Abstract

Disruption of the homologous recombination repair (HRR) pathway was reported in at least half of high-grade serous ovarian cancer (HGSOC) patients. HRR deficiency (HRD) is associated with superior response to PARP inhibition maintenance therapy in both primary and relapsed HGSOC. Moreover, HRD is a prerequisite for combination therapy with Bevacizumab and Olaparib following first-line chemotherapy. Aim of this study was to evaluate if a machine learning framework trained on matrix-assisted laser desorption/ionization imaging mass spectroscopy (MALDI-IMS) data can accurately predict the HRD status in HGSOC tumors.

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