The role of IGF-1 as a major growth factor for myeloma cell lines and the prognostic relevance of the expression of its receptor
Bibliographic record
Abstract
A plethora of myeloma growth factors (MGFs) has been identified, but their relative importance and cooperation have not been determined. We investigated 5 MGFs (interleukin-6 [IL-6], insulin-like growth factor type 1 [IGF-1], hepatocyte growth factor [HGF], HB-epidermal growth factor [HB-EGF], and a proliferation-inducing ligand [APRIL]) in serum-free cultures of human myeloma cell lines (HMCLs). In CD45(-) HMCLs, an autocrine IGF-1 loop promoted autonomous survival whereas CD45(+) HMCLs could not survive without addition of MGFs, mainly IGF-1 and IL-6. IGF-1 was the major one: its activity was abrogated by an IGF-1R inhibitor only, whereas IL-6, HGF, or HB-EGF activity was inhibited by both IGF-1R- and receptor-specific inhibition. APRIL activity was inhibited by its specific inhibitor only. Of the investigated MGFs and their receptors, only expressions of IGF-1R and IL-6R in multiple myeloma cells (MMCs) of patients delineate a group with adverse prognosis. This is mainly explained by a strong association of IGF-1R and IL-6R expression and t(4;14) translocation, but IGF-1R expression without t(4;14) can also have a poor prognosis. Thus, IGF-1-targeted therapy, eventually in combination with anti-IL-6 therapy, could be promising in a subset of patients with MMCs expressing IGF-1R.
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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 | 0.002 |
| 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".