Abstract B01: MicroRNA expression in tumors and liquid biopsy samples from patients with pancreatic ductal adenocarcinoma: Identification of clinically relevant pathways
Notice bibliographique
Résumé
Abstract Background: Pancreatic carcinoma leads to 6.9% and 4% of all cancer-related deaths in the United States and Brazil, respectively. Pancreatic ductal adenocarcinoma (PDAC) comprises ~90% of pancreatic cancer cases and patients have a poor prognosis, mainly due to asymptomatic disease that leads to late diagnosis. Considering that diagnosis of disease in advanced stages is one of the main factors associated with mortality, the identification of circulating biomarkers in tumor and plasma (liquid biopsy) from patients is believed to be a clinically relevant strategy for early disease detection and treatment response monitoring. Objectives: We aimed to identify global microRNA (miRNA) expression changes in primary untreated tumors and plasma from patients diagnosed with PDAC. Deregulated miRNAs were mapped to miRNA-target genes, and PDAC tumorigenesis pathways were identified. Patients and Methods: 24 formalin-fixed, paraffin-embedded (FFPE) tumors and their paired normal pancreatic tissues were needle microdissected. In addition, 4 plasma samples from patients diagnosed with PDAC and 10 age-matched controls from individuals without disease were obtained. All samples were profiled using the TaqMan Array Human MicroRNA Cards (TLDA) (card A, v3.0) (Life Technologies). Data analysis was performed using ExpressionSuite Software v1.0.3. Computational miRNA target gene identification was performed using microRNA Data Integration Portal (mirDIP). Comprehensive pathway enrichment analysis based on identified miRNAs and target genes was performed using Pathway Data Integration Portal (pathDIP). Data were considered significant with Bonferroni corrected p-values. Results and Discussion: 63 miRNAs (33 over- and 30 underexpressed) were significantly deregulated (FC≥2 and p<0.05) in PDAC compared to paired normal pancreatic tissue. In plasma, 25 miRNAs were under- and 16 were overexpressed. Of these, 6 miRNAs were commonly deregulated in both tumor and plasma. Interestingly, 420 genes were identified as targeted by at least 2 of these 6 miRNAs. AKT, Insulin and VEGFR1 signaling pathways were identified as the most significant disease-associated mechanisms affected by miRNA target genes. Conclusions: A 6-miRNA subset is commonly deregulated in plasma and tumors and associated with important signaling pathways in PDAC. miRNAs are likely valuable diagnostic and predictive biomarkers for patients with PDAC. Our data build on existing knowledge that liquid biopsy samples are a clinically useful and minimally invasive source for the development of molecular testing that should be translated to the clinical setting. Financial Support: TFF was awarded grant #2014/00367-4, São Paulo Research Foundation (FAPESP); TFF and NB a CAPES-DS Master's Science fellowship. Computational analysis was supported in part by Canada Research Chair Program (#225404), Canada Foundation for Innovation (CFI #225404, #30865), Ontario Research Fund (#34876), IBM (IJ). Citation Format: Tainara F. Felix,* Natalia Bertoni,* Tomas Tokar, Maria A. M. Rodrigues, Rogerio A. Oliveira, Claudia N. Hasimoto, Juan C. Llanos, Igor Jurisica, Sandra A. Drigo, Robson F. Carvalho, Patricia P. Reis. MicroRNA expression in tumors and liquid biopsy samples from patients with pancreatic ductal adenocarcinoma: Identification of clinically relevant pathways [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr B01.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».