The Potential for Targeting G2/M Cell Cycle Checkpoint Kinases in Enhancing the Efficacy of Radiotherapy
Bibliographic record
Abstract
Radiotherapy is one of the main cancer treatments being used for ~50% of all cancer patients. Conventional radiotherapy typically utilises X-rays (photons); however, there is increasing use of particle beam therapy (PBT), such as protons and carbon ions. This is because PBT elicits significant benefits through more precise dose delivery to the cancer than X-rays, but also due to the increases in linear energy transfer (LET) that lead to more enhanced biological effectiveness. Despite the radiotherapy type, the introduction of DNA damage ultimately drives the therapeutic response through stimulating cancer cell death. To combat this, cells harbour cell cycle checkpoints that enables time for efficient DNA damage repair. Interestingly, cancer cells frequently have mutations in key genes such as TP53 and ATM that drive the G1/S checkpoint, whereas the G2/M checkpoint driven through ATR, Chk1 and Wee1 remains intact. Therefore, targeting the G2/M checkpoint through specific inhibitors is considered an important strategy for enhancing the efficacy of radiotherapy. In this review, we focus on inhibitors of Chk1 and Wee1 kinases and present the current biological evidence supporting their utility as radiosensitisers with different radiotherapy modalities, as well as clinical trials that have and are investigating their potential for cancer patient benefit.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".