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Commentary: Identification of durable, assessable biomarker signatures for parasite-induced cholangiocarcinoma
June 2016
by Jordan Plieskatt of GWU  |  Email the author


Identification of durable, assessable biomarker signatures for parasite-induced cholangiocarcinoma
By Jordan Plieskatt of The George Washington University
Biomarkers that are present in body fluids, such as blood or urine, have the potential to form the basis of affordable diagnostic tests to detect diseases in their earliest stages and monitor disease progression. Such diagnostic tests allow appropriate treatment for the disease stage and can allow vital treatment where early intervention is crucial or diagnosis currently difficult.
Biomarkers for infection-related cancers and neglected tropical diseases
In particular, the management of neglected tropical diseases (NTDs) will benefit from biomarker-based diagnostic tests. NTDs are estimated to affect a billion people1 in low- to middle-income countries. Many occur in remote locations, which—along with the poverty individuals often face—makes access to healthcare and medicines difficult.
Screening for such diseases allows infections to be treated before potentially severe complications develop, including infection-related cancers. The identification of biomarkers in easily accessible biofluids will aid screening and treatment efforts by enabling straightforward sample collection in the field without complex and expensive equipment.
One such infection-related cancer and NTD is parasite-induced cholangiocarcinoma (CCA), a cancer of the bile ducts that is associated with infection by the food-borne trematode Opisthorchis viverrini (OV).2 This cancer is extremely prevalent in Thailand and surrounding countries of the Mekong river basin, where OV is transmitted by ingestion of raw fish.
Once ingested, the parasite enters the biliary tract, where it causes chronic inflammation that leads to advanced periductal fibrosis (APF) and eventually CCA. Currently, diagnosis and monitoring is done by ultrasound or liver resection, as not all OV infections progress to CCA. A lack of widespread and accessible early screening means that CCA is often diagnosed at a late stage when morbidity and mortality are high; by diagnosis, expected life expectancy is generally under two years.3
The outcomes of such malignancies could be strongly influenced by the identification of assessable and durable biomarkers that are derived from crude body fluids and can be stored in tropical settings. High-throughput analysis of CCA biomarkers using common instrumentation would allow earlier diagnosis of OV-induced CCA, better monitoring of disease progression and ultimately clinical intervention (Figure 1).
Detecting and validating miRNA biomarker signatures
Several characteristics of microRNAs (miRNAs) make them particularly suitable as biomarkers for this purpose. Their presence in accessible biofluids such as blood, urine and saliva makes sample collection relatively straightforward and non-invasive. In addition, they are stable in these biofluids, aiding storage and transportation of samples.
Crucially, miRNA expression alters under disease states; altered miRNA profiles have been identified for diseases, including various cancers,4 and our lab has identified changes to miRNA expression in OV-induced CCA (Figure 2). In particular, we have identified distinct miRNA profiles associated with increasing histological differentiation of the tumor.5
However, developing miRNA biomarker signatures to characterize a disease is complex and challenging. During an initial discovery phase, a large number of miRNAs are screened in a small number of samples, often in a matrix (e.g. tumor tissue) that is not intended to be the ultimate point-of-care sample (e.g. biofluid). The discovery phase is followed by validation of promising candidates in the point-of-care matrix, using a much larger sample set. Potential miRNAs are narrowed down at this point, but this can result in exclusion of promising candidates and critical insights being missed, a particular issue if small sample volumes prevent repeat experiments.
Logistical challenges arise in the validation phase from detecting multiple miRNAs in samples from thousands of patients. Although microarrays and miRNA-seq are commonly used to identify miRNA signatures, using these techniques to validate miRNA signatures is costly and time-consuming. Higher-throughput analysis with qPCR also presents additional limitations.
The most critical barrier to miRNA analysis is sample preparation and purification. Current methods are not only prone to human error, but have profound effects on RNA yield and sample throughput. Often only a small amount of each patient sample is available, having been biobanked prior to study conception. miRNA yield can be a major problem for microarrays and RNA-seq, and further challenges are often encountered.
Although qPCR requires less miRNA, heparin—an anticoagulant often used for blood collection—can contaminate plasma samples and inhibit the reverse transcription step, and consequently interferes with and limits the results achieved through qPCR. These effects can be improved with heparinase treatment,6 but rescuing plasma samples still presents a challenge.
Ideally, miRNAs would be detected directly from small volumes of crude biofluid, with no RNA purification, using an approach that minimizes or eliminates the effects of PCR inhibitors.
Identifying CCA biomarkers
Research in our lab at The George Washington University is part of an ongoing program to identify durable and accessible biomarkers for OV-induced CCA. We have access to a comprehensive set of longitudinal samples from thousands of patients in Thailand that are extensively annotated with clinical information, including positive confirmation of APF or CCA. However, samples consist of a range of biofluids including plasma and urine, and are often limited to only 200 µL of sample available for analysis.
Consequently, using this resource to identify suitable biomarkers of OV-induced CCA relies on detecting multiple miRNAs using a small sample volume, with a technique that can handle different biofluids, including heparin-contaminated plasma samples.
Through initial investigation of tissue and sera with microarrays and RNA-seq, a signature involving 50 to 60 miRNAs was identified, whose expression changes throughout OV-induced CCA. We wanted to further verify this complex and available signature in different biofluid samples; however, due to the limitations outlined above, taking forward this many miRNAs would have been impractical using conventional approaches.
Putting miRNA particle technology to the test
To progress our research, we used Abcam’s Firefly multiplex miRNA particle technology. This multiplex technology allows us to measure as many as 68 miRNAs from a sample in a single well, letting us move all our miRNA candidates forward in the study. Additionally, the assay requires just 30 µL to 50 µL of crude biofluid sample—preserving sample to run as duplicate or a technical replicate.
Initial studies aimed to see if the technology could detect miRNAs from a range of biofluids. In close collaboration with the Abcam team, we firstly used non-study plasma and serum samples to test if heparin interfered with assay readout. The results revealed that heparin had no effect on the assay, removing the need to pre-treat samples, and allowing us to leverage our extensive cohort of samples.
Additionally, the data showed that there was a strong correlation between different sample matrices, meaning that data from serum samples could be directly compared to plasma samples (Figure 3). This again would allow us to use a comprehensive biorepository of study samples from a variety of cohorts in our verification stage.
With the confidence that the protocol and technology could deliver, we could start to assess CCA cancer patient samples from the biorepository. These study samples were extensively annotated with clinical information, including positive confirmation of APF or CCA via ultrasound. It was crucial for our study to ensure that the technology could detect miRNAs from samples that had been stored for up to 10 years, and in different sample matrices, including urine. We used pooled study samples from four different groups of patients: OV negative, OV positive with no signs of cancer, AFP positive and CCA positive. Using a stock oncology panel, we were able to detect miRNAs from serum, urine and saliva with the Firefly assay, including regulation and fold change attributed to the various stages of the disease.
Finally, to see if we could use this technology to detect 50 target miRNAs (out of 68 miRNAs including additional controls) from the CCA signature we had previously identified using microarray and RNA-seq, we developed a custom miRNA panel and analyzed over 400 serum, urine and saliva samples. Out of 50 target miRNAs in the custom panel, we were able to detect 48 (plus additional controls) above the threshold value in our various sample matrices and study groups (Figure 4).
These results highlight the impact the Firefly assay will have for our research, allowing us to explore custom multiplex miRNA signatures in a variety of patient samples and matrices. The challenges we have faced with our research are common amongst all biomarker research; overcoming these has highlighted the potential of multiplex miRNA particle technology to help other researchers as they attempt to identify and validate miRNA biomarker signatures.
Conclusion and outlook
Using microarrays and miRNA-seq, we identified a biomarker signature that had potential to detect OV-induced CCA and differentiate between different disease stages. However, time, cost and small sample volumes meant we were unable to use this multiplex signature in a large number of patient samples.
The multiplex miRNA particle technology we used allowed us to address these key challenges, enabling us to progress faster and further with our research using crude biofluid samples. To run the experiments discussed in this article as individuals would take approximately 15 times the man hours and cost about 10 times as much in materials.
Going forward, this work is pivotal in the ongoing development of an affordable diagnostic test for OV-induced CCA. Further, in our passion to address NTDs and in particular infection-related cancers, this technology presents itself to be adaptable in resource-limited settings.

Jordan Plieskatt is part of the Department of Microbiology, Immunology, and Tropical Medicine faculty and a senior research associate in the Bethony Lab at The George Washington University in Washington, D.C. He has more than 15 years of biotechnology and product development experience focused on the analysis, manufacture and preclinical testing of recombinant proteins and formulated vaccine products.
1 World Health Organization. First WHO report on neglected tropical diseases: working to overcome the global impact of neglected tropical diseases, World Health Organization, Geneva, Switzerland (2010).
2 International Agency for Research on Cancer. Schistomes, liver flukes and helicobacter pylori. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans 61, 218–21 (1994).
3 Farley DR, Weaver AL, Nagorney DM. “Natural history” of unresected cholangiocarcinoma: patient outcome after noncurative intervention. Mayo Clin Proc 70, 425–429 (1995).
4 Iorio MV and Croce CM. MicroRNA dysregulation in cancer: diagnostics, monitoring and therapeutics. A comprehensive review. EMBO Mol Med 4, 143–59 (2012).
5 Plieskatt J, Rinaldi G, Feng Y, Peng Y, Easley S, Jia X, Potriquet J, Pairojkul C, Bhudhisawasdi V, Sripa B, Brindley PJ, Bethony J, Mulvenna J. A microRNA profile associated with Opisthorchis viverrini-induced cholangiocarcinoma in tissue and plasma. BMC Cancer 15, 309 (2015).
6 Plieskatt JL, Feng Y, Rinaldi G, Mulvenna JP, Bethony JM, Brindly PJ. Circumventing qPCR inhibition to amplify miRNAs in plasma. Biomarker Research 2, 13 (2014).
Figure 1
Figure 1. (A) Hypothesized progressive clinical stages of Opisthorchis viverrini (OV)-induced Cholangiocarcinoma.* Following infection, the clinical progression involves intermediary stages including advanced periductal fibrosis. Intervention efficacy decreases as the likelihood of disease progression increases, emphasizing the importance of biomarkers (both diagnostic and prognostic) at the various disease stages where intervention efficacy may be most effective. (B) Normal liver (20x). Normal liver FFPE section stained with hematoxylin and eosin (H&E) and used among control tissue samples for initial miRNA analysis. (C) Papillary Adenocarcinoma (20X). A representative image from FFPE of an invasive component of papillary adenocarcinoma (a subset of CCA) stained with H&E used in initial experiments to identify dysregulated miRNAs. Additional histological classifications were also analyzed including moderately and well differentiated FFPE sections amongst an initial set of 16 CCA cases (FFPE) with paired serum samples along with additional control cases.
*Schematic adapted from first appearance in: Saichua P et al; Microproteinuria during Opisthorchis viverrini infection: a biomarker for advanced renal and hepatobiliary pathologies from chronic opisthorchiasis; PLoS Negl Trop Dis. 2013
Figure 2
Figure 2. Firefly discovery engine visualization of terminology and miRNA prevalence. (A) A visual representation of search result terms related to cholangiocarcinoma and their prevalence in published literature. (B) Visual output of current search results for miRNAs in published literature related to cholangiocarcinoma. A total of 70 miRNAs are currently reported in literature related to CCA including among the most prevalent, miR-21, miR-200, miR-222 and miR-221, which have also been identified in our research.
Figure 3a
Figure 3b
Figure 3. (A) Heatmap output from various sample matrices utilizing a stock Abcam Firefly multiplex circulating panel. Four sample matrices were analyzed including sera and also plasma derived from: sodium citrate, lithium heparin and sodium heparin preparations. Utilizing Abcam’s Firefly particle technology, no detectable interference was seen from residual heparin. In addition, Pearson’s correlation values of 0.90-0.99 were obtained from miRNA analysis from the same individual but different sample preparations (e.g. sera vs lithium heparin) as seen in (B).
Figure 4
Figure 4. Visualization of miRNAs detected across CCA study samples. A customized Abcam Firefly multiplex circulating panel was used to analyze over 400 study samples across multiple matrices (Saliva, urine, sera and plasma) and study groups (including controls, APF and CCA). The Firefly multiplex panel was able to detect the majority of miRNAs from the study samples (green highlights) across all study groups and matrices. From this initial study with the Abcam technology, significantly dysregulated miRNA were identified from the various analyses and are a part of our further research for an OV-induced CCA biomarker.
Code: E061640



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