Functional profiles of curatively treated adenoid cystic carcinoma unveil prognostic features and potentially targetable pathways
Bibliographic record
Abstract
Adenoid cystic carcinoma (ACC) of salivary gland is a slowly growing tumor showing a propensity for delayed recurrence, with decreased survival rates. The identification of poor prognosis patients may help in defining molecular-based targeted strategies in this rare disease orphan of new treatments. Through a gene expression microarray-based approach followed by GSE functional analysis the expression profile of 46 primary untreated ACC samples and of ACC (h-TERT) tumor cells was analyzed. Patients who experienced early relapse showed enrichment in proliferation-related gene sets, including the G2-M checkpoint, E2F and myc targets, and in gene sets related to IFN signaling and aberrant proteostasis (FDR < 0.1), indicating increased mitotic and transcriptional activity in aggressive ACC. Similar functions were enriched in ACC samples classified by immunohistochemical staining as p63-negative, which exhibited increased protein burden and activation of pro-survival stress response pathways compared to p63-positive tumors. Compared to ACC tissues, ACC (h-TERT) cells share transcriptional features of aggressive p63-negative tumors. These data suggest association of specific pathway alterations with histopathological features of ACC, as recapitulated by p63 testing in patient prognostic stratification, anticipating new avenues for therapeutic intervention.
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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 | 0.000 | 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 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".