Biology subjects

ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network,

Publications and source records attributed to ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network,.

3 recordsLinked to original sources

bioRxiv2018

Oncogenic effects of germline mutations in lysosomal storage disease genes

Clinical observations have indicated that patients with Gaucher disease or Fabry disease are at increased risk of cancer. However, a systematic evaluation of the oncogenic effects of causal mutations of lysosomal storage diseases (LSDs) has been lacking. Here we report a comprehensive association analysis between potentially pathogenic germline mutations in LSD genes and cancer interrogating genomic (or exomic) variant datasets derived from the Pan-Cancer Analysis of Whole Genomes project (case cohort), the 1000 Genomes project (primary control cohort), and the Exome Aggregation Consortium that does not include The Cancer Genome Atlas subset (validation control cohort). We show that potentially pathogenic variants (PPVs) in 42 LSD genes are significantly enriched in cancer patients in a histology-dependent manner, cancer risk is higher in individuals with a greater number of PPVs, and cancer develops earlier in PPV carriers. Analysis of tumor genomic and transcriptomic data from the pancreatic adenocarcinoma cohort revealed potential mechanisms that might be involved in the oncogenic contribution of PPVs. Our findings extend the mechanistic understanding of inherited cancer susceptibility and highlight the promise of harnessing available therapeutic strategies to restore lysosomal function for personalized cancer prevention.

genetics↗

bioRxiv2018

Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing

Chromothripsis is a newly discovered mutational phenomenon involving massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in cancer suggest that chromothripsis may be far more common than initially inferred from low resolution DNA copy number data. Here, we analyze the patterns of chromothripsis across 2,658 tumors spanning 39 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of >50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy number states, a considerable fraction of the events involves multiple chromosomes as well as additional structural alterations. In addition to non-homologous end-joining, we detect signatures of replicative processes and templated insertions. Chromothripsis contributes to oncogene amplification as well as to inactivation of genes such as mismatch-repair related genes. These findings show that chromothripsis is a major process driving genome evolution in human cancer.

genomics↗

bioRxiv2018

Passenger mutations in 2500 cancer genomes: Overall molecular functional impact and consequences

The Pan-cancer Analysis of Whole Genomes (PCAWG) project provides an unprecedented opportunity to comprehensively characterize a vast set of uniformly annotated coding and non-coding mutations present in thousands of cancer genomes. Classical models of cancer progression posit that only a small number of these mutations strongly drive tumor progression and that the remaining ones (termed \"putative passengers\") are inconsequential for tumorigenesis. In this study, we leveraged the comprehensive variant data from PCAWG to ascertain the molecular functional impact of each variant. The impact distribution of PCAWG mutations shows that, in addition to high- and low-impact mutations, there is a group of medium-impact putative passengers predicted to influence gene activity. Moreover, the predicted impact relates to the underlying mutational signature: different signatures confer divergent impact, differentially affecting distinct regulatory subsystems and gene categories. We also find that impact varies based on subclonal architecture (i.e., early vs. late mutations) and can be related to patient survival. Finally, we note that insufficient power due to limited cohort sizes precludes identification of weak drivers using standard recurrence-based approaches. To address this, we adapted an additive effects model derived from complex trait studies to show that aggregating the impact of putative passenger variants (i.e. including yet undetected weak drivers) provides significant predictability for cancer phenotypes beyond the PCAWG identified driver mutations (12.5% additive variance). Furthermore, this framework allowed us to estimate the frequency of potential weak driver mutations in the subset of PCAWG samples lacking well-characterized driver alterations.

genomics↗