Chromosomal copy number heterogeneity predicts survival rates across cancers

Erik Dijk, Tom Bosch, Kristiaan J. Lenos, Khalid El Makrini, Lisanne E. Nijman, Hendrik F. B. Essen, Nico Lansu, Michiel Boekhout, Joris H. Hageman, Rebecca C. Fitzgerald, Cornelis J. A. Punt, Jurriaan B. Tuynman, Hugo J. G. Snippert, Geert J. P. L. Kops, Jan Paul Medema, Bauke Ylstra, Louis Vermeulen () and Daniël M. Miedema ()
Additional contact information
Erik Dijk: Vrije Universiteit Amsterdam
Tom Bosch: Amsterdam UMC, University of Amsterdam
Kristiaan J. Lenos: Amsterdam UMC, University of Amsterdam
Khalid El Makrini: Amsterdam UMC, University of Amsterdam
Lisanne E. Nijman: Amsterdam UMC, University of Amsterdam
Hendrik F. B. Essen: Vrije Universiteit Amsterdam
Nico Lansu: Oncode Institute
Michiel Boekhout: Oncode Institute
Joris H. Hageman: Oncode Institute
Rebecca C. Fitzgerald: University of Cambridge
Cornelis J. A. Punt: University Medical Center Utrecht
Jurriaan B. Tuynman: Vrije Universiteit Amsterdam
Hugo J. G. Snippert: Oncode Institute
Geert J. P. L. Kops: Oncode Institute
Jan Paul Medema: Amsterdam UMC, University of Amsterdam
Bauke Ylstra: Vrije Universiteit Amsterdam
Louis Vermeulen: Amsterdam UMC, University of Amsterdam
Daniël M. Miedema: Amsterdam UMC, University of Amsterdam

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: Abstract Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing. Here, we introduce a scalable measure of chromosomal copy number heterogeneity (CNH) that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data and live cell imaging we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 37 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

Date: 2021
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DOI: 10.1038/s41467-021-23384-6

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