Abstract IA-003: Proteogenomic characterizations of pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Pancreatic cancer is one of the deadliest cancers and the five-year survival rate is less than 10%. Pancreatic ductal adenocarcinoma (PDAC) represents more than 90% of all pancreatic malignancies, and is responsible for the majority of pancreatic cancer-related deaths. Towards understanding the underlying molecular alterations that drive PDAC oncogenesis and identify therapeutic targets for personalized treatments, we comprehensively characterized 140 pancreatic cancers and 67 normal adjacent tissues. To ensure robust, downstream analyses, tumor neoplastic cellularity was assessed via multiple, orthogonal strategies using molecular features, and verified via pathological estimation of tumor cellularity based on histological review to select tumors with sufficient tumor cellularity. We also included the analysis of 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole genome sequencing, whole exome sequencing, methylation, RNA-seq, and miRNA-seq were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. These characterizations revealed functional impacts of genomic and epigenomic alterations on proteins and protein modifications, delineated PDAC cell microenvironment compositions and the immune signatures for immunotherapy, also uncovered putative kinase inhibitors that could be tested for therapy. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets. Citation Format: Liwei Cao, Chen Huang, Daniel Cui Zhou, Yingwei Hu, Mamie Lih, Sara R. Savage, Karsten Krug, David J. Clark, Michael Schnaubelt, Lijun Chen, Felipe da Veiga Leprevost, Rodrigo Vargas Eguez, Alexey I. Nesvizhskii, D.R. Mani, Gilbert S. Omenn, Emily S. Boja, Mehdi Mesri, Ana I. Robles, Henry Rodriguez, Oliver F. Bathe, Daniel W. Chan, Ralph H. Hruban, Li Ding, Bing Zhang, Hui Zhang, Clinical Proteomic Tumor Analysis Consortium. Proteogenomic characterizations of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr IA-003.
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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 | 0.001 |
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".