Prognostic Impact of Novel Molecular Subtypes of Small Intestinal Neuroendocrine Tumor
Bibliographic record
Abstract
PURPOSE: Small intestinal neuroendocrine tumors (SINET) are the commonest malignancy of the small intestine; however, underlying pathogenic mechanisms remain poorly characterized. Whole-genome and -exome sequencing has demonstrated that SINETs are mutationally quiet, with the most frequent known mutation in the cyclin-dependent kinase inhibitor 1B gene (CDKN1B) occurring in only ∼8% of tumors, suggesting that alternative mechanisms may drive tumorigenesis. The aim of this study is to perform genome-wide molecular profiling of SINETs in order to identify pathogenic drivers based on molecular profiling. This study represents the largest unbiased integrated genomic, epigenomic, and transcriptomic analysis undertaken in this tumor type. EXPERIMENTAL DESIGN: Here, we present data from integrated molecular analysis of SINETs (n = 97), including whole-exome or targeted CDKN1B sequencing (n = 29), HumanMethylation450 BeadChip (Illumina) array profiling (n = 69), methylated DNA immunoprecipitation sequencing (n = 16), copy-number variance analysis (n = 47), and Whole-Genome DASL (Illumina) expression array profiling (n = 43). RESULTS: Based on molecular profiling, SINETs can be classified into three groups, which demonstrate significantly different progression-free survival after resection of primary tumor (not reached at 10 years vs. 56 months vs. 21 months, P = 0.04). Epimutations were found at a recurrence rate of up to 85%, and 21 epigenetically dysregulated genes were identified, including CDX1 (86%), CELSR3 (84%), FBP1 (84%), and GIPR (74%). CONCLUSIONS: This is the first comprehensive integrated molecular analysis of SINETs. We have demonstrated that these tumors are highly epigenetically dysregulated. Furthermore, we have identified novel molecular subtypes with significant impact on progression-free survival.
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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 | 0.000 | 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".