Abstract 5103: Mining the pancreatic cancer ascites fluid proteome with mass spectrometry
Bibliographic record
Abstract
Abstract Malignant ascites is the build up of excess fluid in the peritoneal cavity resulting from increased fluid leakage from capillaries and decreased lymphatic fluid evacuation due to the presence of peritoneal metastases. Prognosis for pancreatic cancer patients is often poor due to its rapid dissemination to near-by organs and structures, and many pancreatic cancer patients develop peritoneal distention and ascites. Previously, proteomic analysis of ovarian cancer ascites has proven to be useful in mining for potential cancer biomarkers. In this regard, we performed mass spectrometric analysis of pancreatic cancer ascites derived from four patients with pancreatic ductal adenocarcinoma using three pre-fractionation strategies. To reduce the complexity of the ascites proteome, the proteins were fractionated by size exclusion chromatography (SEC), anion exchange chromatography (AEC) and multi-lectin chromatography (MLC). The MLC column was prepared by combining immobilized wheat germ agglutinin (WGA) and Concanavalin A (Con A) on Sepharose 4B. The protein fractions were trypsin-digested and the peptides were separated on a strong cation exchange (SCX) column. Peptide-SCX fractions were desalted and analyzed on a LTQ-Orbitrap mass spectrometer coupled online to a nano-reverse phase HPLC system. The resulting mass spectra were searched against the human forward and reverse IPI 3.62 database using both MASCOT and X!Tandem search engines, followed by integration of these data using Scaffold 2.06 software. A total of 818 non-redundant proteins were identified from all methods combined, with at least 1 peptide and a false positive rate <1.5%. Of the three methods, the MLC was the most efficient and resulted in the most proteins identified. Additionally, the MLC method also resulted in the greatest enrichment for secreted proteins. We previously characterized the proteomes of six pancreatic cancer cell lines and identified 4916 proteins with at least 1 peptide and false positive rate <1.0%. Approximately 35% of the proteins identified in the ascites were also identified in the cell lines. Based on KEGG pathway analysis, 6 pathways were overrepresented in the ascites in comparison to the cell lines, one of which was the complement and coagulation cascade (p=1.97E-23) which is in line with the mechanisms by which ascites formation occurs. Interestingly, another overrepresented pathway was pancreatic secretion (p=1.115E-3) which demonstrates the presence of biologically relevant pancreas-specific molecules in the ascites fluid of pancreatic cancer patients. This, to our knowledge, is the first proteome of pancreatic cancer ascites and represents a source to mine for potential biomarkers and therapeutic targets. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5103. doi:10.1158/1538-7445.AM2011-5103
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".