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Complex phenotypic differences among different acute leukemias cannot befully captured by analyzing the expression levels of one single molecule, suchas a miR, at a time, but requires systematic analysis of large sets of miRs.While a popular approach for analysis of such datasets is principal componentanalysis (PCA), this method is not designed to optimally discriminate differentphenotypes. Moreover, PCA and other low-dimensional representation methodsyield linear or non-linear combinations of all measured miRs. Global human miRexpression was measured in AML, B-ALL, and T-ALL cell lines and patient RNAsamples. By systematically applying support vector machines to all measuredmiRs taken in dyad and triad groups, we built miR networks using cell line dataand validated our findings with primary patient samples. All the coordinatelytranscribed members of the miR-23a cluster (which includes also miR-24 andmiR-27a), known to function as tumor suppressors of acute leukemias, appearedin the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysisshowed that the most connected miR in the B-ALL-centric network, miR-708, ishighly and specifically expressed in B-ALLs, suggesting that miR-708 mightserve as a biomarker for B-ALL. This approach is systematic, quantitative,scalable, and unbiased. Rather than a single signature, our approach yields anetwork of signatures reflecting the redundant nature of biological signalingpathways. The network representation allows for visual analysis of allsignatures by an expert and for future integration of additional information.Furthermore, each signature involves only small sets of miRs, such as dyads andtriads, which are well suited for in depth validation through laboratoryexperiments such as loss- and gain-of-function assays designed to drive changesin leukemia cell survival, proliferation and differentiation.

Uncovering low-dimensional, miR-based signatures of acute myeloid and lymphoblastic leukemias with a machine-learning-driven network approach