Synthetic Cytotoxicity: Digenic Interactions with TEL1/ATM Mutations Reveal Sensitivity to Low Doses of Camptothecin
Bibliographic record
Abstract
Many tumors contain mutations that confer defects in the DNA-damage response and genome stability. DNA-damaging agents are powerful therapeutic tools that can differentially kill cells with an impaired DNA-damage response. The response to DNA damage is complex and composed of a network of coordinated pathways, often with a degree of redundancy. Tumor-specific somatic mutations in DNA-damage response genes could be exploited by inhibiting the function of a second gene product to increase the sensitivity of tumor cells to a sublethal concentration of a DNA-damaging therapeutic agent, resulting in a class of conditional synthetic lethality we call synthetic cytotoxicity. We used the Saccharomyces cerevisiae nonessential gene-deletion collection to screen for synthetic cytotoxic interactions with camptothecin, a topoisomerase I inhibitor, and a null mutation in TEL1, the S. cerevisiae ortholog of the mammalian tumor-suppressor gene, ATM. We found and validated 14 synthetic cytotoxic interactions that define at least five epistasis groups. One class of synthetic cytotoxic interaction was due to telomere defects. We also found that at least one synthetic cytotoxic interaction was conserved in Caenorhabditis elegans. We have demonstrated that synthetic cytotoxicity could be a useful strategy for expanding the sensitivity of certain tumors to DNA-damaging therapeutics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".