Induction of angiogenesis by normal and malignant plasma cells
Bibliographic record
Abstract
Abundant bone marrow angiogenesis is present in almost all myeloma patients requiring therapy and correlated to treatment response and survival. We assessed the expression of 402 angiogenesis-associated genes by Affymetrix DNA microarrays in 466 samples, including CD138-purified myeloma cells (MMCs) from 300 previously untreated patients, in vivo microcirculation by dynamic contrast-enhanced magnetic resonance imaging, and in vitro angiogenesis (AngioKit-assay). Normal bone marrow plasma cells (BMPCs) express a median of 39 proangiogenic (eg, VEGFA, ADM, IGF-1) and 28 antiangiogenic genes (eg, TIMP1, TIMP2). Supernatants of BMPCs unlike those of memory B cells induce angiogenesis in vitro. MMCs do not show a significantly higher median number of expressed proangiogenic (45) or antiangiogenic (31) genes, but 97% of MMC samples aberrantly express at least one of the angiogenic factors HGF, IL-15, ANG, APRIL, CTGF, or TGFA. Supernatants of MMCs and human myeloma cell lines induce significantly higher in vitro angiogenesis compared with BMPCs. In conclusion, BMPCs express a surplus of proangiogenic over antiangiogenic genes transmitting to the ability to induce in vitro angiogenesis. Aberrant expression of proangiogenic and down-regulation of antiangiogenic genes by MMCs further increases the angiogenic stimulus, together leading to bone marrow angiogenesis at various degrees in all myeloma patients.
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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".