Retroelement decay by the exonuclease XRN1 is a viral mimicry dependency in cancer
Bibliographic record
Abstract
Abstract Viral mimicry describes the immune response induced by endogenous stimuli such as dsRNA formed by endogenous retroelements. Activation of viral mimicry has the potential to kill cancer cells or augment anti-tumor immune response. Paradoxically, cancer cells frequently present a dysregulated epigenome, leading to increased expression of retroelements. We previously found that ADAR1 p150 upregulation is an adaptation mechanism to tolerate high retroelement-derived dsRNA levels, leading to a druggable dependency. Here, we systematically identified novel mechanisms of viral mimicry adaptation associated with cancer cell dependencies. We correlated the gene knockout sensitivity from the DepMap dataset and interferon stimulated gene (ISG) expression in the Cancer Cell Line Encyclopedia (CCLE) dataset of 1005 human cell lines and identified pathways such as RNA modification and nucleic acid metabolism. Among the top hits was the RNA decay protein XRN1 as an essential gene for the survival of a subset of cancer cell lines. XRN1-sensitive cancer cell lines have a high level of cytosolic dsRNA and high ISG expression. Furthermore, sensitivity to XRN1 knockout was mediated by MAVS and PKR activation, indicating that the cells die due to XRN1-dependent induction of viral mimicry. XRN1-resistant cell lines had low basal dsRNA levels, but became synthetically dependent on XRN1 upon treatment with viral mimicry inducing drugs such as 5-AZA-CdR or palbociclib. Finally, XRN1-dependency is partly independent of ADAR1 activity. These results confirm the potential for our ISG correlation analysis to discover novel regulators of viral mimicry and show that XRN1 activation is an adaptive mechanism to control high dsRNA stress induced by dysregulated retroelements in cancer cells and creates a dependency that can be explored for novel 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".