The Type 1 Insulin-Like Growth Factor Receptor Pathway
Bibliographic record
Abstract
Research conducted over the past two decades has shown the importance of the type 1 insulin-like growth factor receptor (IGF1R) in tumorigenesis, metastasis, and resistance to existing forms of cancer therapy. The IGF1R itself has only recently been accepted as a credible treatment target, however, perhaps reflecting the potential problems for drug design posed by normal tissue IGF1R expression, and close homology with the insulin receptor. Currently approximately 12 anti-IGF1R therapeutics are undergoing clinical evaluation, including blocking antibodies and tyrosine kinase inhibitors. This review will summarize the principal signaling pathways activated by IGF1R and the preclinical data that validated this receptor as a treatment target. We will review clinical progress in the testing of IGF1R inhibitory drug candidates, the relative benefits and potential toxicities of coinhibition of the insulin receptor, and the rationale for combining IGF1R blockade with other cancer treatments. An understanding of IGF1R signaling is important because it will guide the incorporation of appropriate molecular markers into clinical trial design. This will be key to the identification of patients most likely to benefit, and so will influence the ability of IGF1R inhibition to make the transition from experimental intervention to clinical therapy.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".