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Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer |
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| Author: | Perez-Villatoro, Fernando1,2; Oikkonen, Jaana1; Casado, Julia1,2; |
| Organizations: |
1Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland 2iCAN digital precision cancer medicine flagship, Helsinki, Finland 3Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
4Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital, Turku, Finland
5Department of Obstetrics and Gynecology, Helsinki University and Helsinki University Hospital, Helsinki, Finland 6Center for Child, Adolescent, and Maternal Health Research, Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland 7Computational Medicine, Faculty of Medicine, University of Oulu, Oulu, Finland 8Department of Obstetrics and Gynaecology, Tampere University Hospital, Tampere, Finland 9Medical and Clinical Genetics, University of Helsinki and Helsinki University Hospital, Helsinki, Finland 10Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland 11Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA 12Department of Biochemistry and Developmental Biology, University of Helsinki, Helsinki, Finland |
| Format: | article |
| Version: | published version |
| Access: | open |
| Online Access: | PDF Full Text (PDF, 3.8 MB) |
| Persistent link: | http://urn.fi/urn:nbn:fi-fe2023070481559 |
| Language: | English |
| Published: |
Springer Nature,
2022
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| Publish Date: | 2023-07-04 |
| Description: |
AbstractHomologous 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. see all
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| Series: |
npj precision oncology |
| ISSN: | 2397-768X |
| ISSN-E: | 2397-768X |
| ISSN-L: | 2397-768X |
| Volume: | 6 |
| Issue: | 1 |
| Article number: | 96 |
| DOI: | 10.1038/s41698-022-00339-8 |
| OADOI: | https://oadoi.org/10.1038/s41698-022-00339-8 |
| Type of Publication: |
A1 Journal article – refereed |
| Field of Science: |
3122 Cancers |
| Subjects: | |
| Funding: |
This study was funded by the Sigrid Jusélius Foundation (A.F., L.K.), Cancer Society of Finland (A.F., J.C., L.K.), Academy of Finland (grant number 339805, 322979, 350396 to A.F., grant numbers 314394 and 322178 to L.K., grant number 314398 to SaHi), Paolo Foundation (A.F.), The Finnish Medical Foundation (A.F.), Finnish Cultural Foundation (A.F.), Instrumentarium Foundation (A.F., J.C.), University of Helsinki (A.F.), AstraZeneca (to SaHi), the European Union’s Horizon 2020 research and innovation program under grant agreement No 667403 for HERCULES (SHa, J.H., SaHi) and No 965193 for DECIDER (SHa, J.H., SaHi). We also wish to thank FIMM Genomics core facility, The FINNPEC for the statistical reference data, Johan Staaf for assistance in TNBC data access, and the IT Center for Science (CSC) for computational resources. |
| Copyright information: |
© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
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https://creativecommons.org/licenses/by/4.0/ |