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Systemic structural analysis of alterations reveals a common structural basis of driver mutations in cancer.

ABSTRACT: A major effort in cancer research is to organize the complexities of the disease into fundamental traits. Despite conceptual progress in the last decades and the synthesis of hallmark features, no organizing principles governing cancer beyond cellular features exist. We analyzed experimentally determined structures harboring the most significant and prevalent driver missense mutations in human cancer, covering 73% (n = 168178) of the Catalog of Somatic Mutation in Cancer tumor samples (COSMIC). The results reveal that a single structural element-κ-helix (polyproline II helix)-lies at the core of driver point mutations, with significant enrichment in all major anatomical sites, suggesting that a small number of molecular traits are shared by most and perhaps all types of cancer. Thus, we uncovered the lowest possible level of organization at which carcinogenesis takes place at the protein level. This framework provides an initial scheme for a mechanistic understanding underlying the development of tumors and pinpoints key vulnerabilities.

SUBMITTER: Meirson T

PROVIDER: S-EPMC9846427 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Systemic structural analysis of alterations reveals a common structural basis of driver mutations in cancer.

Meirson Tomer T Bomze David D Schueler-Furman Ora O Stemmer Salomon M SM Markel Gal G

NAR cancer 20230118 1

A major effort in cancer research is to organize the complexities of the disease into fundamental traits. Despite conceptual progress in the last decades and the synthesis of hallmark features, no organizing principles governing cancer beyond cellular features exist. We analyzed experimentally determined structures harboring the most significant and prevalent driver missense mutations in human cancer, covering 73% (<i>n</i> = 168178) of the Catalog of Somatic Mutation in Cancer tumor samples (CO ...[more]

PMID: 36683915

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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

Systemic structural analysis of alterations reveals a common structural basis of driver mutations in cancer.

Publications

Systemic structural analysis of alterations reveals a common structural basis of driver mutations in cancer.

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