Resistance-centered pharmacology of DNA damage response-targeted therapy: Mechanisms, predictive biomarkers, and biomarker-guided adaptive treatment strategies in solid tumors

DNA damage response (DDR)-targeted agents, particularly poly(ADP-ribose) polymerase inhibitors (PARPi), have transformed treatment of homologous recombination (HR)-deficient solid tumors. Yet inevitable therapeutic resistance limits durability of clinical benefit and now defines the central pharmacological challenge of the field, driving development of next-generation DDR inhibitors including saruparib, ART6043, RP-3467, ART0380/alnodesertib, ceralasertib, and peposertib. Existing reviews predominantly catalog DDR around isolated pathways or individual drug classes, failing to capture resistance as a cross-pathway, network-level pharmacological phenomenon. Here, we provide a resistance-mechanism-centered pharmacological framework that systematically connects DDR protein alterations, predictive biomarkers, and matched therapeutic strategies into a unified, clinically actionable roadmap. Pan-cancer analysis of TCGA Pan-Cancer Atlas data (10,348 tumors across 31 cancer types) reveals statistically significant pathway co-alteration patterns (Spearman ρ = 0.76–0.89; FDR < 0.05) that reframe DDR dysfunction as coordinated network-level disruption rather than isolated single-pathway loss. We classify clinical resistance into six mechanistically distinct categories — BRCA1/2 reversion mutations, 53BP1/Shieldin-mediated HR restoration, replication fork stabilization, Polθ-mediated theta-mediated end joining, ABCB1-driven drug efflux, and cGAS-STING–mediated immune evasion — and pharmacologically map each to detectable biomarkers and matched therapeutic strategies, supported by 2024–2026 clinical trial data including PETRA, EvoPAR-Prostate01/02, STELLA, MEDIOLA, TOPACIO, ATHENA-COMBO, CAPRI, and the practice-changing DUO-O trial. We propose a biomarker-guided adaptive treatment algorithm integrating longitudinal circulating tumor DNA monitoring, functional RAD51 foci assays, and AI-driven multi-omics integration to enable real-time resistance detection and mechanism-guided therapy switching. This framework advances DDR-targeted oncology from static biomarker selection toward a dynamic, resistance-aware, mechanism-matched therapeutic strategy for patients with metastatic solid tumors.