Abstract 4731: Therapeutic targeting of RNA splicing through inhibition of protein arginine methylation
Bibliographic record
Abstract
Abstract Mutations in RNA splicing factors commonly occur in myeloid leukemia and solid tumors. These mutations occur in a heterozygous manner and confer dependency on the wild-type allele. Studies have shown that splicing factor mutant cancers are vulnerable to further perturbation of splicing, by pharmacological intervention that directly targets core splicing factors. Our project identifies the use of PRMT inhibitors, as plausible alternative therapeutic strategy to treat spliceosomal mutant leukemia. The data show that splicing factor mutant leukemia exhibit greater sensitivity to the use of inhibitors against Type I PRMTs and PRMT5, in comparison to their wild-type counterparts. As the need for new therapeutic strategies in cancer treatment increases, this study identifies PRMT inhibitors as a potential therapeutic intervention against cancers with splicing factor mutations. Citation Format: Jia Yi Fong, Diana Low, Luca Pignata, Kimihito Cojin Kawabata, Stanley CW Lee, Cheryl Koh, Daniele Musiani, Enrico Massignani, Cheng Mun Wun, Pierre-Alexis V. Goy, Yudao Shen, Heike Wollmann, Florence PH Gay, Genna Luciani, Dalia Barsyte, Jian Jin, Ari M. Melnick, Tiziana Bonaldi, Omar Abdel-Wahab, Ernesto Guccione. Therapeutic targeting of RNA splicing through inhibition of protein arginine methylation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4731.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".