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Record W4409628907 · doi:10.1158/1538-7445.am2025-5407

Abstract 5407: Identifying synthetic lethality targets in DDX41-mutated MDS/AML

2025· article· en· W4409628907 on OpenAlexaff
Vincent Maranda, Frederick S. Vizeacoumar, Franco J. Vizeacoumar, Yuliang Wu

Bibliographic record

VenueCancer Research · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSynthetic lethalityLethalityMedicineCancer researchBiologyGeneticsDNA repairGene

Abstract

fetched live from OpenAlex

DDX41 (DEAD-box helicase 41) is a member of the DEAD-box helicase family, critical for RNA metabolism and genome stability. Mutations in DDX41 are strongly associated with myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), particularly in patients carrying inherited mutations or somatic alterations. Among these, the R525H missense mutation in DDX41 is found in over 70% of affected individuals, impairing helicase activity and contributing to disease pathology. Directly targeting mutant DDX41 (e.g., R525H) with inhibitors remains challenging due to the structural similarity of mutant and wild-type (WT) proteins, as well as the conserved helicase domains shared by the 37 DEAD-box helicases in humans. To overcome these challenges, we are leveraging synthetic lethality (SL) as an innovative therapeutic strategy. Bioinformatic analyses identified potential SL partners of DDX41, including WRN and DDX23. Functional validation experiments revealed that inhibiting WRN helicase selectively impairs the growth of DDX41-deficient cells. In DDX41-knockout HeLa cells, treatment with WRN inhibitors NSC 617145 and HRO761 significantly reduced cell growth compared to WT controls. Similarly, in DDX41-knockdown U2OS cells, WRN inhibitors markedly inhibited cell proliferation compared to WT cells. Future studies aim to elucidate the molecular mechanisms underlying SL interactions between DDX41 and its partners, providing insights into the therapeutic potential of targeting these vulnerabilities. This work offers a promising avenue for developing novel treatments for MDS/AML patients harboring DDX41 mutations, ultimately improving clinical outcomes. Citation Format: Sohaumn Mondal, Shizuo Yang, Vincent Maranda, Frederick Vizeacoumar, Franco Vizeacoumar, Yuliang Wu. Identifying synthetic lethality targets in DDX41-mutated MDS/AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5407.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.105
GPT teacher head0.471
Teacher spread0.367 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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