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

IMAXT Consortium

Research output: Contribution to journalArticlepeer-review

38 Scopus citations


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.

Original languageEnglish
Pages (from-to)585-590
Number of pages6
Issue number7868
StatePublished - 22 Jul 2021
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This project was generously supported by the BC Cancer Foundation at BC Cancer and Cycle for Survival supporting Memorial Sloan Kettering Cancer Center. S.P.S. holds the Nicholls Biondi Chair in Computational Oncology and is a Susan G. Komen Scholar (GC233085). S.A. holds the Nan and Lorraine Robertson Chair in Breast Cancer and is a Canada Research Chair in Molecular Oncology (950-230610). Additional funding provided by the Terry Fox Research Institute Grant 1082, Canadian Cancer Society Research Institute Impact program Grant 705617, CIHR Grant FDN-148429, Breast Cancer Research Foundation award (BCRF-18-180, BCRF-19-180 and BCRF-20-180), MSK Cancer Center Support Grant/Core Grant (P30 CA008748), National Institutes of Health Grant (1RM1 HG011014-01), CCSRI Grant (705636), the Cancer Research UK Grand Challenge Program, Canada Foundation for Innovation (40044) to S.A., S.P.S. and A.B.-C. We thank S. P. Otto, E. Laks, D. Min and E. Zaikova for their contribution to the project.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.


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