Human MicroRNAs: Powerful Biomarkers for Cancer Diagnostics
Authors: Zvi Bentwich
Journal: Clinical Cancer Research (2007)

PL05-02    Background:  MicroRNAs are a family of 20-25 nucleotides non-coding small RNAs, that suppress translation of target genes by binding to their mRNA, thereby regulating the expression of at least 30% of all human genes. The total number of human microRNAs is at least 800, and though highly conserved throughout evolution, a significant proportion of them are primate specific and are highly expressed in developmental tissues. Previous studies have highlighted the regulatory function of individual microRNAs in diverse biological processes, however, their association with cancer and development and their tissue specificity are clearly emerging as some of their dominant features. The importance of microRNAs in cancer is underscored by the finding that a large proportion of them are located in cancer-associated genomic regions and that a strong association has been found between altered microRNA expression and cancer that in some instances is associated with a single type of tumor, while in others, it occurs in more than one tumor type. 3%~5% of new cancer cases are metastatic cancers of unknown primary. The process of identifying the tissue source of primary tumor is lengthy, costly and often inaccurate. Several mRNA-based classifiers have been applied for classification of tissue origin, none have achieved a high enough sensitivity combined with essential biological significance. Genome-wide expression profiles of microRNAs were found to be informative for tumor classification, but the biological significance of individual microRNAs or their clinical utility has not been sufficiently investigated. Rosetta Genomics has developed a powerful platform for profiling and characterizing all known and several hundreds novel human microRNAs. We shall present the application of this platform to determine the tissue origin for cancers of unknown primary, based on the development of a highly accurate microRNA-based decision tree for tumor classification. We measured the expression-levels of hundreds of microRNAs in several fresh and paraffin-embedded samples from 15 different tumor tissues, and constructed a transparent classifier based on a limited number of microRNAs, with a clear relevance of these microRNAs to specific biological roles.   Methods:  In order to develop a microRNA-based tumor classifier, we collected and profiled 228 fresh-frozen and FFPE samples representing 15 different primary tumor types. The tumor tissues included 13 epithelial tumor sites as well as non-epithelial cancers, with a selection of histological sub-types. Since formalin-fixed paraffin-embedded samples (FFPE) are an invaluable source for tumor samples, we developed a method for extracting RNA from FFPE samples which preserves the microRNA fraction. We compared RNA extracted from fresh-frozen samples, formalin-fixed samples, or FFPE blocks, and demonstrated that the RNA quantity and quality was similar for all preservation methods. Furthermore, the microRNA profile was stable in FFPE samples for as long as 11 years of storage. MicroRNA profiling was performed on Rosetta Genomics’ miRdicator TM  microarrays , containing probes for all microRNA in miRBase and additional microRNAs which were predicted and validated internally at Rosetta Genomics. The microarray is highly reproducible and demonstrates a sensitivity of 0.1 fmole, with a linear dynamic range of about 3 orders of magnitude, and 10-fold signal reduction for a single nucleotide mismatch. We built a tumor classifier using the microRNA expression levels by applying a binary tree classification scheme . This framework is set up to utilize the potential specificity of microRNAs in tissue differentiation and embryogenesis: different microRNAs may be involved in various stages of tissue specification.   Results:   Several hundreds (~500) tumor and normal tissue samples were obtained from patients with 15 distinct tumor types and were subjected to microRNA expression profiling. The tumor tissues included 13 epithelial tumor sites as well as non-epithelial cancers, with a selection of histological sub-types Using our algorithm for tissue classification, correct tissue source was identified using only 28 microRNAs. This resulted in a small number (2~3) of microRNA features per node in the decision tree, totaling 28 microRNAs for the full classifier. All of the node classifiers had a p-value of less than 0.001, indicating the high degree of specificity of the microRNA expression levels between the tissue types in the two branches. Overall, the accuracy of determining tissue origin of tumors reached 94%, with increased accuracy observed in subsets of tumor categories. The design of the algorithm allows us to easily trace the strong points in the classification, and to incorporate additional information where required. These results demonstrate the robustness and stability of the classifier, and suggest an essential biological significance for the 28 microRNAs used.   Conclusion:  1) A simple microRNA-based algorithm for identifying tissue origin of tumors has been developed. 2) The tissue specificity of  microRNAs permits the identification of tumor origin based on a surprisingly small number of microRNAs. 3) The accuracy of this tool in determining the tissue origin of the primary unknown cancer is high and at least comparable to other, more complex, molecular tools currently available. 4) MicroRNAs constitute an important new player in molecular diagnostics of cancer. 5) The results support the notion that microRNAs are useful biomarkers that may be used to classify tumors and to augment current clinical capabilities.