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

机译：通过机器学习驱动的网络方法发现基于miR的低维特征性急性髓样和淋巴细胞白血病

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Complex phenotypic differences among different acute leukemias cannot be fully captured by analyzing the expression levels of one single molecule, such as 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 component analysis (PCA), this method is not designed to optimally discriminate different phenotypes. Moreover, PCA and other low-dimensional representation methods yield linear or non-linear combinations of all measured miRs. Global human miR expression was measured in AML, B-ALL, and TALL cell lines and patient RNA samples. By systematically applying support vector machines to all measured miRs taken in dyad and triad groups, we built miR networks using cell line data and validated our findings with primary patient samples. All the coordinately transcribed members of the miR-23a cluster (which includes also miR-24 and miR-27a), known to function as tumor suppressors of acute leukemias, appeared in the AML, B-ALL and T-ALL centric networks. Subsequent qRT-PCR analysis showed that the most connected miR in the B-ALL-centric network, miR-708, is highly and specifically expressed in B-ALLs, suggesting that miR-708 might serve as a biomarker for B-ALL. This approach is systematic, quantitative, scalable, and unbiased. Rather than a single signature, our approach yields a network of signatures reflecting the redundant nature of biological signaling pathways. The network representation allows for visual analysis of all signatures by an expert and for future integration of additional information. Furthermore, each signature involves only small sets of miRs, such as dyads and triads, which are well suited for in depth validation through laboratory experiments. In particular, loss-and gain-of-function assays designed to drive changes in leukemia cell survival, proliferation and differentiation will benefit from the identification of multi-miR signatures that characterize leukemia subtypes and their normal counterpart cells of origin.
机译：一次分析一个单一分子（例如miR）的表达水平无法完全捕获不同急性白血病之间复杂的表型差异，但需要对大量miR进行系统分析。尽管分析此类数据集的一种流行方法是主成分分析（PCA），但该方法并非旨在最佳地区分不同的表型。此外，PCA和其他低维表示方法可得出所有测得的miR的线性或非线性组合。在AML，B-ALL和TALL细胞系以及患者RNA样品中测量了总体人类miR表达。通过将支持向量机系统地应用于在二重体和三重体组中测得的所有miR，我们使用细胞系数据构建了miR网络，并用主要患者样本验证了我们的发现。 miR-23a簇（也包括miR-24和miR-27a）的所有协调转录成员（已知是急性白血病的肿瘤抑制因子）出现在以AML，B-ALL和T-ALL为中心的网络中。随后的qRT-PCR分析表明，以B-ALL为中心的网络中连接最紧密的miR-miR-708在B-ALL中高度特异性地表达，这表明miR-708可能是B-ALL的生物标记。这种方法是系统的，定量的，可伸缩的且无偏见的。我们的方法不是单一签名，而是产生了一个反映网络生物信号通路冗余性质的签名网络。网络表示允许专家对所有签名进行可视化分析，并在将来集成其他信息。此外，每个签名仅涉及一小部分miR，例如dyad和triad，非常适合通过实验室实验进行深度验证。特别是，旨在驱动白血病细胞存活，增殖和分化变化的功能丧失和获得功能测定法将受益于鉴定表征白血病亚型及其起源的正常对应细胞的多重miR信号。

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