Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms
Bibliographic record
Abstract
Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous. Here, we use a multi-omics approach to uncover the molecular factors underlying this heterogeneity. Transcriptomic analysis of 84 PNEN specimens, drawn from two cohorts, is substantiated with proteomic profiling and identifies four subgroups: Proliferative, PDX1-high, Alpha cell-like and Stromal/Mesenchymal. The Proliferative subgroup, consisting of both well- and poorly differentiated specimens, is associated with inferior overall survival probability. The PDX1-high and Alpha cell-like subgroups partially resemble previously described subtypes, and we further uncover distinctive metabolism-related features in the Alpha cell-like subgroup. The Stromal/Mesenchymal subgroup exhibits molecular characteristics of YAP1/WWTR1(TAZ) activation suggestive of Hippo signaling pathway involvement in PNENs. Whole-exome sequencing reveals subgroup-enriched mutational differences, supported by activity inference analysis, and identifies hypermorphic proto-oncogene variants in 14.3% of sequenced PNENs. Our study reveals differences in cellular signaling axes that provide potential directions for PNEN patient stratification and treatment strategies.
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.
How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".