Abstract 5103: Mining the pancreatic cancer ascites fluid proteome with mass spectrometry
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».