Sun, R. X. | bioRxiv Science

bioRxiv2018

Accurate Reference-Free Somatic Variant-Calling by Integrating Genomic, Sequencing and Population Data

The detection of somatic single nucleotide variants (SNVs) is critical in both research and clinical applications. Studies of human cancer typically use matched normal (reference) samples from a distant tissue to increase SNV prediction accuracy. This process both doubles sequencing costs and poses challenges when reference samples are not readily available, such as for many cell-lines. To address these challenges, we created S22S: an approach for the prediction of somatic mutations without need for matched reference tissue. S22S takes underlying sequence data, augments them with genomic background context and population frequency information, and classifies SNVs as somatic or non-somatic. We validated S22S using primary tumor/normal pairs from four tumor types, spanning two different sequencing technologies. S22S robustly identifies somatic SNVs, with the area under the precision recall curve reaching 0.97 in kidney clear cell carcinoma, comparable to the best tumor/normal analysis pipelines. S22S is freely available at http://labs.oicr.on.ca/Boutros-lab/software/s22s.

bioinformatics↗

bioRxiv2018

The Origins and Consequences of Localized and Global Somatic Hypermutation

Cancer is a disease of the genome, but the dramatic inter-patient variability in mutation number is poorly understood. Tumours of the same type can differ by orders of magnitude in their mutation rate. To understand potential drivers and consequences of the underlying heterogeneity in mutation rate across tumours, we evaluated both local and global measures of mutation density: both single-stranded and double-stranded DNA breaks in 2,460 tumours of 38 cancer types. We find that SCNAs in thousands of genes are associated with elevated rates of point-mutations, while similarly point-mutation patterns in dozens of genes are associated with specific patterns of DNA double-stranded breaks. These candidate drivers of mutation density are enriched for known cancer drivers, and preferentially occur early in tumour evolution, appearing clonally in all cells of a tumour. To supplement this understanding of global mutation density, we developed and validated a tool called SeqKat to identify localized \"rainstorms\" of point-mutations (kataegis). We show that rates of kataegis differ by four orders of magnitude across tumour types, with malignant lymphomas showing the highest. Tumours with TP53 mutations were 2.6-times more likely to harbour a kataegic event than those without, and 239 SCNAs were associated with elevated rates of kataegis, including loss of the tumour-suppressor CDKN2A. We identify novel subtypes of kataegic events not associated with aberrant APOBEC activity, and find that these are localized to specific cellular regions, enriched for MYC-target genes. Kataegic events were associated with patient survival in some, but not all tumour types, highlighting a combination of global and tumour-type specific effects. Taken together, we reveal a landscape of genes driving localized and tumour-specific hyper-mutation, and reveal novel mutational processes at play in specific tumour types.

cancer biology↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗