Abstract Protein lactylation emerges as a pivotal metabolic rheostat, translating microenvironmental lactate flux into stable programs that orchestrate cancer treatment resistance. This review synthesizes recent advances under the framework of “Lactylation Switch in Cancer Vulnerabilities.” We dissect the dominant enzymatic pathways (AARS1/2, KATs, HDACs) and non-enzymatic mechanisms (MGO/LGSH), alongside their critical structural underpinnings. Furthermore, we delineate how lactylation signals are interpreted by specific readers (e.g., TRIM33), directly reprogram non-histone protein function through structural metamorphosis (e.g., disrupting p53, enhancing XLF), and engage in complex crosstalk with other PTMs, as exemplified by the synergistic interplay between histone H3 lysine 18 lactylation (H3K18la) and histone H3 lysine 27 acetylation (H3K27ac) in T-cell acute lymphoblastic leukemia (T-ALL). This interplay coordinately drives metabolic-epigenetic reprogramming, which specifically rewires intra- and extratumoral survival mechanisms. Lactylation fundamentally establishes a therapy-adaptive state by simultaneously enhancing intrinsic resistance (e.g., BLM K24la-mediated DNA repair) and extrinsic resistance (e.g., histone lactylation-driven PD-L1 upregulation). Critically, preclinical and clinical studies in validated models demonstrate that targeting this lactylation network (e.g., LDHA inhibition with stiripentol, KAT inhibitors, or site-specific blockers) yields striking synergistic effects, potentiating tumor sensitivity to chemotherapy, radiotherapy, and immunotherapy. Looking forward, we outline key translational paths, including deciphering stringent enzyme-substrate specificity for targeted inhibition, developing structure-based drug design, leveraging lactylomic signatures as predictive biomarkers, and addressing current mechanistic and technological gaps. This work not only establishes lactylation as a central mechanism of therapeutic resistance but also provides a novel conceptual paradigm for understanding how metabolic signals dynamically encode cancer cell vulnerabilities, offering transformative opportunities for precision oncology.
Yang et al. (2026) studied this question.