Abstract IA005: Therapeutic vulnerabilities of cohesin-mutant myeloid malignancies
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".