Gene context drift identifies drug targets to mitigate cancer treatment resistance

Amir Jassim1, Birgit V Nimmervoll1, Sabrina Terranova1

  • 1Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.

Cancer Cell
|June 27, 2025
PubMed

Insights

A new computational pipeline, REsistance through COntext DRift (RECODR), identifies cancer treatment resistance by analyzing gene co-expression context. This approach reveals hidden resistance mechanisms, aiding in designing more effective combination therapies.

Area of Science:

  • Computational biology
  • Genomics
  • Cancer research

Background:

  • Cancer treatment failure is often due to unpredictable resistance mechanisms.
  • Existing computational approaches struggle to identify complex resistance patterns.

Purpose of the Study:

  • To introduce REsistance through COntext DRift (RECODR), a novel computational pipeline.
  • To leverage gene co-expression context for predicting and understanding cancer treatment resistance.

Main Methods:

  • Utilizing single-cell RNA sequencing profiles.
  • Building co-expression graph networks.
  • Applying a graph-embedding approach to measure changes in gene co-expression context.

Main Results:

  • RECODR identified resistance mechanisms previously undetectable by other methods.
  • Successfully designed effective combination treatments for preclinical models (choroid plexus carcinoma).
  • Predicted potential new treatments for medulloblastoma and triple-negative breast cancer.

Conclusions:

  • RECODR effectively unravels the complexity of cancer treatment resistance.
  • Detecting context-specific gene interaction changes is key to understanding the resistant phenotype.
  • This pipeline offers a promising tool for developing improved combination cancer therapies.

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