Development and clinical application of an integrative genomic approach to personalized cancer therapy.

Uzilov AV1, Ding W1, Fink MY1,2, Antipin Y1, Brohl AS1,3, Davis C1, Lau CY1, Pandya C1, Shah H1, Kasai Y1, Powell J1, Micchelli M1, Castellanos R1,Zhang Z1, Linderman M1, Kinoshita Y4, Zweig M1, Raustad K1, Cheung K1, Castillo D1, Wooten M1, Bourzgui I1, Newman LC1, Deikus G1, Mathew B1, Zhu J1, Glicksberg BS1, Moe AS1, Liao J1, Edelmann L1, Dudley JT1, Maki RG5, Kasarskis A1, Holcombe RF5, Mahajan M1, Hao K1, Reva B1, Longtine J4,Starcevic D1, Sebra R1, Donovan MJ4, Li S1, Schadt EE6, Chen R7.

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Abstract

BACKGROUND:

Personalized therapy provides the best outcome of cancer care and its implementation in the clinic has been greatly facilitated by recent convergence of enormous progress in basic cancer research, rapid advancement of new tumor profiling technologies, and an expanding compendium of targeted cancer therapeutics.

METHODS:

We developed a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants. To provide high sensitivity in known cancer mutation hotspots, Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was also employed. We integrated the resulting data with cancer knowledge bases and developed a specific workflow for each cancer type to improve interpretation of genomic data.

RESULTS:

We returned genomics findings to 46 patients and their physicians describing somatic alterations and predicting drug response, toxicity, and prognosis. Mean 17.3 cancer-relevant somatic mutations per patient were identified, 13.3-fold, 6.9-fold, and 4.7-fold more than could have been detected using CHPv2, Oncomine Cancer Panel (OCP), and FoundationOne, respectively. Our approach delineated the underlying genetic drivers at the pathway level and provided meaningful predictions of therapeutic efficacy and toxicity. Actionable alterations were found in 91 % of patients (mean 4.9 per patient, including somatic mutations, copy number alterations, gene expression alterations, and germline variants), a 7.5-fold, 2.0-fold, and 1.9-fold increase over what could have been uncovered by CHPv2, OCP, and FoundationOne, respectively. The findings altered the course of treatment in four cases.