Molecular characterization of metastatic pancreatic neuroendocrine tumors (PNETs) using whole-genome and transcriptome sequencing
Why this work is in the frame
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Bibliographic record
Abstract
Pancreatic neuroendocrine tumors (PNETs) are a genomically and clinically heterogeneous group of pancreatic neoplasms often diagnosed with distant metastases. Recurrent somatic mutations, chromosomal aberrations, and gene expression signatures in PNETs have been described, but the clinical significance of these molecular changes is still poorly understood, and the clinical outcomes of PNET patients remain highly variable. To help identify the molecular factors that contribute to PNET progression and metastasis, and as part of an ongoing clinical trial at the BC Cancer Agency (clinicaltrials.gov ID: NCT02155621 ), the genomic and transcriptomic profiles of liver metastases from five patients (four PNETs and one neuroendocrine carcinoma) were analyzed. In four of the five cases, we identified biallelic loss of MEN1 and DAXX as well as recurrent regions with loss of heterozygosity. Several novel findings were observed, including focal amplification of MYCN concomitant with loss of APC and TP53 in one sample with wild-type MEN1 and DAXX . Transcriptome analyses revealed up-regulation of MYCN target genes in this sample, confirming a MYCN -driven gene expression signature. We also identified a germline NTHL1 fusion event in one sample that resulted in a striking C>T mutation signature profile not previously reported in PNETs. These varying molecular alterations suggest different cellular pathways may contribute to PNET progression, consistent with the heterogeneous clinical nature of this disease. Furthermore, genomic profiles appeared to correlate well with treatment response, lending support to the role of prospective genotyping efforts to guide therapy in PNETs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it