Cross-Species Regulatory Network Analysis Identifies a Synergistic Interaction between FOXM1 and CENPF that Drives Prostate Cancer Malignancy

Abstract

Summary To identify regulatory drivers of prostate cancer malignancy, we have assembled genome-wide regulatory networks (interactomes) for human and mouse prostate cancer from expression profiles of human tumors and of genetically engineered mouse models, respectively. Cross-species computational analysis of these interactomes has identified FOXM1 and CENPF as synergistic master regulators of prostate cancer malignancy. Experimental validation shows that FOXM1 and CENPF function synergistically to promote tumor growth, by coordinated regulation of target gene expression and activation of key signaling pathways associated with prostate cancer malignancy. Furthermore, co-expression of FOXM1 and CENPF is a robust prognostic indicator of poor survival and metastasis. Thus, genome-wide cross-species interrogation of regulatory networks represents a valuable strategy to identify causal mechanisms of human cancer.

A Systems Approach to Drug Discovery in Alzheimer's Disease

“…The corresponding human PC interactome was produced by ARACNe analysis of a set of gene expression profiles from~200 patient-derived PC samples, representing the full spectrum of disease progression. Comparison of the human and murine interactomes, using a novel algorithm, revealed that 70% of the regulatory programs in PC are highly conserved between these 2 species, including those of 2 synergistic master regulators (MRs) of progression to aggressive disease (forkhead box protein M1 and centromere protein F), inferred by the Master Regulator Inference algorithm (MARINa) [7,[12][13][14][15], and experimentally validated both in mouse and in human tissue. However, the analysis also showed that 30% of the programs are not conserved, including those representing a few PC-related genes that would thus be unlikely to produce patient-relevant results if studied or targeted in a murine context.…”

“…To address this impasse and to avoid findings that are idiosyncratic to a specific biological model but fail to generalize to the human context, we propose a cross-species approach that has been highly successful in the study of human malignancies [4]. Specifically, we propose assembling accurate, genome-wide regulatory models for both mouse models of AD and for their human counterpart to assess systematically the subset of the regulatory logic of AD neurons that is conserved or divergent between the two species.…”

“…The MARINa algorithm was developed to infer the regulators that are causally responsible for implementing a phenotype-specific gene expression signature initially from multiple gene expression profiles [12,14], and, more recently, even from a single gene expression profile [7,40]. This is accomplished by measuring the enrichment of over-and underexpressed genes in positively regulated and repressed targets in the regulon of every possible regulator protein represented in the interactome, thus providing an accurate and extremely robust predictor of the differential activity of a regulator.…”

“…Murine interactomes including neuron-specific interactomes [8,9], have been obtained by harvesting cells from mouse models representing diverse genetic backgrounds, tissues, and/or environmental stimuli, including small molecule perturbation in vivo [7]. For instance, to assemble an accurate murine interactome for cross-species analysis of prostate cancer (PC) progression, 13 distinct transgenic PC models were perturbed in vivo with 13 small molecule compounds and dimethyl sulfoxide to generate a set of 364 gene expression profiles from murine PC tissues and normal controls.…”

A basal stem cell signature identifies aggressive prostate cancer phenotypes

“…To identify potential TFs that regulate each phenotype, we used the master regulator interference algorithm (MARINa), which has been used to identify master regulators for human high-grade glioma, murine prostate cancer, and normal formation of germinal centers (40)(41)(42). We created a network of TFs and their targets by combining transcriptional and genomic data from multiple databases (43)(44)(45)(46).…”