HRProfiler Detects Homologous Recombination Deficiency in Breast and Ovarian Cancers Using Whole-Genome and Whole-Exome Sequencing Data
HRProfiler is a machine learning approach that reliably identifies homologous recombination deficiency in whole-exome-sequenced breast and ovarian cancers, outperforming other tools and providing clinically useful insights. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI. See related commentary by Lim and Ju, p. 2348.
Authors
- Nischalan Pillay (ORCID: https://orcid.org/0000-0003-0579-4105)
- Erik N. Bergstrom (ORCID: https://orcid.org/0000-0001-7740-022X)
- Akanksha Farswan (ORCID: https://orcid.org/0000-0003-3889-089X)
- Ammal Abbasi (ORCID: https://orcid.org/0000-0003-0623-2243)
- Christopher D. Steele (ORCID: https://orcid.org/0000-0002-6110-0086)
- Rana R. McKay (ORCID: https://orcid.org/0000-0002-0581-7963)
- Azhar Khandekar (ORCID: https://orcid.org/0000-0002-0521-1195)
- Ludmil B. Alexandrov (ORCID: https://orcid.org/0000-0003-3596-4515)
Institutions
- University of San Diego (US)
- Royal National Orthopaedic Hospital (GB)
- University of California San Diego (US)
- London Cancer (GB)
- San Diego Biomedical Research Institute (US)
- Royal National Orthopaedic Hospital NHS Trust (GB)
- CRUK Lung Cancer Centre of Excellence (GB)
- University College London (GB)
Publication Details
- Journal
- Cancer Research
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1158/0008-5472.can-24-2639
- Citations
- 3
- Primary Topic
- CRISPR and Genetic Engineering
- Type
- article
- Field-Weighted Citation Impact
- 2.42