Deciphering context-specific Axitinib escape pathways via multi-omics and explainable machine learning.

ABSTRACT:

Background

Resistance remains a major barrier to targeted cancer therapies. Axitinib, a VEGF receptor inhibitor with anti-angiogenic activity, is effective in several cancers but shows heterogeneous patient responses, reflecting context-specific molecular adaptations. A comprehensive multi-omics approach is needed to define these mechanisms and uncover compensatory survival pathways limiting Axitinib efficacy.

Methodology

We conducted a high-throughput analysis of ~ 1000 pan-cancer cell lines treated with 44 FDA-approved targeted drugs. Basal transcriptomic (~ 36,000 transcripts) and proteomic (~ 9000 proteins) profiles were integrated to predict cell-line-specific drug response using a multi-classifier machine learning framework. Multiple models, including ensemble, linear,

SUBMITTER: Gupta S 

PROVIDER: S-EPMC12613443 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature