Standardized decision support in next generation sequencing reports of somatic cancer variants

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.

Keywords: Cancer; Genomics; Next-generation sequencing; Report; Variant annotation.

Figure 1

Variant analysis flowchart of next generation sequencing tests performed in clinical laboratories 1 KG: 1000 Genomes Project; AACR: American Association for Cancer Research; ASCO: American Society of Clinical Oncology; COSMIC: Catalog of Somatic Mutations in Cancer; dbSNP: database of single nucleotide polymorphisms; ESMO: European Society for Medical Oncology; ESP6500: Exome Sequencing Project; PharmGKB, Pharmacogenomics Knowledge Base TCGA: The Cancer Genome Atlas.

Figure 2

Somatic variant classification system and corresponding action‐items.

Figure 3

Illustrative examples of sequencing results describing somatic cancer variants with structured evidence‐based classification.

Figure 3

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Figure 4

Statistics of internal knowledge database. Upper chart shows number of genes with emerging predictive associations in different tumor types. Citations of the same emerging clinical and preclinical associations are displayed in the lower part.