1. Reported by R5 李霖昆 Supervised by 楊慕華 大夫 Genomics-Driven Oncology: Framework for an Emerging Paradigm Review article Journal of Clinical Oncology 31, 15, 1806–1814, May 20 th, 2013 Levi A. Garraway

2. Outline  Introduction  Principle and hypothesis of genomics-driven cancer medicine  Hypothesis testing  Question encountered  Conclusion

3.  In 1973:  Masaharu Sakurai and Avery A. Sandburg  Karyotype abnomality - leukemia - prognosis  After 3 years: AML  minor or major karyotypic alteration  In mid 1980s:  Guide leukemia Tx  Clinical trial design: patient stratification  Cancer Gene (oncogen / tumor suppressor gene)  Comprise normal genes: derangement  Oncogenesis, tumor progression, response to Tx  Tumor virus

4.  In 1985:  Somatic genetic derangement  Diagnostic and prognostic impact  Patient stratification  In 1990s and 2000s:  Trastuzumab, Imatinib  CRC, NSCLC, melanoma  New treatment paradigm

5. Outline  Introduction  Principle and hypothesis of genomics-driven cancer medicine  Hypothesis testing  Question encountered  Conclusion

6.  During past decades  Tumor biology, genomics technology, computational innovation, drug discovery  Translational cancer research  Driver genetic alteration  Dysregulated protein: Cancer cells depend on  Targeted agents  Hypothesis of Cancer genome era  Genomic information to guide Tx  3 principles

7. Principle 1: molecular pathway  Somatic / germline genetic mutation  Mitogenic signal transduction pathway  Cell cycle control  Apoptosis  Ubquitin proteolysis  WNT-β catenin signaling: self-renwal  Differentiation  DNA repair pathways  Checkpoints  Epigenetic/chromatin modification  Metabolism

10. Mutant K-RAS @ Undruggable oncoprotein #Downstream pathway: MEK inhibitor (NSCLC) #Coexist mutation: CDKN2A (CDK inhibitor), PIK3CA

11. Epigenetic regulation

12. Metabolic pathway DNA methylation and Histone demethylation

13. Principle 2: anti-cancer agents  In 2004:  11 targeted agents, 4 category entering clinical trial  RTK, angiogenic, serine/theonine kinases, cell growth/protein translation  In 2012:  19 targeted agents have approval  150 compound in study

17. Principle 3: Technology  Formalin-fixed paraffin-embedded tumor tissue  Difficult to identify > 2-3 genes  Allele-based mutational profiling technologies  Mass spectrometric genotyping  Allele-specific PCR  Hundreds of mutation can be identified  Applied to Formalin-fixed paraffin-embedded tumor tissue  Under estimate the actionable tumor genetic event

18.  Massicely parallel sequencing (MPS)  DNA based alteration, test for RNA  Mutation identified > Tx developed  Costly  Focus the scope, reduced the cost and time  Genome based patient stratication and therapeutic guidence

19. Outline  Introduction  Principle and hypothesis of genomics-driven cancer medicine  Hypothesis testing  Question encountered  Conclusion

21. Outline  Introduction  Principle and hypothesis of genomics-driven cancer medicine  Hypothesis testing  Question encountered  Conclusion

22. Question 1  Which mutational profiling approaches will be most enabling for genomics-driven cancer medicine?  Genomic/epigenomic profile  Technical and analytic validation: sensitivity, specificity, time, cost, data storage and transfer

23. Question 2  What interpretive frameworks may render complex genomic data accessible to oncologists?  Usually not evidence based  Data integration to prevent premature and inappropriate use of the genomic data  Science driven computational algorithms  Rule based  Knowledge based

24. Question 3  What clinical trial designs will optimally interrogate the utility of tumor genomic information?  More subtypes: selection of patients of specific genomic profile  Genotype - to - phenotype construct  Phenotype - to - genotype approach  Early cancer drug development  Empirical pharmacology  mechanism-based framework

25. Question 4  How will oncologists and patients handle the return of large-scale genomic information? Return  Beneficence and respect: return results to patients  Incentive to participate clinical trial Not return  Need genetic counselor  Uncertain significance of some mutation

26. Conclusion  Comprehensive genomic information – better Tx outcome  Genomic driven paradigm is complementary :  Immunotherapy  Targeting microenvironment  Stem cell based Tx  Conventional Tx  Genomic profile must be evaluated as part of clinical features  Drug toxicity, tumor heterogeneity, complexity of tumor genomic information  may limited the role  Work hard at work worth doing