The Impact of Radiation-Induced DNA Damage on cGAS-STING-Mediated Immune Responses to Cancer
Bibliographic record
Abstract
Radiotherapy is a major modality used to combat a wide range of cancers. Classical radiobiology principles categorize ionizing radiation (IR) as a direct cytocidal therapeutic agent against cancer; however, there is an emerging appreciation for additional antitumor immune responses generated by this modality. A more nuanced understanding of the immunological pathways induced by radiation could inform optimal therapeutic combinations to harness radiation-induced antitumor immunity and improve treatment outcomes of cancers refractory to current radiotherapy regimens. Here, we summarize how radiation-induced DNA damage leads to the activation of a cytosolic DNA sensing pathway mediated by cyclic GMP-AMP (cGAMP) synthase (cGAS) and stimulator of interferon genes (STING). The activation of cGAS-STING initiates innate immune signaling that facilitates adaptive immune responses to destroy cancer. In this way, cGAS-STING signaling bridges the DNA damaging capacity of IR with the activation of CD8+ cytotoxic T cell-mediated destruction of cancer-highlighting a molecular pathway radiotherapy can exploit to induce antitumor immune responses. In the context of radiotherapy, we further report on factors that enhance or inhibit cGAS-STING signaling, deleterious effects associated with cGAS-STING activation, and promising therapeutic candidates being investigated in combination with IR to bolster immune activation through engaging STING-signaling. A clearer understanding of how IR activates cGAS-STING signaling will inform immune-based treatment strategies to maximize the antitumor efficacy of radiotherapy, improving therapeutic outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".