Abstract
Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants.
Highlights
Structural variants (SVs) such as inversions, tandem duplications, large deletions and more complex chromosomal rearrangements are implicated as driver events in multiple cancers (Latysheva & Babu, 2016)
We show the full utility of the improved prioritisation and visualisation approaches in samples with structural variants leading to oncogenic gene fusions
For the genes of interest (GOI), as a supplement to the prioritisation implementation we have provided a list of 300+ genes commonly associated with cancer, including genes involved in the MAPK and PI3K pathways, DNA damage response, immuno-oncology and others
Summary
Structural variants (SVs) such as inversions, tandem duplications, large deletions and more complex chromosomal rearrangements are implicated as driver events in multiple cancers (Latysheva & Babu, 2016). Clinical detection of SVs in Mendelian diseases has been considered by e.g., Noll et al (2016) but to our knowledge no prioritisation approach for oncology is publicly available. Some well understood examples include TMPRSS2-ERG in prostate cancer (Tomlins et al, 2008), FGFR1,3-TACC1,3 in bladder and other cancers (The Cancer Genome Atlas Research Network, 2014), EGFRv3 deletion in glioblastoma and other tumours (Sugawa et al, 1990) and EML4-ALK in lung cancer (Soda et al, 2007). How to cite this article Ahdesmäki et al (2017), Prioritisation of structural variant calls in cancer genomes.
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