4.8 Article

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

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NATURE
卷 595, 期 7868, 页码 585-+

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NATURE PORTFOLIO
DOI: 10.1038/s41586-021-03648-3

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资金

  1. BC Cancer Foundation at BC Cancer and Cycle for Survival supporting Memorial Sloan Kettering Cancer Center
  2. Susan G. Komen [GC233085]
  3. Nan and Lorraine Robertson Chair in Breast Cancer
  4. Canada Research Chair in Molecular Oncology [950-230610]
  5. Terry Fox Research Institute [1082]
  6. Canadian Cancer Society Research Institute Impact program [705617]
  7. CIHR Grant [FDN-148429]
  8. Breast Cancer Research Foundation [BCRF-18-180, BCRF-19-180, BCRF-20-180]
  9. MSK Cancer Center Support Grant/Core Grant [P30 CA008748]
  10. National Institutes of Health Grant [1RM1 HG011014-01]
  11. CCSRI Grant [705636]
  12. Cancer Research UK Grand Challenge Program
  13. Canada Foundation for Innovation [40044]
  14. Nicholls Biondi Chair in Computational Oncology

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Progress in defining genomic fitness landscapes in cancer, particularly those associated with copy number alterations (CNAs), has been hindered by the absence of time-series single-cell sampling and temporal statistical models. This study generated a large number of genomes from long-term time-series single-cell whole-genome sequencing of breast epithelium and primary triple-negative breast cancer (TNBC) patient-derived xenografts, revealing the impact of TP53 mutation and cisplatin chemotherapy on clonal fitness dynamics defined by CNAs. The findings show that TP53 mutation alters the fitness landscape, redistributing fitness across different clones associated with distinct CNAs, and that drug treatment can lead to the emergence of drug-resistant clones while eradicating high-fitness clones in untreated settings.
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 models(1-7). 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 model(8,9) 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.

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