Abstract B01: MicroRNA expression in tumors and liquid biopsy samples from patients with pancreatic ductal adenocarcinoma: Identification of clinically relevant pathways
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".