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

Annamaria Salvati1, Viola Melone1, Alessandro Giordano1,2

  • 1Laboratory 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.

Insights

Artificial Intelligence (AI) and multi-omics are revolutionizing cancer therapy by identifying new drug targets and patient associations. This approach is particularly advancing treatments for gynecologic and breast cancers, overcoming drug resistance and improving patient outcomes.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Histone post-translational modifications (PTMs) regulate chromatin and gene expression; their dysregulation drives cancer progression, metastasis, and therapy resistance.
  • Translating complex histone landscape data from multi-omics technologies into clinical applications remains a significant challenge.
  • Conventional treatments for solid tumors, especially gynecologic and breast cancers, often face limitations due to widespread chromatin remodeling dysregulation and acquired drug resistance.

Purpose of the Study:

  • To review how Artificial Intelligence (AI)-driven multi-omics integration is transforming therapeutic strategies for solid tumors.
  • To highlight the potential of AI in identifying novel drug-target-patient associations for precision oncology.
  • To focus on the application of AI-powered drug repurposing for epigenetic therapies in gynecologic and breast cancers.

Main Methods:

  • Integration of high-throughput omics data with AI algorithms.
  • Analysis of drug-target-patient associations for identifying novel therapeutic strategies.
  • Focus on AI-driven patient stratification and biomarker-guided therapy optimization.

Main Results:

  • AI-driven multi-omics integration is uncovering new therapeutic opportunities with high accuracy.
  • AI-powered repurposing of epigenetic drugs (epi-drugs) shows clinical advances in enhancing tumor sensitivity to immunotherapy.
  • AI strategies offer new avenues to overcome drug resistance and cancer relapse in aggressive cancers.

Conclusions:

  • The convergence of AI, multi-omics, and chromatin remodeler targeting promises a transformative shift in solid tumor management.
  • AI-driven approaches can bridge the gap between biological insights and therapeutic innovation in precision oncology.
  • Combination strategies involving AI-driven patient stratification and biomarker-guided therapy are crucial for maximizing clinical benefits.

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