Type I Insulin-like Growth Factor Receptor Induces Pulmonary Tumorigenesis
Bibliographic record
Abstract
Despite the type I insulin-like growth factor receptor (IGF-IR) being highly expressed in more than 80% of human lung tumors, a transgenic model of IGF-IR overexpression in the lung has not been created. We produced two novel transgenic mouse models in which IGF-IR is overexpressed in either lung type II alveolar cells (surfactant protein C [SPC]-IGFIR) or Clara cells (CCSP-IGFIR) in a doxycycline-inducible manner. Overexpression of IGF-IR in either cell type caused multifocal adenomatous alveolar hyperplasia with papillary and solid adenomas. These tumors expressed thyroid transcription factor 1 and Kruppel-like factor 5 in most tumor cells. Similar to our previous work with lung tumors that developed in the mouse mammary tumor virus-IGF-II transgenic mice, the lung tumors that develop in the SPC-IGFIR and CCSP-IGFIR transgenic mice expressed high levels of the cyclic adenosine monophosphate response element binding protein that was localized primarily to the nucleus. Although elevated IGF-IR expression can initiate lung tumor development, tumors can become independent of IGF-IR signaling as IGF-IR down-regulation in established tumors produced tumor regression in some, but not all, of the tumors. These findings implicate IGF-IR as an important initiator of lung tumorigenesis and suggest that the SPC-IGFIR and CCSP-IGFIR transgenic mice can be used to further our understanding of human lung cancer and the role IGF-IR plays in this disease.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".