Adeno-Associated Virus Vector Mediated Expression of an Oncogenic Retroviral Envelope Protein Induces Lung Adenocarcinomas in Immunocompetent Mice
Bibliographic record
Abstract
Lung cancer is the most common cause of cancer-related death worldwide. A poor overall survival rate of 16% necessitates the need for novel treatment strategies. Mouse models of lung cancer are important tools for analyzing the significance of somatic mutations in the initiation and progression of lung cancer. Of additional importance, however, are animal models of virally induced cancers. JSRV is a simple betaretrovirus that causes contagious lung cancer in sheep known as ovine pulmonary adenocarcinoma and closely resembles human lung adenocarcinoma. Previously we showed that expression of the JSRV envelope (Env) from an AAV vector induced lung tumors in immunodeficient mice, but not in immunocompetent mice. Because of the importance of studying lung cancer in the context of an intact immune system we sought to improve our mouse model. In this report, we employed the use of a strong JSRV enhancer-promoter combination to express Env at high levels and demonstrate for the first time, lung tumor induction in immunocompetent mice. This occurred despite a robust Env-specific antibody-mediated immune response. The PI3K/Akt and MAPK pathways were activated in both immunocompetent and immunodeficient mice, however, differential activation of PTEN, GSKα, p70S6K, p38MAPK, ATF2 and STAT5 was observed. A JSRV Env lung tumor-derived cell line was shown to have a similar signal transduction activation profile as Env-induced lung tumors in C57BL/6 mice. Given the similarities between our model and pulmonary adenocarcinomas in humans, and the ease with which tumors can be induced in any transgenic mouse, this system can be used to uncover novel mechanisms involved lung tumorigenesis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".