Chronic Viral Mimicry Induction following p53 Loss Promotes Immune Evasion
Bibliographic record
Abstract
Abstract Epigenetic therapies facilitate transcription of immunogenic repetitive elements that cull cancer cells through “viral mimicry” responses. Paradoxically, cancer-initiating events also facilitate transcription of repetitive elements. Contributions of repetitive element transcription toward cancer initiation, and the mechanisms by which cancer cells evade lethal viral mimicry responses during tumor initiation remain poorly understood. In this report, we characterize premalignant lesions of the fallopian tube along with syngeneic epithelial ovarian cancer models to explore the earliest events of tumorigenesis following the loss of the p53 tumor suppressor protein. We report that p53 loss permits the transcription of immunogenic repetitive elements and chronic viral mimicry activation that increases cellular tolerance of cytosolic nucleic acids and diminishes cellular immunogenicity. This selection process can be partially attenuated pharmacologically. Altogether, these results reveal that viral mimicry conditioning following p53 loss promotes immune evasion and may represent a pharmacologic target for early cancer interception. Significance: Our landmark discovery of viral mimicry characterized repetitive elements as immunogenic stimuli that cull cancer cells. If expressed repetitive elements cull cancer cells, why does every human cancer express repetitive elements? Our report offers an exciting advancement toward understanding this paradox and how to exploit this mechanism for cancer interception. See related commentary by Murayama and Cañadas, p. 670
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".