Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer
Abstract Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer...
| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-12-01
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| Series: | npj Precision Oncology |
| Online Access: | https://doi.org/10.1038/s41698-022-00339-8 |
| _version_ | 1797642138926710784 |
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| author | Fernando Perez-Villatoro Jaana Oikkonen Julia Casado Anastasiya Chernenko Doga C. Gulhan Manuela Tumiati Yilin Li Kari Lavikka Sakari Hietanen Johanna Hynninen Ulla-Maija Haltia Jaakko S. Tyrmi Hannele Laivuori Panagiotis A. Konstantinopoulos Sampsa Hautaniemi Liisa Kauppi Anniina Färkkilä |
| author_facet | Fernando Perez-Villatoro Jaana Oikkonen Julia Casado Anastasiya Chernenko Doga C. Gulhan Manuela Tumiati Yilin Li Kari Lavikka Sakari Hietanen Johanna Hynninen Ulla-Maija Haltia Jaakko S. Tyrmi Hannele Laivuori Panagiotis A. Konstantinopoulos Sampsa Hautaniemi Liisa Kauppi Anniina Färkkilä |
| author_sort | Fernando Perez-Villatoro |
| collection | DOAJ |
| description | Abstract Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC). We used machine learning and statistics to generate improved criteria to identify HRD in HGSC (ovaHRDscar). ovaHRDscar significantly predicted clinical outcomes in three independent patient cohorts with higher precision than previous methods. Characterization of 98 spatiotemporally distinct metastatic samples revealed low intra-patient variation and indicated the primary tumor as the preferred site for clinical sampling in HGSC. Further, our approach improved the prediction of clinical outcomes in triple-negative breast cancer (tnbcHRDscar), validated in two independent patient cohorts. In conclusion, our tumor-specific, systematic approach has the potential to improve patient selection for HR-targeted therapies. |
| first_indexed | 2024-03-11T13:55:52Z |
| format | Article |
| id | doaj.art-1510497742624a49b8fc5fba11d6c5dc |
| institution | Directory Open Access Journal |
| issn | 2397-768X |
| language | English |
| last_indexed | 2024-03-11T13:55:52Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Precision Oncology |
| spelling | doaj.art-1510497742624a49b8fc5fba11d6c5dc2023-11-02T06:46:14ZengNature Portfolionpj Precision Oncology2397-768X2022-12-016111310.1038/s41698-022-00339-8Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancerFernando Perez-Villatoro0Jaana Oikkonen1Julia Casado2Anastasiya Chernenko3Doga C. Gulhan4Manuela Tumiati5Yilin Li6Kari Lavikka7Sakari Hietanen8Johanna Hynninen9Ulla-Maija Haltia10Jaakko S. Tyrmi11Hannele Laivuori12Panagiotis A. Konstantinopoulos13Sampsa Hautaniemi14Liisa Kauppi15Anniina Färkkilä16Research Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiDepartment of Biomedical Informatics, Harvard Medical SchoolResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiDepartment of Obstetrics and Gynecology, University of Turku and Turku University HospitalDepartment of Obstetrics and Gynecology, University of Turku and Turku University HospitalDepartment of Obstetrics and Gynecology, Helsinki University and Helsinki University HospitalCenter for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere UniversityCenter for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere UniversityDana-Farber Cancer Institute, Harvard Medical SchoolResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiResearch Program in Systems Oncology, University of HelsinkiAbstract Homologous recombination DNA-repair deficiency (HRD) is a common driver of genomic instability and confers a therapeutic vulnerability in cancer. The accurate detection of somatic allelic imbalances (AIs) has been limited by methods focused on BRCA1/2 mutations and using mixtures of cancer types. Using pan-cancer data, we revealed distinct patterns of AIs in high-grade serous ovarian cancer (HGSC). We used machine learning and statistics to generate improved criteria to identify HRD in HGSC (ovaHRDscar). ovaHRDscar significantly predicted clinical outcomes in three independent patient cohorts with higher precision than previous methods. Characterization of 98 spatiotemporally distinct metastatic samples revealed low intra-patient variation and indicated the primary tumor as the preferred site for clinical sampling in HGSC. Further, our approach improved the prediction of clinical outcomes in triple-negative breast cancer (tnbcHRDscar), validated in two independent patient cohorts. In conclusion, our tumor-specific, systematic approach has the potential to improve patient selection for HR-targeted therapies.https://doi.org/10.1038/s41698-022-00339-8 |
| spellingShingle | Fernando Perez-Villatoro Jaana Oikkonen Julia Casado Anastasiya Chernenko Doga C. Gulhan Manuela Tumiati Yilin Li Kari Lavikka Sakari Hietanen Johanna Hynninen Ulla-Maija Haltia Jaakko S. Tyrmi Hannele Laivuori Panagiotis A. Konstantinopoulos Sampsa Hautaniemi Liisa Kauppi Anniina Färkkilä Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer npj Precision Oncology |
| title | Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| title_full | Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| title_fullStr | Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| title_full_unstemmed | Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| title_short | Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| title_sort | optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
| url | https://doi.org/10.1038/s41698-022-00339-8 |
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