Ameloblastoma driver mutations revealed by next‐generation sequencing of formalin‐fixed paraffin‐embedded specimens (1048.18)
Bibliographic record
Abstract
Rare cancer types are not only understudied, but are typically represented by formalin‐fixed paraffin‐embedded (FFPE) (rather than freshly‐frozen) specimens that are suboptimal for genomic analysis. Ameloblastoma is one such rare tumor type, thought to arise from ameloblasts, the cells that deposit enamel during tooth development. Though typically benign, ameloblastomas are locally destructive to the jaw and face, and new non‐surgical interventions are needed. To discover novel driver mutations and therapeutic targets, we optimized methods and performed whole‐transcriptome sequencing and/or targeted exon sequencing (TruSeq Cancer Panel) of 8 FFPE cases. Identified mutations were verified, and then evaluated on a larger, independent set of 22 FFPE cases by PCR and Sanger sequencing. From the analysis, we identified recurrent somatic mutations in three key developmental or signaling pathways, including Hedgehog, fibroblast growth factor, and MAP kinase pathways. Functional interrogation of a novel Hedgehog pathway mutation confirmed increased basal pathway activity, and defined the response profile to various pharmacologic Hedgehog inhibitors. Together, our results define new ameloblastoma drivers and nominate new molecularly‐directed therapies for this rare but disfiguring disease. More generally, our findings validate a robust approach for discovering driver mutations in rare cancers. Grant Funding Source : Supported by Stanford University Department of Pathology
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".