Functional profiling of ovarian cancer models reveals Bcl-xL/NOTCH targeting to overcome resistance

Platinum resistance remains a major therapeutic challenge in ovarian cancer (OC) and is one of the main causes leading to disease relapse and mortality. Although PARP inhibitors have improved outcomes for a subset of patients, most women continue to rely on platinum-taxol based chemotherapy and ultimately develop recurrent, treatment-resistant disease with limited further therapy options. Therefore, there is a critical need to identify actionable, patient-specific vulnerabilities that would help as an alternative for chemoresistant patients. To identify such therapeutic opportunities, we established 35 patient-derived models from 22 OC patients, representing seven OC subtypes and preserving key molecular features of individual patients. High-throughput drug profiling across a library of 528 oncology-focused compounds generated over 29,000 drug response measurements, revealing inter-patient heterogeneity and sensitivity patterns. Among these, a subset of models exhibited a pronounced dependency on the anti-apoptotic protein Bcl-xL with minimal effects observed on patient-derived fibroblasts and healthy bone marrow, suggesting a therapeutic window. Proteomics-based comparison of Bcl-xL-sensitive and -resistant subclones identified activation of NOTCH signaling as a determinant to reduced response to Bcl-xL inhibition. Blocking of NOTCH signaling with gamma-secretase inhibitors restored sensitivity to Bcl-xL targeting and resensitized resistant cells to Carboplatin, resulting in sustained cytotoxicity in long-term washout assays and ex vivo cultures. Similar effects were observed with both Bcl-xL protein degrader and small molecule inhibitor, supporting robust targeting of Bcl-xL through different modalities. Together, these findings define a NOTCH-modulated Bcl-xL survival axis as a therapeutic vulnerability in platinum resistant OC. More broadly, this study demonstrates how translational drug profiling of physiologically relevant disease models can generate insights with clinical potential and provide precision oncology framework for identifying rational combination strategies to overcome chemoresistance.