Genomic and transcriptomic landscapes of metastatic neuroendocrine neoplasms from distinct primary sites and their clinical implications
Bibliographic record
Abstract
Neuroendocrine neoplasms (NENs) encompass a highly heterogeneous group of neoplasms with varying prognoses and molecular alterations. Molecular profiling studies have furthered our understanding of NENs, but the majority of previous studies have focused on primary tumors and on mutational landscapes using DNA sequencing data. Here, we describe the genomic and transcriptomic landscapes of 28 metastatic NENs across different primary anatomical sites (PASs) and their potential clinical implications. Although our cohort is small, our analyses provide further insights on the molecular commonalities and distinctions between metastatic NENs of different PASs. Comparison to several reference transcriptome data sets revealed that despite considerable whole genome and transcriptome variability in NENs, the metastatic NENs are still more like each other than other cancer types. Our study also highlights the potential utility of NEN transcriptome data for molecular classification and clinical decision making.
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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 | 0.001 | 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".