Clonal fitness inferred from time-series modelling of single-cell cancer genomes
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-seri...
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| Format: | Article |
| Language: | English |
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Springer Science and Business Media LLC
2022
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| Online Access: | https://hdl.handle.net/1721.1/138810 |
| _version_ | 1826197388892569600 |
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| author | Boyden, Edward S. |
| author_facet | Boyden, Edward S. |
| author_sort | Boyden, Edward S. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:46:57Z |
| format | Article |
| id | mit-1721.1/138810 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:46:57Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1388102022-01-05T03:27:59Z Clonal fitness inferred from time-series modelling of single-cell cancer genomes Boyden, Edward S. 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. 2022-01-04T19:46:07Z 2022-01-04T19:46:07Z 2021 2022-01-04T19:38:49Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/138810 Boyden, Edward S. 2021. "Clonal fitness inferred from time-series modelling of single-cell cancer genomes." Nature, 595 (7868). en 10.1038/S41586-021-03648-3 Nature Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Springer Science and Business Media LLC PMC |
| spellingShingle | Boyden, Edward S. Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title | Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title_full | Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title_fullStr | Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title_full_unstemmed | Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title_short | Clonal fitness inferred from time-series modelling of single-cell cancer genomes |
| title_sort | clonal fitness inferred from time series modelling of single cell cancer genomes |
| url | https://hdl.handle.net/1721.1/138810 |
| work_keys_str_mv | AT boydenedwards clonalfitnessinferredfromtimeseriesmodellingofsinglecellcancergenomes |