Characterization of a Novel Primary Mammary Tumor Cell Line Reveals that Cyclin D1 Is Regulated by the Type I Insulin-Like Growth Factor Receptor
Bibliographic record
Abstract
The importance of type I insulin-like growth factor receptor (IGF-IR) overexpression in mammary tumorigenesis was recently shown in two separate transgenic models. One of these models, the MTB-IGFIR transgenics, was generated in our lab to overexpress IGF-IR in mammary epithelial cells in a doxycycline (Dox)-inducible manner. To complement this transgenic model, primary cells that retained Dox-inducible expression of IGF-IR were isolated from a transgenic mammary tumor. This cell line, RM11A, expressed high levels of IGF-IR, phosphorylated Akt, and phosphorylated extracellular signal-regulated kinase 1/2 in the presence of Dox. IGF-IR overexpression provided the primary tumor cells with a survival advantage in serum-free media and seemed to induce ligand-independent activation of the IGF-IR because RM11A cells cultured in the presence of Dox were largely nonresponsive to exogenous IGFs. IGF-IR overexpression also augmented the growth of RM11A cells in vivo because injection of these cells into mammary glands of wild-type mice produced palpable tumors in 15.8 +/- 3.4 days when the mice were administered Dox, compared with 57.8 +/- 6.3 days in the absence of Dox. DNA microarray analysis revealed a number of genes regulated by IGF-IR, one of which was cyclin D1. Suppression of IGF-IR expression in vitro or in vivo was associated with a decrease in cyclin D1 protein, suggesting that at least some of the proliferative actions of IGF-IR are mediated through cyclin D1. Therefore, this article characterizes the first primary murine mammary tumor cell line with inducible IGF-IR expression. These cells provide a powerful in vitro/in vivo model to examine the function of IGF-IR in mammary 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".