Glycoprotein Nonmetastatic B Is an Independent Prognostic Indicator of Recurrence and a Novel Therapeutic Target in Breast Cancer
Bibliographic record
Abstract
PURPOSE: Although the murine orthologue of glycoprotein nonmetastatic B (GPNMB), Osteoactivin, promotes breast cancer metastasis in an in vivo mouse model, its importance in human breast cancer is unknown. We have examined the significance of GPNMB expression as a prognostic indicator of recurrence and assessed its potential as a novel therapeutic target in breast cancer. EXPERIMENTAL DESIGN: The clinical significance of GPNMB expression in breast cancer was addressed by analyzing GPNMB levels in several published gene expression data sets and two independent tissue microarrays derived from human breast tumors. GPNMB-expressing human breast cancer cell lines were further used to validate a toxin-conjugated anti-GPNMB antibody as a novel therapeutic agent. RESULTS: GPNMB expression correlates with shorter recurrence times and reduced overall survival of breast cancer patients. Epithelial-specific GPNMB staining is an independent prognostic indicator for breast cancer recurrence. GPNMB is highly expressed in basal and triple-negative breast cancers and is associated with increased risk of recurrence within this subtype. GPNMB expression confers a more migratory and invasive phenotype on breast cancer cells and sensitizes them to killing by CDX-011 (glembatumumab vedotin), a GPNMB-targeted antibody-drug conjugate. CONCLUSIONS: GPNMB expression is associated with the basal/triple-negative subtype and is a prognostic marker of poor outcome in patients with breast cancer. CDX-011 (glembatumumab vedotin) is a promising new targeted therapy for patients with metastatic triple-negative breast cancers, a patient population that currently lacks targeted-therapy options.
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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".