Loss of Tumorigenicity and Metastatic Potential in Carcinoma Cells Expressing the Extracellular Domain of the Type 1 Insulin-Like Growth Factor Receptor
Bibliographic record
Abstract
The receptor for the type 1 insulin-like growth factor (IGF-IR) was identified as a major regulator of the malignant phenotype and a target for cancer therapy. In the present study, a novel IGF-IR mutant consisting of the entire extracellular domain of the receptor (IGFIR(933)) was genetically engineered and expressed in highly metastatic H-59 murine lung carcinoma cells. We show here that the cells expressed a truncated heterotetramer (beta(m)-alpha-alpha-beta(m)) that was secreted into the medium and could neutralize the effects of exogenous IGF-I, thus diminishing IGF-I-induced signaling and blocking IGF-I-mediated cellular functions such as cell proliferation, invasion, and survival. In vivo, tumor incidence and growth rate were markedly reduced in mice inoculated s.c. with H-59/IGFIR(933) cells. Moreover, after the intrasplenic/portal inoculation of these cells, there was a 90% reduction in the incidence of hepatic metastases and a significant increase in the long-term, disease-free survival of the mice compared with controls. Our results identify the IGFIR(933) as a potent antitumorigenic and antimetastatic agent with potential applications for cancer gene therapy.
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 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) | 0.000 | 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 | 0.001 |
| 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; 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".