Splicing modulators impair DNA damage response and induce killing of cohesin-mutant MDS/AML
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 cancer, including myelodysplastic syndromes (MDS) and secondary acute myeloid leukemia (AML), where they are associated with poor outcomes. Here, we show that cohesin mutations are biomarkers of sensitivity to drugs targeting the splicing-factor SF3B1 (H3B-8800 and E-7107). We identify drug-induced alterations in splicing and corresponding reduced gene expression of a large number of DNA repair genes, including BRCA1 and BRCA2, as the mechanism underlying this sensitivity in cell line models, primary patient samples and patient-derived xenograft (PDX) models of AML. We find that DNA damage repair genes are particularly sensitive to exon skipping induced by SF3B1 modulators given their long length and large number of exons per transcript. Furthermore, we demonstrate that treatment of cohesin-mutant cells with SF3B1 modulators not only results in impaired DNA damage response and accumulation of DNA damage, but it significantly sensitizes cells to subsequent killing by PARP inhibitors and chemotherapy, and leads to improved overall survival of PDX models of cohesin-mutant AML in vivo . Our findings expand the potential therapeutic benefits of SF3B1 splicing modulators to include cohesin-mutant MDS and AML, and we propose this as a broader strategy for therapeutic targeting of other DNA damage-repair deficient cancers. One Sentence Summary We identify an unexpected effect of SF3B1 splicing inhibitors on regulation of DNA damage repair genes and show efficacy of combination treatment in cohesin-mutant MDS and AML.
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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) | 0.001 | 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".