Abstract 4560: Integrative proteomic profiling of pancreatic cancer cell line supernatants, pancreatic juice and ascites fluid for the identification of novel pancreatic cancer biomarkers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 ».