Language
English
Publication Date
9-8-2025
Journal
Cancer Cell
DOI
10.1016/j.ccell.2025.06.005
PMID
40578362
Abstract
Cancer treatment often fails because combinations of different therapies evoke complex resistance mechanisms that are hard to predict. We introduce REsistance through COntext DRift (RECODR): a computational pipeline that combines co-expression graph networks of single-cell RNA sequencing profiles with a graph-embedding approach to measure changes in gene co-expression context during cancer treatment. RECODR is based on the idea that gene co-expression context, rather than expression level alone, reveals important information about treatment resistance. Analysis of tumors treated in preclinical and clinical trials using RECODR unmasked resistance mechanisms -invisible to existing computational approaches- enabling the design of highly effective combination treatments for mice with choroid plexus carcinoma, and the prediction of potential new treatments for patients with medulloblastoma and triple-negative breast cancer. Thus, RECODR may unravel the complexity of cancer treatment resistance by detecting context-specific changes in gene interactions that determine the resistant phenotype.
Keywords
Humans, Animals, Drug Resistance, Neoplasm, Mice, Triple Negative Breast Neoplasms, Gene Expression Regulation, Neoplastic, Single-Cell Analysis, Neoplasms, Medulloblastoma, Female, Gene Expression Profiling, Gene Regulatory Networks, DNA repair. cancer. choroid plexus. choroid plexus carcinoma. combination therapy. graph networks. machine learning. radiation. treatment resistance. triple-negative breast cancer
Published Open-Access
yes
Recommended Citation
Jassim, Amir; Nimmervoll, Birgit V; Terranova, Sabrina; et al., "Gene Context Drift Identifies Drug Targets to Mitigate Cancer Treatment Resistance" (2025). Faculty and Staff Publications. 5393.
https://digitalcommons.library.tmc.edu/baylor_docs/5393