Translating Genomics in Cancer Care

Restricted access

There is increasing enthusiasm for genomics and its promise in advancing personalized medicine. Genomic information has been used to personalize health care for decades, spanning the fields of cardiovascular disease, infectious disease, endocrinology, metabolic medicine, and hematology. However, oncology has often been the first test bed for the clinical translation of genomics for diagnostic, prognostic, and therapeutic applications. Notable hereditary cancer examples include testing for mutations in BRCA1 or BRCA2 in unaffected women to identify those at significantly elevated risk for developing breast and ovarian cancers, and screening patients with newly diagnosed colorectal cancer for mutations in 4 mismatch repair genes to reduce morbidity and mortality in their relatives. Somatic genomic testing is also increasingly used in oncology, with gene expression profiling of breast tumors and EGFR testing to predict treatment response representing commonly used examples. Health technology assessment provides a rigorous means to inform clinical and policy decision-making through systematic assessment of the evidentiary base, along with precepts of clinical effectiveness, cost-effectiveness, and consideration of risks and benefits for health care delivery and society. Although this evaluation is a fundamental step in the translation of any new therapeutic, procedure, or diagnostic test into clinical care, emerging developments may threaten this standard. These include “direct to consumer” genomic risk assessment services and the challenges posed by incidental results generated from next-generation sequencing (NGS) technologies. This article presents a review of the evidentiary standards and knowledge base supporting the translation of key cancer genomic technologies along the continuum of validity, utility, cost-effectiveness, health service impacts, and ethical and societal issues, and offers future research considerations to guide the responsible introduction of NGS technologies into health care. It concludes that significant evidentiary gaps remain in translating genomic technologies into routine clinical practice, particularly in efficacy, health outcomes, cost-effectiveness, and health services research. These caveats are especially germane in the context of NGS, wherein efforts are underway to translate NGS results despite their limited accuracy, lack of proven efficacy, and significant computational and counseling challenges. Further research across these domains is critical to inform the effective, efficient, and equitable translation of genomics into cancer care.

Correspondence: Yvonne Bombard, PhD, University of Toronto, Institute of Health Policy, Management, and Evaluation, The Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, Ontario M5B 1T8, Canada. E-mail: yvonne.bombard@utoronto.ca
  • 1.

    PascheBAbsherD. Whole-genome sequencing: a step closer to personalized medicine. JAMA2011;305:15961597.

  • 2.

    HamburgMACollinsFS. The path to personalized medicine. N Engl J Med2010;363:301304.

  • 3.

    BurkeWPsatyBM. Personalized medicine in the era of genomics. JAMA2007;298:16821684.

  • 4.

    OffitK. Personalized medicine: new genomics, old lessons. Hum Genet2011;130:314.

  • 5.

    ChanISGinsburgGS. Personalized medicine: progress and promise. Annu Rev Genomics Hum Genet2011;12:217244.

  • 6.

    RobsonMOffitK. Clinical practice. Management of an inherited predisposition to breast cancer. N Engl J Med2007;357:154162.

  • 7.

    WeitzelJNBlazerKRMacdonaldDJ. Genetics, genomics, and cancer risk assessment: state of the art and future directions in the era of personalized medicine. CA Cancer J Clinin press.

    • Search Google Scholar
    • Export Citation
  • 8.

    PaikSShakSTangG. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med2004;351:28172826.

    • Search Google Scholar
    • Export Citation
  • 9.

    9.PaikSTangGShakS. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol2006;24:37263734.

    • Search Google Scholar
    • Export Citation
  • 10.

    KrisMGNataleRBHerbstRS. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: a randomized trial. JAMA2003;290:21492158.

    • Search Google Scholar
    • Export Citation
  • 11.

    Perez-SolerRChachouaAHammondLA. Determinants of tumor response and survival with erlotinib in patients with non-small-cell lung cancer. J Clin Oncol2004;22:32383247.

    • Search Google Scholar
    • Export Citation
  • 12.

    JohnsonAPSikichNJEvansG. Health technology assessment: a comprehensive framework for evidence-based recommendations in Ontario. Int J Technol Assess Health Care2009;25:141150.

    • Search Google Scholar
    • Export Citation
  • 13.

    TeutschSMBradleyLAPalomakiGE. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med2009;11:314.

    • Search Google Scholar
    • Export Citation
  • 14.

    StadlerZKThomPRobsonME. Genome-wide association studies of cancer. J Clin Oncol2010;28:42554267.

  • 15.

    BamshadMJNgSBBighamAW. Exome sequencing as a tool for Mendelian disease gene discovery. Nat Rev Genet2011;12:745755.

  • 16.

    DomchekSMBradburyAGarberJE. Multiplex genetic testing for cancer susceptibility: out on the high wire without a net?J Clin Oncol2013;31:12671270.

    • Search Google Scholar
    • Export Citation
  • 17.

    BombardYRobsonMEOffitK. Revealing the incidentalome when targeting the tumor genome. JAMA2013;310:795796.

  • 18.

    CassaCASavageSKTaylorPL. Disclosing pathogenic genetic variants to research participants: quantifying an emerging ethical responsibility. Genome Res2012;22:421428.

    • Search Google Scholar
    • Export Citation
  • 19.

    BombardYOffitKRobsonME. Risks to relatives in genomic research: a duty to warn?Am J Bioeth2012;12:1214.

  • 20.

    MillerFAChristensenRGiacominiMRobertJS. Duty to disclose what? Querying the putative obligation to return research results to participants. J Med Ethics2008;34:210213.

    • Search Google Scholar
    • Export Citation
  • 21.

    BredenoordALKroesHYCuppenE. Disclosure of individual genetic data to research participants: the debate reconsidered. Trends Genet2011;27:4147.

    • Search Google Scholar
    • Export Citation
  • 22.

    KnoppersBMJolyYSimardJDurocherF. The emergence of an ethical duty to disclose genetic research results: international perspectives. Eur J Hum Genet2006;14:11701178.

    • Search Google Scholar
    • Export Citation
  • 23.

    BergJSKhouryMJEvansJP. Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet Med2011;13:499504.

    • Search Google Scholar
    • Export Citation
  • 24.

    BieseckerLG. Incidental variants are critical for genomics. Am J Hum Genet2013;92:648651.

  • 25.

    GoddardKAWhitlockEPBergJS. Description and pilot results from a novel method for evaluating return of incidental findings from next-generation sequencing technologies. Genet Med2013;15:721728.

    • Search Google Scholar
    • Export Citation
  • 26.

    GreenRCBergJSGrodyWW. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet Med2013;15:565574.

    • Search Google Scholar
    • Export Citation
  • 27.

    KohaneISMasysDRAltmanRB. The incidentalome: a threat to genomic medicine. JAMA2006;296:212215.

  • 28.

    BieseckerLGBurkeWKohaneI. Next-generation sequencing in the clinic: are we ready?Nat Rev Genet2012;13:818824.

  • 29.

    McGuireALBurkeW. An unwelcome side effect of direct-to-consumer personal genome testing: raiding the medical commons. JAMA2008;300:26692671.

    • Search Google Scholar
    • Export Citation
  • 30.

    Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: genetic testing strategies in newly diagnosed individuals with colorectal cancer aimed at reducing morbidity and mortality from Lynch syndrome in relatives. Genet Med2009;11:3541.

    • Search Google Scholar
    • Export Citation
  • 31.

    Blue Cross Blue Shield Association. Epidermal growth factor receptor mutations and tyrosine kinase inhibitor therapy in advanced non-small-cell lung cancer. Assessment Program2011;25:129.

    • Search Google Scholar
    • Export Citation
  • 32.

    NakanoHSodaHTakasuM. Heterogeneity of epidermal growth factor receptor mutations within a mixed adenocarcinoma lung nodule. Lung Cancer2008;60:136140.

    • Search Google Scholar
    • Export Citation
  • 33.

    SchrijverIAzizNFarkasDH. Opportunities and challenges associated with clinical diagnostic genome sequencing: a report of the association for molecular pathology. J Mol Diagn2012;14:525540.

    • Search Google Scholar
    • Export Citation
  • 34.

    OrmondKEWheelerMTHudginsL. Challenges in the clinical application of whole-genome sequencing. Lancet2011;375:17491751.

  • 35.

    Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group. Recommendations from the EGAPP Working Group: can tumor gene expression profiling improve outcomes in patients with breast cancer?Genet Med2009;11:6673.

    • Search Google Scholar
    • Export Citation
  • 36.

    ContiRVeenstraDLArmstrongK. Personalized medicine and genomics: challenges and opportunities in assessing effectiveness, cost-effectiveness, and future research priorities. Med Decis Making2010;30:328340.

    • Search Google Scholar
    • Export Citation
  • 37.

    LamHYClarkMJChenR. Performance comparison of whole-genome sequencing platforms. Nat Biotechnol2011;30:7882.

  • 38.

    U.S. Preventive Services Task Force (USPSTF)genetic risk assessment and BRCA mutation testing for breast and ovarian cancer susceptibility: recommendation statement. Ann Intern Med2005;143:355361.

    • Search Google Scholar
    • Export Citation
  • 39.

    McBrideCMBowenDBrodyLC. Future health applications of genomics: priorities for communication, behavioral, and social sciences research. Am J Prev Med2011;38:556565.

    • Search Google Scholar
    • Export Citation
  • 40.

    GrosseSDKhouryMJ. What is the clinical utility of genetic testing?Genet Med2006;8:448450.

  • 41.

    LammensCRAaronsonNKWagnerA. Genetic testing in Li-Fraumeni syndrome: uptake and psychosocial consequences. J Clin Oncol2010;28:30083014.

    • Search Google Scholar
    • Export Citation
  • 42.

    HaydenMRBombardY. Psychosocial effects of predictive testing for Huntington’s disease. Adv Neurol2005;96:226239.

  • 43.

    WigginsSWhytePHugginsM. The psychological consequences of predictive testing for Huntington’s disease. Canadian Collaborative Study of Predictive Testing. N Engl J Med1992;327:14011405.

    • Search Google Scholar
    • Export Citation
  • 44.

    LammensCRBleikerEMAaronsonNK. Regular surveillance for Li-Fraumeni syndrome: advice, adherence and perceived benefits. Fam Cancer2010;9:647654.

    • Search Google Scholar
    • Export Citation
  • 45.

    AspinwallLGLeafSLDolaER. CDKN2A/p16 genetic test reporting improves early detection intentions and practices in high-risk melanoma families. Cancer Epidemiol Biomarkers Prev2008;17:15101519.

    • Search Google Scholar
    • Export Citation
  • 46.

    PartridgeAHWinerEP. Informing clinical trial participants about study results. JAMA2002;288:363365.

  • 47.

    ShalowitzDIMillerFG. Communicating the results of clinical research to participants: attitudes, practices, and future directions. PLoS Medicine2008;5:e91.

    • Search Google Scholar
    • Export Citation
  • 48.

    MurphyJScottJKaufmanD. Public expectations for return of results from large-cohort genetic research. Am J Bioeth2008;8:3643.

  • 49.

    BlossCSOrnowskiLSilverE. Consumer perceptions of direct-to-consumer personalized genomic risk assessments. Genet Med2010;12:556566.

  • 50.

    BlossCSWineingerNEDarstBF. Impact of direct-to-consumer genomic testing at long term follow-up. J Med Genet2013;50:393400.

  • 51.

    FacioFMBrooksSLoewensteinJ. Motivators for participation in a whole-genome sequencing study: implications for translational genomics research. Eur J Hum Genet2011;19:12131217.

    • Search Google Scholar
    • Export Citation
  • 52.

    GollustSEGordonESZayacC. Motivations and perceptions of early adopters of personalized genomics: perspectives from research participants. Public Health Genomics2012;15:2230.

    • Search Google Scholar
    • Export Citation
  • 53.

    GoddardKAKnausWAWhitlockE. Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research. Genet Med2012;14:633642.

    • Search Google Scholar
    • Export Citation
  • 54.

    PalmerSByfordSRafteryJ. Economics notes: types of economic evaluation. BMJ1999;318:1349.

  • 55.

    MvunduraMGrosseSDHampelHPalomakiGE. The cost-effectiveness of genetic testing strategies for Lynch syndrome among newly diagnosed patients with colorectal cancer. Genet Med2010;12:93104.

    • Search Google Scholar
    • Export Citation
  • 56.

    Health Quality Ontario. Epidermal growth factor receptor mutation (EGFR) testing for prediction of response to EGFR-targeting tyrosine kinase inhibitor (TKI) drugs in patients with advanced non-small-cell lung cancer: an evidence-based analysis. Ont Health Technol Assess Ser2010;10:148.

    • Search Google Scholar
    • Export Citation
  • 57.

    CulyerAJBombardY. An equity framework for health technology assessments. Med Decis Making2012;32:428441.

  • 58.

    LymanGHCoslerLEKudererNMHornbergerJ. Impact of a 21-gene RT-PCR assay on treatment decisions in early-stage breast cancer: an economic analysis based on prognostic and predictive validation studies. Cancer2007;109:10111018.

    • Search Google Scholar
    • Export Citation
  • 59.

    YauchRLSettlemanJ. Recent advances in pathway-targeted cancer drug therapies emerging from cancer genome analysis. Curr Opin Genet Dev2012;22:4549.

    • Search Google Scholar
    • Export Citation
  • 60.

    PalomakiGEMcClainMRMelilloS. EGAPP supplementary evidence review: DNA testing strategies aimed at reducing morbidity and mortality from Lynch syndrome. Genet Med2009;11:4265.

    • Search Google Scholar
    • Export Citation
  • 61.

    HallMJ. Counterpoint: implementing population genetic screening for Lynch syndrome among newly diagnosed colorectal cancer patients—will the ends justify the means?J Natl Compr Canc Netw2010;8:606611.

    • Search Google Scholar
    • Export Citation
  • 62.

    KayeCI. Genetic service delivery: infrastructure, assessment and information. Public Health Genomics2012;15:164171.

  • 63.

    LehouxPWilliams-JonesB. Mapping the integration of social and ethical issues in health technology assessment. Int J Technol Assess Health Care2007;23:916.

    • Search Google Scholar
    • Export Citation
  • 64.

    BombardYAbelsonJSimeonovDGauvinFP. Eliciting social values and ethics in health technology assessment: a participatory approach. Soc Sci Med2011;73:135144.

    • Search Google Scholar
    • Export Citation
  • 65.

    DaviesCWetherellMBarnettESeymour-SmithS. Opening the Box: Evaluating the Citizens Council of NICE. London: School of Health & Social Welfare and Psychology Discipline, The Open University; 2005.

    • Search Google Scholar
    • Export Citation
  • 66.

    DeJeanDGiacominiMSchwartzLMillerFA. Ethics in Canadian health technology assessment: a descriptive review. Int J Technol Assess Health Care2009;25:463469.

    • Search Google Scholar
    • Export Citation
  • 67.

    BombardYAbelsonJSimeonovDGauvinFP. Citizens’ perspectives on personalized medicine: a qualitative public deliberation study. Eur J Hum Genet2013;21:11971201.

    • Search Google Scholar
    • Export Citation
  • 68.

    MoynihanRDoustJHenryD. Preventing overdiagnosis: how to stop harming the healthy. BMJ2012;344:e3502.

  • 69.

    KhouryMJGwinnMYoonPW. The continuum of translation research in genomic medicine: how can we accelerate the appropriate integration of human genome discoveries into health care and disease prevention?Genet Med2007;9:665674.

    • Search Google Scholar
    • Export Citation
  • 70.

    AndersonKJacobsonJSHeitjanDF. Cost-effectiveness of preventive strategies for women with a BRCA1 or a BRCA2 mutation. Ann Intern Med2006;144:397406.

    • Search Google Scholar
    • Export Citation
  • 71.

    Health Quality Ontario. Gene expression profiling for guiding adjuvant chemotherapy decisions in women with early breast cancer: an evidence-based and economic analysis. Ont Health Technol Assess Ser2010;10:157.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 130 130 7
PDF Downloads 58 58 4
EPUB Downloads 0 0 0