Abstract 4260: Standardized decision support in next-generation sequencing (NGS) reports of somatic cancer variants
Authors: Rodrigo Dienstmann, Fei Dong, Darrell Borger, Dora Dias Santagata, Leif W. Ellisen, Long P. Le, A. John Iafrate
Journal: Cancer Research (2014)

Abstract Introduction: Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non-recurrent variants. Many such variants occur in tumor genes that have well-established biological and clinical relevance and are putative targets of molecular therapy, however, most variants are still of unknown significance. Prioritizing and reporting genetic variants identified via NGS technologies remains a major challenge. Methods: Based on a systematic framework for cancer variant annotation/prioritization, we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. In parallel with the variant interpretation pipeline, we developed a comprehensive knowledge database (Kdb) that integrates tumor types, genes, variants, response/resistance patterns to approved and experimental agents and PubMed identifiers. Results: As physicians rely heavily on limited subsets of data and frequently apply rules based on strength of evidence in the decision-making process, NGS results are presented in a very discrete manner, avoiding detailed descriptions of each genomic aberration. Reportable variants are grouped in three categories: (i) actionable, which support treatment recommendation, enrollment in clinical trials and/or have prognostic or diagnostic implications; (ii) biologically relevant but not clearly actionable, including novel variants lacking functional preclinical validation in known cancer genes; and (iii) variants of unknown significance. Predictive associations are reported according to tumor type and classified in a hierarchical way based on the strength of evidence: (i) late trials; (ii) early trials; (iii) case reports; and (iv) preclinical data being explored in clinical trials. In our internal Kdb, tumor types with the largest number of emerging predictive associations are acute leukemias, non-small cell lung cancer, brain tumors, melanoma, colorectal, ovarian and breast cancers, comprising more than 100 unique gene - drug interactions. Further interpretation of gene variants requires a team with strong background in cancer biology, careful evaluation of the published literature and ability to match a patient's tumor genotype to clinical trials in the context of multiple aberrations. Conclusions: With large amounts of data being generated as high throughput sequencing assays enter the clinical realm, there is a growing need to better communicate relevant findings in a timely manner while remaining cognizant of the potential consequences of misuse or overinterpretation of genomic information. The scientific knowledge on actionable events should be presented the report, so that physicians can make evidence-based decisions and patients may benefit from treatment with matched targeted agents. We hope that our experience in this process will help other institutions implement similar programs. Citation Format: Rodrigo Dienstmann, Fei Dong, Darrell Borger, Dora Dias Santagata, Leif W. Ellisen, Long P. Le, A. John Iafrate. Standardized decision support in next-generation sequencing (NGS) reports of somatic cancer variants. [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 4260. doi:10.1158/1538-7445.AM2014-4260