When <scp>DNA</scp> damage responses meet tumor immunity: From mechanism to therapeutic opportunity
Bibliographic record
Abstract
DNA damage is a prevalent phenomenon in the context of cancer progression. Evidence suggests that DNA damage responses (DDR) are pivotal in overcoming tumor immune evasion. Alternatively, traditional radiotherapy and chemotherapy operate by inducing DNA damage, consequently stimulating the immune system to target tumors. The intricate interplay between signaling pathways involved in DDR and immune activation underscores the significance of considering both factors in developing improved immunotherapies. By delving deeper into the mechanisms underlying immune activation brought on by DNA damage, it becomes possible to identify novel treatment approaches that boost the anticancer immune response while minimizing undesirable side effects. This review explores the mechanisms behind DNA damage-induced antitumor immune responses, the importance of DNA damage in antitumor immunity, and potential therapeutic approaches for cancer immunotherapy targeting DDR. Additionally, we discuss the challenges of combination therapy and strategies for integrating DNA damage-targeting therapies with current cancer immunotherapy. In summary, this review highlights the critical role of DNA damage in tumor immunology, underscoring the potential of DDR inhibitors as promising therapeutic modalities for cancer treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".