Whole‐exome sequencing reveals novel vacuolar ATPase genes’ variants and variants in genes involved in lysosomal biology and autophagosomal formation in oral granular cell tumors
Bibliographic record
Abstract
BACKGROUND: Granular cell tumors (GCTs) are rare neuroectodermal soft tissue neoplasms that mainly affect the skin of the upper limbs and trunks and the oral cavity. GCTs are derived from Schwann cells and, ultrastructurally, their intracytoplasmic granules are considered autophagosomes or autophagolysosomes and are consistent with myelin accumulation. METHODS: In this study, a convenience set of 22 formalin-fixed, paraffin-embedded samples of oral GCTs, all but one sample located at the tongue, was screened for mutations by whole-exome (WES) or targeted sequencing. RESULTS: WES revealed two novel variants in genes of the vacuolar ATPase (V-ATPase) complex: ATP6AP1 frameshift c.746_749del, leading to p.P249Hfs*4, and ATP6V1A non-synonymous c.G868A, leading to p.D290N. Each of these mutations occurred in one case. With regard to the samples that were wild type for these V-ATPase variants, at least two samples presented variants in genes that are part of endosomal/lysosomal/autophagosomal networks including ABCA8, ABCC6, AGAP3, ATG9A, CTSB, DNAJC13, GALC, NPC1, SLC15A3, SLC31A2, and TMEM104. CONCLUSION: Although the mechanisms involved in oral GCT initiation and progression remain unclear, our results suggest that oral GCTs have V-ATPase variants similarly to GCTs from other tissues/organs, and additionally show variants in lysosomes/endosomes/autophagosomal genes.
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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 | 0.001 |
| 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".