Biology subjects

Korbel, J.

Publications and source records attributed to Korbel, J..

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↗

bioRxiv2017

Assessing the Gene Regulatory Landscape in 1,188 Human Tumors

Cancer is characterised by somatic genetic variation, but the effect of the majority of non-coding somatic variants and the interface with the germline genome are still unknown. We analysed the whole genome and RNA-Seq data from 1,188 human cancer patients as provided by the Pan-cancer Analysis of Whole Genomes (PCAWG) project to map cis expression quantitative trait loci of somatic and germline variation and to uncover the causes of allele-specific expression patterns in human cancers. The availability of the first large-scale dataset with both whole genome and gene expression data enabled us to uncover the effects of the non-coding variation on cancer. In addition to confirming known regulatory effects, we identified novel associations between somatic variation and expression dysregulation, in particular in distal regulatory elements. Finally, we uncovered links between somatic mutational signatures and gene expression changes, including TERT and LMO2, and we explained the inherited risk factors in APOBEC-related mutational processes. This work represents the first large-scale assessment of the effects of both germline and somatic genetic variation on gene expression in cancer and creates a valuable resource cataloguing these effects.

cancer biology↗

bioRxiv2017

Framework For Quality Assessment Of Whole Genome, Cancer Sequences

Working with cancer whole genomes sequenced over a period of many years in different sequencing centres requires a validated framework to compare the quality of these sequences. The Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC), a project a cohort of over 2800 donors provided us with the challenge of assessing the quality of the genome sequences. A non-redundant set of five quality control (QC) measurements were assembled and used to establish a star rating system. These QC measures reflect known differences in sequencing protocol and provide a guide to downstream analyses of these whole genome sequences. The resulting QC measures also allowed for exclusion samples of poor quality, providing researchers within PCAWG, and when the data is released for other researchers, a good idea of the sequencing quality. For a researcher wishing to apply the QC measures for their data we provide a Docker Container of the software used to calculate them. We believe that this is an effective framework of quality measures for whole genome, cancer sequences, which will be a useful addition to analytical pipelines, as it has to the PCAWG project.

genomics↗