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Lecture III: Interpreting genomic information for clinical care

Lecture III: Interpreting genomic information for clinical care. Richard L. Haspel , MD, PhD Karen L. Kaul, MD, PhD Henry M. Rinder, MD, PhD. Coming to a clinic near you…. Why Pathologists? We have access, we know testing. Personalized Risk Prediction, Medication Dosing, Diagnosis/

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Lecture III: Interpreting genomic information for clinical care

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  1. Lecture III: Interpreting genomic information for clinical care Richard L. Haspel, MD, PhD Karen L. Kaul, MD, PhD Henry M. Rinder, MD, PhD TRiG Curriculum: Lecture 3

  2. Coming to a clinic near you… TRiG Curriculum: Lecture 3

  3. Why Pathologists? We have access, we know testing Personalized Risk Prediction, Medication Dosing, Diagnosis/ Prognosis Pathologists Access to patient’s genome Physician sends sample to Pathology (blood/tissue) Just another laboratory test TRiG Curriculum: Lecture 3

  4. 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 TRiG Curriculum: Lecture 3

  5. Risk prediction Pathologists involved in preventive medicine Predict risk of disease Predict drug response (pharmacogenomics) Germline Heritable genomic targets Does not change during lifetime What we could test for? Something New Just another laboratory test TRiG Curriculum: Lecture 3

  6. 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 TRiG Curriculum: Lecture 3

  7. 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 TRiG Curriculum: Lecture 3

  8. Multi-gene assays in breast cancer Look familiar? TRiG Curriculum: Lecture 3

  9. Multi-gene assays to determine risk score, need for additional chemo For use in ER+, node negative cancer TRiG Curriculum: Lecture 3

  10. 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 another laboratory test Cuzick J, et al. J ClinOncol. 2011; 29: 4273 TRiG Curriculum: Lecture 3

  11. Analyzed 8,101 genes on chip microarrays • Reference= pooled cell lines • Breast cancer subgroups Perou CM, et al. Nature. 2000; 406, 747 TRiG Curriculum: Lecture 3

  12. Cancer Treatment: NGS in AML Welch JS, et al. JAMA, 2011;305, 1577 TRiG Curriculum: Lecture 3

  13. 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 TRiG Curriculum: Lecture 3

  14. 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 TRiG Curriculum: Lecture 3

  15. Cancer Treatment: NGS of Tumor Jones SJM, et al. Genome Biol. 2010;11:R82 TRiG Curriculum: Lecture 3

  16. 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 TRiG Curriculum: Lecture 3

  17. Methods and Results • Analysis • Whole genome • Transcriptome • Findings • Upregulation of RET oncogene • Downregulation of PTEN TRiG Curriculum: Lecture 3

  18. X 1 month pre-anti-RET Anti-RET added 1 month on anti-Ret TRiG Curriculum: Lecture 3

  19. X TRiG Curriculum: Lecture 3

  20. Why Pathologists? We have access, we know testing Personalized Tumor Treatment Plan Pathologists Access to tumor genome Would like to identify tumor, know prognosis, treatment options TRiG Curriculum: Lecture 3

  21. 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.” Welch JS, et al. JAMA, 2011;305:1577 TRiG Curriculum: Lecture 3

  22. 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 TRiG Curriculum: Lecture 3

  23. Laboratory Testing: Micro Identifying outbreak source Serotyping Pulsed field electrophoresis Next-generation sequencing analysis TRiG Curriculum: Lecture 3

  24. 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 TRiG Curriculum: Lecture 3

  25. 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 TRiG Curriculum: Lecture 3

  26. Risk Prediction: Timeline Factor V Leiden • Single gene • Multigene assays • Direct-to-consumer • Next generation sequencing AlsmadiOA, et al. BMC Genomics 2003 4:21 TRiG Curriculum: Lecture 3

  27. TRiG Curriculum: Lecture 3

  28. 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 TRiG Curriculum: Lecture 3

  29. 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 TRiG Curriculum: Lecture 3

  30. Just another test: Selection • 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 Menkes MS, et al. NEJM 1986;315:1250; Hung RJ, et al. Nature Genetics. 2008; 452:633 TRiG Curriculum: Lecture 3

  31. A B C D Statistics: Classic case-control study Lung Cancer + - + Vitamin E Low Level - AD/BC = Odds ratio (OR) ~ Relative risk (RR) TRiG Curriculum: Lecture 3

  32. A B C D GWAS: (Case-control)N Lung Cancer + - + SNP 1 - TRiG Curriculum: Lecture 3

  33. A B C D GWAS: (Case-control)N Lung Cancer + - + SNP 2 - TRiG Curriculum: Lecture 3

  34. A B C D GWAS: (Case-control)N Lung Cancer + - + SNP 3 - TRiG Curriculum: Lecture 3

  35. A B C D GWAS: (Case-control)N Lung Cancer + - + SNP X X - Up to1,000,000 SNPs (however many on chip) TRiG Curriculum: Lecture 3

  36. 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 another laboratory test Lagakos SW. NEJM 2006;354:16 TRiG Curriculum: Lecture 3

  37. Other criteria: Reproducibility: only single population Physiologic hypothesis: anti-oxidant (determined pre-study) TRiG Curriculum: Lecture 3

  38. Table 1 | Lung cancer risk and rs8034191 genotype Cases/controls From different populations Other criteria: Reproducibility: many populations Physiologic hypothesis: mutation in carcinogen binding receptor (determined post-study) TRiG Curriculum: Lecture 3

  39. Why Pathologists? We have access, we know testing Personal Risk Prediction Pathologists Access to patient’s chip results Would like to determine patient risk for disease Not so simple!! TRiG Curriculum: Lecture 3

  40. 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 Ng PC, et al. Nature. 2009; 461: 724 TRiG Curriculum: Lecture 3

  41. DTC: A simplistic calculation Post-test probability Pre-test probability How about family history? Environment? Ng PC, et al. Nature. 2009; 461: 724 TRiG Curriculum: Lecture 3

  42. Calculating pre-test probability is not so simple Parmigiani G, et al. Ann Intern Med. 2007; 147: 441 TRiG Curriculum: Lecture 3

  43. “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 TRiG Curriculum: Lecture 3

  44. 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? Ashley EA, et al. Lancet. 2010; 375: 1525 TRiG Curriculum: Lecture 3

  45. Pharmacogenomics may guide care Need validation in clinical trials TRiG Curriculum: Lecture 3

  46. Other variants detected TRiG Curriculum: Lecture 3

  47. Clinical Risk determination (prevalence X post test probability = clinical risk) Pre-test probability Post-test probability TRiG Curriculum: Lecture 3

  48. Why Pathologists? We have access, we know testing Personal Risk Prediction Pathologists Access to patient’s whole genome! Would like to determine patient risk for disease Not so simple!! TRiG Curriculum: Lecture 3

  49. 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 TRiG Curriculum: Lecture 3

  50. “No methods exist for statistical integration of such conditionally dependent risks” • Strength of association based on # of Medline articles TRiG Curriculum: Lecture 3

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