ZNF687 Mutations in Severe Paget Disease of Bone Associated with Giant Cell Tumor
Bibliographic record
Abstract
Paget disease of bone (PDB) is a skeletal disorder characterized by focal abnormalities of bone remodeling, which result in enlarged and deformed bones in one or more regions of the skeleton. In some cases, the pagetic tissue undergoes neoplastic transformation, resulting in osteosarcoma and, less frequently, in giant cell tumor of bone (GCT). We performed whole-exome sequencing in a large family with 14 PDB-affected members, four of whom developed GCT at multiple pagetic skeletal sites, and we identified the c.2810C>G (p.Pro937Arg) missense mutation in the zinc finger protein 687 gene (ZNF687). The mutation precisely co-segregated with the clinical phenotype in all affected family members. The sequencing of seven unrelated individuals with GCT associated with PDB (GCT/PDB) identified the same mutation in all individuals, unravelling a founder effect. ZNF687 is highly expressed during osteoclastogenesis and osteoblastogenesis and is dramatically upregulated in the tumor tissue of individuals with GCT/PDB. Interestingly, our preliminary findings showed that ZNF687, indicated as a target gene of the NFkB transcription factor by ChIP-seq analysis, is also upregulated in the peripheral blood of PDB-affected individuals with (n = 5) or without (n = 6) mutations in SQSTM1, encouraging additional studies to investigate its potential role as a biomarker of PDB risk.
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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 | 0.001 |
| 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 | 0.001 | 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".