Role of IGF-1R in Mediating Breast Cancer Invasion and Metastasis
Bibliographic record
Abstract
In this review we bring forward what is currently known about the role of type I insulin-like growth factor receptor (IGF-1R) in mediating breast cancer invasion and metastasis. We begin by addressing how activated IGF-1R could allow pre-cancerous cells to become invasive. To this effect, we discuss clinical reports suggesting that activation of IGF-1R could stimulate ductal carcinoma in situs to become invasive. In the same light, we review basic research from our laboratory showing that IGF-1R differentially regulates the expression of breast cancer progression genes when pre-malignant breast epithelial cells were stimulated with insulin-like growth factor-I (IGF-I) over time. The discussion then turns toward the ability of IGF-1R to stimulate invasion of breast cancer cells that have acquired a malignant phenotype. At this stage of breast cancer, it appears that IGF-I stimulates cells to invade in part by inducing urokinase plasminogen activator. Finally, we consider the potential role of IGF-1R in regulating breast cancer metastases by facilitating angiogenesis and lymphangiogenesis. In support of this idea, there is evidence for IGF-1R in both of these processes through the induction of vascular endothelial growth factors (VEGF(165) and VEGF(121)). Thus, IGF-1R affords breast cancer cells many opportunities to become invasive and eventually metastatic. We conclude that disrupting IGF-1R signaling has many important implications in the treatment and management of breast cancer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".