Molecular Analysis of an Intestinal Neuroendocrine/Non-neuroendocrine Neoplasm (MiNEN) Reveals MLH1 Methylation-driven Microsatellite Instability and a Monoclonal Origin: Diagnostic and Clinical Implications
Bibliographic record
Abstract
Mixed neuroendocrine/non-neuroendocrine neoplasms (MiNEN) are rare mixed epithelial neoplasms in which a neuroendocrine component is combined with a non-neuroendocrine component. Here, we provide the clinical, pathologic, and molecular report of a 73-year-old-man presenting with an intestinal MiNEN. The lesion was composed of a well-differentiated G3 neuroendocrine tumor and a colloid adenocarcinoma. The molecular characterization was performed using a multigene next-generation sequencing panel. The neoplasm displayed microsatellite instability due to MLH1 promoter methylation. The extended molecular profile documented the same mutations affecting ARID1A, ASXL1, BLM, and RNF43 genes in both components, indicating a monoclonal origin of the tumor. Regarding component-specific gene mutations, BRCA2 was specifically altered in the neuroendocrine area. It may represent a new actionable target for precision oncology in MiNEN, but the lack of its alteration in the colloid component calls for further considerations on intratumor heterogeneity. The most important finding with potential immediate implications regards the presence of microsatellite instability: it indicates that this molecular alteration should become part of the diagnostic algorithm for these rare neoplasms.
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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 | 0.000 |
| 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".