A machine learning-driven framework integrating cell death and senescence signatures for multi-target drug design and immunotherapy optimization in ovarian cancer.

Ovarian cancer (OC) remains therapeutic challenge due to its complex molecular heterogeneity and therapy-induced adaptive resistance. While non-apoptotic cell death and senescence pathways contribute to tumor evolution and immunosuppression, their integration into predictive models for multi-target drug design and immunotherapy optimization is underexplored. Machine learning was used to identify key genes that governing cell death and senescence (CDS). The resulting Cell Death and Senescence Learning Signature (CDSLS) was validated across multiple OC cohorts (n = 1858) and immunotherapy datasets. Multi-omics analyses, including single-cell RNA sequencing, were used to map the tumor microenvironment and identify conserved therapeutic targets. Functional validation of the hub gene RB1 included in vitro and in vivo experiments to assess its role in senescence, DNA damage, and T-cell activation. Patients with high scores predicting poor survival and immunosuppression. Knocking down RB1 promoted proliferation and suppressed senescence, while overexpression induced senescence, amplified DNA damage signaling, and enhanced CD8+ T cell activation. In vivo, RB1-overexpressing tumors showed restrained growth and elevated immune infiltration. Targeted affinity small molecule compounds (e.g., ZINC001175043471) were predicted using artificial intelligence tools to target RB1. Drug sensitivity analysis linked CDSLS to differential responses to brivanib, azacitidine, and other agents. Our framework supports the use of AI in identifying conserved binding sites, predicting mutational escape, and provide a basis for future analysis for OC.