ErbB2 enhances mammary tumorigenesis, oncogene-independent recurrence and metastasis in a model of IGF-IR-mediated mammary tumorigenesis
Bibliographic record
Abstract
BACKGROUND: The type I insulin-like growth factor receptor (IGF-IR) and ErbB2 (Her-2) are receptor tyrosine kinases implicated in human breast cancer. Both proteins are currently the subject of targeted therapeutics that are used in the treatment of breast cancer or which are in clinical trials. The focus of this study was to utilize our inducible model of IGF-IR overexpression to explore the interaction of these two potent oncogenes. RESULTS: ErbB2 was overexpressed in our RM11A cell line, a murine tumor cell line that overexpresses human IGF-IR in an inducible manner. ErbB2 conferred an accelerated tumor onset and increased tumor incidence after injection of RM11A cells into the mammary glands of syngeneic wild type mice. This was associated with increased proliferation immediately after tumor cell colonization of the mammary gland; however, this effect was lost after tumor establishment. ErbB2 overexpression also impaired the regression of established RM11A tumors following IGF-IR downregulation and enhanced their metastatic potential. CONCLUSION: This study has revealed that even in the presence of vast IGF-IR overexpression, a modest increase in ErbB2 can augment tumor establishment in vivo, mediate resistance to IGF-IR downregulation and facilitate metastasis. This supports the growing evidence suggesting a possible advantage of using IGF-IR and ErbB2-directed therapies concurrently in the treatment of breast cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".