Abstract
BackgroundAn increasing number of precision oncology programmes are being launched world-wide. To support this development, we present the Cancer Variant Explorer (CVE), an R package with an interactive Shiny web browser interface.ResultsLeveraging Oncotator and the Drug Gene Interaction Database, CVE offers exploration of variants within single or multiple tumour exomes to identify drivers, resistance mechanisms and to assess druggability. We present example applications including the analysis of an individual patient and a cohort-wide study, and provide a first extension of CVE by adding a tumour-specific co-expression network.ConclusionsThe CVE package allows interactive variant prioritisation to expedite the analysis of cancer sequencing studies. Our framework also includes the prioritisation of druggable targets, allows exploratory analysis of tissue specific networks and is extendable for specific applications by virtue of its modular design. We encourage the use of CVE within translational research studies and molecular tumour boards. The CVE package is available via Bioconductor (http://bioconductor.org/packages/CVE/).
Highlights
An increasing number of precision oncology programmes are being launched world-wide
We present a first extension of Cancer Variant Explorer (CVE) functionality for the exploration of variants in melanoma using a tumour-type specific co-expression network
While targeting specific single nucleotide variant (SNV) has been proven successful in some cancers, pan-cancer analysis revealed that not all tumour entities are primarily driven by point mutations, with copy number changes dominating in many cancer types
Summary
An increasing number of precision oncology programmes are being launched world-wide. To support this development, we present the Cancer Variant Explorer (CVE), an R package with an interactive Shiny web browser interface. Analysis of circulating tumour DNA (ctDNA) successfully enabled non-invasive monitoring of the evolution of different tumour clones and treatment resistance over the course of the disease [6,7,8,9]. This convergence of discovery, technology and therapeutic development has created an opportunity to test whether systematic knowledge of genomic information can successfully guide targeted therapy and improve patient outcomes (reviewed in [10, 11])
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