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

Tumor suppressor p53 plays a pivotal role in suppressing cancer, so various drugs has been suggested to upregulate its function. However, drug resistance is still the biggest hurdle to be overcome. To address this, we developed a deep learning model called AnoDAN (anomalous gene detection using generative adversarial networks and graph neural networks for overcoming drug resistance) that unravels the hidden resistance mechanisms and identifies a combinatorial target to overcome the resistance. Our findings reveal that the TGF-beta signaling pathway, alongside the p53 signaling pathway, mediates the resistance, with THBS1 serving as a core regulatory target in both pathways. Experimental validation in lung cancer cells confirms the effects of THBS1 on responsiveness to a p53 reactivator. Wefurther discovered the positive feedback loop between THBS1 and the TGF-beta pathway as the main source of resistance. This study enhances our understanding of p53 regulation and offers insights into overcoming drug resistance.

Automated summary

This study developed a deep learning model called AnoDAN to overcome drug resistance in the tumor suppressor p53 by unraveling hidden resistance mechanisms and identifying targets. The study revealed the crucial role of TGF-beta and p53 signaling pathways in drug resistance, with THBS1 identified as a core regulatory target in both pathways.