A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing

Abstract

As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.

A decision tool to guide the ethics review of a challenging breed of emerging genomic projects

“…Each group analyzed the data separately and reported results back to a central analysis team. Some centers expressed interest in publishing their findings, 3 which at the time raised some concern that the benchmarking activity might be better categorized as research.…”

<b>Optimizing an ion semiconductor sequencing data analysis method to identify somatic mutations in the genomes of cancer cells in clinical tissue </b><b>samples </b>

“…A data analysis pipeline sequentially applies a suite of programs with adjustable parameters, and different combinations of programs and thresholds may produce diverse results. For example, a recent study showed that one pipeline produces more than 4-fold more single-nucleotide variants (SNVs) from the same dataset than other pipelines (1). Published studies compared different pipelines that employ open source and commercial software, and most of them analyze NGS data generated by using Illumina's platform (1,15,39).…”

“…For example, a recent study showed that one pipeline produces more than 4-fold more single-nucleotide variants (SNVs) from the same dataset than other pipelines (1). Published studies compared different pipelines that employ open source and commercial software, and most of them analyze NGS data generated by using Illumina's platform (1,15,39). There is no publicly available datasets applicable to WES of clinical samples of cancer tissues collected at single institute, which are sequenced at sufficient depths using the Ion Proton System.…”

Why Genetic Effects of Radiation are Observed in Mice but not in Humans

“…Such a study would provide a decisive explanation about the genetic effects of radiation in humans. However, currently, whole genome sequencing techniques are reasonably effective at detecting base-change mutations, but are not yet good enough to detect copy number changes (insertions and deletions) (38). Currently, one of the best methods to detect deletion mutations would be to use the array-based comparative genomic hybridization (CGH) method (see Fig.…”