Jaagsiekte sheep retrovirus detected in human lung cancer tissue arrays
Bibliographic record
Abstract
BACKGROUND: Adenocarcinoma is the most common type of non-small cell lung cancer and is frequently observed in non-smoking patients. Adenocarcinoma in-situ (formerly referred to as bronchioloalveolar carcinoma) is a subset of lung adenocarcinoma characterized by growth along alveolar septae without evidence of stromal, vascular, or pleural invasion, that disproportionately affects never-smokers, women, and Asians. Adenocarcinoma in-situ is morphologically and histologically similar to a contagious lung neoplasm of sheep called ovine pulmonary adenocarcinoma (OPA). OPA is caused by infection with the exogenous betaretrovirus, jaagsiekte sheep retrovirus (JSRV), whose envelope protein (Env) is a potent oncogene. Several studies have reported that a proportion of human lung adenocarcinomas are immunopositive for an antigen related to the Gag protein of JSRV, however other groups have been unable to verify these observations by PCR. METHODS: Here we examine human lung cancer tissue arrays (TA) for evidence of JSRV Env protein and DNA by immunohistochemical staining and PCR, respectively. RESULTS: Our results reveal that a subset of human lung cancers express an antigen that reacts with a JSRV Env-specific monoclonal antibody in immunohistochemistry and that exogenous JSRV-like env and gag sequences can be amplified from TA tumor samples, albeit inefficiently. CONCLUSIONS: While a causative role has not been established, these data suggest that a JSRV-like virus might infect humans. With next generation sequencing approaches, a JSRV-like virus in human lung cancers may be identified which could have profound implications for prevention, diagnosis and therapy.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".