Abstract 5340: Bioinformatic analyses approaches for personalized oncogenomics

Abstract The Personalized Oncogenomics initiative at the British Columbia Cancer Agency aims to identify tumor-specific therapeutic targets in cancer patients with late stage disease who have failed standard therapy. Comprehensive profiling of individual patients' tumor(s) at the DNA and RNA level allows for characterization of altered pathways and hence identification of therapeutics designed to specifically target them. Data for each individual study included whole genome and transcriptome sequence of the fresh biopsy and whole genome sequence of patient's blood. When tissues were available, transcriptome of a matched normal sample and genome of the commonly formalin-fixed paraffin-embedded primary tumor were also sequenced. All sequencing experiments were performed on Illumina machines. Genomic data was examined, depending on the case, for germline and/or somatic mutations. These included single nucleotide variants, small insertions and deletions and copy number variations. All sequence data were assembled de novo in order to identify rearrangements causing gene fusions; transcriptome data also revealed allelic expression of variants and provided a profile for the entire transcribed genome. Differential abundance estimation was run against a rich repository of publicly available data from The Cancer Genome Atlas project and transcriptome datasets available in-house. The variants and pathways were then mapped to drug databases as well as clinical trial records. This was followed by an extensive literature search for evidence of drug combinations, drug-drug interactions and efficacy of a drug for a particular cancer type, especially those not recognized as the approved disease group for the drug under consideration. The project has sequenced 50 patients; the average length of time between acquiring the biopsy and delivering a report to clinical oncologists was 37 days. Bioinformatic analysis of the sequence data led to identification of informative or actionable targets in up to 80% of cases. The findings were not restricted to target identification but also led to change of diagnosis, treatment and characterization of tumor evolution. De novo assembly of the data in a non-small cell lung cancer patient led to the identification of the well-characterized EML4-ALK oncogenic fusion which had been missed through the use of clinically approved fluorescence in situ hybridization test. Analysis of two separate malignant masses in another patient revealed two divergent and unique tumors. Two different therapeutic were prescribed in order to target these; this led to the disappearance of both tumors and disease stabilization for 7 months. Through the design of an efficient and automated bioinformatics pipeline, individual patient's tumor specimen(s) were profiled in a clinically relevant time frame. This in turn enabled the delivery of targeted therapies and disease stabilization in patients who had no remaining standard therapeutic options. Citation Format: Katayoon Kasaian, Yaoqing Shen, Sreeja Leelakumari, Peter Eirew, Yvonne Y. Li, Erin Pleasance, Richard Corbett, Karen L. Mungall, Jacquie Schein, Andrew J. Mungall, Yongjun Zhao, Richard A. Moore, Stephen Yip, Karen Gelmon, Howard Lim, Daniel Renouf, Robyn Roscoe, Yussanne Ma, Marco A. Marra, Janessa Laskin, Steven JM Jones. Bioinformatic analyses approaches for personalized oncogenomics. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5340. doi:10.1158/1538-7445.AM2014-5340

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