Abstract B1: Validation of a panel of candidate pancreatic cancer serum biomarkers identifies SYCN and REG1B to significantly improve the performance of CA19.9.
Notice bibliographique
Résumé
The identification of new serum biomarkers with high sensitivity and specificity to aid in the detection and management of pancreatic cancer is an important priority in the field. Given that proteins which are secreted or shed from tumor cells and their microenvironment have the highest chance of reaching the circulation and serving as serum biomarkers, we previously characterized the proteomes of pancreatic cancer-related proximal biological fluids. Specifically, the proteomes of conditioned media (cell culture supernatants) from six pancreatic cancer cell lines (BxPc3, MIA-PaCa2, PANC1, CAPAN1, CFPAC1 and SU.86.86) and the normal human pancreatic ductal epithelial cell line HPDE, as well as pancreatic juice from patients with pancreatic ductal adenocarcinoma (PDAC) were analyzed using strong cation exchange liquid chromatography, followed by LC-MS/MS on an LTQ-Orbitrap mass spectrometer. Over 3,500 non-redundant proteins were identified with high confidence (≥ 2 peptides). Subsequent bioinformatics-based biomarker filtration criteria, incorporating both quantitative and qualitative evaluations, led to the generation of candidate pancreatic cancer biomarkers for validation. The present study details the validation of five selected candidates, regenerating islet-derived 1 beta (REG1B), syncollin (SYCN), anterior gradient homolog 2 protein (AGR2), polymeric immunoglobulin receptor (PIGR), and lysyl oxidase-like 2 (LOXL2), using enzyme-linked immunosorbent assays (ELISAs) in 388 serum samples. The samples included 182 PDAC, 92 healthy controls, 44 benign pancreatic diseases and 70 samples from individuals with other gastrointestinal cancers. Statistical analyses showed three of the tested candidates to be significantly elevated in PDAC versus healthy controls (p=1.95E-13, p=3.43E-11, 9.80E-07 for REG1B, SYCN and PIGR, respectively). CA19.9, the marker currently used clinically for pancreatic cancer management was also assessed (p=2.88E-24 in PDAC versus healthy controls). Individually, CA19.9 performed superiorly to the tested candidates in this sample set; however in combination, the addition of REG1B and SYCN was able to significantly improve the area under the receiver operating characteristic curve of CA19.9 to 0.92 (95% confidence interval of 0.8864-0.9486; p-value of panel in comparison to AUC of CA19.9 alone = 0.0012). The present study details a large validation of five candidates identified through comprehensive proteomics strategies, through which the addition of REG1B and SYCN in a panel with CA19.9 was found to improve the performance of CA19.9 alone. Further validation of these candidates in larger sample sets for purposes of pancreatic cancer detection and management is highly warranted, as is assessment of their performance when used in combination with other, yet untested, candidates. This work was supported by the Ontario Institute for Cancer Research, Grant # 10 NOV-498 (EPD Principal Investigator). Citation Format: Shalini Makawita, Apostolos Dimitromanolakis, Antoninus Soosaipillai, Ireena Soleas, Alison Chan, Steven Gallinger, Randy S. Haun, Ivan Blasutig, Eleftherios P. Diamandis. Validation of a panel of candidate pancreatic cancer serum biomarkers identifies SYCN and REG1B to significantly improve the performance of CA19.9. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Progress and Challenges; Jun 18-21, 2012; Lake Tahoe, NV. Philadelphia (PA): AACR; Cancer Res 2012;72(12 Suppl):Abstract nr B1.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».