Abstract 17: Proteogenomic characterization of pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is a highly aggressive cancer with 5-year survival rate less than 10%. Although numerous efforts have been made to characterize the functional driver genes of PDAC, the impacts of genomic alterations on protein modifications and molecular mechanism of this cancer type are still not well defined. To this end, Investigators from The Clinical Proteomic Tumor Analysis Consortium (CPTAC) conducted the first comprehensive characterization of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues using whole genome sequencing, whole exome sequencing, methylation, RNA-seq, miRNA-seq, proteomics, phosphoproteomics, and glycoproteomics. To address the inherent low neoplastic cellularity of pancreatic cancer, multiple orthogonal strategies using molecular features and histology imaging were deployed to deconvolute the cellularity and identify 105 tumors with sufficient neoplastic purity for downstream analyses. Proteomics, phosphoproteomics, and glycoproteomics analyses by mass spectrometry identified and quantified 11,662 proteins, 51,469 phosphosites, and 34,024 glycopeptides, respectively. Genomic data revealed that 97% of the cancers harbored KRAS alterations in our cohort. In addition, we found that somatic mutations and copy number alterations could impact gene and protein expression, as well as protein modifications such as phosphorylation and glycosylation. Glycoproteomic analyses uncovered tumor-associated alterations in glycoprotein biosynthesis and glycoproteins as potential targets for diagnosis or therapeutic intervention. Over-expressed kinase substrates and their corresponding kinases were identified with integration of global proteomic and phosphoproteomic measurements, thus identifying potential therapeutic targets. The results of our molecular and cellular subtyping revealed a small group of immune-hot subtype tumors that could benefit from immunotherapy, as well as the underlying mechanisms associated with major groups of immune-cold subtypes, including endothelial cell remodeling, glycolysis, and cell junction dysregulation. Non-negative matrix factorization (NMF)-based proteogenomics subtyping revealed two clusters with strong prognostic relevance. Finally, we identified proteins and glycoproteins overexpressed in early stage pancreatic cancer that may serve as candidates for early detection. This comprehensive proteogenomic characterization of PDAC provides a valuable resource for uncovering the molecular mechanisms of this cancer and thus paves the way for discovery of novel early detection and therapeutic targets. Citation Format: Liwei Cao, Chen Huang, Daniel Cui Zhou, Oliver F. Bathe, Daniel W. Chan, Ralph H. Hruban, Li Ding, Bing Zhang, Hui Zhang, Clinical Proteomic Tumor Analysis Consortium Investigators. Proteogenomic characterization of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 17.
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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 | 0.000 |
| 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 | 0.001 | 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".