Reported by R5 李霖昆Supervised by 楊慕華 大夫

Genomics-Driven Oncology: Framework for anEmerging Paradigm

Review article

Journal of Clinical Oncology 31, 15, 1806–1814, May 20th , 2013

Levi A. Garraway

Outline

Introduction Principle and hypothesis of

genomics-driven cancer medicine Hypothesis testing Question encountered Conclusion

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

In 1985: Somatic genetic derangement Diagnostic and prognostic impact Patient stratification

In 1990s and 2000s: Trastuzumab, Imatinib CRC, NSCLC, melanoma

New treatment paradigm

Outline

Introduction Principle and hypothesis of

genomics-driven cancer medicine

Hypothesis testing Question encountered Conclusion

During past decades Tumor biology, genomics technology,

computational innovation, drug discovery

Translational cancer research Driver genetic alteration

Dysregulated protein: Cancer cells depend on

Targeted agentsHypothesis of Cancer genome era

Genomic information to guide Tx3 principles

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

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

Epigenetic regulation

Metabolic pathway

DNA methylation and Histone demethylation

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

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 PCRHundreds of mutation can be identifiedApplied to Formalin-fixed paraffin-

embedded tumor tissueUnder estimate the actionable tumor

genetic event

Massicely parallel sequencing (MPS) DNA based alteration, test for RNA Mutation identified > Tx developed CostlyFocus the scope, reduced the cost and

time

Genome based patient stratication and therapeutic guidence

Outline

Introduction Principle and hypothesis of

genomics-driven cancer medicine Hypothesis testing Question encountered Conclusion

Outline

Introduction Principle and hypothesis of

genomics-driven cancer medicine Hypothesis testing Question encountered Conclusion

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

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

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

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

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