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Genomics-Driven Oncology: Revolutionizing Cancer Treatment

Delve into the emerging paradigm of genomics-driven cancer medicine, exploring principles, hypothesis testing, and the transformative impact on oncology. Learn about genetic alterations, targeted agents, and the challenges and opportunities in utilizing genomic information for tailored treatments.

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Genomics-Driven Oncology: Revolutionizing Cancer Treatment

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

  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

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

  9. Epigenetic regulation

  10. Metabolic pathway DNA methylation and Histone demethylation

  11. 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

  12. 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

  13. 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

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

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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

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