A miRNA-regulatory network explains how dysregulated miRNAs perturb oncogenic processes across diverse cancers

MicroRNAs (miRNAs) mediate degradation (Baek et al. 2008) or translational repression (Selbach et al. 2008) of gene transcripts associated with an array of biological processes including many of the hallmarks of cancer (Hanahan and Weinberg 2000, 2011; Dalmay and Edwards 2006; Ruan et al. 2009). Not surprisingly, dysregulated miRNAs can be readily detected in tumor biopsies (Jiang et al. 2009) and are known to be diagnostic and prognostic indicators (Zen and Zhang 2012). In some cases miRNAs have also been shown to be potential therapeutic targets (Garofalo and Croce 2011; Nana-Sinkam and Croce 2011). Conservative estimates suggest that each human miRNA regulates several hundred transcripts (Baek et al. 2008; Selbach et al. 2008), and thus miRNA-mediated regulation results in statistically significant gene coexpression signatures that are readily discovered through transcriptome profiling (Lim et al. 2005; Wang and Wang 2006; Weber et al. 2006; Brueckner et al. 2007; Chang et al. 2007; Grimson et al. 2007; He et al. 2007; Johnson et al. 2007; Karginov et al. 2007; Linsley et al. 2007; Frankel et al. 2008; Georges et al. 2008; Hendrickson et al. 2008; Ozen et al. 2008; Sengupta et al. 2008; Ceppi et al. 2009; Fasanaro et al. 2009; Tan et al. 2009; Tsai et al. 2009; Valastyan et al. 2009; Malzkorn et al. 2010; Wang et al. 2010). Together these studies motivated us to build a generalized framework for the inference of miRNA regulatory networks for genes discovered to be coexpressed through analysis of genome-wide transcriptome profiles. There are two commonly used strategies to identify the miRNA regulator(s) responsible for the observed coexpression of a set of genes: (1) Enrichment of predicted 3′ UTR binding sites for a known miRNA (Kertesz et al. 2007; Betel et al. 2008, 2010; Friedman et al. 2009); or (2) de novo identification of a 3′ UTR motif that is complementary to a seed sequence of a miRNA in miRBase (Linhart et al. 2008; Fan et al. 2009; Goodarzi et al. 2009; Kozomara and Griffiths-Jones 2011). Algorithms utilizing the first strategy incorporate some combination of seed complementarity, cross-species conservation, and thermodynamic properties of the binding site. These algorithms include PITA (Kertesz et al. 2007), TargetScan (Friedman et al. 2009), and both miRanda (Betel et al. 2008) and miRSVR (Betel et al. 2010) from microRNA.org. While the combined modeling of two or more miRNA-binding properties within these algorithms boosts signal (Supplemental Table 1), the multiple hypotheses testing required to identify bona fide miRNA-binding sites unfortunately also simultaneously leads to high false-negative rates (∼32%–52%) (Sethupathy et al. 2006). Therefore, inference of a comprehensive miRNA regulatory network would require the integration of best-performing algorithms from this class with algorithms that utilize the second strategy. We have recently developed a novel algorithm miRvestigator to accurately associate 3′ UTR motifs to complementary miRNA seed sequences (Plaisier et al. 2011). However, for this algorithm to be effective it has to be coupled to a second algorithm Weeder (Pavesi et al. 2006) that can accurately detect de novo cis-regulatory motifs that are conserved within the 3′ UTRs of the coexpressed genes (Linhart et al. 2008; Fan et al. 2009). miRvestigator converts relative conservation of nucleotides at each position of a cis-regulatory motif discovered by Weeder into a profile hidden Markov model (HMM). Using this HMM, the Viterbi algorithm, and a background distribution of all possible k-mer sequences (6-, 7-, or 8-mer), miRvestigator accurately identifies the most likely miRNA that binds the conserved 3′ UTR element to mediate the observed coregulation (Plaisier et al. 2011). Here, we report the construction of a generalized framework for the inference of regulation by miRNAs (FIRM). First, we have compiled a compendium of transcriptome profiles from studies that had interrogated differential expression of genes in response to targeted perturbation of specific miRNAs (Lim et al. 2005; Weber et al. 2006; Brueckner et al. 2007; Chang et al. 2007; Grimson et al. 2007; He et al. 2007; Johnson et al. 2007; Karginov et al. 2007; Linsley et al. 2007; Frankel et al. 2008; Georges et al. 2008; Hendrickson et al. 2008; Ozen et al. 2008; Sengupta et al. 2008; Ceppi et al. 2009; Fasanaro et al. 2009; Tan et al. 2009; Tsai et al. 2009; Valastyan et al. 2009; Malzkorn et al. 2010; Wang et al. 2010). Second, using this compendium of miRNA-perturbed transcriptomes we demonstrate that functional miRNA-binding sites (8 bp of complementarity) preferentially reside in the 3′ UTRs. Further, we demonstrate that using preferential 3′ UTR localization as a heuristic significantly increases sensitivity and specificity of miRNA-binding site discovery by Weeder-miRvestigator. Third, using the compendium of miRNA-perturbed transcriptomes we have identified and integrated the best performing algorithms into a generalized framework for inference of miRNA regulatory networks. Finally, we demonstrate the utility of this framework by applying it to a set of 2240 coexpression signatures from 46 different cancers. The original study was able to associate only four signatures to putative regulation by a known miRNA (Goodarzi et al. 2009). In contrast, using the integrated framework we were able to explain 1324 signatures as potential outcomes of regulation by specific miRNAs in miRBase. By applying functional enrichment and semantic similarity we have identified within this expansive network specific miRNAs associated with hallmarks of cancer. Further, filtering gene coexpression signatures for specific hallmarks of cancer such as “tissue invasion and metastasis” generated a metastatic cancer–miRNA regulatory network of 33 miRNAs. Importantly, this revealed that a relatively small subset of miRNAs regulate multiple oncogenic processes across different cancers. Through in-depth analyses of data from prior studies as well as new data from targeted miRNA-perturbation experiments, we have experimentally validated the role of miR-29 family members in lung adenocarcinoma and discovered gene targets for regulation by the relatively unknown miR-767-5p. These analyses and validations demonstrate how the cancer–miRNA regulatory network can be used to accelerate discovery of miRNA-based biomarkers and potentially therapeutics.