The Folate Cycle Enzyme MTHFR Is a Critical Regulator of Cell Response to MYC-Targeting Therapies
Bibliographic record
Abstract
Abstract Deciphering the impact of metabolic intervention on response to anticancer therapy may elucidate a path toward improved clinical responses. Here, we identify amino acid–related pathways connected to the folate cycle whose activation predicts sensitivity to MYC-targeting therapies in acute myeloid leukemia (AML). We establish that folate restriction and deficiency of the rate-limiting folate cycle enzyme MTHFR, which exhibits reduced-function polymorphisms in about 10% of Caucasians, induce resistance to MYC targeting by BET and CDK7 inhibitors in cell lines, primary patient samples, and syngeneic mouse models of AML. Furthermore, this effect is abrogated by supplementation with the MTHFR enzymatic product CH3-THF. Mechanistically, folate cycle disturbance reduces H3K27/K9 histone methylation and activates a SPI1 transcriptional program counteracting the effect of BET inhibition. Our data provide a rationale for screening MTHFR polymorphisms and folate cycle status to nominate patients most likely to benefit from MYC-targeting therapies. Significance: Although MYC-targeting therapies represent a promising strategy for cancer treatment, evidence of predictors of sensitivity to these agents is limited. We pinpoint that folate cycle disturbance and frequent polymorphisms associated with reduced MTHFR activity promote resistance to BET inhibitors. CH3-THF supplementation thus represents a low-risk intervention to enhance their effects. See related commentary by Marando and Huntly, p. 1791. This article is highlighted in the In This Issue feature, p. 1775
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".