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Updated: Jan 11, 2026

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

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

Samriddhi Gupta¹, Khyati Patni¹, Simarpreet Kaur²

¹Department of Computational Biology, Indraprastha Institute of Information Technology Delhi (IIIT-Delhi), New Delhi, 110020, India.

Journal of Translational Medicine

|November 13, 2025

Summary

This summary is machine-generated.

Axitinib resistance in cancer is context-specific. Multi-omics and AI reveal distinct adaptations in blood cancers and solid tumors, guiding personalized re-sensitization strategies.

Keywords:

Axitinib Drug resistance Explainable AI Machine learning Molecular targeted therapy

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Methods for Evaluating the Role of c-Fos and Dusp1 in Oncogene Dependence

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Published on: January 7, 2019

Area of Science:

Oncology
Computational Biology
Genomics

Background:

Targeted cancer therapies like Axitinib face resistance, limiting efficacy.
Patient responses to Axitinib are heterogeneous due to molecular adaptations.
Comprehensive multi-omics analysis is crucial to understand resistance mechanisms.

Purpose of the Study:

To define mechanisms of Axitinib resistance using a multi-omics approach.
To identify compensatory survival pathways limiting Axitinib efficacy.
To develop predictive models for Axitinib response.

Main Methods:

High-throughput transcriptomic and proteomic profiling of ~1000 pan-cancer cell lines.
Machine learning framework to predict cell-line-specific drug response.
Explainable AI (LIME) to identify resistance features and hierarchical clustering for subtype discovery.

Main Results:

Axitinib showed the highest predictive accuracy among 44 drugs.
Machine learning reliably classified resistant/sensitive cell lines from multi-omics data.
Two distinct Axitinib resistance subtypes identified: blood cancers (purine metabolism, growth factors) and solid tumors (hypoxia adaptation, ECM remodeling, EMT, immune evasion).

Conclusions:

Axitinib resistance is driven by tissue- and context-specific adaptations.
Multi-omics and explainable AI reveal distinct resistance strategies.
Precision re-sensitization approaches tailored to tumor context are necessary.