Abstract 2718: Genomic and microRNAome subtraction identifies pathogenic viral sequences in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is by far the most common type of pancreatic cancer. It constitutes about 90% of tumors of the exocrine pancreas. The aggressive nature of PDAC along with its poor diagnostic markers contributes to a high lethality of this disease. Studies from other cancer entities implicate the role of viruses in tumor development. However, there have been no established reports about virus(es) associated with pancreatic cancer. The present study identified a virus by genomic and digital microRNAome subtraction between healthy and PDAC patients, suggesting that this virus may play a role in carcinogenesis. Experimental procedures: Representational difference analysis (RDA) was utilized to perform an experimental genome-wide search for DNA sequences that occur in PDAC but not in normal tissues. Ten healthy and ten PDAC tissue DNA samples were used for the generation of representations and subtractive hybridization. The difference products (DPs) so obtained were paired end sequenced on the Illumina MiSeq platform. The sequencing data were aligned against the viral RefSeq database. We also did in silico subtraction of microRNA content (miRNA-seq) of PDAC samples. Eight PDAC samples were analyzed for viral sequences using relevant RefSeq databases. The two approaches (RDA and miRNA-seq) were pursued in parallel. RT-qPCR analysis and the in vitro functional characterization of viral sequences in PDAC cell lines were respectively performed using hydrolysis probes and second generation microRNA mimic systems. Stable cell lines expressing viral microRNA and luciferase were produced using lentivirus for mice in vivo studies. Results and Summary: We found DNA sequences of a virus in the difference products of RDA whose signature was also implicated in the microRNA analysis. Further, using RT-qPCR, we identified that a particular microRNA from this virus is expressed at significantly higher levels in PDAC samples than in normal tissue. This observation was also verified by droplet digital PCR analysis. Non-metastatic PDAC cell lines overexpressing viral microRNA showed significant invasion against relevant controls, interestingly with no significant change in proliferation. We are currently analyzing the functional consequences of these viral sequences and their involvement in carcinogenesis. Conclusion: From two different robust approaches, these findings strongly indicate viral sequences associated with PDAC that could affect pathways relevant to tumor development. Citation Format: Mohanachary Amaravadi, Agnes Hotz-Wagenblatt, Sandeep Kumar Botla, Pouria Jandaghi, Mehdi Manoochehri, Nathalia Giese, Markus W. Büchler, Andrea S. Bauer, Jörg D. Hoheisel. Genomic and microRNAome subtraction identifies pathogenic viral sequences in pancreatic ductal adenocarcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2718. doi:10.1158/1538-7445.AM2015-2718
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".