Molecular Characterization of Apocrine Salivary Duct Carcinoma
Bibliographic record
Abstract
Contemporary classification and treatment of salivary duct carcinoma (SDC) require its thorough molecular characterization. Thirty apocrine SDCs were analyzed by the Ion Ampliseq Cancer HotSpot panel v2 for mutations in 50 cancer-related genes. Mutational findings were corroborated by immunohistochemistry (eg, TP53, BRAF, β-catenin, estrogen, and androgen receptors) or Sanger sequencing/SNaPshot polymerase chain reaction. ERBB2 (HER2), PTEN, FGFR1, CDKN2A/P16, CMET, EGFR, MDM2, and PIK3CA copy number changes were studied by fluorescence in situ hybridization. TP53 mutations (15/27, 56%), PTEN loss (11/29, 38%, including 2 cases with PTEN mutation), PIK3CA hotspot mutations (10/30, 33%), HRAS hotspot mutations (10/29; 34%), and ERBB2 amplification (9/29, 31%, including 1 case with mutation) represented the 5 most common abnormalities. There was no correlation between genetic changes and clinicopathologic parameters. There was substantial overlap between genetic changes: 8 of 9 cases with ERBB2 amplification also harbored a PIK3CA, HRAS, and TP53 mutation and/or PTEN loss. Six of 10 cases with PIK3CA mutation also had an HRAS mutation. These findings provide a molecular rationale for dual targeting of mitogen-activated protein kinase and phosphoinositide 3-kinase pathways in SDC. FGFR1 amplification (3/29, 10%) represents a new potential target. On the basis of studies of breast carcinomas, the efficacy of anti-ERBB2 therapy will likely be decreased in SDC with ERBB2 amplification co-occurring with PIK3CA mutation or PTEN loss. Therefore, isolated ERBB2 testing is insufficient for theranostic stratification of apocrine SDC. On the basis of the prevalence and type of genetic changes, apocrine SDC appears to resemble one subtype of breast carcinoma-"luminal androgen receptor positive/molecular apocrine."
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".