Endogenous Retrovirus Transcript Levels Are Associated with Immunogenic Signatures in Multiple Metastatic Cancer Types
Bibliographic record
Abstract
Abstract Next-generation sequencing of solid tumors has revealed variable signatures of immunogenicity across tumors, but underlying molecular characteristics driving such variation are not fully understood. Although expression of endogenous retrovirus (ERV)-containing transcripts can provide a source of tumor-specific neoantigen in some cancer models, associations between ERV levels and immunogenicity across different types of metastatic cancer are not well established. We performed bioinformatics analysis of genomic, transcriptomic, and clinical data across an integrated cohort of 199 patients with metastatic breast, colorectal, and pancreatic ductal adenocarcinoma tumors. Within each cancer type, we identified a subgroup of viral mimicry tumors in which increased ERV levels were coupled with transcriptional signatures of autonomous antiviral response and immunogenicity. In addition, viral mimicry colorectal and pancreatic tumors showed increased expression of DNA demethylation gene TET2. Taken together, these data demonstrate the existence of an ERV-associated viral mimicry phenotype across three distinct metastatic cancer types, while indicating links between ERV abundance, epigenetic dysregulation, and immunogenicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".