Targeting the insulin-like growth factor 1 receptor (IGF1R) signaling pathway for cancer therapy
Bibliographic record
Abstract
INTRODUCTION: The IGF system controls growth, differentiation, and development at the cellular, organ and organismal levels. IGF1 receptor (IGF1R) signaling is dysregulated in many cancers. Numerous clinical trials are currently assessing therapies that inhibit either growth factor binding or IGF1R itself. Therapeutic benefit, often in the form of stable disease, has been reported for many different cancer types. AREAS COVERED: Canonical IGF signaling and non-canonical pathways involved in carcinogenesis. Three recent insights into IGF1R signaling, namely hybrid receptor formation with insulin receptor (INSR), insulin receptor substrate 1 nuclear translocation, and evidence for IGF1R/INSR as dependence receptors. Different approaches to targeting IGF1R and mechanisms of acquired resistance. Possible mechanisms by which IGF1R signaling supports carcinogenesis and specific examples in different human tumors. EXPERT OPINION: Pre-clinical data justifies IGF1R as a target and early clinical trials have shown modest efficacy in selected tumor types. Future work will focus upon assessing the usefulness or disadvantages of simultaneously targeting the IGF1R and INSR, biomarker development to identify potentially responsive patients, and the use of IGF1R inhibitors in combination therapies or as an adjunct to conventional chemotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".