Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy

A murine preclinical syngeneic transplantation model for breast cancer precision medicine

We previously demonstrated that altered activity of lysophosphatidic acid in murine mammary glands promotes tumorigenesis. We have now established and characterized a heterogeneous collection of mouse-derived syngeneic transplants (MDSTs) as preclinical platforms for the assessment of personalized pharmacological therapies. Detailed molecular and phenotypic analyses revealed that MDSTs are the most heterogeneous group of genetically engineered mouse models (GEMMs) of breast cancer yet observed. Response of MDSTs to trametinib, a mitogen-activated protein kinase (MAPK) kinase inhibitor, correlated with RAS/MAPK signaling activity, as expected from studies in xenografts and clinical trials providing validation of the utility of the model. Sensitivity of MDSTs to talazoparib, a poly(adenosine 5'-diphosphate-ribose) polymerase (PARP) inhibitor, was predicted by PARP1 protein levels and by a new PARP sensitivity predictor (PSP) score developed from integrated analysis of drug sensitivity data of human cell lines. PSP score-based classification of The Cancer Genome Atlas breast cancer suggested that a subset of patients with limited therapeutic options would be expected to benefit from PARP-targeted drugs. These results indicate that MDSTs are useful models for studies of targeted therapies, and propose novel potential biomarkers for identification of breast cancer patients likely to benefit from personalized pharmacological treatments.

AURKA Overexpression Is Driven by FOXM1 and MAPK/ERK Activation in Melanoma Cells Harboring BRAF or NRAS Mutations: Impact on Melanoma Prognosis and Therapy

The cell cycle-related genes AURKA and FOXM1 are overexpressed in melanoma. We show here that AURKA overexpression is associated with poor prognosis in three independent cohorts of melanoma patients and correlates with the presence of genomic amplification of AURKA locus and BRAF^V600E mutation. AURKA overexpression may also be driven by increased promoter activation through elements such as ETS and FOXM1 found within the 5' proximal promoter region. Activated MAPK/ERK signaling pathway mediates robust AURKA promoter activation, thereby knockdown of BRAF^V600E and ERK inhibition results in reduced AURKA transcription and expression. We show a positive correlation between FOXM1 and AURKA expression in three independent cohorts of melanoma patients. FOXM1 silencing decreases expression of AURKA and late cell cycle genes in melanoma cells. We further found that FOXM1 expression levels are significantly higher in tumors carrying the BRAF^V600E mutation compared with the wild-type BRAF (BRAF^wt). Accordingly, the knockdown of BRAF^V600E also reduces the expression of FOXM1 in BRAF^V600E cells. Moreover, Aurora kinase A and FOXM1 inhibition by either genetic knockdown or pharmacologic inhibitors impair melanoma growth and survival both in culture and in vivo, underscoring their therapeutic value for melanoma patients who fail to benefit from BRAF/MEK signaling inhibition.

The recurrent architecture of tumour initiation, progression and drug sensitivity

Recent studies across multiple tumour types are starting to reveal a recurrent regulatory architecture in which genomic alterations cluster upstream of functional master regulator (MR) proteins, the aberrant activity of which is both necessary and sufficient to maintain tumour cell state. These proteins form small, hyperconnected and autoregulated modules (termed tumour checkpoints) that are increasingly emerging as optimal biomarkers and therapeutic targets. Crucially, as their activity is mostly dysregulated in a post-translational manner, rather than by mutations in their corresponding genes or by differential expression, the identification of MR proteins by conventional methods is challenging. In this Opinion article, we discuss novel methods for the systematic analysis of MR proteins and of the modular regulatory architecture they implement, including their use as a valuable reductionist framework to study the genetic heterogeneity of human disease and to drive key translational applications.

Fused Regression for Multi-source Gene Regulatory Network Inference

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method’s utility in learning from data collected on different experimental platforms.

Gene regulatory networks describing related biological processes are thought to share conserved interaction structure. This assumption motivates a great deal of work in model systems–where discovery of gene regulation may be more experimentally tractable–but is difficult to directly evaluate using existing methods. The presence of shared structure in a well studied model system or process should make the problem of network inference in a related process easier, but this information is not often applied to the discovery of global gene regulatory networks. Further, to be able to successfully translate findings between different organisms, it is important to be able to identify where regulatory structure is different. We provide a method based on penalized fused regression for inferring gene regulatory networks given prior knowledge about the similarity of interactions in each network. This method is demonstrated on synthetic data, and applied to the problem of inferring networks in distantly related bacterial organisms. We then introduce an extension of the method to deal with the condition of uncertainty over the degree of regulatory conservation by simultaneously inferring gene conservation and interaction weights.

Mapping Complex Traits in a Diversity Outbred F1 Mouse Population Identifies Germline Modifiers of Metastasis in Human Prostate Cancer

It is unclear how standing genetic variation affects the prognosis of prostate cancer patients. To provide one controlled answer to this problem, we crossed a dominant, penetrant mouse model of prostate cancer to Diversity Outbred mice, a collection of animals that carries over 40 million SNPs. Integration of disease phenotype and SNP variation data in 493 F1 males identified a metastasis modifier locus on Chromosome 8 (LOD = 8.42); further analysis identified the genes Rwdd4, Cenpu, and Casp3 as functional effectors of this locus. Accordingly, analysis of over 5,300 prostate cancer patient samples revealed correlations between the presence of genetic variants at these loci, their expression levels, cancer aggressiveness, and patient survival. We also observed that ectopic overexpression of RWDD4 and CENPU increased the aggressiveness of two human prostate cancer cell lines. In aggregate, our approach demonstrates how well-characterized genetic variation in mice can be harnessed in conjunction with systems genetics approaches to identify and characterize germline modifiers of human disease processes.

On a FOX hunt: functions of FOX transcriptional regulators in bladder cancer

Genomic and transcriptional studies have identified discrete molecular subtypes of bladder cancer. These observations could be the starting point to identify new treatments. Several members of the forkhead box (FOX) superfamily of transcription factors have been found to be differentially expressed in the different bladder cancer subtypes. In addition, the FOXA protein family are key regulators of embryonic bladder development and patterning. Both experimental and clinical data support a role for FOXA1 and FOXA2 in urothelial carcinoma. FOXA1 is expressed in embryonic and adult urothelium and its expression is altered in urothelial carcinomas and across disparate molecular bladder cancer subtypes. FOXA2 is normally absent from the adult urothelium, but developmental studies identified FOXA2 as a marker of a transient urothelial progenitor cell population during bladder development. Studies also implicate FOXA2 in bladder cancer and several other FOX proteins might be involved in development and/or progression of this disease; for example, FOXA1 and FOXO3A have been associated with clinical patient outcomes. Future studies should investigate to what extent and by which mechanisms FOX proteins might be directly involved in bladder cancer pathogenesis and treatment responses.

NANOG reprograms prostate cancer cells to castration resistance via dynamically repressing and engaging the AR/FOXA1 signaling axis

The pluripotency transcription factor NANOG has been implicated in tumor development, and NANOG-expressing cancer cells manifest stem cell properties that sustain tumor homeostasis, mediate therapy resistance and fuel tumor progression. However, how NANOG converges on somatic circuitry to trigger oncogenic reprogramming remains obscure. We previously reported that inducible NANOG expression propels the emergence of aggressive castration-resistant prostate cancer phenotypes. Here we first show that endogenous NANOG is required for the growth of castration-resistant prostate cancer xenografts. Genome-wide chromatin immunoprecipitation sequencing coupled with biochemical assays unexpectedly reveals that NANOG co-occupies a distinctive proportion of androgen receptor/Forkhead box A1 genomic loci and physically interacts with androgen receptor and Forkhead box A1. Integrative analysis of chromatin immunoprecipitation sequencing and time-resolved RNA sequencing demonstrates that NANOG dynamically alters androgen receptor/Forkhead box A1 signaling leading to both repression of androgen receptor-regulated pro-differentiation genes and induction of genes associated with cell cycle, stem cells, cell motility and castration resistance. Our studies reveal global molecular mechanisms whereby NANOG reprograms prostate cancer cells to a clinically relevant castration-resistant stem cell-like state driven by distinct NANOG-regulated gene clusters that correlate with patient survival. Thus, reprogramming factors such as NANOG may converge on and alter lineage-specific master transcription factors broadly in somatic cancers, thereby facilitating malignant disease progression and providing a novel route for therapeutic resistance.

Regulation of MMP13 by antitumor microRNA-375 markedly inhibits cancer cell migration and invasion in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignancies. Recently developed molecular targeted therapies are not available for patients with ESCC. After curative surgical resection, patients frequently suffer distant metastasis and recurrence. Exploration of novel ESCC metastatic pathways may lead to the development of new treatment protocols for this disease. Accordingly, we have sequentially identified microRNA (miRNA)-mediated metastatic pathways in several cancers. Our past studies of miRNA expression signatures have shown that microRNA-375 (miR-375) is frequently reduced in several types of cancers, including ESCC. In the present study, we aimed to investigate novel miR-375-mediated metastatic pathways in ESCC cells. The expression of miR-375 was downregulated in ESCC tissues, and ectopic expression of this miRNA markedly inhibited cancer cell migration and invasion, suggesting that miR-375 acted as an antimetastatic miRNA in ESCC cells. Our strategies for miRNA target searching demonstrated that matrix metalloproteinase 13 (MMP13) was directly regulated by miR-375 in ESCC cells. Overexpression of MMP13 was observed in ESCC clinical tissues, and the expression of MMP13 promoted cancer cell aggressiveness. Moreover, oncogenic genes, including CENPF, KIF14 and TOP2A, were shown to be regulated downstream of MMP13. Taken together, these findings demonstrated that the antitumor miR-375/oncogenic MMP13 axis had a pivotal role in ESCC aggressiveness. These results provide novel insights into the potential mechanisms of ESCC pathogenesis.

A validated gene regulatory network and GWAS identifies early regulators of T cell-associated diseases

Early regulators of disease may increase understanding of disease mechanisms and serve as markers for presymptomatic diagnosis and treatment. However, early regulators are difficult to identify because patients generally present after they are symptomatic. We hypothesized that early regulators of T cell-associated diseases could be found by identifying upstream transcription factors (TFs) in T cell differentiation and by prioritizing hub TFs that were enriched for disease-associated polymorphisms. A gene regulatory network (GRN) was constructed by time series profiling of the transcriptomes and methylomes of human CD4(+) T cells during in vitro differentiation into four helper T cell lineages, in combination with sequence-based TF binding predictions. The TFs GATA3, MAF, and MYB were identified as early regulators and validated by ChIP-seq (chromatin immunoprecipitation sequencing) and small interfering RNA knockdowns. Differential mRNA expression of the TFs and their targets in T cell-associated diseases supports their clinical relevance. To directly test if the TFs were altered early in disease, T cells from patients with two T cell-mediated diseases, multiple sclerosis and seasonal allergic rhinitis, were analyzed. Strikingly, the TFs were differentially expressed during asymptomatic stages of both diseases, whereas their targets showed altered expression during symptomatic stages. This analytical strategy to identify early regulators of disease by combining GRNs with genome-wide association studies may be generally applicable for functional and clinical studies of early disease development.

Integrated Classification of Prostate Cancer Reveals a Novel Luminal Subtype with Poor Outcome

Prostate cancer is a biologically heterogeneous disease with variable molecular alterations underlying cancer initiation and progression. Despite recent advances in understanding prostate cancer heterogeneity, better methods for classification of prostate cancer are still needed to improve prognostic accuracy and therapeutic outcomes. In this study, we computationally assembled a large virtual cohort (n = 1,321) of human prostate cancer transcriptome profiles from 38 distinct cohorts and, using pathway activation signatures of known relevance to prostate cancer, developed a novel classification system consisting of three distinct subtypes (named PCS1-3). We validated this subtyping scheme in 10 independent patient cohorts and 19 laboratory models of prostate cancer, including cell lines and genetically engineered mouse models. Analysis of subtype-specific gene expression patterns in independent datasets derived from luminal and basal cell models provides evidence that PCS1 and PCS2 tumors reflect luminal subtypes, while PCS3 represents a basal subtype. We show that PCS1 tumors progress more rapidly to metastatic disease in comparison with PCS2 or PCS3, including PSC1 tumors of low Gleason grade. To apply this finding clinically, we developed a 37-gene panel that accurately assigns individual tumors to one of the three PCS subtypes. This panel was also applied to circulating tumor cells (CTC) and provided evidence that PCS1 CTCs may reflect enzalutamide resistance. In summary, PCS subtyping may improve accuracy in predicting the likelihood of clinical progression and permit treatment stratification at early and late disease stages. Cancer Res; 76(17); 4948-58. ©2016 AACR.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

Phosphoproteome Integration Reveals Patient-Specific Networks in Prostate Cancer

We used clinical tissue from lethal metastatic castration-resistant prostate cancer (CRPC) patients obtained at rapid autopsy to evaluate diverse genomic, transcriptomic, and phosphoproteomic datasets for pathway analysis. Using Tied Diffusion through Interacting Events (TieDIE), we integrated differentially expressed master transcriptional regulators, functionally mutated genes, and differentially activated kinases in CRPC tissues to synthesize a robust signaling network consisting of druggable kinase pathways. Using MSigDB hallmark gene sets, six major signaling pathways with phosphorylation of several key residues were significantly enriched in CRPC tumors after incorporation of phosphoproteomic data. Individual autopsy profiles developed using these hallmarks revealed clinically relevant pathway information potentially suitable for patient stratification and targeted therapies in late stage prostate cancer. Here, we describe phosphorylation-based cancer hallmarks using integrated personalized signatures (pCHIPS) that shed light on the diversity of activated signaling pathways in metastatic CRPC while providing an integrative, pathway-based reference for drug prioritization in individual patients.

Functional characterization of somatic mutations in cancer using network-based inference of protein activity

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.

Transforming Big Data into Cancer-Relevant Insight: An Initial, Multi-Tier Approach to Assess Reproducibility and Relevance

The Cancer Target Discovery and Development (CTD(2)) Network was established to accelerate the transformation of "Big Data" into novel pharmacologic targets, lead compounds, and biomarkers for rapid translation into improved patient outcomes. It rapidly became clear in this collaborative network that a key central issue was to define what constitutes sufficient computational or experimental evidence to support a biologically or clinically relevant finding. This article represents a first attempt to delineate the challenges of supporting and confirming discoveries arising from the systematic analysis of large-scale data resources in a collaborative work environment and to provide a framework that would begin a community discussion to resolve these challenges. The Network implemented a multi-tier framework designed to substantiate the biological and biomedical relevance as well as the reproducibility of data and insights resulting from its collaborative activities. The same approach can be used by the broad scientific community to drive development of novel therapeutic and biomarker strategies for cancer. Mol Cancer Res; 14(8); 675-82. ©2016 AACR.

Androgen-induced Long Noncoding RNA (lncRNA) SOCS2-AS1 Promotes Cell Growth and Inhibits Apoptosis in Prostate Cancer Cells

Long noncoding RNAs (lncRNA) have been associated with the development of cancer. However, the interplay between lncRNAs and androgen receptor (AR) signaling in prostate cancer is still unclear. Here, we identified lncRNAs induced by androgen in AR-positive prostate cancer cells, where induction was abolished by AR knockdown as well as an anti-androgen, bicalutamide. By combining these data, we identified an androgen-regulated lncRNA, suppressor of cytokine signaling 2-antisense transcript 1 (SOCS2-AS1), the expression of which was higher in castration-resistant prostate cancer model cells, i.e long-term androgen-deprived (LTAD) cells, than in parental androgen-dependent LNCaP cells. SOCS2-AS1 promoted castration-resistant and androgen-dependent cell growth. We found that SOCS2-AS1 knockdown up-regulated genes related to the apoptosis pathway, including tumor necrosis factor superfamily 10 (TNFSF10), and sensitized prostate cancer cells to docetaxel treatment. Moreover, we also demonstrated that SOCS2-AS1 promotes androgen signaling by modulating the epigenetic control for AR target genes including TNFSF10 These findings suggest that SOCS2-AS1 plays an important role in the development of castration-resistant prostate cancer by repressing apoptosis.

Loss of CENP-F results in distinct microtubule-related defects without chromosomal abnormalities

Microtubule (MT)-binding centromere protein F (CENP-F) was previously shown to play a role exclusively in chromosome segregation during cellular division. Many cell models of CENP-F depletion show a lag in the cell cycle and aneuploidy. Here, using our novel genetic deletion model, we show that CENP-F also regulates a broader range of cellular functions outside of cell division. We characterized CENP-F(+/+) and CENP-F(-/-) mouse embryonic fibroblasts (MEFs) and found drastic differences in multiple cellular functions during interphase, including cell migration, focal adhesion dynamics, and primary cilia formation. We discovered that CENP-F(-/-) MEFs have severely diminished MT dynamics, which underlies the phenotypes we describe. These data, combined with recent biochemical research demonstrating the strong binding of CENP-F to the MT network, support the conclusion that CENP-F is a powerful regulator of MT dynamics during interphase and affects heterogeneous cell functions.

Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis

BACKGROUND

Pancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression.

METHODS

Multidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis.

RESULTS

Rank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities.

CONCLUSIONS

It is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Dysregulation of miRNAs-COUP-TFII-FOXM1-CENPF axis contributes to the metastasis of prostate cancer

Although early detection and treatment of prostate cancer (PCa) improves outcomes, many patients still die of metastatic PCa. Here, we report that metastatic PCa exhibits reduced levels of the microRNAsmiR-101 and miR-27a. These micro-RNAs (miRNAs) negatively regulate cell invasion and inhibit the expression of FOXM1 and CENPF, two master regulators of metastasis in PCa. Interestingly, the repression of FOXM1 and CENPF by these miRNAs occurs through COUP-TFII, a member of the orphan nuclear receptors family. Loss of miR-101 positively correlates with the increase of COUP-TFII-FOXM1-CENPF activity in clinical PCa data sets, implicating clinical relevance of such regulation. Further studies show that COUP-TFII is a critical factor controlling metastatic gene networks to promote PCa metastasis. Most importantly, this miRNA-COUP-TFII-FOXM1-CENPF regulatory axis is also involved in the development of enzalutaminde resistance. Taken together, our findings highlight the contribution of specific miRNAs through the regulation of the COUP-TFII-FOXM1-CENPF cascade in PCa metastasis and drug resistance.

The orphan nuclear receptor COUP-TFII is highly expressed in metastatic prostate cancers and its overexpression accelerates prostate tumour progression in mouse models. Here, the author show that that loss of miR-101 and miR-27a in prostate cancer cells can lead to COUP-TFII expression which in turn directly regulates FOXM1 and CENPF favouring prostate cancer metastasis.

ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information

Summary: The accurate reconstruction of gene regulatory networks from large scale molecular profile datasets represents one of the grand challenges of Systems Biology. The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) represents one of the most effective tools to accomplish this goal. However, the initial Fixed Bandwidth (FB) implementation is both inefficient and unable to deal with sample sets providing largely uneven coverage of the probability density space. Here, we present a completely new implementation of the algorithm, based on an Adaptive Partitioning strategy (AP) for estimating the Mutual Information. The new AP implementation (ARACNe-AP) achieves a dramatic improvement in computational performance (200× on average) over the previous methodology, while preserving the Mutual Information estimator and the Network inference accuracy of the original algorithm. Given that the previous version of ARACNe is extremely demanding, the new version of the algorithm will allow even researchers with modest computational resources to build complex regulatory networks from hundreds of gene expression profiles.

Availability and Implementation: A JAVA cross-platform command line executable of ARACNe, together with all source code and a detailed usage guide are freely available on Sourceforge (http://sourceforge.net/projects/aracne-ap). JAVA version 8 or higher is required.

Contact:califano@c2b2.columbia.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Advances in prostate cancer research models: From transgenic mice to tumor xenografting models

The identification of the origin and molecular characteristics of prostate cancer (PCa) has crucial implications for personalized treatment. The development of effective treatments for PCa has been limited; however, the recent establishment of several transgenic mouse lines and/or xenografting models is better reflecting the disease in vivo. With appropriate models, valuable tools for elucidating the functions of specific genes have gone deep into prostate development and carcinogenesis. In the present review, we summarize a number of important PCa research models established in our laboratories (PSA-Cre-ER^T2/PTEN transgenic mouse models, AP-OX model, tissue recombination-xenografting models and PDX models), which represent advances of translational models from transgenic mouse lines to human tumor xenografting. Better understanding of the developments of these models will offer new insights into tumor progression and may help explain the functional significance of genetic variations in PCa. Additionally, this understanding could lead to new modes for curing PCa based on their particular biological phenotypes.

Optimizing mouse models for precision cancer prevention

As cancer has become increasingly prevalent, cancer prevention research has evolved towards placing a greater emphasis on reducing cancer deaths and minimizing the adverse consequences of having cancer. 'Precision cancer prevention' takes into account the collaboration of intrinsic and extrinsic factors in influencing cancer incidence and aggressiveness in the context of the individual, as well as recognizing that such knowledge can improve early detection and enable more accurate discrimination of cancerous lesions. However, mouse models, and particularly genetically engineered mouse (GEM) models, have yet to be fully integrated into prevention research. In this Opinion article, we discuss opportunities and challenges for precision mouse modelling, including the essential criteria of mouse models for prevention research, representative success stories and opportunities for more refined analyses in future studies.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

Multidisciplinary intervention of early, lethal metastatic prostate cancer: Report from the 2015 Coffey-Holden Prostate Cancer Academy Meeting

BACKGROUND

The 2015 Coffey-Holden Prostate Cancer Academy Meeting, themed: "Multidisciplinary Intervention of Early, Lethal Metastatic Prostate Cancer," was held in La Jolla, California from June 25 to 28, 2015.

METHODS

The Prostate Cancer Foundation (PCF) sponsors an annual, invitation-only, action-tank-structured meeting on a critical topic concerning lethal prostate cancer. The 2015 meeting was attended by 71 basic, translational, and clinical investigators who discussed the current state of the field, major unmet needs, and ideas for addressing earlier diagnosis and treatment of men with lethal prostate cancer for the purpose of extending lives and making progress toward a cure.

RESULTS

The questions addressed at the meeting included: cellular and molecular mechanisms of tumorigenesis, evaluating, and targeting the microenvironment in the primary tumor, advancing biomarkers for clinical integration, new molecular imaging technologies, clinical trials, and clinical trial design in localized high-risk and oligometastatic settings, targeting the primary tumor in advanced disease, and instituting multi-modal care of high risk and oligometastatic patients.

DISCUSSION

This article highlights the current status, greatest unmet needs, and anticipated field changes that were discussed at the meeting toward the goal of optimizing earlier interventions to potentiate cures in high-risk and oligometastatic prostate cancer patients.

Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression

Follicular lymphoma, the most common indolent subtype of non-Hodgkin lymphoma, is associated with a relatively long overall survival rate ranging from 6 to 10 years from the time of diagnosis. However, in 20% to 60% of follicular lymphoma patients, transformation to aggressive diffuse large B-cell lymphoma (DLBCL) reduces median survival to only 1.2 years. The specific functional and genetic determinants of follicular lymphoma transformation remain elusive, and genomic alterations underlying disease advancement have only been identified for a subset of cases. Therefore, to identify candidate drivers of follicular lymphoma transformation, we performed systematic analysis of a B-cell-specific regulatory model exhibiting follicular lymphoma transformation signatures using the Master Regulator Inference algorithm (MARINa). This analysis revealed FOXM1, TFDP1, ATF5, HMGA1, and NFYB to be candidate master regulators (MR) contributing to disease progression. Accordingly, validation was achieved through synthetic lethality assays in which RNAi-mediated silencing of MRs individually or in combination reduced the viability of (14;18)-positive DLBCL (t-DLBCL) cells. Furthermore, specific combinations of small-molecule compounds targeting synergistic MR pairs induced loss of viability in t-DLBCL cells. Collectively, our findings indicate that MR analysis is a valuable method for identifying bona fide contributors to follicular lymphoma transformation and may therefore guide the selection of compounds to be used in combinatorial treatment strategies.

Integrative analysis identifies targetable CREB1/FoxA1 transcriptional co-regulation as a predictor of prostate cancer recurrence

Identifying prostate cancer-driving transcription factors (TFs) in addition to the androgen receptor promises to improve our ability to effectively diagnose and treat this disease. We employed an integrative genomics analysis of master TFs CREB1 and FoxA1 in androgen-dependent prostate cancer (ADPC) and castration-resistant prostate cancer (CRPC) cell lines, primary prostate cancer tissues and circulating tumor cells (CTCs) to investigate their role in defining prostate cancer gene expression profiles. Combining genome-wide binding site and gene expression profiles we define CREB1 as a critical driver of pro-survival, cell cycle and metabolic transcription programs. We show that CREB1 and FoxA1 co-localize and mutually influence each other's binding to define disease-driving transcription profiles associated with advanced prostate cancer. Gene expression analysis in human prostate cancer samples found that CREB1/FoxA1 target gene panels predict prostate cancer recurrence. Finally, we showed that this signaling pathway is sensitive to compounds that inhibit the transcription co-regulatory factor MED1. These findings not only reveal a novel, global transcriptional co-regulatory function of CREB1 and FoxA1, but also suggest CREB1/FoxA1 signaling is a targetable driver of prostate cancer progression and serves as a biomarker of poor clinical outcomes.

Cross-Study Comparison Reveals Common Genomic, Network, and Functional Signatures of Desiccation Resistance in Drosophila melanogaster

Repeated attempts to map the genomic basis of complex traits often yield different outcomes because of the influence of genetic background, gene-by-environment interactions, and/or statistical limitations. However, where repeatability is low at the level of individual genes, overlap often occurs in gene ontology categories, genetic pathways, and interaction networks. Here we report on the genomic overlap for natural desiccation resistance from a Pool-genome-wide association study experiment and a selection experiment in flies collected from the same region in southeastern Australia in different years. We identified over 600 single nucleotide polymorphisms associated with desiccation resistance in flies derived from almost 1,000 wild-caught genotypes, a similar number of loci to that observed in our previous genomic study of selected lines, demonstrating the genetic complexity of this ecologically important trait. By harnessing the power of cross-study comparison, we narrowed the candidates from almost 400 genes in each study to a core set of 45 genes, enriched for stimulus, stress, and defense responses. In addition to gene-level overlap, there was higher order congruence at the network and functional levels, suggesting genetic redundancy in key stress sensing, stress response, immunity, signaling, and gene expression pathways. We also identified variants linked to different molecular aspects of desiccation physiology previously verified from functional experiments. Our approach provides insight into the genomic basis of a complex and ecologically important trait and predicts candidate genetic pathways to explore in multiple genetic backgrounds and related species within a functional framework.

Immunopathologic Assessment of PTEN Expression

Immunohistochemistry (IHC) is an excellent technique used routinely to define the phenotype in pathology laboratories through the analysis of molecular expression in cells and tissues. The PTEN protein is ubiquitously expressed in the majority of human tissues, and allelic or complete loss of PTEN is frequently observed in different types of malignancies leading to an activation of the AKT/mTOR pathways. IHC-based analyses are best to determine the level of PTEN expression in histological samples, but not to assess partial or heterozygous deletions, for which FISH analyses are more appropriate. Interpretation of the IHC results is the most critical point in the assessment of PTEN expression, since it is used both as a prognostic factor and as a tool to guide therapeutic intervention and response to therapy. Importantly, analyses of well-known downstream markers, such as AKT or mTOR, may be used to further analyze PTEN functional status.

Integrative Analysis Reveals the Transcriptional Collaboration between EZH2 and E2F1 in the Regulation of Cancer-Related Gene Expression

Overexpression of EZH2 is frequently linked to the advanced and metastatic stage of cancers. The mechanisms of its oncogenic function can be context specific, and may vary depending on the protein complexes that EZH2 interacts with. To identify novel transcriptional collaborators of EZH2 in cancers, a computational approach was developed that integrates protein-DNA binding data, cell perturbation gene expression data, and compendiums of tumor expression profiles. This holistic approach identified E2F1, a known mediator of the Rb tumor suppressor, as a transcriptional collaborator of EZH2 in castration-resistant prostate cancer. Subsequent analysis and experimental validation found EZH2 and E2F1 cobind to a subset of chromatin sites lacking H3K27 trimethylation, and activate genes that are critical for prostate cancer progression. The collaboration of EZH2 and E2F1 in transcriptional regulation is also observed in diffuse large B-cell lymphoma cell lines, where activation of the transcriptional network is concordant with the cellular response to the EZH2 inhibitor.

IMPLICATIONS

The direct collaboration between EZH2 and Rb/E2F1 pathway provides an innovative mechanism underlying the cascade of tumor progression, and lays the foundation for the development of new anticancer targets/strategies.

HDAC6 activity is a non-oncogene addiction hub for inflammatory breast cancers

INTRODUCTION

Inflammatory breast cancer (IBC) is the most lethal form of breast cancers with a 5-year survival rate of only 40 %. Despite its lethality, IBC remains poorly understood which has greatly limited its therapeutic management. We thus decided to utilize an integrative functional genomic strategy to identify the Achilles' heel of IBC cells.

METHODS

We have pioneered the development of genetic tools as well as experimental and analytical strategies to perform RNAi-based loss-of-function studies at a genome-wide level. Importantly, we and others have demonstrated that these functional screens are able to identify essential functions linked to certain cancer phenotypes. Thus, we decided to use this approach to identify IBC specific sensitivities.

RESULTS

We identified and validated HDAC6 as a functionally necessary gene to maintain IBC cell viability, while being non-essential for other breast cancer subtypes. Importantly, small molecule inhibitors for HDAC6 already exist and are in clinical trials for other tumor types. We thus demonstrated that Ricolinostat (ACY1215), a leading HDAC6 inhibitor, efficiently controls IBC cell proliferation both in vitro and in vivo. Critically, functional HDAC6 dependency is not associated with genomic alterations at its locus and thus represents a non-oncogene addiction. Despite HDAC6 not being overexpressed, we found that its activity is significantly higher in IBC compared to non-IBC cells, suggesting a possible rationale supporting the observed dependency.

CONCLUSION

Our finding that IBC cells are sensitive to HDAC6 inhibition provides a foundation to rapidly develop novel, efficient, and well-tolerated targeted therapy strategies for IBC patients.

A basal stem cell signature identifies aggressive prostate cancer phenotypes

Evidence from numerous cancers suggests that increased aggressiveness is accompanied by up-regulation of signaling pathways and acquisition of properties common to stem cells. It is unclear if different subtypes of late-stage cancer vary in stemness properties and whether or not these subtypes are transcriptionally similar to normal tissue stem cells. We report a gene signature specific for human prostate basal cells that is differentially enriched in various phenotypes of late-stage metastatic prostate cancer. We FACS-purified and transcriptionally profiled basal and luminal epithelial populations from the benign and cancerous regions of primary human prostates. High-throughput RNA sequencing showed the basal population to be defined by genes associated with stem cell signaling programs and invasiveness. Application of a 91-gene basal signature to gene expression datasets from patients with organ-confined or hormone-refractory metastatic prostate cancer revealed that metastatic small cell neuroendocrine carcinoma was molecularly more stem-like than either metastatic adenocarcinoma or organ-confined adenocarcinoma. Bioinformatic analysis of the basal cell and two human small cell gene signatures identified a set of E2F target genes common between prostate small cell neuroendocrine carcinoma and primary prostate basal cells. Taken together, our data suggest that aggressive prostate cancer shares a conserved transcriptional program with normal adult prostate basal stem cells.

Predicting Drug Response in Human Prostate Cancer from Preclinical Analysis of In Vivo Mouse Models

Although genetically engineered mouse (GEM) models are often used to evaluate cancer therapies, extrapolation of such preclinical data to human cancer can be challenging. Here, we introduce an approach that uses drug perturbation data from GEM models to predict drug efficacy in human cancer. Network-based analysis of expression profiles from in vivo treatment of GEM models identified drugs and drug combinations that inhibit the activity of FOXM1 and CENPF, which are master regulators of prostate cancer malignancy. Validation of mouse and human prostate cancer models confirmed the specificity and synergy of a predicted drug combination to abrogate FOXM1/CENPF activity and inhibit tumorigenicity. Network-based analysis of treatment signatures from GEM models identified treatment-responsive genes in human prostate cancer that are potential biomarkers of patient response. More generally, this approach allows systematic identification of drugs that inhibit tumor dependencies, thereby improving the utility of GEM models for prioritizing drugs for clinical evaluation.

Pathway and network analysis of cancer genomes

Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.

DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package.

AVAILABILITY AND IMPLEMENTATION

The diggit package is freely available under the GPL-2 license from Bioconductor (http://www.bioconductor.org).

Targeting of mutant p53-induced FoxM1 with thiostrepton induces cytotoxicity and enhances carboplatin sensitivity in cancer cells

FoxM1 is an oncogenic Forkhead transcription factor that is overexpressed in ovarian cancer. However, the mechanisms by which FoxM1 is deregulated in ovarian cancer and the extent to which FoxM1 can be targeted in ovarian cancer have not been reported previously. In this study, we showed that MDM2 inhibitor Nutlin-3 upregulated p53 protein and downregulated FoxM1 expression in several cancer cell lines with wild type TP53 but not in cell lines with mutant TP53. FoxM1 downregulation was partially blocked by cycloheximide or actinomycin D, and pulse-chase studies indicate Nutlin-3 enhances FoxM1 mRNA decay. Knockdown of p53 using shRNAs abrogated the FoxM1 downregulation by Nutlin-3, indicating a p53-dependent mechanism. FoxM1 inhibitor, thiostrepton, induces apoptosis in cancer cell lines and enhances sensitivity to cisplatin in these cells. Thiostrepton downregulates FoxM1 expression in several cancer cell lines and enhances sensitivity to carboplatin in vivo. Finally, FoxM1 expression is elevated in nearly all (48/49) ovarian tumors, indicating that thiostrepton target gene is highly expressed in ovarian cancer. In summary, the present study provides novel evidence that both amorphic and neomorphic mutations in TP53 contribute to FoxM1 overexpression and that FoxM1 may be targeted for therapeutic benefits in cancers.

Transcriptional master regulator analysis in breast cancer genetic networks

Gene regulatory networks account for the delicate mechanisms that control gene expression. Under certain circumstances, gene regulatory programs may give rise to amplification cascades. Such transcriptional cascades are events in which activation of key-responsive transcription factors called master regulators trigger a series of gene expression events. The action of transcriptional master regulators is then important for the establishment of certain programs like cell development and differentiation. However, such cascades have also been related with the onset and maintenance of cancer phenotypes. Here we present a systematic implementation of a series of algorithms aimed at the inference of a gene regulatory network and analysis of transcriptional master regulators in the context of primary breast cancer cells. Such studies were performed in a highly curated database of 880 microarray gene expression experiments on biopsy-captured tissue corresponding to primary breast cancer and healthy controls. Biological function and biochemical pathway enrichment analyses were also performed to study the role that the processes controlled - at the transcriptional level - by such master regulators may have in relation to primary breast cancer. We found that transcription factors such as AGTR2, ZNF132, TFDP3 and others are master regulators in this gene regulatory network. Sets of genes controlled by these regulators are involved in processes that are well-known hallmarks of cancer. This kind of analyses may help to understand the most upstream events in the development of phenotypes, in particular, those regarding cancer biology.

Targeted inhibition of metastatic melanoma through interference with Pin1-FOXM1 signaling

Melanoma is the most lethal form of skin cancer and successful treatment of metastatic melanoma remains challenging. BRAF/MEK inhibitors only show a temporary benefit due to rapid occurrence of resistance, whereas immunotherapy is mainly effective in selected subsets of patients. Thus, there is a need to identify new targets to improve treatment of metastatic melanoma. To this extent, we searched for markers that are elevated in melanoma and are under regulation of potentially druggable enzymes. Here, we show that the pro-proliferative transcription factor FOXM1 is elevated and activated in malignant melanoma. FOXM1 activity correlated with expression of the enzyme Pin1, which we found to be indicative of a poor prognosis. In functional experiments, Pin1 proved to be a main regulator of FOXM1 activity through MEK-dependent physical regulation during the cell cycle. The Pin1-FOXM1 interaction was enhanced by BRAF(V600E), the driver oncogene in the majority of melanomas, and in extrapolation of the correlation data, interference with\ Pin1 in BRAF(V600E)-driven metastatic melanoma cells impaired both FOXM1 activity and cell survival. Importantly, cell-permeable Pin1-FOXM1-blocking peptides repressed the proliferation of melanoma cells in freshly isolated human metastatic melanoma ex vivo and in three-dimensional-cultured patient-derived melanoids. When combined with the BRAF(V600E)-inhibitor PLX4032 a robust repression in melanoid viability was obtained, establishing preclinical value of patient-derived melanoids for prognostic use of drug sensitivity and further underscoring the beneficial effect of Pin1-FOXM1 inhibitory peptides as anti-melanoma drugs. These proof-of-concept results provide a starting point for development of therapeutic Pin1-FOXM1 inhibitors to target metastatic melanoma.

Inhibition of the autocrine IL-6-JAK2-STAT3-calprotectin axis as targeted therapy for HR-/HER2+ breast cancers

Rodriguez-Barrueco et al. found that HR⁻/HER2⁺ cells secrete high levels of IL-6, inducing the activation of STAT3, which in turn promotes a second autocrine stimulus to increase S100A8/9 complex (calprotectin) production and secretion. Inhibition of the IL-6–JAK2–STAT3–calprotectin axis with FDA-approved drugs, alone and in combination with HER2 inhibitors, reduced the tumorigenicity of HR⁻/HER2⁺ breast cancers.

HER2-positive (HER2⁺) breast adenocarcinomas are a heterogeneous group in which hormone receptor (HR) status influences therapeutic decisions and patient outcome. By combining genome-wide RNAi screens with regulatory network analysis, we identified STAT3 as a critically activated master regulator of HR⁻/HER2⁺ tumors, eliciting tumor dependency in these cells. Mechanistically, HR⁻/HER2⁺ cells secrete high levels of the interleukin-6 (IL-6) cytokine, inducing the activation of STAT3, which in turn promotes a second autocrine stimulus to increase S100A8/9 complex (calprotectin) production and secretion. Increased calprotectin levels activate signaling pathways involved in proliferation and resistance. Importantly, we demonstrated that inhibition of the IL-6–Janus kinase 2 (JAK2)–STAT3–calprotectin axis with FDA-approved drugs, alone and in combination with HER2 inhibitors, reduced the tumorigenicity of HR⁻/HER2⁺ breast cancers, opening novel targeted therapeutic opportunities.

Identification of neurodegenerative factors using translatome-regulatory network analysis

For degenerative disorders of the CNS, the main obstacle to therapeutic advancement has been the challenge of identifying the key molecular mechanisms underlying neuronal loss. We developed a combinatorial approach including translational profiling and brain regulatory network analysis to search for key determinants of neuronal survival or death. Following the generation of transgenic mice for cell type-specific profiling of midbrain dopaminergic neurons, we established and compared translatome libraries reflecting the molecular signature of these cells at baseline or under degenerative stress. Analysis of these libraries by interrogating a context-specific brain regulatory network led to the identification of a repertoire of intrinsic upstream regulators that drive the dopaminergic stress response. The altered activity of these regulators was not associated with changes in their expression levels. This strategy can be generalized for the identification of molecular determinants involved in the degeneration of other classes of neurons.

The Regulatory Machinery of Neurodegeneration in In Vitro Models of Amyotrophic Lateral Sclerosis

Neurodegenerative phenotypes reflect complex, time-dependent molecular processes whose elucidation may reveal neuronal class-specific therapeutic targets. The current focus in neurodegeneration has been on individual genes and pathways. In contrast, we assembled a genome-wide regulatory model (henceforth, "interactome"), whose unbiased interrogation revealed 23 candidate causal master regulators of neurodegeneration in an in vitro model of amyotrophic lateral sclerosis (ALS), characterized by a loss of spinal motor neurons (MNs). Of these, eight were confirmed as specific MN death drivers in our model of familial ALS, including NF-κB, which has long been considered a pro-survival factor. Through an extensive array of molecular, pharmacological, and biochemical approaches, we have confirmed that neuronal NF-κB drives the degeneration of MNs in both familial and sporadic models of ALS, thus providing proof of principle that regulatory network analysis is a valuable tool for studying cell-specific mechanisms of neurodegeneration.

MicroRNA-205 inhibits cancer cell migration and invasion via modulation of centromere protein F regulating pathways in prostate cancer

OBJECTIVES

To investigate the functional roles of microRNA-205 in the modulation of novel cancer pathways in prostate cancer cells.

METHODS

Functional studies of microRNA-205 were carried out to investigate cell proliferation, migration and invasion in prostate cancer cell lines (PC3 and DU145) by restoration of mature microRNA. In silico database and genome-wide gene expression analyses were carried out to identify molecular targets and pathways mediated by microRNA-205. Loss-of-function studies were applied to microRNA-205 target genes.

RESULTS

Restoration of microRNA-205 in cancer cell lines significantly inhibited cancer cell migration and invasion. Our data showed that the centromere protein F gene was overexpressed in prostate cancer clinical specimens and was a direct target of microRNA-205 regulation. Silencing of centromere protein F significantly inhibited cancer cell migration and invasion. Furthermore, MCM7, an oncogenic gene functioning downstream of centromere protein F, was identified by si-centromere protein F transfectants in prostate cancer cells.

CONCLUSIONS

Loss of tumor-suppressive microRNA-205 seems to enhance cancer cell migration and invasion in prostate cancer through direct regulation of centromere protein F. Our data describing pathways regulated by tumor-suppressive microRNA-205 provide new insights into the potential mechanisms of prostate cancer oncogenesis and metastasis.

Deregulation of the FOXM1 target gene network and its coregulatory partners in oesophageal adenocarcinoma

Background

Survival rates for oesophageal adenocarcinoma (OAC) remain disappointingly poor and current conventional treatment modalities have minimal impact on long-term survival. This is partly due to a lack of understanding of the molecular changes that occur in this disease. Previous studies have indicated that the transcription factor FOXM1 is commonly upregulated in this cancer type but the impact of this overexpression on gene expression in the context of OAC is largely unknown. FOXM1 does not function alone but works alongside the antagonistically-functioning co-regulatory MMB and DREAM complexes.

Methods

To establish how FOXM1 affects gene expression in OAC we have identified the FOXM1 target gene network in OAC-derived cells using ChIP-seq and determined the expression of both its coregulatory partners and members of this target gene network in OAC by digital transcript counting using the Nanostring gene expression assay.

Results

We find co-upregulation of FOXM1 with its target gene network in OAC. Furthermore, we find changes in the expression of its coregulatory partners, including co-upregulation of LIN9 and, surprisingly, reduced expression of LIN54. Mechanistically, we identify LIN9 as the direct binding partner for FOXM1 in the MMB complex. In the context of OAC, both coregulator (eg LIN54) and target gene (eg UHRF1) expression levels are predictive of disease stage.

Conclusions

Together our data demonstrate that there are global changes to the FOXM1 regulatory network in OAC and the expression of components of this network help predict cancer prognosis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12943-015-0339-8) contains supplementary material, which is available to authorized users.

Identifying candidate drivers of alcohol dependence-induced excessive drinking by assembly and interrogation of brain-specific regulatory networks

BACKGROUND

A systems biology approach based on the assembly and interrogation of gene regulatory networks, or interactomes, was used to study neuroadaptation processes associated with the transition to alcohol dependence at the molecular level.

RESULTS

Using a rat model of dependent and non-dependent alcohol self-administration, we reverse engineered a global transcriptional regulatory network during protracted abstinence, a period when relapse rates are highest. We then interrogated the network to identify master regulator genes that mechanistically regulate brain region-specific signatures associated with dependent and non-dependent alcohol self-administration. Among these, the gene coding for the glucocorticoid receptor was independently identified as a master regulator in multiple brain regions, including the medial prefrontal cortex, nucleus accumbens, central nucleus of the amygdala, and ventral tegmental area, consistent with the view that brain reward and stress systems are dysregulated during protracted abstinence. Administration of the glucocorticoid antagonist mifepristone in either the nucleus accumbens or ventral tegmental area selectively decreased dependent, excessive, alcohol self-administration in rats but had no effect on non-dependent, moderate, alcohol self-administration.

CONCLUSIONS

Our study suggests that assembly and analysis of regulatory networks is an effective strategy for the identification of key regulators of long-term neuroplastic changes within specific brain regions that play a functional role in alcohol dependence. More specifically, our results support a key role for regulatory networks downstream of the glucocorticoid receptor in excessive alcohol drinking during protracted alcohol abstinence.

Enhanced expression of centromere protein F predicts clinical progression and prognosis in patients with prostate cancer

Centromere protein F (CENPF) is a protein associated with the centromere-kinetochore complex and chromosomal segregation during mitosis. Previous studies have demonstrated that the upregulation of CENPF may be used as a proliferation marker of malignant cell growth in tumors. The overexpression of CENPF has also been reported to be associated with a poor prognosis in human cancers. However, the clinical significance of CENPF in prostate cancer (PCa) has not yet been fully elucidated. Thus, the aim of the present study was to determine the association of CENPF with tumor progression and prognosis in patients with PCa. The expression of CENPF at the protein level in human PCa and non-cancerous prostate tissues was detected by immunohistochemical analysis, which was further validated using a microarray-based dataset (NCBI GEO accession no: GSE21032) at the mRNA level. Subsequently, the association of CENPF expression with the clinicopathological characteristics of the patients with PCa was statistically analyzed. Immunohistochemistry and dataset analysis revealed that CENPF expression was significantly increased in the PCa tissues compared with the non-cancerous prostate tissues [immunoreactivity score (IRS): PCa, 177.98 ± 94.096 vs. benign, 121.30 ± 89.596, P < 0.001; mRNA expression in the dataset: PCa, 5.67 ± 0.47 vs. benign, 5.40 ± 0.11; P < 0.001]. Additionally, as revealed by the dataset, the upregulation of CENPF mRNA expression in the PCa tissues significantly correlated with a higher Gleason score (GS, P = 0.005), an advanced pathological stage (P = 0.008), the presence of metastasis (P < 0.001), a shorter overall survival (P=0.003) and prostate-specific antigen (PSA) failure (P < 0.001). Furthermore, both univariate and multivariate analyses revealed that the upregulation of CENPF was an independent predictor of poor biochemical recurrence (BCR)-free survival (P < 0.001 and P = 0.012, respectively). Our data suggest that the increased expression of CENPF plays an important role in the progression of PCa. More importantly, the increased expression of CENPF may efficiently predict poor BCR-free survival in patients with PCa.

miR-204 is dysregulated in metastatic prostate cancer in vitro

During cancer progression, the genome instability incurred rearrangement could possibly turn some of the tumor suppressor micro-RNAs into pro-oncogenic ones. We aimed to investigate miR-204 in the context of prostate cancer progression using a cell line model of different levels of genome instability (LNCaP, PC3, VCaP and NCI H660), as demonstrated by the availability of ERG fusion. We studied the effect of miR-204 modulation on master transcription factors important for lineage development, cell differentiation and prostate cancer bone marrow metastasis. We followed c-MYB, ETS1 and RUNX2 transcript and protein expression and the miR-204 affected global proteome. We further investigated if these transcription factors exert an effect on miR-204 expression (qPCR, luciferase reporter assay) by silencing them using esiRNA. We found dualistic miR-204 effects, either acting as a tumor suppressor on c-MYB, or as an oncomiR on ETS1. RUNX2 and ETS1 regulation by miR-204 was ERG fusion dependent, demonstrating regulatory circuitry disruption in advanced metastatic models. miR-204 also differentially affected mRNA splicing and protein stability. miR-204 levels were found dependent on cancer hypermethylation and supported by positive feedback induced by all three transcription factors. In this regulatory circuitry among miR-204, c-MYB, RUNX2 and ETS1, the c-MYB was found to induce all three other members, but its expression was differentially affected by the methylation status in lymph node vs. bone metastasis. We demonstrate that not only tumor suppressor micro-RNA loss, but also significant genome rearrangement-driven regulatory loop perturbations play a role in the advanced cancer progression, conferring better pro-survival and metastatic potential.

A systems approach to drug discovery in Alzheimer's disease

In the articles included in this volume, one feels a strong frustration among the writers with the slow course of therapeutics development for Alzheimer's disease and with the clinical failure of targeted therapeutic agents despite substantial progress in our understanding of the biology and biochemistry of the disease.