University of Oulu

Perez-Villatoro, F., Oikkonen, J., Casado, J. et al. Optimized detection of homologous recombination deficiency improves the prediction of clinical outcomes in cancer. npj Precis. Onc. 6, 96 (2022).

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)
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Language: English
Published: Springer Nature, 2022
Publish Date: 2023-07-04


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.

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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
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
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.
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