Cytosolic DNA structures produced by mismatch-repair deficiency coordinate anti-tumor immunity in colorectal cancer
Bibliographic record
Abstract
Abstract Patients with the microsatellite instable (MSI) subtype of colorectal cancer (CRC) have better prognosis and immunotherapy response than patients with the chromosomally instable (CIN) subtype due to improved cytotoxic T cell responses from high neoantigen levels and production of the chemokines CXCL10 and CCL5 that recruit cytotoxic T cells. This high chemokine production in MSI CRCs is due to constitutive activation of the cytosolic DNA (cyDNA) sensor STING by specific features of MSI cyDNA that lead to more effective STING pathway activation. Here, we investigate the features of MSI and CIN cyDNA to identify structures that more effectively activate STING. We find that MSI cyDNA is enriched in G-quadruplexes which improve STING and CD8 + T cell activation. Additionally, MSI micronuclei are also more efficient at inducing chemokine expression than CIN micronuclei. However, micronuclei are less effective than free cyDNA at inducing anti-tumor immunity and instead lead to increased Treg activation and IL10 production. Overall, these data highlight the role of specific cyDNA structures in anti-tumor immunity and provide essential knowledge for improved design of therapeutic DNA-based STING agonists that could be combined with immune checkpoint therapies to improve the prognosis of poorly immunogenic tumors like CIN CRCs.
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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".