The Underlying Mechanisms and Emerging Strategies to Overcome Resistance in Breast Cancer

Despite advances in early detection and targeted therapies, breast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Resistance develops through the interplay of tumor-intrinsic heterogeneity and tumor-extrinsic influences, including the tumor microenvironment and immune-metabolic interactions. This complexity drives therapeutic evasion, metastatic progression, and poor outcomes. Resistance mechanisms include drug efflux, genetic mutations, and altered signaling pathways. Additional contributors are cancer stem cell plasticity, exosomal RNA transfer, stromal remodeling, epigenetic alterations, and metabolic reprogramming. Microbial influences and immune evasion further reduce treatment effectiveness. Collectively, these processes converge on regulated cell death (RCD) pathways-apoptosis, ferroptosis, and pyroptosis-where metabolic shifts and immune suppression recalibrate cell death thresholds. Nutrient competition, hypoxia-driven signaling, and lactate accumulation weaken antitumor immunity and reinforce resistance niches. In this review, we synthesize the genetic, metabolic, epigenetic, immunological, and microenvironmental drivers of BC resistance within a unified framework. We highlight the convergence of these mechanisms on RCD and immune-metabolic signaling as central principles. Artificial intelligence (AI) is emphasized as a cross-cutting connector that links major domains of resistance biology. AI supports early detection through ctDNA and imaging, predicts efflux- and mutation-driven resistance, models apoptotic and ferroptotic vulnerabilities, and stratifies high-risk patients such as TNBC patients.