Abstract
In personalized cancer genomic medicine, characterizing a patient’s molecular profile based on comprehensive information is important for maximizing treatment benefits. However, current cancer genome analysis is centered on single nucleotide variation (SNV), gene expression, and copy number variation (CNV) but places little emphasis on structural variations (SV) beside fusions. To date, investigation of SVs has been limited because SV analysis entails a cumbersome annotation process. This study describes the design, development, and implementation of an annotation tool for SV, termed SVAnnotator. Detailed annotation was performed on the results of SV detection of 2,781 whole genome samples from the ICGC/TCGA PanCancer Analysis of Whole Genomes (PCAWG) with identifications of fusion, exon skipping, gene disruption, and tandem duplication SVs. These annotations of SVs will facilitate understanding of molecular events and further enhance utilities of precision medicine in stratifications, pathogenicity assessments and drug responses. Frequent novel SV events in MACROD2, FHIT, WWOX and CCSER1 were observed across many cancers. Importantly, SV events were frequently identified in well-established tumor suppressor genes including RB1, NF1, PTEN and TP53. As such, it is plausible that potential therapeutic opportunities are overlooked when SV analysis is not appropriately performed. Given the frequency of SVs detected in our study, SVanalysis with detailed annotation should be a routine part of comprehensive precision medicine analysis, and further studies are warranted to enhance clinical benefits as well as our understanding of uncharacterized SV events.
Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network
