The IGF-Trap: Novel Inhibitor of Carcinoma Growth and Metastasis
Bibliographic record
Abstract
The IGFI receptor promotes malignant progression and has been recognized as a target for cancer therapy. Clinical trials with anti-IGFIR antibodies provided evidence of therapeutic efficacy but exposed limitations due in part to effects on, and the compensatory function of, the insulin receptor system. Here, we report on the production, characterization, and biologic activity of a novel, IGF-targeting protein (the IGF-Trap) comprising a soluble form of hIGFIR and the Fc portion of hIgG1. The IGF-Trap has a high affinity for hIGFI and hIGFII but low affinity for insulin, as revealed by surface plasmon resonance. It efficiently blocked IGFIR signaling in several carcinoma cell types and inhibited tumor cell proliferation, migration, and invasion in vitro. In vivo, the IGF-Trap showed favorable pharmacokinetic properties and could suppress the growth of established breast carcinoma tumors when administered therapeutically into tumor-bearing mice, improving disease-free survival. Moreover, IGF-Trap treatment markedly reduced experimental liver metastasis of colon and lung carcinoma cells, increasing tumor cell apoptosis and reducing angiogenesis. Finally, when compared with an anti-IGFIR antibody or IGF-binding protein-1 that were used at similar or higher concentrations, the IGF-Trap showed superior therapeutic efficacy to both inhibitors. Taken together, we have developed a targeted therapeutic molecule with highly potent anticancer effects that could address limitations of current IGFIR-targeting agents.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".