Histopathology and proteomics are synergistic for High-Grade Serous Ovarian Cancer platinum response prediction.

Autor:	Kilim O; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.; Semmelweis University, Data-Driven Health Division of National Laboratory, Budapest, Hungary., Olar A; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.; Eötvös Loránd University, Department of Informatics, Budapest, Hungary., Biricz A; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.; Semmelweis University, Data-Driven Health Division of National Laboratory, Budapest, Hungary., Madaras L; Semmelweis University, 2nd Department of Pathology, Budapest, Hungary., Pollner P; Eötvös Loránd University, Department of Biological Physics, Budapest, Hungary.; Semmelweis University, Data-Driven Health Division of National Laboratory, Budapest, Hungary., Szállási Z; Danish Cancer Institute, Copenhagen, Denmark.; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Sztupinszki Z; Danish Cancer Institute, Copenhagen, Denmark.; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.; Harvard Medical School, Boston, MA, USA., Csabai I; Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Jun 03. Date of Electronic Publication: 2024 Jun 03.
DOI:	10.1101/2024.06.01.24308293
Abstrakt:	Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E) pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained Whole Slide Images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts. This method outperforms the Homologous Recombination Deficiency (HRD) score in predicting platinum response and overall patient survival. The study sets new performance benchmarks and explores the intersection of histology and proteomics, highlighting phenotypes related to treatment response pathways, including homologous recombination, DNA damage response, nucleotide synthesis, apoptosis, and ER stress. This integrative approach has the potential to improve personalized treatment and provide insights into the therapeutic vulnerabilities of HGSOC. Competing Interests: Competing Interests Z.S. is an inventor on a patent used in the myChoice HRD assay.
Databáze:	MEDLINE
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