ceRNA networks Cupid simultaneos reconstruct on of microRNA-target and

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Hua-Sheng Chiu, David Llobet-Navas, Xuerui Yang, et al. Genome Res. published online November 5, 2014Access the most recent version at doi:10.1101/gr.178194.114 Cupid: simultaneous reconstruction of microRNA-target andceRNA networksP<P AcceptedManuscript CreativeCommons License

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Published by Cold Spring Harbor Laboratory Press

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Cupid: simultaneous reconstruction of

microRNA-target and ceRNA networks

Hua-Sheng Chiu1,2,3,7,12, David Llobet-Navas8,12, Xuerui Yang9, Wei-Jen Chung1,2,3, Alberto Ambesi-Impiombato4,5, Archana Iyer1, Hyunjae Ryan Kim10, Elena G. Seviour11, Zijun Luo11, Vasudha Sehgal11, Tyler Moss11, Yiling Lu11, Prahlad Ram11, José Silva8, Gordon B Mills11, Andrea Califano1,2,3,4,5,6,* & Pavel Sumazin7,*

1

2

3

4

5

6 Department of Systems Biology, Center for Computational Biology and Bioinformatics, Department of Biomedical Informatics, Institute for Cancer Genetics, Herbert Irving Comprehensive Cancer Center, Department of Biochemistry and Molecular Biophysics,

Columbia University, New York, New York, 10032, USA

Texas Children's Cancer Center, Baylor College of Medicine, Houston, Texas, 77030, USA

Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, New York, 10029, USA 78

9 MOE Key Laboratory of Bioinformatics, Tsinghua-Peking Center for Life Sciences,

School of Life Sciences, Tsinghua University, Beijing 100084, China

Laboratory of RNA Molecular Biology, Rockefeller University, 1230 York Avenue, Box 301, New York, NY 10065, USA

Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas, 77030, USA

These authors contributed equally to this work 101112

* Correspondence: califano@c2b2.columbia.edu,

Running title: Cupid: miRNA-target and ceRNA networks

Key words: microRNA, ceRNA, reverse phase protein array (RPPA), breast cancer

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Abstract

We introduce a method for simultaneous prediction of microRNA-target interactions and their mediated competitive endogenous RNA (ceRNA) interactions. Using high-throughput validation assays in breast cancer cell lines, we show that our integrative approach significantly improves on microRNA-target prediction accuracy as assessed by both mRNA and protein level measurements. Our biochemical assays support nearly 500 microRNA-target interactions with evidence for regulation in breast-cancer tumors. Moreover, these assays constitute the most extensive validation platform for computationally inferred networks of microRNA-target interactions in breast-cancer tumors, providing a useful benchmark to ascertain future improvements.

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Introduction

MicroRNAs (miRNAs) regulate RNA stability and mRNA translation (Filipowicz et al. 2008) and their dysregulation has been implicated in a wide range of human diseases including cancer (Garzon et al. 2009). Consequently, establishing accurate and comprehensive repertoires of miRNA-target interactions is a necessary step toward elucidating their mechanistic role in pathophysiology. Dissecting miRNA regulation, however, has proven challenging because candidate miRNA binding sites are ubiquitous and their regulatory effects are context specific (Liu et al. 2005; Lu et al. 2005; Mukherji et al. 2011). As a result, and despite of their relatively low accuracy, computational prediction methods that incorporate context-specific data are preferred for screening for miRNA-target interactions in tumor contexts (Carroll et al. 2013; Erhard et al. 2014).

To address these challenges, we introduce Cupid, an integrative framework for the context-specific inference of miRNA targets. Cupid integrates sequence-based evidence and functional clues derived from RNA and miRNA expression analysis, predicting candidate miRNA binding sites and associated target genes using ensemble machine learning classifiers that are trained on validated interactions. Candidate interactions emerging from this step are then refined based on independent, context specific clues, including their predicted ability to mediate competitive endogenous RNA (ceRNA) interactions, where mRNA compete for shared miRNA regulators (Fig. 1A) (Tay et al. 2014). Thus, Cupid simultaneously infers both interaction types (ceRNA and miRNA-target interactions). In addition, we considered evidence for combinatorial regulation by multiple miRNA species (Boissonneault et al. 2009; Xu et al. 2011) (Fig. 1B) and for indirect miRNA regulation through effector proteins (Fig. 1C). Taken individually, these clues are predictive of bona fide miRNA-target interactions and can significantly improve the tradeoff between precision and recall.

We show that Cupid predictions outperform other leading algorithms, based on multiple experimental assays, including PAR-CLIP data, miRNA perturbation followed by mRNA

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and protein expression profiles, and 3’ luciferase activity assays. Critically, while Cupid predicts fewer interactions than other methods (Fig. 1D-F), its predictions are much more likely to be consistent with experimental evidence. This is critical since high false-positive prediction rates are a key limitation of current miRNA-target prediction methods.

Results

Algorithm outline and miRNA-target prediction in breast cancer tumors

Cupid is implemented in three sequential steps (Fig. 1A). First, Cupid re-evaluates candidate miRNA binding sites in 3’ UTRs, as inferred by TargetScan (Lewis et al. 2005), miRanda (John et al. 2004) and PITA (Kertesz et al. 2007). This is accomplished by integrating their algorithm-specific scores, their location in the 3’ UTR, and their cross-species conservation. Then, miRNA-target interactions are predicted by further integrating information about selected sites, their multiplicity, and the statistical dependency between the expression profiles of miRNA and putative targets.

Likelihoods for each predictive feature are computed based on a positive gold standard set of 588 experimentally confirmed miRNA-target interactions, representing 1481 binding sites in TarBase (Papadopoulos et al. 2009), TRANSFAC (Matys et al. 2006) and miRecords (Xiao et al. 2009). They are then integrated using a support vector machine (SVM) algorithm (Chang and Lin 2011). Finally, Cupid assesses whether inferred targets compete for their predicted miRNA regulators. In the following sections, we discuss results from Cupid-inferred miRNA targets, based on gene expression profile data of TCGA breast cancer samples (The Cancer Genome Atlas 2012).

Step I (miRNA binding site analysis): miRNA binding sites in 3’ UTRs were predicted and scored independently by TargetScan, miRanda and PITA. Taken together, these algorithms predicted a total of 37M candidate binding sites (Fig. 1D). Each site was associated with the following features: (a) TargetScan, miRanda and PITA confidence scores, when available; (b) PhastCons (Siepel et al. 2005) species-conservation scores; and (c) relative distance from the 3’ and 5’ ends of the target 3’ UTR. These features Page | 4