Kannan Krishnaswamy, Srinivasan Alagarsamy, Kannan Aarthi, Ali Nawab
Biomolecular Integrations, Little Rock, AR 72205, USA.
Nanobio Diagnostics, West Chester, PA 19382, USA.
Cancers (Basel). 2025 Sep 8;17(17):2938. doi: 10.3390/cancers17172938.
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.
尽管在早期检测和靶向治疗方面取得了进展，但乳腺癌（BC）仍然是全球女性癌症相关死亡的主要原因。耐药性通过肿瘤内在异质性和肿瘤外在影响（包括肿瘤微环境和免疫代谢相互作用）的相互作用而产生。这种复杂性导致治疗逃逸、转移进展和不良预后。耐药机制包括药物外排、基因突变和信号通路改变。其他因素包括癌症干细胞可塑性、外泌体RNA转移、基质重塑、表观遗传改变和代谢重编程。微生物影响和免疫逃逸进一步降低了治疗效果。总体而言，这些过程汇聚在程序性细胞死亡（RCD）途径——凋亡、铁死亡和焦亡——代谢变化和免疫抑制会重新校准细胞死亡阈值。营养竞争、缺氧驱动的信号传导和乳酸积累削弱了抗肿瘤免疫力并加强了耐药微环境。在本综述中，我们在一个统一的框架内综合了乳腺癌耐药性的遗传、代谢、表观遗传、免疫和微环境驱动因素。我们强调这些机制在RCD和免疫代谢信号传导上的汇聚是核心原则。人工智能（AI）被视为连接耐药生物学主要领域的交叉连接器。AI通过循环肿瘤DNA（ctDNA）和成像支持早期检测，预测外排和突变驱动的耐药性，模拟凋亡和铁死亡易感性，并对高危患者（如三阴性乳腺癌患者）进行分层。
