Lecture III: Interpreting genomic information for
clinical careRichard L. Haspel, MD, PhD
Karen L. Kaul, MD, PhDHenry M. Rinder, MD, PhD
TRiG Curriculum: Lecture 3 1March 2012
Coming to a clinic near you…
2TRiG Curriculum: Lecture 3March 2012
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
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
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
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
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
Multi-gene assays in breast cancer
Look familiar?
8TRiG Curriculum: Lecture 3March 2012
Multi-gene assays to determine risk score, need for additional chemo
For use in ER+, node negative cancer
9TRiG Curriculum: Lecture 3March 2012
• Oncotype similar predictive value to combined four immunohistochemical stains (ER,PR, HER2, Ki-67)
• May offer standardization lacking in IHC• Need to validate
– Prospective trialsJust anotherlaboratory test
10TRiG Curriculum: Lecture 3
Cuzick J, et al. J Clin Oncol. 2011; 29: 4273
March 2012
• 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
TRiG Curriculum: Lecture 3 12
Cancer Treatment: NGS in AML
Welch JS, et al. JAMA, 2011;305, 1577
March 2012
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
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 later14TRiG Curriculum: Lecture 3March 2012
Cancer Treatment: NGS of Tumor
Jones SJM, et al. Genome Biol. 2010;11:R82
15TRiG Curriculum: Lecture 3March 2012
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
Methods and Results
• Analysis– Whole genome– Transcriptome
• Findings– Upregulation of
RET oncogene– Downregulation of
PTEN
17TRiG Curriculum: Lecture 3March 2012
X
1 month pre-anti-RET Anti-RET added 1 month on anti-Ret18TRiG Curriculum: Lecture 3March 2012
X
19TRiG Curriculum: Lecture 3March 2012
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
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
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
Laboratory Testing: Micro • Identifying outbreak
source
– Serotyping
– Pulsed field electrophoresis
– Next-generation sequencing analysis
23TRiG Curriculum: Lecture 3March 2012
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
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
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
27TRiG Curriculum: Lecture 3March 2012
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
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 basisPearson TA, Manolio TA. JAMA 2008; 298:1335
29TRiG Curriculum: Lecture 3March 2012
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
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
GWAS: (Case-control)N
Lung Cancer+ -
+
-
A B
C D
SNP 1
32TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
+ -
+
-
A B
C D
SNP 2
Lung Cancer
33TRiG Curriculum: Lecture 3March 2012
GWAS: (Case-control)N
+ -
+
-
A B
C D
SNP 3
Lung Cancer
34TRiG Curriculum: Lecture 3March 2012
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
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< 0.00000005
Just anotherlaboratory test
Lagakos SW. NEJM 2006;354:16
36TRiG Curriculum: Lecture 3March 2012
Other criteria: Reproducibility: only single populationPhysiologic hypothesis: anti-oxidant (determined pre-study)
37TRiG Curriculum: Lecture 3March 2012
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
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
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
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
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
• “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
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
Pharmacogenomics may guide care
Need validation in clinical trials
45TRiG Curriculum: Lecture 3March 2012
Other variants detected
46TRiG Curriculum: Lecture 3March 2012
Clinical Risk determination (prevalence X post test probability = clinical risk)
Pre-testprobability
Post-testprobability
47TRiG Curriculum: Lecture 3March 2012
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
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
• “No methods exist for statistical integration of such conditionally dependent risks”
• Strength of association based on # of Medline articles
50TRiG Curriculum: Lecture 3March 2012
In the end: Is the info actionable?
NEJM. 1994;330:1029
51TRiG Curriculum: Lecture 3March 2012
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