57 research outputs found

    Class IA PI3Kinase Regulatory Subunit, p85α, Mediates Mast Cell Development through Regulation of Growth and Survival Related Genes

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    Stem cell factor (SCF) mediated KIT receptor activation plays a pivotal role in mast cell growth, maturation and survival. However, the signaling events downstream from KIT are poorly understood. Mast cells express multiple regulatory subunits of class 1A PI3Kinase (PI3K) including p85α, p85β, p50α, and p55α. While it is known that PI3K plays an essential role in mast cells; the precise mechanism by which these regulatory subunits impact specific mast cell functions including growth, survival and cycling are not known. We show that loss of p85α impairs the growth, survival and cycling of mast cell progenitors (MCp). To delineate the molecular mechanism (s) by which p85α regulates mast cell growth, survival and cycling, we performed microarray analyses to compare the gene expression profile of MCps derived from WT and p85α-deficient mice in response to SCF stimulation. We identified 151 unique genes exhibiting altered expression in p85α-deficient cells in response to SCF stimulation compared to WT cells. Functional categorization based on DAVID bioinformatics tool and Ingenuity Pathway Analysis (IPA) software relates the altered genes due to lack of p85α to transcription, cell cycle, cell survival, cell adhesion, cell differentiation, and signal transduction. Our results suggest that p85α is involved in mast cell development through regulation of expression of growth, survival and cell cycle related genes

    Pan-cancer analysis of whole genomes

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    Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

    Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

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    Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts
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