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Updated: Sep 10, 2025

Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
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Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics,

Annabella Di Mauro1, Massimiliano Berretta2, Mariachiara Santorsola1

  • 1Istituto Nazionale Tumori di Napoli, IRCCS "G. Pascale", Via M. Semmola, 80131 Naples, Italy.

International Journal of Molecular Sciences
|August 28, 2025
PubMed

Insights

Precision oncology faces challenges with single-target therapies due to cancer

Area of Science:

  • Oncology
  • Genomics
  • Computational Biology

Background:

  • Precision oncology has advanced cancer treatment by targeting specific oncogenic drivers.
  • However, the effectiveness of single-target therapies is limited by cancer's complexity and adaptability.
  • Solid tumors often develop resistance to monotherapies due to multifactorial origins.

Purpose of the Study:

  • To advocate for a shift towards multi-targeted therapies enhanced by artificial intelligence (AI).
  • To highlight the potential of AI in analyzing high-throughput multi-omic data for rational combination therapy design.

Main Methods:

  • Review of current limitations in precision oncology.
  • Exploration of AI applications in drug discovery, repurposing, and clinical trial optimization.
  • Integration of multi-omic data for comprehensive cancer understanding.

Main Results:

  • AI can overcome limitations of single-target therapies by enabling sophisticated combination strategies.
  • AI facilitates rational drug design and repurposing for complex oncogenic pathways.
  • AI enhances prediction of treatment response and optimizes clinical trial design.

Conclusions:

  • A paradigm shift towards AI-enhanced, multi-targeted therapies is necessary for durable cancer treatment.
  • Harnessing AI and multi-omic data will allow oncology to address cancer's biological intricacy more effectively.
  • This approach promises to move beyond reductionist strategies for improved patient outcomes.
Keywords:
artificial intelligencedrug repurposingnext-generation sequencingprecision oncologytarget therapytumor heterogeneity

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