Recombinant Human Insulin-like Growth Factor Binding Protein 3 Inhibits Growth of Human Epidermal Growth Factor Receptor-2–Overexpressing Breast Tumors and Potentiates Herceptin Activity <i>In vivo</i>
Bibliographic record
Abstract
Clinical studies indicate that Herceptin (trastuzumab), a recombinant humanized monoclonal antibody directed against the human epidermal growth factor receptor-2 (HER-2) tyrosine kinase growth factor receptor, provides a significant but transient survival advantage to a subset of patients with HER-2-overexpressing metastatic breast cancer when given as a first-line agent. Increased insulin-like growth factor (IGF)-I receptor (IGF-IR) signaling has recently been identified as a potential factor adversely influencing the response to Herceptin. We examined the effect of recombinant human IGF binding protein 3 (rhIGFBP-3), an antagonist of IGF-IR signaling, in Herceptin-resistant breast cells in vitro and in tumors in vivo. Consistent with results obtained using HER-2- or IGF-IR-transfected cells (MCF-7/HER2-18 and SKBR3/IGF-IR, respectively), we found that rhIGFBP-3 significantly reduced IGF-I-induced IGF-IR phosphorylation and displayed a synergistic interaction with Herceptin against cultured HER-2-overexpressing breast cancer cells in vitro. We show, for the first time, the antitumor activity of rhIGFBP-3 against advanced-stage MCF-7/HER2-18-transfected human breast cancer xenografts and its potentiation of Herceptin activity. We also provide evidence that IGF-IR activation counters the early suppressive effect of Herceptin on HER-2 signaling via Akt and p44/p42 mitogen-activated protein kinase (MAPK), and that inhibition of HER-2-overexpressing human breast tumor growth by rhIGFBP-3 is associated with restored down-regulation of Akt and p44/p42 MAPK phosphorylation in vitro and in vivo. These results emphasize the merit of evaluating simultaneous blockade of the HER-2 and IGF-IR pathways using combination therapy with rhIGFBP-3 plus Herceptin in human clinical trials of patients with HER-2-positive 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 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".