A prosurvival DNA damage-induced cytoplasmic interferon response is mediated by end resection factors and is limited by Trex1
Bibliographic record
Abstract
Radiotherapy and chemotherapy are effective treatment methods for many types of cancer, but resistance is common. Recent findings indicate that antiviral type I interferon (IFN) signaling is induced by these treatments. However, the underlying mechanisms still need to be elucidated. Expression of a set of IFN-stimulated genes comprises an IFN-related DNA damage resistance signature (IRDS), which correlates strongly with resistance to radiotherapy and chemotherapy across different tumors. Classically, during viral infection, the presence of foreign DNA in the cytoplasm of host cells can initiate type I IFN signaling. Here, we demonstrate that DNA-damaging modalities used during cancer therapy lead to the release of ssDNA fragments from the cell nucleus into the cytosol, engaging this innate immune response. We found that the factors that control DNA end resection during double-strand break repair, including the Bloom syndrome (BLM) helicase and exonuclease 1 (EXO1), play a major role in generating these DNA fragments and that the cytoplasmic 3'-5' exonuclease Trex1 is required for their degradation. Analysis of mRNA expression profiles in breast tumors demonstrates that those with lower Trex1 and higher BLM and EXO1 expression levels are associated with poor prognosis. Targeting BLM and EXO1 could therefore represent a novel approach for circumventing the IRDS produced in response to cancer 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) | 0.000 | 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 | 0.000 |
| 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".