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基于多组学和人工智能驱动的女性恶性肿瘤表观遗传治疗药物重新定位

Multi-omics based and AI-driven drug repositioning for epigenetic therapy in female malignancies.

作者信息

Salvati Annamaria, Melone Viola, Giordano Alessandro, Lamberti Jessica, Palumbo Domenico, Palo Luigi, Rea Dilia, Memoli Domenico, Simonis Vittoria, Alexandrova Elena, Silvestro Francesco, Rizzo Francesca, Weisz Alessandro, Tarallo Roberta, Nassa Giovanni

机构信息

Laboratory of Molecular Medicine and Genomics, Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", University of Salerno, via S. Allende, 1, Baronissi, 84081, SA, Italy.

Genome Research Center for Health - CRGS, Baronissi, SA, 84081, Italy.

出版信息

J Transl Med. 2025 Jul 25;23(1):837. doi: 10.1186/s12967-025-06856-x.

DOI:10.1186/s12967-025-06856-x
PMID:40713639
Abstract

Histone post-translational modifications (PTMs) have long been recognized as critical regulators of chromatin dynamics and gene expression, with aberrations in these processes driving tumorigenesis, immune escape, metastasis, and therapy resistance. While multi-omics technologies are generating ever more detailed maps of the histone landscape, translating these insights into clinical practice remains challenging. The ongoing convergence of high-throughput omics technologies and Artificial Intelligence (AI) is revolutionizing drug repositioning strategies, offering new precision tools to identify histone-targeted therapies for solid tumors. In this review, we explore how AI-driven multi-omics integration is currently reshaping therapeutic opportunities by uncovering novel drug-target-patient associations with unprecedented accuracy. Special focus is given to gynecologic and breast cancers, where chromatin remodeling dysregulation is particularly widespread, conventional therapeutic approaches have demonstrated substantial limitations and drug resistance represents a major clinical obstacle. These aggressive and lethal cancers exemplify areas where AI-powered repurposing of epi-drugs is making tangible clinical advances, enhancing tumor sensitivity to treatments like immunotherapy, but also offering new avenues to overcome challenging phenomena such as drug resistance and cancer relapse. We critically discuss these challenges and the effectiveness of a combination strategy approaches based on AI-driven patient stratification and biomarker-guided therapy optimization to maximize clinical benefits. In an era where precision oncology demands both specific drugs and the application of smarter strategies, the integration of AI, multi-omics, and targeting of chromatin remodelers may herald a transformative shift in the management of solid tumors, bridging the gap between biological insights and therapeutic innovation.

摘要

长期以来,组蛋白翻译后修饰(PTMs)一直被认为是染色质动力学和基因表达的关键调节因子,这些过程中的异常会驱动肿瘤发生、免疫逃逸、转移和治疗耐药性。虽然多组学技术正在生成越来越详细的组蛋白图谱,但将这些见解转化为临床实践仍然具有挑战性。高通量组学技术与人工智能(AI)的不断融合正在彻底改变药物重新定位策略,提供新的精准工具来识别针对实体瘤的组蛋白靶向疗法。在这篇综述中,我们探讨了人工智能驱动的多组学整合目前如何通过以前所未有的准确性揭示新的药物-靶点-患者关联来重塑治疗机会。特别关注妇科和乳腺癌,其中染色质重塑失调尤为普遍,传统治疗方法已显示出很大局限性,而耐药性是一个主要临床障碍。这些侵袭性和致命性癌症例证了人工智能驱动的表观遗传药物重新定位正在取得切实临床进展的领域,增强了肿瘤对免疫疗法等治疗的敏感性,同时也提供了新途径来克服耐药性和癌症复发等具有挑战性的现象。我们批判性地讨论了这些挑战以及基于人工智能驱动的患者分层和生物标志物引导的治疗优化的联合策略方法的有效性,以最大化临床益处。在精准肿瘤学需要特定药物和更智能策略应用的时代,人工智能、多组学与染色质重塑因子靶向的整合可能预示着实体瘤管理的变革性转变,弥合生物学见解与治疗创新之间的差距。

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AI-Driven Drug Discovery: A Comprehensive Review.人工智能驱动的药物发现:全面综述。
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3D genome folding in epigenetic regulation and cellular memory.3D基因组折叠在表观遗传调控和细胞记忆中的作用
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T-cell immune checkpoint inhibition plus hypomethylation for locally advanced HER2-negative breast cancer: a phase 2 neoadjuvant window trial of decitabine and pembrolizumab followed by standard neoadjuvant chemotherapy.T细胞免疫检查点抑制联合低甲基化治疗局部晚期HER2阴性乳腺癌:地西他滨和帕博利珠单抗序贯标准新辅助化疗的2期新辅助窗口期试验
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Epigenetic drugs in cancer therapy.用于癌症治疗的表观遗传药物。
Cancer Metastasis Rev. 2025 Feb 26;44(1):37. doi: 10.1007/s10555-025-10253-7.
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Learning and actioning general principles of cancer cell drug sensitivity.学习并践行癌细胞药物敏感性的一般原则。
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Targeted intervention of tumor microenvironment with HDAC inhibitors and their combination therapy strategies.组蛋白去乙酰化酶抑制剂对肿瘤微环境的靶向干预及其联合治疗策略
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Improving drug repositioning with negative data labeling using large language models.利用大语言模型通过阴性数据标注改进药物重新定位。
J Cheminform. 2025 Feb 4;17(1):16. doi: 10.1186/s13321-025-00962-0.
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