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 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 | 0.001 | 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".