Abstract A1-44: Clinical cancer sequencing and integrated analysis of whole genomes, exomes and transcriptomes
Authors: Malachi Griffith, Obi L. Griffith, Avinash Ramu, J Benjamin Ainscough, Kilannin Krysiak, Mayank Choudhary, Zachary Skidmore, Benjamin Tan, Govindan Ramaswamy, Brian Van Tine, Matthew J. Ellis, Timothy J. Ley, Richard K. Wilson, Elaine R. Mardis
Journal: Cancer Research (2015)

Abstract Deep and comprehensive tumor sequencing in a clinical context presents unique challenges compared to discovery-based cancer genomics. To explore these challenges, we have developed a comprehensive approach for identification of clinically actionable events in patient tumors by integrated analysis of whole genome, exome, and transcriptome sequencing. To demonstrate the utility of this approach we sequenced the DNA and RNA for both tumor and matched normal tissue of a diverse set of 21 cancer cases (1 ALL, 2 AML, 6 breast, 1 gastrointestinal adenocarcinoma, 1 gastrointestinal stromal tumor, 1 lung, 1 low grade glioma, 1 high grade glioma, 1 leiomyosarcoma, 1 signet ring gastric and 5 pancreatic). Each case represented a patient with advanced disease. These tumors varied substantially in their purity, heterogeneity, extraction method, sample quality, and sample amount. Each tumor/normal pair was sequenced to ~30-90X whole genome coverage, ~150-300X exome coverage for tumor and normal, and varying transcriptome coverage depending on sample quality (at least one lane of Illumina HiSeq2000 data each). Integrating the analysis of all three data types allowed for more sensitive and interpretable identification of clinically relevant tumor associated mutations than any single approach. For example, combining exome and whole genome data increased detection of variants in sub-clones and low purity tumors. Combining WGS and RNA-seq data allowed confirmation of the expression effect of focal amplifications, identification of variant biased allele-specific expression and confirmation of gene fusion products predicted by structural variants. To maximize the potential for at least one clinically actionable finding in each case, our analysis goal was to identify, annotate, visualize and prioritize single nucleotide variants (SNVs), small indels, translocations, copy number variants, gene fusions, and expression of aberrant mRNA isoforms. We accomplished these tasks by creating a clinical sequencing pipeline that incorporates existing and novel bioinformatics methods into the analysis infrastructure of the Genome Institute's Genome Modeling System (GMS). A maximum turnaround time of 30 days was targeted for every case from sample receipt to complete report generation. Events were prioritized according to potential clinical relevance with particular attention paid to focal amplifications, SNVs and indels with ‘driver’ characteristics, gene fusions, and aberrantly expressed genes. These candidates were further prioritized by a suite of tools we are developing to help researchers and clinicians assess clinical actionability including: DGIdb (www.dgidb.org) a drug-gene interaction resource created to facilitate mining the druggable genome, DoCM (www.docm.info) a database of canonical mutations, and CIViC (www.civicdb.org) an open interface for clinical interpretation of variants in cancer. Citation Format: Malachi Griffith, Obi L. Griffith, Avinash Ramu, J Benjamin Ainscough, Kilannin Krysiak, Mayank Choudhary, Zachary Skidmore, Benjamin Tan, Govindan Ramaswamy, Brian Van Tine, Matthew J. Ellis, Timothy J. Ley, Richard K. Wilson, Elaine R. Mardis. Clinical cancer sequencing and integrated analysis of whole genomes, exomes and transcriptomes. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-44.