Reprogramming of Nucleotide Metabolism Mediates Synergy between Epigenetic Therapy and MAP Kinase Inhibition
Bibliographic record
Abstract
Abstract Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare but often lethal cancer that is diagnosed at a median age of 24 years. Optimal management of patients is not well defined, and current treatment remains challenging, necessitating the discovery of novel therapeutic approaches. The identification of SMARCA4-inactivating mutations invariably characterizing this type of cancer provided insights facilitating diagnostic and therapeutic measures against this disease. We show here that the BET inhibitor OTX015 acts in synergy with the MEK inhibitor cobimetinib to repress the proliferation of SCCOHT in vivo. Notably, this synergy is also observed in some SMARCA4-expressing ovarian adenocarcinoma models intrinsically resistant to BETi. Mass spectrometry, coupled with knockdown of newly found targets such as thymidylate synthase, revealed that the repression of a panel of proteins involved in nucleotide synthesis underlies this synergy both in vitro and in vivo, resulting in reduced pools of nucleotide metabolites and subsequent cell-cycle arrest. Overall, our data indicate that dual treatment with BETi and MEKi represents a rational combination therapy against SCCOHT and potentially additional ovarian cancer subtypes.
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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".