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Deep learning untangles the resistance mechanism of p53 reactivator in lung cancer cells

Soo Min Lee1, Younghyun Han1, Kwang-Hyun Cho1

  • 1Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.

Iscience
|November 30, 2023
PubMed

Insights

This study introduces AnoDAN, a deep learning model to overcome cancer drug resistance by identifying resistance mechanisms. It reveals THBS1 and the TGF-β pathway as key targets for enhancing p53 reactivator therapy.

Area of Science:

  • Oncology
  • Computational Biology
  • Genetics

Background:

  • The tumor suppressor p53 is crucial for cancer suppression, but drug resistance remains a significant challenge.
  • Developing strategies to overcome drug resistance is essential for effective cancer therapy.

Purpose of the Study:

  • To develop a deep learning model, AnoDAN, to identify mechanisms of drug resistance and discover combinatorial targets.
  • To investigate the role of the TGF-β signaling pathway and THBS1 in mediating resistance to p53-targeting drugs.

Main Methods:

  • Development of AnoDAN, a deep learning model integrating generative adversarial networks and graph neural networks.
  • Analysis of signaling pathways involved in drug resistance.
  • Experimental validation in lung cancer cells to assess the impact of THBS1 modulation.

Main Results:

  • AnoDAN identified the TGF-β signaling pathway and THBS1 as key mediators of resistance to p53 reactivators.
  • THBS1 was confirmed as a core regulatory target in both p53 and TGF-β pathways.
  • A positive feedback loop between THBS1 and TGF-β was identified as a primary resistance mechanism.

Conclusions:

  • The study enhances understanding of p53 regulation and drug resistance mechanisms.
  • AnoDAN provides a novel approach for identifying combinatorial targets to overcome cancer drug resistance.
  • Targeting THBS1 in conjunction with p53 reactivators shows promise for improving lung cancer treatment.
Keywords:
CancerCancer systems biologyMachine learningMathematical biosciencesMolecular biologyPharmaceutical science

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