Abstract B36: Developing circRNA signatures as a biomarker for the early diagnosis of pancreatic carcinoma
Bibliographic record
Abstract
Abstract Background: At the time of diagnosis, pancreatic ductal adenocarcinoma (PDAC) is typically already advanced and incurable. Current research has concentrated on finding tumor markers for early detection while the cancer is still localized and amenable to therapy, however, these markers remain elusive. The studies described focus on developing exonic circular RNAs (circRNA) as a novel set of diagnostic/prognostic biomarkers for PDAC. CircRNAs found in mammalian cells, are backsplice variants of transcripts that are derived from approximately 15% of actively transcribed genes. The prevalence, stability and cell-specific expression patterns of circRNAs suggest that they could be exploited as an indirect or surrogate readout of transcriptional activity in normal and diseased states. Both coding and non-coding RNAs are encapsulated within cytoplasmic endosomes, which are subsequently released as extracellular or circulating microvesicles called exosomes. Exosomes have become a promising research focus as a source for biomarkers. Objective: We are interested in elucidating whether aberrantly expressed genes in PDAC produce different types of circRNAs that become enriched in tumor-secreted exosomes. Hypothesis: Exosomal circRNA (exo-circRNA) expression patterns are potentially specific to different stages/types of PDAC and therefore can be used in disease sub-typing and prognosis. Methods: Exosomes were isolated from a normal pancreatic exocrine cell line (htert-HPNE) as well as three PDAC cell lines ranging from well to poorly differentiated, including PANC-1, BxPC3and MIAPaCa-2. The size and relative abundance of exosomes was quantified by transmission electron microscopy (TEM). The expression of common exosomal markers (CD63, CD9, CD81, and HSP70) and the PDAC exosomal marker glypican-1 (GPC-1) was evaluated by flow cytometry. RNA was purified from exosomes (exo-RNA) and the rRNA depleted samples were subject to circular RNA isolation. Exo-circRNA was used to construct RNA-Seq libraries. Sequencing of the generated libraries was performed on the Illumina Nextseq platform using 2x100 reads V1 chemistry at a targeted depth of 25 million paired end reads per library. The four read libraries were mapped to the human reference genome GRCh38.p3 using BWA-MEM, and analyzed using two bioinformatics platforms, “CIRI” and “find_circ”. Comparison of membership and expression levels was made between a normal cell line and well-, moderately- and poorly differentiated, PDAC cell lines. Results: Exosome size ranged from 20nm to 80nm. These structures demonstrated some diversity in size and marker expression when comparing cell lines. The smallest structures were observed from BxPC3 cell. Here, we show for the first time the presence of circRNAs in exosomes collected from PDAC cell lines. RNA-seq analyses revealed a number of interesting circRNA species that show cell line specificity. Preliminary examination of PANC-1 RNA-seq libraries from the ENCODE database identified over 800 circRNA isoforms from total cellular transcriptome. The number of circRNA isoforms for PANC-1 cells decreased when using an enriched exo-circRNA library for alignment to approximately 19 putative circRNA markers. Furthermore, circRNA isoforms for each of the cell lines examined were distinct. Interestingly, no circRNAs of genes known to be overexpressed in PDAC (such as K-RAS) were found in the fraction of exo-cricRNA for any of the cell lines tested. Impact: The studies described demonstrate that specific circRNAs can be readily extracted from the exosomes of conditioned media. We hope that this novel tool can be further developed to help to diagnose pancreatic carcinoma when it is amenable to surgical resection and/or chemotherapy, thereby reducing the mortality associated with this disease. Citation Format: Jessica Kalra, Keith Laderoute, Daniel Renouf, David Shaeffer, Marcel Bally. Developing circRNA signatures as a biomarker for the early diagnosis of pancreatic carcinoma. [abstract]. In: Proceedings of the AACR Special Conference on Noncoding RNAs and Cancer: Mechanisms to Medicines ; 2015 Dec 4-7; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2016;76(6 Suppl):Abstract nr B36.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".