Gene context drift identifies drug targets to mitigate cancer treatment resistance PMID: 40578362

Target-related Cytokines

2026-09-23

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

1. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. Electronic address: [email protected].
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: [email protected].

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.