Prognostic Impact of Novel Molecular Subtypes of Small Intestinal Neuroendocrine Tumor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it