Review article
Genomics-Driven Oncology: Framework for an
Emerging Paradigm
Levi A. Garraway
Journal of Clinical Oncology 31, 15,
1806–1814, May 20th , 2013
Reported by R5 李霖昆
Supervised by 楊慕華 大夫
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 agents
Hypothesis of Cancer genome era
Genomic information to guide Tx
3 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 PCR
Hundreds of mutation can be identified
Applied to Formalin-fixed paraffin-embedded tumor
tissue
Under estimate the actionable tumor genetic event
 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
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