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Supplementary Data from Treatment Represents a Key Driver of Metastatic Cancer Evolution

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posted on 2023-03-31, 05:21 authored by Ditte S. Christensen, Johanne Ahrenfeldt, Mateo Sokač, Judit Kisistók, Martin K. Thomsen, Lasse Maretty, Nicholas McGranahan, Nicolai J. Birkbak
Supplementary Data from Treatment Represents a Key Driver of Metastatic Cancer Evolution

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Lundbeck Foundation

Aarhus University Research Foundation

Danish Cancer Society

Novo Nordisk Foundation

Wellcome Trust and the Royal Society

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ARTICLE ABSTRACT

Metastasis is the main cause of cancer death, yet the evolutionary processes behind it remain largely unknown. Here, through analysis of large panel-based genomic datasets from the AACR Genomics Evidence Neoplasia Information Exchange project, including 40,979 primary and metastatic tumors across 25 distinct cancer types, we explore how the evolutionary pressure of cancer metastasis shapes the selection of genomic drivers of cancer. The most commonly affected genes were TP53, MYC, and CDKN2A, with no specific pattern associated with metastatic disease. This suggests that, on a driver mutation level, the selective pressure operating in primary and metastatic tumors is similar. The most highly enriched individual driver mutations in metastatic tumors were mutations known to drive resistance to hormone therapies in breast and prostate cancer (ESR1 and AR), anti-EGFR therapy in non–small cell lung cancer (EGFR T790M), and imatinib in gastrointestinal cancer (KIT V654A). Specific mutational signatures were also associated with treatment in three cancer types, supporting clonal selection following anticancer therapy. Overall, this implies that initial acquisition of driver mutations is predominantly shaped by the tissue of origin, where specific mutations define the developing primary tumor and drive growth, immune escape, and tolerance to chromosomal instability. However, acquisition of driver mutations that contribute to metastatic disease is less specific, with the main genomic drivers of metastatic cancer evolution associating with resistance to therapy. This study leverages large datasets to investigate the evolutionary landscape of established cancer genes to shed new light upon the mystery of cancer dissemination and expand the understanding of metastatic cancer biology.

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