Standardized decision support in next generation sequencing reports of somatic cancer variants. Issue 5 (4th April 2014)

• Record Type: Journal Article
• Authors: Dienstmann, Rodrigo; Dong, Fei; Borger, Darrell; Dias-Santagata, Dora; Ellisen, Leif W.; Le, Long P.; Iafrate, A. John
• Is Part Of: Molecular oncology. Volume 8:Issue 5(2014:Jul.)
• Page Start: 859
• Page End: 873
• Publication Date: 2014-04-04
• Subjects: Cancer -- Genomics -- Next‐generation sequencing -- Report -- Variant annotation; Cancer -- Molecular aspects -- Periodicals; 616.994005
• DOI: 10.1016/j.molonc.2014.03.021
• Languages: English
• ISSNs: 1574-7891

Abstract

Abstract : Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non‐recurrent variants. Prioritizing genetic variants identified via next generation sequencing technologies remains a major challenge. 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. 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. Herein we describe a systematic framework for variant annotation and prioritization, and we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. We hope that our experience developing a comprehensive knowledge database of emerging predictive markers matched to targeted therapies will help other institutions implement similar programs. Highlights: Annotation and prioritization of cancer gene variants is critical for clinical interpretation. Actionable variants support treatment, enrolment in clinical trials and/or have prognostic/diagnostic implications. Biologically relevant alterations should also be included in the reports of cancer genomic tests. Structured reports with standardized vocabulary enable evidence‐based decisions and matched targeted therapies. A "knowledge database" with emerging preclinical/clinical predictive markers supports report generation.