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Divergent mutational processes distinguish hypoxic and normoxic tumours

Author

Listed:
  • Vinayak Bhandari

    (University of Toronto
    Ontario Institute for Cancer Research)

  • Constance H. Li

    (University of Toronto
    Ontario Institute for Cancer Research
    University of California)

  • Robert G. Bristow

    (University of Manchester
    The Christie NHS Foundation Trust
    CRUK Manchester Institute and Centre)

  • Paul C. Boutros

    (University of Toronto
    University of California
    University of Toronto
    Vector Institute for Artificial Intelligence)

Abstract

Many primary tumours have low levels of molecular oxygen (hypoxia), and hypoxic tumours respond poorly to therapy. Pan-cancer molecular hallmarks of tumour hypoxia remain poorly understood, with limited comprehension of its associations with specific mutational processes, non-coding driver genes and evolutionary features. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we quantify hypoxia in 1188 tumours spanning 27 cancer types. Elevated hypoxia associates with increased mutational load across cancer types, irrespective of underlying mutational class. The proportion of mutations attributed to several mutational signatures of unknown aetiology directly associates with the level of hypoxia, suggesting underlying mutational processes for these signatures. At the gene level, driver mutations in TP53, MYC and PTEN are enriched in hypoxic tumours, and mutations in PTEN interact with hypoxia to direct tumour evolutionary trajectories. Overall, hypoxia plays a critical role in shaping the genomic and evolutionary landscapes of cancer.

Suggested Citation

  • Vinayak Bhandari & Constance H. Li & Robert G. Bristow & Paul C. Boutros, 2020. "Divergent mutational processes distinguish hypoxic and normoxic tumours," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-019-14052-x
    DOI: 10.1038/s41467-019-14052-x
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    Cited by:

    1. Yuefan Huang & Vakul Mohanty & Merve Dede & Kyle Tsai & May Daher & Li Li & Katayoun Rezvani & Ken Chen, 2023. "Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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