Insulin Receptor Isoform A and Insulin-like Growth Factor II as Additional Treatment Targets in Human Osteosarcoma
Bibliographic record
Abstract
Despite the frequent presence of an insulin-like growth factor I receptor (IGFIR)-mediated autocrine loop in osteosarcoma (OS), interfering with this target was only moderately effective in preclinical studies. Here, we considered other members of the IGF system that might be involved in the molecular pathology of OS. We found that, among 45 patients with OS, IGF-I and IGFBP-3 serum levels were significantly lower, and IGF-II serum levels significantly higher, than healthy controls. Increased IGF-II values were associated with a decreased disease-free survival. After tumor removal, both IGF-I and IGF-II levels returned to normal values. In 23 of 45 patients, we obtained tissue specimens and found that all expressed high mRNA level of IGF-II and >IGF-I. Also, isoform A of the insulin receptor (IR-A) was expressed at high level in addition to IGFIR and IR-A/IGFIR hybrids receptors (HR(A)). These receptors were also expressed in OS cell lines, and simultaneous impairment of IGFIR, IR, and Hybrid-Rs by monoclonal antibodies, siRNA, or the tyrosine kinase inhibitor BMS-536924, which blocks both IGFIR and IR, was more effective than selective anti-IGFIR strategies. Also, anti-IGF-II-siRNA treatment in low-serum conditions significantly inhibited MG-63 OS cells that have an autocrine circuit for IGF-II. In summary, IGF-II rather than IGF-I is the predominant growth factor produced by OS cells, and three different receptors (IR-A, HR(A), and IGFIR) act complementarily for an IGF-II-mediated constitutive autocrine loop, in addition to the previously shown IGFIR/IGF-I circuit. Cotargeting IGFIR and IR-A is more effective than targeting IGF-IR alone in inhibiting OS growth.
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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) | 0.000 | 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) | 0.001 | 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".