Show Notes
Jassim A et al., Cancer Cell - This episode summarizes Jassim et al.'s introduction of RECODR, a graph-embedding pipeline that measures gene co-expression context drift from sc/snRNA-seq to reveal drivers of tumorigenesis and treatment resistance and to nominate combination therapies validated in mouse models and predicted for human tumors. Key terms: RECODR, gene context drift, single-cell RNA-seq, treatment resistance, combination therapy.
Study Highlights:
The authors developed RECODR, which combines co-expression graph networks with Node2Vec/Word2Vec embeddings and alignment to quantify gene context drift between treatment states. In a mouse choroid plexus carcinoma (CPC) model RECODR prioritized ATM as a vulnerability and guided use of an ATM inhibitor; it also identified PARP1 as a target to mitigate monotherapy resistance. For combination AZD1390 and radiation resistance RECODR revealed an expanded immune-like program and nominated dasatinib, which in specific combination schedules produced marked survival benefit in mice. Applied to paired human medulloblastoma and TNBC samples, RECODR detected context-drift signatures and proposed subtype-specific therapies for testing.
Conclusion:
Measuring changes in gene co-expression context (gene context drift) with RECODR reveals resistance mechanisms invisible to expression-level analyses and can nominate context-specific targets and combination regimens for preclinical and clinical testing.
Music:
Enjoy the music based on this article at the end of the episode.
Article title:
Gene context drift identifies drug targets to mitigate cancer treatment resistance
First author:
Jassim A
Journal:
Cancer Cell
DOI:
10.1016/j.ccell.2025.06.005
Reference:
Jassim A, Nimmervoll BV, Terranova S, et al. Gene context drift identifies drug targets to mitigate cancer treatment resistance. Cancer Cell. 2025;43:1608–1621. 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/
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Episode link: https://basebybase.com/episodes/gene-context-drift-identifies-drug-targets-to-mitigate-cancer-treatment-resistance
QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2025-09-13.
QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited sections include: gene context drift concept; RECODR pipeline and four context-drift metrics; single-cell RNA-seq co-expression graphs; Node2Vec/Word2Vec embedding and Procrustes alignment; CPC mouse model with AZD1390 and radiation; PARP1 inhibitor AZD9574; dasatinib targeting of an immune-like network; transl
- transcript topics: Gene context drift concept; RECODR pipeline and context drift metrics; Single-cell RNA-seq and co-expression graphs; Node2Vec and Word2Vec embedding; Four context-drift metrics: neighbor number, graph reach, neighbor cosine, index gene cosine; CPC mouse model and DNA repair remodeling
QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 6
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0
Metadata Audited:
- article_doi
- article_title
- article_journal
- license
Factual Items Audited:
- DOI alignment between metadata and canonical article
- Article title alignment with canonical form
- Journal alignment with provided journal label
- License variant CC BY 4.0 alignment
- RECODR concept: context drift as resistance mechanism
- Single-cell RNA-seq data used to build co-expression graphs
QC result: Pass.
