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Computational Analysis Reveals the Temporal Acquisition of Pathway Alterations during the Evolution of Cancer

Ahrenfeldt, Johanne; Christensen, Ditte S; Sokac, Mateo; Kisistok, Judit; McGranahan, Nicholas; Birkbak, Nicolai J; (2022) Computational Analysis Reveals the Temporal Acquisition of Pathway Alterations during the Evolution of Cancer. Cancers , 14 (23) , Article 5817. 10.3390/cancers14235817. Green open access

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Abstract

Cancer metastasis is the lethal developmental step in cancer, responsible for the majority of cancer deaths. To metastasise, cancer cells must acquire the ability to disseminate systemically and to escape an activated immune response. Here, we endeavoured to investigate if metastatic dissemination reflects acquisition of genomic traits that are selected for. We acquired mutation and copy number data from 8332 tumours representing 19 cancer types acquired from The Cancer Genome Atlas and the Hartwig Medical Foundation. A total of 827,344 non-synonymous mutations across 8332 tumour samples representing 19 cancer types were timed as early or late relative to copy number alterations, and potential driver events were annotated. We found that metastatic cancers had a significantly higher proportion of clonal mutations and a general enrichment of early mutations in p53 and RTK/KRAS pathways. However, while individual pathways demonstrated a clear time-separated preference for specific events, the relative timing did not vary between primary and metastatic cancers. These results indicate that the selective pressure that drives cancer development does not change dramatically between primary and metastatic cancer on a genomic level, and is mainly focused on alterations that increase proliferation.

Type: Article
Title: Computational Analysis Reveals the Temporal Acquisition of Pathway Alterations during the Evolution of Cancer
Location: Switzerland
Open access status: An open access version is available from UCL Discovery
DOI: 10.3390/cancers14235817
Publisher version: https://doi.org/10.3390/cancers14235817
Language: English
Additional information: © 2022 by the Authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Keywords: metastasis; cancer evolution; bioinformatics; cancer biology; cancer genomics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Oncology
URI: https://discovery.ucl.ac.uk/id/eprint/10163781
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