Genomic imbalances in precancerous tissues signal oral cancer risk
Bibliographic record
Abstract
Oral cancer develops through a series of histopathological stages: through mild (low grade), moderate, and severe (high grade) dysplasia to carcinoma in situ and then invasive disease. Early detection of those oral premalignant lesions (OPLs) that will develop into invasive tumors is necessary to improve the poor prognosis of oral cancer. Because no tools exist for delineating progression risk in low grade oral lesions, we cannot determine which of these cases require aggressive intervention. We undertook whole genome analysis by tiling-path array comparative genomic hybridization for a rare panel of early and late stage OPLs (n = 62), all of which had extensive longitudinal follow up (>10 years). Genome profiles for oral squamous cell carcinomas (n = 24) were generated for comparison. Parallel analysis of genome alterations and clinical parameters was performed to identify features associated with disease progression. Genome alterations in low grade dysplasias progressing to invasive disease more closely resembled those observed for later stage disease than they did those observed for non-progressing low grade dysplasias. This was despite the histopathological similarity between progressing and non-progressing cases. Strikingly, unbiased computational analysis of genomic alteration data correctly classified nearly all progressing low grade dysplasia cases. Our data demonstrate that high resolution genomic analysis can be used to evaluate progression risk in low grade OPLs, a marked improvement over present histopathological approaches which cannot delineate progression risk. Taken together, our data suggest that whole genome technologies could be used in management strategies for patients presenting with precancerous oral lesions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".