ICE Bioscience Posters

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Poly (ADP-ribose) polymerase inhibitors (PARPi), such as Olaparib, have revolutionized the targeted treatment of ovarian cancer. However, the emergence of acquired drug resistance significantly limits their long-term clinical efficacy. The molecular mechanisms driving Olaparib resistance are highly heterogeneous, involving dynamic cellular state transitions and complex multi-level regulations that cannot be fully captured by bulk profiling alone. In this study, we established a comprehensive multi-omics integration framework (scRNA-seq, ATAC-seq, WES, and bulk RNA-seq) to delineate the evolutionary trajectory and chromatin regulatory landscape of Olaparib-resistant ovarian cancer cells. Furthermore, we developed an AI-driven drug screening pipeline integrating machine learning feature extraction and deep learning affinity prediction to identify potential reversal agents. This study provides a systematic multi-dimensional map of PARPi resistance and offers a reliable translational workflow for discovering synergistic combination therapies.