Searching for biomarkers in LCM samples from prostate cancer miRCURY LNA™ Universal RT microRNA PCR
The Zehner lab
lab at Virginia Commonwealth University is working on identifying microRNA biomarkers that can be used to detect tumor progression, follow treatment outcomes, and suggest alternative therapies. By using LCM they can survey all the different cell-types that comprise the tumor versus its adjacent normal environment.
1. What is the main focus of the research conducted in your lab?
The main focus of research in my laboratory is the identification of micoRNAs which promote prostate cancer. Currently, the usefulness of PSA for early detection of prostate cancer has become debatable. More reliable biomarkers are needed to accurately detect prostate cancer early. With the finding that microRNAs are secreted as stable particles, the determination of microRNAs which drive prostate tumorgenesis offers alternatives as biomarkers to detect and stage tumor progression, follow treatment outcomes, and suggest alternative therapies. Ultimately, this is the goal of our research.
2. How did your research lead to the study of microRNAs?
Our lab came to the study of microRNAs by a circuitous route. For years we had been studying transcriptional regulation when we noticed that the 3'UTR of a gene of interest was as conserved as coding regions. Obviously, it must serve some biological function to be so conserved, but what? We later discovered that the 3'UTR contained binding sites for several microRNAs, one of which was microRNA-17-3p. We subsequently documented that microRNA-17-3p is reduced by 90% in RNA extracted from human prostate tumor cells compared to normal epithelium or stroma as retrieved by laser capture microdissection (LCM) [ Zhang et al. Clin. Exp. Metastasis 26:965-979,2009
]. More importantly, this change in microRNA-17-3p expression was never detected in RNA extracted from whole tumors compared to tumor- free material. Our result documents the heterogeneous composition of solid whole tumors like prostate and the necessity to move to cell-specific analysis rather than whole tumor studies to find dysregulated microRNAs that contribute to cancer.
3. What is the aim of your current project?
Based on our previous results, we have expanded our LCM analysis to search for other microRNAs whose differential expression could contribute to prostate cancer. We are conducting this analysis in a variety of human prostate cancer cell lines such as p69, M12, F6, DU145, PC3 and the LnCap series. Today, we realize there is a delicate balance of interaction within the tumor microenvironment, which if interrupted, could tip the outcome towards cancer. With the use of LCM we can survey all the different cell-types that comprise the tumor versus its adjacent normal environment to determine how this balance is interrupted and eventually what needs to be restored. Thus, we are not limited to just cell lines, which can't accurately duplicate the tumor microenvironment. In addition, we can search for microRNAs that could become relevant biomarkers for the diagnosis, staging, and ultimate treatment of prostate cancer.
4. What were the key factors for you in choosing a microRNA qPCR system?
Since we are using LCM our amount of high quality RNA is limiting. Thus, we need a qPCR method which is highly specific, reproducible, and reliable yet distinguish different microRNA family members, which differ by only a single nucleotide change.
5. What do you find are the main advantages of the miRCURY LNA™ microRNA PCR system?
One major advantage for using the Exiqon LNA™ system is that our precious RNA can be quickly converted into cDNA and stockpiled. At a later date cDNA can be analyzed for any number of different microRNAs. This is superior to other qPCR methods such as Taqman, where the decision of which microRNA to analyze has to be made at the time of conversion of RNA to cDNA. Exiqon's methodology allows for flexibility so that we can readjust our microRNA analysis to microRNAs of interest as they are discovered. The LNA™ technology also enables us to easily discern between nearly identical family members and we have found great reproducibility between duplicate microRNA array screens using different amounts of input RNA, i.e., 20 ng versus 50 ng. Once normalized, Ct values were spot on. Moreover, Exiqon technical services have worked with us in maximizing the best results from our analysis. Thus, we find Exiqon methodology is a big improvement over what is available and for this reason, we will continue to use their products in our research.
6. How is the project proceeding so far?
We have screened Exiqon's human panel of microRNAs with RNA extracted from several isogenic human prostate cell lines that represent various stages of prostate cancer progression; for example, p69, a normal epithelial cell line, versus its highly tumorigenic, metastatic variant. It is customary to next confirm differences from the microRNA array screens by single microRNA analysis. In the literature it has been reported that roughly 50% of those microRNAs purported to be differentially expressed in microRNA array screens hold up in single microRNA qPCR analysis. Although in some cases the actual fold difference between the array and single microRNA analysis didn't produce the exact same number, the overall direction was usually maintained. For example, we found 24 microRNAs differentially expressed between our cell lines. By single microRNA analysis, twenty of these held true. Another two were found to be so lowly expressed in one cell line, that relevant comparisons across all cell lines could not be made. It is always difficult to get Ct values within the reproducible linear range across a variety of cell lines and is not a reflection on Exiqon reagents. Overall, our result was substantially better than the 50% confirmation rate reported in the literature.
7. What were some of the specific challenges in this project and how did you overcome them?
We have found that one needs to look for the best endogenous normalization RNA and not assume those used by others are necessarily the best for your model system. This is worth spending some time analyzing up-front before analyzing full plates of single microRNAs without the right normalization standard. While both RNU6B and SNORD48 are considered to be small RNAs that are produced at consistent rates, we found that RNU6B varied from cell line to cell line in our system, and SNORD48 was more consistent between and within both cell lines and RT reactions.
8. What are the next steps/future perspectives for this research?
Our next step is to modulate expression of these dysregulated microRNAs in the appropriate cell line and assess the resulting effect on cell proliferation, migration and invasion and ultimately on tumor growth and metastasis via orthotopic injection into male, athymic nude mice. Currently, we are beginning to analyze blood, urine and semen samples from prostate cancer patients to compare to the results generated from these studies. By this dual approach we hope to bridge the gap between basic and translational research to enhance our search for relevant biomarkers.