Details

Autor(en) / Beteiligte

• Ahdesmäki, Miika
• Chapman, Brad
• Luke, Sally
• Carr, Hedley
• Stetson, Daniel
• Hofmann, Oliver
• Johnson, Justin

Titel

Abstract 3604: Validated structural variant detection with prioritisation of known cancer related changes

Ist Teil von

• Cancer research (Chicago, Ill.), 2016-07, Vol.76 (14_Supplement), p.3604-3604

Erscheinungsjahr

2016

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Abstract Reliable detection of structural variation (SV) is playing an increasingly important role in cancer diagnostics and treatments. We introduce a best practice integrative workflow for detecting large structural variations (SVs) such as deletions, duplications, inversions, fusions and translocations from standard DNA re-sequencing techniques, annotating and prioritising for clinically actionable events. While established RNA-Seq fusion workflows exist to detect and filter fusion events in tumors detecting large structural changes from exome- and WGS data remains challenging. Current DNA-based SV callers predict a large percentage of false positive events, and the resulting event lists are not prioritised for validation. To streamline follow-up studies we automate running multiple structural variant callers (Manta, WHAM, Lumpy, MetaSV), identifying potentially disruptive large scale events. We evaluate the integrated workflow sensitivity and specificity against the ICGC-TCGA DREAM Mutation Calling Challenge data set, optimise filtering criteria based on quality and supporting information, and annotate the filtered calls using known cancer related alterations from CIViC and publicly available gene lists from AstraZeneca. This enables us to prioritise known and potentially interesting events over intergenic events or non-whole exon deletions. We show validation results from test data and clinically actionable inversions, duplications and fusion events from cancer cell lines. Our approach is based on a two tier approach of first focusing on known events that yield fusion transcripts such as the TACC3-FGFR3 tandem duplication and ALK-EML4 inversion, and secondarily on events in oncogenes and tumor suppressors of interest. While whole genome sequencing is the preferred approach to detecting these events, we also show how by design or by the nature of the breakpoint being close enough to exons, hybrid capture data can also be surprisingly useful for SV inference. Citation Format: Miika Ahdesmäki, Brad Chapman, Sally Luke, Hedley Carr, Daniel Stetson, Oliver Hofmann, Justin Johnson. Validated structural variant detection with prioritisation of known cancer related changes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3604.