The inhibitory anti-FGFR3 antibody, PRO-001, is cytotoxic to t(4;14) multiple myeloma cells
Bibliographic record
Abstract
The association of fibroblast growth factor receptor 3 (FGFR3) expression with t(4;14) multiple myeloma (MM) and the demonstration of the transforming potential of this receptor tyrosine kinase (RTK) make it a particularly attractive target for drug development. We report here a novel and highly specific anti-FGFR3-neutralizing antibody (PRO-001). PRO-001 binds to FGFR3 expressed on transformed cells and inhibits FGFR3 autophosphorylation and downstream signaling. The antibody inhibited the growth of FGFR3-expressing FDCP cells (IC(50) of 0.5 microg/mL) but not that of cells expressing FGFR1 or FGFR2, and potently inhibited FGFR3-dependent solid tumor growth in a mouse xenograft model. Furthermore, PRO-001 inhibited the growth of the FGFR3-expressing, human myeloma cell line, UTMC2. Inhibition of viability was still observed when cells were cocultured with stroma or in the presence of IL-6 or IGF-1. PRO-001 did not inhibit constitutive activation of K650E, G384D, and Y373C FGFR3 in myeloma cell lines and failed to inhibit the growth of these cells. Most importantly, however, PRO-001 induced cytotoxic responses in primary t(4;14)(+) MM samples with an increase in apoptotic index of 20% to 80% as determined by annexin V staining. The data demonstrate that PRO-001 is a potent and specific inhibitor of FGFR3 and deserves further study for the treatment of FGFR3-expressing myeloma.
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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 | 0.001 |
| 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".