Mixed Adenoneuroendocrine Carcinomas of the Gastrointestinal Tract: Targeted Next-Generation Sequencing Suggests a Monoclonal Origin of the Two Components
Bibliographic record
Abstract
BACKGROUND: Mixed adenoneuroendocrine carcinomas (MANECs) of the gastrointestinal tract are rare neoplasms characterized by coexisting exocrine and neuroendocrine neoplastic components. MANECs' histogenetic classification and molecular characterization remain unclear, significantly affecting the identification of innovative therapeutic options for these tumors. METHODS: The exocrine and neuroendocrine components of 6 gastrointestinal MANECs were microdissected and subjected to the simultaneous mutation assessment in selected regions of 54 cancer-associated genes using Ion Torrent semiconductor-based next-generation sequencing. Sanger sequencing and immunohistochemistry were used as validation of the mutational status. RESULTS: A total of 20 driver gene somatic mutations were observed among the 12 neoplastic components investigated. In 11 of 12 (91.7%) samples, at least one mutation was detected; 7 samples (58.3%) were found to have multiple mutations. TP53 gene mutations were the most frequent genetic alterations observed in the series, occurring in 11/12 samples (91.7%). Somatic mutations in other genes were detected at lower frequencies: ATM, CTNNB1, ERBB4, JAK3, KDR, KRAS, RB1. CONCLUSIONS: Five of the 6 MANECs presented an overlapping mutational profile in both components, suggesting a monoclonal origin of the two MANEC components.
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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 | 0.000 | 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 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".