Clonal fitness inferred from time-series modelling of single-cell cancer genomes

Sohrab Salehi, Farhia Kabeer, Nicholas Ceglia, Mirela Andronescu, Marc J. Williams, Kieran R. Campbell, Tehmina Masud, Beixi Wang, Justina Biele, Jazmine Brimhall, David Gee, Hakwoo Lee, Jerome Ting, Allen W. Zhang, Hoa Tran, Ciara O’Flanagan, Fatemeh Dorri, Nicole Rusk, Teresa Ruiz Algara, So Ra Lee, Brian Yu Chieh Cheng, Peter Eirew, Takako Kono, Jenifer Pham, Diljot Grewal, Daniel Lai, Richard Moore, Andrew J. Mungall, Marco A. Marra, Andrew McPherson, Alexandre Bouchard-Côté, Samuel Aparicio () and Sohrab P. Shah ()
Additional contact information
Sohrab Salehi: BC Cancer
Farhia Kabeer: BC Cancer
Nicholas Ceglia: Memorial Sloan Kettering Cancer Center
Mirela Andronescu: BC Cancer
Marc J. Williams: Memorial Sloan Kettering Cancer Center
Kieran R. Campbell: University of Toronto
Tehmina Masud: BC Cancer
Beixi Wang: BC Cancer
Justina Biele: BC Cancer
Jazmine Brimhall: BC Cancer
David Gee: BC Cancer
Hakwoo Lee: BC Cancer
Jerome Ting: BC Cancer
Allen W. Zhang: BC Cancer
Hoa Tran: BC Cancer
Ciara O’Flanagan: BC Cancer
Fatemeh Dorri: BC Cancer
Nicole Rusk: Memorial Sloan Kettering Cancer Center
Teresa Ruiz Algara: BC Cancer
So Ra Lee: BC Cancer
Brian Yu Chieh Cheng: BC Cancer
Peter Eirew: BC Cancer
Takako Kono: BC Cancer
Jenifer Pham: BC Cancer
Diljot Grewal: Memorial Sloan Kettering Cancer Center
Daniel Lai: BC Cancer
Richard Moore: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Andrew J. Mungall: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Marco A. Marra: Canada’s Michael Smith Genome Sciences Centre, BC Cancer
Andrew McPherson: Memorial Sloan Kettering Cancer Center
Alexandre Bouchard-Côté: University of British Columbia
Samuel Aparicio: BC Cancer
Sohrab P. Shah: Memorial Sloan Kettering Cancer Center

Nature, 2021, vol. 595, issue 7868, 585-590

Abstract: Abstract Progress in defining genomic fitness landscapes in cancer, especially those defined by copy number alterations (CNAs), has been impeded by lack of time-series single-cell sampling of polyclonal populations and temporal statistical models1–7. Here we generated 42,000 genomes from multi-year time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts (PDXs), revealing the nature of CNA-defined clonal fitness dynamics induced by TP53 mutation and cisplatin chemotherapy. Using a new Wright–Fisher population genetics model8,9 to infer clonal fitness, we found that TP53 mutation alters the fitness landscape, reproducibly distributing fitness over a larger number of clones associated with distinct CNAs. Furthermore, in TNBC PDX models with mutated TP53, inferred fitness coefficients from CNA-based genotypes accurately forecast experimentally enforced clonal competition dynamics. Drug treatment in three long-term serially passaged TNBC PDXs resulted in cisplatin-resistant clones emerging from low-fitness phylogenetic lineages in the untreated setting. Conversely, high-fitness clones from treatment-naive controls were eradicated, signalling an inversion of the fitness landscape. Finally, upon release of drug, selection pressure dynamics were reversed, indicating a fitness cost of treatment resistance. Together, our findings define clonal fitness linked to both CNA and therapeutic resistance in polyclonal tumours.

Date: 2021
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DOI: 10.1038/s41586-021-03648-3

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