Deoxyuridine-rich cytoplasmic DNA antagonizes STING-dependent innate immune responses and sensitizes resistant tumors to anti-PD-L1 therapy
Bibliographic record
Abstract
ABSTRACT DNA damage and cytoplasmic DNA induce type-1 interferon (IFN-1) and potentiate responses to immune checkpoint inhibitors. Our prior work found that inhibitors of the DNA damage response kinase ATR (ATRi) induce IFN-1 and deoxyuridine (dU) incorporation by DNA polymerases, akin to antimetabolites. Whether and how dU incorporation is required for ATRi-induced IFN-1 signaling is not known. Here, we show that ATRi-dependent IFN-1 responses require uracil DNA glycosylase (UNG)-initiated base excision repair and STING. Quantitative analyses of nine distinct nucleosides reveals that ATRi induce dU incorporation more rapidly in UNG wild-type than knockout cells, and that induction of IFN-1 is associated with futile cycles of repair. While ATRi induce similar numbers of micronuclei in UNG wild-type and knockout cells, dU containing micronuclei and cytoplasmic DNA are increased in knockout cells. Surprisingly, DNA fragments containing dU block STING-dependent induction of IFN-1, MHC-1, and PD-L1. Furthermore, UNG knockout sensitizes cells to IFN-γ in vitro , and potentiates responses to anti-PD-L1 in resistant tumors in vivo . These data demonstrate an unexpected and specific role for dU-rich DNA in suppressing STING-dependent IFN-1 responses, and show that UNG-deficient tumors have a heightened response to immune checkpoint inhibitors. STATEMENT OF SIGNIFICANCE Antimetabolites disrupt nucleotide pools and increase dU incorporation by DNA polymerases. We show that unrepaired dU potentiates responses to checkpoint inhibitors in mouse models of cancer. Patients with low tumor UNG may respond to antimetabolites combined with checkpoint inhibitors, and patients with high tumor UNG may respond to UNG inhibitors combined with checkpoint inhibitors.
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".