Author response: Kinase-dead ATM protein is highly oncogenic and can be preferentially targeted by Topo-isomerase I inhibitors
Bibliographic record
Abstract
Cancer is a genetic disease. To remain healthy, therefore, it is essential that cells do not accrue too many dangerous mutations in their DNA that allow cancers to grow and develop. An enzyme called ATM helps to do just that. DNA damage activates ATM, which, in turn, adds phosphate groups to other proteins. These newly tagged proteins then stop cells dividing until the DNA has been repaired. Human cancers often switch off ATM, either by completely deleting the enzyme or mutating it. This renders ATM unable to add phosphate groups to proteins, and so allows the cancer cells to continue proliferating even in the face of DNA damage. Yamamoto et al. wanted to know whether cancers that completely lack ATM behave differently from cancers that contain an inactive version of the enzyme. Studying mice that were engineered to have an inactive version of ATM in their blood cells showed that such mice developed blood cancers faster than mice have no ATM in their blood cells. In particular, cancer cells with the inactive form of the ATM enzyme accumulated more DNA damage than cells that lacked the enzyme completely. Using biochemical techniques, Yamamoto et al. then showed that the inactive form of ATM can prevent other enzymes from repairing DNA. Drugs that inhibit one of these repair enzymes – called topo-isomerase I – are already used in cancer treatments. These drugs were particularly effective on tumors with the inactive version of the ATM enzyme. As ATM is commonly mutated in human cancers, the next steps that follow on from this research are to develop methods to test which cancers contain the inactive form of the ATM enzyme. Clinical trials could then investigate how effectively topo-isomerase I inhibitors treat these specific types of cancer.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.134 |
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".