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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.

All cancers are caused by somatic mutations. However, understanding of the biological processes generating these mutations is limited. The catalogue of somatic mutations from a cancer genome bears the signatures of the mutational processes that have been operative. Here, we analysed 4,938,362 mutations from 7,042 cancers and extracted more than 20 distinct mutational signatures. Some are present in many cancer types, notably a signature attributed to the APOBEC family of cytidine deaminases, whereas others are confined to a single class. Certain signatures are associated with age of the patient at cancer diagnosis, known mutagenic exposures or defects in DNA maintenance, but many are of cryptic origin. In addition to these genome-wide mutational signatures, hypermutation localized to small genomic regions, kataegis, is found in many cancer types. The results reveal the diversity of mutational processes underlying the development of cancer with potential implications for understanding of cancer etiology, prevention and therapy.

Medulloblastomas are the most common malignant brain tumors in children1. Identifying and understanding the genetic events that drive these tumors is critical for the development of more effective diagnostic, prognostic and therapeutic strategies. Recently, our group and others described distinct molecular subtypes of medulloblastoma based on transcriptional and copy number profiles2–5. Here, we utilized whole exome hybrid capture and deep sequencing to identify somatic mutations across the coding regions of 92 primary medulloblastoma/normal pairs. Overall, medulloblastomas exhibit low mutation rates consistent with other pediatric tumors, with a median of 0.35 non-silent mutations per megabase. We identified twelve genes mutated at statistically significant frequencies, including previously known mutated genes in medulloblastoma such as CTNNB1, PTCH1, MLL2, SMARCA4 and TP53. Recurrent somatic mutations were identified in an RNA helicase gene, DDX3X, often concurrent with CTNNB1 mutations, and in the nuclear co-repressor (N-CoR) complex genes GPS2, BCOR, and LDB1, novel findings in medulloblastoma. We show that mutant DDX3X potentiates transactivation of a TCF promoter and enhances cell viability in combination with mutant but not wild type beta-catenin. Together, our study reveals the alteration of Wnt, Hedgehog, histone methyltransferase and now N-CoR pathways across medulloblastomas and within specific subtypes of this disease, and nominates the RNA helicase DDX3X as a component of pathogenic beta-catenin signaling in medulloblastoma.

DNA methylation plays a key role in regulating eukaryotic gene expression. Although mitotically heritable and stable over time, patterns of DNA methylation frequently change in response to cell differentiation, disease and environmental influences. Several methods have been developed to map DNA methylation on a genomic scale. Here, we benchmark four of these approaches by analyzing two human embryonic stem cell lines derived from genetically unrelated embryos and a matched pair of colon tumor and adjacent normal colon tissue obtained from the same donor. Our analysis reveals that methylated DNA immunoprecipitation sequencing (MeDIP-seq), methylated DNA capture by affinity purification (MethylCap-seq), reduced representation bisulfite sequencing (RRBS) and the Infinium HumanMethylation27 assay all produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies., Stem Cell and Regenerative Biology

Bisulfite sequencing measures absolute levels of DNA methylation at single-nucleotide resolution, providing a robust platform for molecular diagnostics. We optimized bisulfite sequencing for genome-scale analysis of clinical samples: here we outline how restriction digestion targets bisulfite sequencing to hotspots of epigenetic regulation and describe a statistical method for assessing significance of altered DNA methylation patterns. Thirty nanograms of DNA was sufficient for genome-scale analysis and our protocol worked well on formalin-fixed, paraffin-embedded samples., Stem Cell and Regenerative Biology