Abstract PR-009: Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms
Bibliographic record
Abstract
Abstract Background Pancreatic neuroendocrine neoplasms (PNENs) are biologically and clinically heterogeneous neoplasms with variable patient outcomes. Our study aims to uncover the molecular factors that underlie the clinical and biological heterogeneity among PNENs for a better understanding and potential classification of this disease. Methods Formalin-fixed paraffin-embedded primary tumour specimens from 92 patients with PNEN were procured for the study and split into two cohorts for discovery and validation purposes. Next-generation sequencing profiled the exome and transcriptome, and quantitative mass-spectrometry profiled the global proteome of specimens. Non-negative matrix factorization was used to identify subgroups, followed by differential analysis to identify subgroup-specific features. To corroborate subgroup-specific RNA- and protein- level distinctions, activities of key cellular regulators were inferred from target gene expression or co-expression signatures. Results Unsupervised clustering analysis of transcriptome data identified four robust molecular subgroups that were substantiated by proteome analysis (p=0.0005) within the discovery cohort and validated with the validation cohort and an external cohort of PNENs from a previous microarray-based gene expression study. A proliferative subgroup was enriched with neuroendocrine carcinomas and specimens with >20% Ki67, concomitant with reduced survival probability (p=0.0024; logrank test) and higher mRNA expression and protein abundance of cell cycle-related genes. Increased mRNA expression of ARX or PDX1 (adjusted p<0.05) was found in two of the subgroups similar to a previous report. The ARX-high subgroup was characterized by enrichment of oxidative phosphorylation genes and increased relative abundance of mitochondrial proteins, while oncogenic Ras mutations were found in the PDX1-high subgroup. A fourth subgroup exhibited enrichment of stromal/mesenchymal molecular features and Hippo signaling pathway. Inferred activities of key cellular regulators further supported observed alterations within subgroups. Conclusions We identified four robust molecular subgroups among PNENs with clinicopathological associations and biological distinctions that may provide potential new directions for patient stratification and treatment strategies to facilitate treatment decisions. Citation Format: Kevin C. Yang, Steve Kalloger, John Aird, Michael Lee, Christopher Rushton, Sandra E. Spencer Miko, Karen L. Mungall, Andrew J. Mungall, Shane Colborne, Ryan D. Morin, Jonathan M. Loree, Marco A. Marra, Daniel J. Renouf, Gregg B. Morin, David F. Schaeffer, Sharon M. Gorski. Proteotranscriptomic classification and characterization of pancreatic neuroendocrine neoplasms [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2020 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2020;80(22 Suppl):Abstract nr PR-009.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".