Endogenous Retroelements and the Viral Mimicry Response in Cancer Therapy and Cellular Homeostasis
Bibliographic record
Abstract
Features of the cancer epigenome distinguish cancers from their respective cell of origin and establish therapeutic vulnerabilities that can be exploited through pharmacologic inhibition of DNA- or histone-modifying enzymes. Epigenetic therapies converge with cancer immunotherapies through "viral mimicry," a cellular state of active antiviral response triggered by endogenous nucleic acids often derived from aberrantly transcribed endogenous retrotransposons. This review describes the initial characterization and expansion of viral mimicry-inducing approaches as well as features that "prime" cancers for viral mimicry induction. Increased understanding of viral mimicry in therapeutic contexts suggests potential physiologic roles in cellular homeostasis. SIGNIFICANCE: Recent literature establishes elevated cytosolic double strand RNA (dsRNA) levels as a cancer-specific therapeutic vulnerability that can be elevated by viral mimicry-inducing therapies beyond tolerable thresholds to induce antiviral signaling and increase dependence on dsRNA stress responses mediated by ADAR1. Improved understanding of viral mimicry signaling and tolerance mechanisms reveals synergistic treatment combinations with epigenetic therapies that include inhibition of BCL2, ADAR1, and immune checkpoint blockade. Further characterization of viral mimicry tolerance may identify contexts that maximize efficacy of conventional cancer therapies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".