136: Gene Context Drift Identifies Drug Targets to Mitigate Cancer Treatment Resistance

️ Episode 136: Gene Context Drift Identifies Drug Targets to Mitigate Cancer Treatment Resistance In this episode of PaperCast Base by Base, we explore RECODR, a graph‑embedding pipeline that reads single‑cell and single‑nucleus transcriptomes as co‑expression networks to quantify “gene context drift” during therapy and expose druggable vulnerabilities that drive relapse across aggressive cancers. Study Highlights:RECODR constructs co‑expression graph networks from sc/snRNA‑seq data and uses Node2Vec/Word2Vec embeddings to score how each gene’s neighborhood shifts between normal, tumor, and treatment states, revealing resistance drivers that expression level alone misses. In a choroid plexus carcinoma mouse model, context drift pinpointed DNA‑damage response dependencies, nominating ATM as a primary vulnerability and predicting that PARP1 would be inactive in untreated tumors but synergistic after ATM inhibition or radiation. Preclinical trials validated these predictions: the brain‑penetrant PARP1 inhibitor AZD9574 enhanced the ATM inhibitor AZD1390 or radiotherapy in relapsed tumors, while dasatinib—prioritized from an immune‑like resistance program—mitigated failure of the combined ATM inhibitor–radiation regimen when timed to resistant outgrowth. Applying RECODR to paired patient medulloblastoma and triple‑negative breast cancer profiles uncovered relapse‑associated context drift programs and proposed repurposable, blood–brain‑barrier–penetrant drugs tailored to subtype‑specific resistance biology. Conclusion:Gene context drift provides a practical framework to anticipate resistance pathways and design precisely timed combination schedules that remain effective where single agents fail. Reference:Jassim A, Nimmervoll BV, Terranova S, et al. Gene context drift identifies drug targets to mitigate cancer treatment resistance. Cancer Cell. 2025;43(9):1608–1621. doi:10.1016/j.ccell.2025.06.005. License:This episode is based on an open‑access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/ Support:If you'd like to support Base by Base, you can make a one‑time or monthly donation here: https://basebybase.castos.com/

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