HR-SC—an academic-developed machine learning framework to classify HRD-positive ovarian cancer patients and predict sensitivity to olaparib
The study demonstrates that HR-SC is a novel, clinically feasible solution with a low failure rate for predicting HRR status in OC patients and underscores the importance of leveraging ML approaches for advancing precision oncology in the era of personalized medicine.
Authors
- E. Pujade-Lauraine
- Antonio González-Martı́n (ORCID: https://orcid.org/0000-0001-8376-9576)
- Federica Perrone (ORCID: https://orcid.org/0000-0003-4406-8245)
- Lara Paracchini (ORCID: https://orcid.org/0000-0003-1832-5471)
- Silvia Derio
- Laura Arenare (ORCID: https://orcid.org/0000-0001-7100-4517)
- Keiichi Fujiwara (ORCID: https://orcid.org/0000-0002-7388-0243)
- Giovanni Scambia (ORCID: https://orcid.org/0000-0002-9503-9041)
- I. Ray-Coquard
- Paul Buderath (ORCID: https://orcid.org/0000-0003-0941-5635)
- I. Treilleux
- Luca Beltrame (ORCID: https://orcid.org/0000-0003-3631-6544)
- Daniela Califano (ORCID: https://orcid.org/0000-0001-6945-3209)
- A. Sergi (ORCID: https://orcid.org/0000-0002-6130-8717)
- Maurizio D’Incalci (ORCID: https://orcid.org/0000-0001-8784-1360)
- Maria Luisa Dalessandro (ORCID: https://orcid.org/0000-0001-7375-0089)
- Domenica Lorusso (ORCID: https://orcid.org/0000-0003-0981-0598)
- Regina Berger (ORCID: https://orcid.org/0000-0003-2952-6797)
- Magali Provansal
- Sergio Marchini (ORCID: https://orcid.org/0000-0003-1783-7651)
- Laura Mannarino (ORCID: https://orcid.org/0000-0002-0290-4228)
- Philipp Harter (ORCID: https://orcid.org/0000-0002-9663-5005)
- Toon Van Gorp (ORCID: https://orcid.org/0000-0002-2564-721X)
- Sakari Hietanen (ORCID: https://orcid.org/0000-0003-4734-4743)
- Chiara Romualdi (ORCID: https://orcid.org/0000-0003-4792-9047)
- Sandro Pignata (ORCID: https://orcid.org/0000-0002-8836-2633)
- Marco Masseroli (ORCID: https://orcid.org/0000-0003-2574-1174)
- Angelo Velle (ORCID: https://orcid.org/0000-0002-4010-6390)
Institutions
- Humanitas University (IT)
- University of Padua (IT)
- Centre Léon Bérard (FR)
- Essen University Hospital (DE)
- Arcagy Gineco (FR)
- Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" (IT)
- Kliniken Essen-Mitte (DE)
- IRCCS Humanitas Research Hospital (IT)
- Politecnico di Milano (IT)
Publication Details
- Journal
- ESMO Open
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1016/j.esmoop.2025.105060
- Citations
- 2
- Primary Topic
- PARP inhibition in cancer therapy
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- Fonds Wetenschappelijk Onderzoek
- Ministero della Salute
- Associazione Italiana per la Ricerca sul Cancro
- Vlaamse regering