Abstract 4560: Integrative proteomic profiling of pancreatic cancer cell line supernatants, pancreatic juice and ascites fluid for the identification of novel pancreatic cancer biomarkers
Bibliographic record
Abstract
Abstract Pancreatic cancer is a highly lethal malignancy for which circulating biomarkers with high sensitivity and specificity are urgently needed. Proteins secreted or shed from tumor cells and their microenvironment have the highest chance of reaching the circulation and serving as measurable indicators of disease status. In this respect, we extensively characterized the proteomes of pancreatic cancer cell culture supernatants, in conjunction with proximal biological fluids, for the identification of novel candidate biomarkers. Specifically, we characterized the secretomes of the pancreatic cancer cell lines MIA-PaCa2, BxPc-3, Capan1, SU.86.86, CFPAC-1 and Panc1, along with the normal pancreatic ductal epithelial cell line HPDE, through multidimensional separation using strong-cation exchange chromatography, and reverse-phase chromatography coupled online to an LTQ-Orbitrap hybrid mass spectrometer. For each cell line, optimal growth conditions to achieve increased protein secretion with minimal cell death were first selected for, and each cell line was analyzed using three biological replicates. Generated spectra were searched against both MASCOT and X!Tandem using the human IPI 3.62 forward and reverse database and results were integrated using Scaffold 2.06 software. This resulted in a non-redundant list of between 2000 and 3500 proteins identified in the conditioned media from each of the cell lines, with a false positive rate of approximately 1%. Together, a total of 5009 non-redundant proteins with unique gene names were identified in the seven cell lines combined. Upon performing gene ontology annotations, approximately 27% of the proteins were found to localize to the extracellular or plasma membrane components. We subsequently performed proteomic analysis of pancreatic juice from cancer and chronic pancreatitis patients, as well as ascites fluid from pancreatic cancer patients. Through integration of the proteins identified in the pancreatic juice and ascites fluid analyses with that of the cell line conditioned media, we prioritized a list of candidate biomarkers for verification in serum from cancer versus controls through established quantitative methods. To aid in our candidate selection, we also performed multiple pathway and tissue specificity analyses using publically available databases, and made comparisons to proteins identified in relevant tissue proteomics and microarray studies found in the literature. Our analysis identified many previously studied pancreatic cancer biomarkers, which serves to validate our discovery approach. To our knowledge, this study comprises the most exhaustive proteome from pancreatic cancer cell lines, and the proteins identified through integration of the multiple biological sources can now serve as a mine for novel biomarkers and therapeutic targets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4560.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".