Viral mimicry evasion: a new role for oncogenic <scp>KRAS</scp> mutations
Bibliographic record
Abstract
“Viral mimicry” refers to the induction of an innate immune response and interferon signaling by endogenous stimuli such as double‐stranded RNA (dsRNA). This response has been shown to have strong cancer therapeutic potential, including by enhancing the effectiveness of immune checkpoint inhibition (ICI) therapies, and may represent a tumor suppression mechanism that needs to be overcome for malignant transformation to proceed. In a recent study, Zhou and colleagues identify KRAS, a frequently mutated oncogene, as a negative regulator of dsRNA and viral mimicry in an ICI‐resistant colorectal cancer model. Oncogenic KRASG12D mutations downregulate the RNA‐binding protein DDX60 by activating the AKT signaling pathway, which inhibits STAT3, a critical transcription factor regulating DDX60 and other interferon‐stimulated genes. Overexpression of DDX60, which competitively binds to dsRNA to prevent RISC‐mediated degradation, or targeting of KRASG12D elevated dsRNA levels, resulting in viral mimicry activation and potentiation of ICI treatment. These results establish KRAS as a promising target to sensitize immune “cold” tumors to ICI therapy and demonstrate the potential role of oncogenic mutations in viral mimicry evasion during tumorigenesis.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".