Dataset Information

Mutational and functional analysis reveals ADAMTS18 metalloproteinase as a novel driver in melanoma.

ABSTRACT: The disintegrin-metalloproteinases with thrombospondin domains (ADAMTS) genes have been suggested to function as tumor suppressors as several have been found to be epigenetically silenced in various cancers. We performed a mutational analysis of the ADAMTS gene family in human melanoma and identified a large fraction of melanomas to harbor somatic mutations. To evaluate the functional consequences of the most commonly mutated gene, ADAMTS18, six of its mutations were biologically examined. ADAMTS18 mutations had little effect on melanoma cell growth under standard conditions, but reduced cell dependence on growth factors. ADAMTS18 mutations also reduced adhesion to laminin and increased migration in vitro and metastasis in vivo. Melanoma cells expressing mutant ADAMTS18 had reduced cell migration after short hairpin RNA-mediated knockdown of ADAMTS18, suggesting that ADAMTS18 mutations promote growth, migration, and metastasis in melanoma.

SUBMITTER: Wei X

PROVIDER: S-EPMC3058631 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature

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Mutational and functional analysis reveals ADAMTS18 metalloproteinase as a novel driver in melanoma.

Wei Xiaomu X Prickett Todd D TD Viloria Cristina G CG Molinolo Alfredo A Lin Jimmy C JC Cardenas-Navia Isabel I Cruz Pedro P Rosenberg Steven A SA Davies Michael A MA Gershenwald Jeffrey E JE López-Otín Carlos C Samuels Yardena Y

Molecular cancer research : MCR 20101013 11

The disintegrin-metalloproteinases with thrombospondin domains (ADAMTS) genes have been suggested to function as tumor suppressors as several have been found to be epigenetically silenced in various cancers. We performed a mutational analysis of the ADAMTS gene family in human melanoma and identified a large fraction of melanomas to harbor somatic mutations. To evaluate the functional consequences of the most commonly mutated gene, ADAMTS18, six of its mutations were biologically examined. ADAMT ...[more]

PMID: 21047771

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Project description:Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

Dataset Information

Mutational and functional analysis reveals ADAMTS18 metalloproteinase as a novel driver in melanoma.

Publications

Mutational and functional analysis reveals ADAMTS18 metalloproteinase as a novel driver in melanoma.

Similar Datasets

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