Modelling DNA Repair Pathways: Recent Advances and Future Directions
Bibliographic record
Abstract
BACKGROUND: A major class of chemotherapy drugs targets the genome of cancer cells. These DNA damaging agents induce damage to the DNA helix, resulting in the programmed death of cancer cells. An overactivated DNA repair mechanism in cancer cells can reduce the efficacy of these drugs, thereby eliminating their therapeutic benefit and developing an acquired resistance to these otherwise effective drugs. A promising approach to enhance the therapeutic window of DNA damaging agents is to target the DNA repair pathways causing this type of resistance. METHODS: Computational approaches have been applied successfully to study many of these DNA repair mechanisms at different scales and focusing on various aspects. The ultimate goal of these studies has been to identify the key players in developing resistance to DNA damaging agents and to design regulators for their activities. This review covers the most important and recent computational efforts toward this goal. This includes modelling the mechanisms involved in DNA repair and identifying novel pharmacological inhibitors for their activities. RESULTS: We focus here mainly on the pathways associated with an acquired drug resistance to DNA damaging agents, concentrating on the recent advances in modelling the key mechanisms and foreseeing the future directions in this field. CONCLUSION: We hope that this short, yet comprehensive review can help in discovering novel strategies to overcome the resistance effects inherent in various cancer treatments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".