Divergent Routes to Oral Cancer
Bibliographic record
Abstract
Most head and neck squamous cell carcinoma (HNSCC) patients present with late-stage cancers, which are difficult to treat. Therefore, early diagnosis of high-risk premalignant lesions and incipient cancers is important. HNSCC is currently perceived as a single progression mechanism, resulting in immortal invasive cancers. However, we have found that approximately 40% of primary oral SCCs are mortal in culture, and these have a better prognosis. About 60% of oral premalignancies (dysplasias) are also mortal. The mortal and immortal tumors are generated in vivo as judged by p53 mutations and loss of p16(INK4A) expression being found only in the original tumors from which the immortal cultures were derived. To investigate the relationships of dysplasias to SCCs, we did microarray analysis of primary cultures of 4 normal oral mucosa biopsies, 19 dysplasias, and 16 SCCs. Spectral clustering using the singular value decomposition and other bioinformatic techniques showed that development of mortal and immortal SCCs involves distinct transcriptional changes. Both SCC classes share most of the transcriptional changes found in their respective dysplasias but have additional changes. Moreover, high-risk dysplasias that subsequently progress to SCCs more closely resemble SCCs than nonprogressing dysplasias. This indicates for the first time that there are divergent mortal and immortal pathways for oral SCC development via intermediate dysplasias. We believe that this new information may lead to new ways of classifying HNSCC in relation to prognosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".