast cancer
• Gene chips/Next generation sequencing of tumors– Expression profiling– Exome– Transcriptome– Whole genome
7TRiG Curriculum: Lecture 3March 2012
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Multi-gene assays in breast cancer
Look familiar?
8TRiG Curriculum: Lecture 3March 2012
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Multi-gene assays to determine risk score, need for additional chemo
For use in ER+, node negative cancer
9TRiG Curriculum: Lecture 3March 2012
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• Oncotype similar predictive value to
combined four immunohistochemical
stains (ER,PR, HER2, Ki-67)• May offer standardization lacking in IHC• Need to validate
– Prospective trials
Just anotherlaboratory test
10TRiG Curriculum: Lecture 3
Cuzick J, et al. J Clin Oncol. 2011; 29: 4273
March 2012
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• Analyzed 8,101 genes on chip microarrays
• Reference= pooled cell lines
• Breast cancer subgroups
Perou CM, et al. Nature. 2000; 406, 747
11TRiG Curriculum: Lecture 3March 2012
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TRiG Curriculum: Lecture 3 12
Cancer Treatment: NGS in AML
Welch JS, et al. JAMA, 2011;305, 1577
March 2012
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Case History
• 39 year old female with APML by morphology
• Cytogenetics and RT-PCR unable to detect PML-RAR fusion
• Clinical question: Treat with ATRA versus allogeneic stem cell transplant
13TRiG Curriculum: Lecture 3March 2012
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The Findings: Led to appropriate treatment
• Analysis– Paired-end NGS
• Findings – Cytogenetically
cryptic event: novel fusion
• Analysis took 7 weeks
• ATRA Treatment• Patient still alive 15
months later
14TRiG Curriculum: Lecture 3March 2012
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Cancer Treatment: NGS of Tumor
Jones SJM, et al. Genome Biol. 2010;11:R82
15TRiG Curriculum: Lecture 3March 2012
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Case History
• 78 year old male• Poorly differentiated
papillary adenocarcinoma of tongue
• Metastatic to lymph nodes
• Failed chemotherapy• Decision to use next-
generation sequencing methods
16TRiG Curriculum: Lecture 3March 2012
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Methods and Results
• Analysis– Whole genome– Transcriptome
• Findings– Upregulation of
RET oncogene– Downregulation of
PTEN
17TRiG Curriculum: Lecture 3March 2012
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X
1 month pre-anti-RET Anti-RET added 1 month on anti-Ret
18TRiG Curriculum: Lecture 3March 2012
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X
19TRiG Curriculum: Lecture 3March 2012
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Why Pathologists? We have access, we know testing
PersonalizedTumor TreatmentPlan
Would like to identify tumor, know prognosis,treatment options
Pathologists
Access to tumor genome
20TRiG Curriculum: Lecture 3March 2012
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Why pathologists?
“However, to fully use this potentially transformative technology to make informed clinical decisions, standards will have to be developed that allow for CLIA-CAP certification of whole-genome sequencing and for direct reporting of relevant results to treating physicians.”
21TRiG Curriculum: Lecture 3
Welch JS, et al. JAMA, 2011;305:1577
March 2012
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What we will cover today:
• Review current and future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
22TRiG Curriculum: Lecture 3March 2012
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Laboratory Testing: Micro
• Identifying outbreak source
– Serotyping
– Pulsed field electrophoresis
– Next-generation sequencing analysis
23TRiG Curriculum: Lecture 3March 2012
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Laboratory testing: Pre-natal• Amniocentesis/ Chorionic
villus sampling– Karyotyping– Single gene testing
• Multigene assays– “Universal Genetic Test”
available for 100+ diseases
• Next generation methods– Fetal DNA in maternal
plasma, detection of Trisomy 21
Fan HC, et al. PNAS. 2008;105:16266 Srinivasan BS, et al. Reprod Biomed Online. 2010;21:537-51
24TRiG Curriculum: Lecture 3March 2012
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What we will cover today:• Review current and
future molecular testing:
– Somatic analysis/ Diagnosis/Prognosis
• Cancer
– Laboratory testing• Microbiology• Pre-natal testing
– Risk Assessment• Pathologists involved in
preventive medicine
25TRiG Curriculum: Lecture 3March 2012
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Risk Prediction: Timeline
• Single gene
• Multigene assays– Direct-to-
consumer
• Next generation sequencing
Alsmadi OA, et al. BMC Genomics 2003 4:21
Factor V Leiden
26TRiG Curriculum: Lecture 3March 2012
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27TRiG Curriculum: Lecture 3March 2012
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Hereditary Risk Prediction: How is risk calculated?
• Analysis of SNPs (up to a million)– Genome wide
association studies (GWAS)
• Case-control studies– Odds ratios
• Using odds ratios to determine individual patient risk
28TRiG Curriculum: Lecture 3March 2012
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Just another test: Case-control study
• Adequate selection criteria for cases/controls
• # of patients = reasonable ORs (<=1.3)
• Assays appropriate– Enough variation– Proper controls
• Statistics appropriate• Detect known variants• Reproducible results
– Different populations– Different samples
• Pathophysiologic basis
Pearson TA, Manolio TA. JAMA 2008; 298:1335
29TRiG Curriculum: Lecture 3March 2012
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Just another test: Selection
Menkes MS, et al. NEJM 1986;315:1250; Hung RJ, et al. Nature Genetics. 2008; 452:633
• Lung cancer risk• “Old School Study”
– Cases and controls were matched based on age, smoking status, race and month of blood collection
• “Genomic Study”: – Cases and controls
were frequency matched by sex, age center, referral (or of residence) area and period of recruitment
30TRiG Curriculum: Lecture 3March 2012
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Statistics: Classic case-control study
Lung Cancer+ -
Vitamin ELow Level
+
-
A B
C D
AD/BC = Odds ratio (OR) ~ Relative risk (RR)31TRiG Curriculum: Lecture 3March 2012
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GWAS: (Case-control)N
Lung Cancer+ -
+
-
A B
C D
SNP 1
32TRiG Curriculum: Lecture 3March 2012
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GWAS: (Case-control)N
+ -
+
-
A B
C D
SNP 2
Lung Cancer
33TRiG Curriculum: Lecture 3March 2012
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GWAS: (Case-control)N
+ -
+
-
A B
C D
SNP 3
Lung Cancer
34TRiG Curriculum: Lecture 3March 2012
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GWAS: (Case-control)N
+ -
+
-
A B
C D
X
Up to1,000,000 SNPs (howevermany on chip)
SNP X
Lung Cancer
35TRiG Curriculum: Lecture 3March 2012
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A word about statistics…• 20 tests, “significant” if
p=0.05– (.95)N = chance all tests
“not significant”– 1- (.95)N = chance one
test “significant– 1- (.95)20= 64% – Bonferroni correction p =
0.0025
• Need to adjust for number of tests run– For 1 million SNP
GWAS p< 0.00000005Just anotherlaboratory test
Lagakos SW. NEJM 2006;354:16
36TRiG Curriculum: Lecture 3March 2012
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Other criteria: Reproducibility: only single populationPhysiologic hypothesis: anti-oxidant (determined pre-study)
37TRiG Curriculum: Lecture 3March 2012
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Cases/controlsFrom differentpopulations
Other criteria:Reproducibility: many populationsPhysiologic hypothesis: mutation in carcinogen binding receptor (determined post-study)
38TRiG Curriculum: Lecture 3
Table 1 | Lung cancer risk and rs8034191 genotype
March 2012
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Why Pathologists? We have access, we know testing
PersonalRisk Prediction
Would like to determine patientrisk for disease
Pathologists
Access to patient’schip results
Not so simple!!39TRiG Curriculum: Lecture 3March 2012
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Risk Prediction: Not easy to do!!
• Based on case-control study design = variable results
• No quality control of associations– Need for Clinical Grade
Database• Ease of use• Continually updated• Clinically relevant
SNPs/variations
• Pre-test probability assessment
40TRiG Curriculum: Lecture 3
Ng PC, et al. Nature. 2009; 461: 724
March 2012
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DTC: A simplistic calculation
How about family history? Environment?
Pre-test probability
Post-test probability
41TRiG Curriculum: Lecture 3
Ng PC, et al. Nature. 2009; 461: 724
March 2012
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Calculating pre-test
probability is not so simple
TRiG Curriculum: Lecture 3 42
Parmigiani G, et al. Ann Intern Med. 2007; 147: 441
March 2012
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• “Avg” (average risk for your ethnic group = pre-test probability): 8%
• OR from SNP is 0.75 ***25% decreased risk****• “You” (post-test
probability): 8% x 0.75 = 6%
• Absolute decreased risk: = 2%
• Same OR if 80% vs. 60%
• Absolute decreased risk: 20%
Just another laboratory test
43TRiG Curriculum: Lecture 3March 2012
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Hereditary Risk Prediction: NGS
• 40 year old male with family history of CAD and sudden cardiac death
• Whole genome sequencing performed on DNA from whole blood
• How to approach analysis?
44TRiG Curriculum: Lecture 3
Ashley EA, et al. Lancet. 2010; 375: 1525
March 2012
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Pharmacogenomics may guide care
Need validation in clinical trials
45TRiG Curriculum: Lecture 3March 2012
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Other variants detected
46TRiG Curriculum: Lecture 3March 2012
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Clinical Risk determination (prevalence X post test probability = clinical risk)
Pre-testprobability
Post-testprobability
47TRiG Curriculum: Lecture 3March 2012
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Why Pathologists? We have access, we know testing
PersonalRisk Prediction
Would like to determine patientrisk for disease
Pathologists
Access to patient’swhole genome!
Not so simple!!
48TRiG Curriculum: Lecture 3March 2012
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Risk Prediction: Not easy to do!!
• Based on case-control study design = variable results
• No quality control of associations– Need for Clinical Grade
Database• Ease of use• Continually updated• Clinically relevant
SNPs/variations
• Pre-test probability assessment
49TRiG Curriculum: Lecture 3March 2012
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• “No methods exist for statistical integration of such conditionally dependent risks”
• Strength of association based on # of Medline articles
50TRiG Curriculum: Lecture 3March 2012
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In the end: Is the info actionable?
NEJM. 1994;330:1029
51TRiG Curriculum: Lecture 3March 2012
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Summary
• Genomic-era technologies involve– Typical roles of pathologists
• Cancer diagnosis/prognosis/guide treatment
• Laboratory testing (e.g., microbiology)– New roles for pathologists
• Predict disease risk• Predict drug response
– We control the specimens
• Just another test– Issues with case-control studies– Issues of pre- and post-test probability
• Accurately assessing pre-test probability– Need to validate
52TRiG Curriculum: Lecture 3
Roychowdhury S, et al. Sci Transl Med. 2011; 3: 111ra121
March 2012