ATM Regulates 3-Methylpurine-DNA Glycosylase and Promotes Therapeutic Resistance to Alkylating Agents
Bibliographic record
Abstract
UNLABELLED: Alkylating agents are a first-line therapy for the treatment of several aggressive cancers, including pediatric glioblastoma, a lethal tumor in children. Unfortunately, many tumors are resistant to this therapy. We sought to identify ways of sensitizing tumor cells to alkylating agents while leaving normal cells unharmed, increasing therapeutic response while minimizing toxicity. Using an siRNA screen targeting over 240 DNA damage response genes, we identified novel sensitizers to alkylating agents. In particular, the base excision repair (BER) pathway, including 3-methylpurine-DNA glycosylase (MPG), as well as ataxia telangiectasia mutated (ATM), were identified in our screen. Interestingly, we identified MPG as a direct novel substrate of ATM. ATM-mediated phosphorylation of MPG was required for enhanced MPG function. Importantly, combined inhibition or loss of MPG and ATM resulted in increased alkylating agent-induced cytotoxicity in vitro and prolonged survival in vivo. The discovery of the ATM-MPG axis will lead to improved treatment of alkylating agent-resistant tumors. SIGNIFICANCE: Inhibition of ATM and MPG-mediated BER cooperate to sensitize tumor cells to alkylating agents, impairing tumor growth in vitro and in vivo with no toxicity to normal cells, providing an ideal therapeutic window.
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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".