2. Academic Validation
  3. Gene context drift identifies drug targets to mitigate cancer treatment resistance

Gene context drift identifies drug targets to mitigate cancer treatment resistance

  • Cancer Cell. 2025 Jun 20:S1535-6108(25)00255-7. doi: 10.1016/j.ccell.2025.06.005.
Amir Jassim 1,  Birgit V Nimmervoll 2,  Sabrina Terranova 2,  Erica Nathan 2,  Linda Hu 2,  Jessica T Taylor 2,  Katherine E Masih 3,  Lisa Ruff 2,  Matilde Duarte 2,  Elizabeth Cooper 2,  Gunjan Katyal 2,  Melika Akhbari 2,  Reuben J Gilbertson 2,  Jennifer C Coleman 2,  Joseph S Toker 2,  Colton Terhune 2,  Gabriel Balmus 4,  Stephen P Jackson 2,  Hailong Liu 5,  Tao Jiang 6,  Michael D Taylor 7,  Kui Hua 2,  Jean E Abraham 8,  Mariella G Filbin 9,  Anthony Hill 10,  Anarita Patrizi 10,  Neil Dani 11,  Aviv Regev 12,  Maria K Lehtinen 9,  Richard J Gilbertson 13
Affiliations

Affiliations

  • 1 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. Electronic address: amir.jassim@cruk.cam.ac.uk.
  • 2 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK.
  • 3 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Genetics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethseda, MD 20892, USA.
  • 4 UK Dementia Research Institute at the University of Cambridge and Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge CB2 0AH, UK; Department of Molecular Neuroscience, Transylvanian Institute of Neuroscience, 400191 Cluj-Napoca, Romania.
  • 5 Department of Radiotherapy, Beijing Tiantan Hospital Capital Medical University, Beijing 100070, China.
  • 6 Department of Pediatric Neurosurgery, Beijing Tiantan Hospital Capital Medical University, Beijing 100070, China.
  • 7 Texas Children's Cancer and Hematology Center, Houston, TX 77030, USA; Department of Pediatrics, Hematology/Oncology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Texas Children's Hospital, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA.
  • 8 Department of Oncology, University of Cambridge, Box 197 Cambridge Biomedical Campus, Cambridge CB2 0XZ, UK; Precision Breast Cancer Institute, Box 197 Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK.
  • 9 Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, USA.
  • 10 Schaller Research Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • 11 Department of Cell and Developmental Biology, Vanderbilt School of Medicine, Nashville, TN 37232, USA.
  • 12 Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • 13 Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK; Department of Oncology, University of Cambridge, Box 197 Cambridge Biomedical Campus, Cambridge CB2 0XZ, UK. Electronic address: richard.gilbertson@cruk.cam.ac.uk.
PMID: 40578362 DOI: 10.1016/j.ccell.2025.06.005
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

DNA repair; cancer; choroid plexus; choroid plexus carcinoma; combination therapy; graph networks; machine learning; radiation; treatment resistance; triple-negative breast cancer.

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