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Abstract IA005: Therapeutic vulnerabilities of cohesin-mutant myeloid malignancies

2024· article· en· W4399505160 on OpenAlexaboutno aff
Zuzana Tóthová

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsnot available
Fundersnot available
KeywordsMyeloid leukemiaCancer researchCohesinBiologyDNA repairRNA splicingGeneticsGeneChromatinRNA

Abstract

fetched live from OpenAlex

Abstract Splicing modulation is a promising treatment strategy pursued to date only in splicing-factor mutant cancers; however, its therapeutic potential is poorly understood outside of this context. Like splicing factors, genes encoding components of the cohesin complex are frequently mutated in myeloid malignancies, including 15-20% of myelodysplastic syndromes (MDS) and secondary acute myeloid leukemia (AML), where they are associated with poor outcomes. I will discuss our recent findings identifying cohesin mutations as biomarkers of sensitivity to drugs targeting splicing-factor SF3B1 (H3B-8800 and E-7107) and describe the mechanism by which drug-induced alterations in splicing of DNA repair genes, such as BRCA1 and BRCA2, underlie this sensitivity. We have demonstrated that treatment of cohesin-mutant cells with SF3B1 modulators results in impaired DNA damage response, accumulation of DNA damage, and increased sensitivity to PARP inhibitors and a panel of chemotherapeutic agents in vitro and in vivo, using AML cell lines and patient-derived xenograft models. Furthermore, we identified RAD51 foci formation as a predictive biomarker of sensitivity to SF3B1 splicing modulation alone or followed by sequential treatment with PARP inhibition and chemotherapy, and have identified additional non-cohesin mutant subtypes of MDS/AML and ovarian and breast cancer which are sensitive to this therapeutic approach. Our findings expand the potential therapeutic benefits of SF3B1 splicing modulators to include cohesin-mutant MDS/AML and we propose this as broader strategy for therapeutic targeting of other DNA damage-repair deficient cancers. Citation Format: Zuzana Tothova. Therapeutic vulnerabilities of cohesin-mutant myeloid malignancies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA005.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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