Genomic dissection of gastrointestinal and lung neuroendocrine neoplasm
Bibliographic record
Abstract
OBJECTIVE: Neuroendocrine neoplasms (NENs) are relatively rare and heterogeneous malignancies with two major subtypes: low-grade neuroendocrine tumor (NET) and high-grade neuroendocrine carcinoma (NEC). Comprehensive molecular characterization of NENs is needed to refine our understanding of the biological underpinnings of different NEN subtypes and to predict disease progression more accurately. METHODS: We performed whole-exome sequencing (WES) of NEN samples from 49 patients (25 NETs and 24 NECs) arising from the stomach, intestines or lung. Clinicopathologic features were assessed and associated with molecular events. RESULTS: , tissue type-specific mutation profiles of NENs were observed in our cohort compared to those reported in pancreatic NETs. Importantly, we observed significant genomic instability, with increased copy number alterations observed across the NEN genome, which was more profound in NECs and independently correlated with poor overall survival (OS) (P<0.001). NECs could be further stratified into two molecular subtypes based on OS (P<0.001) and the chromosomal instability score (CIS). Interestingly, we discovered that the gain of whole chromosome 5 occurred at the early stage of NEN development, followed by the loss of 5q exclusively in NECs (P<0.001). CONCLUSIONS: These findings provide novel insights into the molecular characteristics of NENs and highlight the association of genomic stability with clinical outcomes.
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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 | 0.001 |
| 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".