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

Epigenetic Regulation in Prostate Cancer Progression

Purpose of Review

An important number of newly identified molecular alterations in prostate cancer affect gene encoding master regulators of chromatin biology epigenetic regulation. This review will provide an updated view of the key epigenetic mechanisms underlying prostate cancer progression, therapy resistance, and potential actionable mechanisms and biomarkers.

Recent Findings

Key players in chromatin biology and epigenetic master regulators has been recently described to be crucially altered in metastatic CRPC and tumors that progress to AR independency. As such, epigenetic dysregulation represents a driving mechanism in the reprograming of prostate cancer cells as they lose AR-imposed identity.

Summary

Chromatin integrity and accessibility for transcriptional regulation are key features altered in cancer progression, and particularly relevant in nuclear hormone receptor-driven tumors like prostate cancer. Understanding how chromatin remodeling dictates prostate development and how its deregulation contributes to prostate cancer onset and progression may improve risk stratification and treatment selection for prostate cancer patients.

Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm

We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm's value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.

LSD1 activates a lethal prostate cancer gene network independently of its demethylase function

Medical castration that interferes with androgen receptor (AR) function is the principal treatment for advanced prostate cancer. However, clinical progression is universal, and tumors with AR-independent resistance mechanisms appear to be increasing in frequency. Consequently, there is an urgent need to develop new treatments targeting molecular pathways enriched in lethal prostate cancer. Lysine-specific demethylase 1 (LSD1) is a histone demethylase and an important regulator of gene expression. Here, we show that LSD1 promotes the survival of prostate cancer cells, including those that are castration-resistant, independently of its demethylase function and of the AR. Importantly, this effect is explained in part by activation of a lethal prostate cancer gene network in collaboration with LSD1's binding protein, ZNF217. Finally, that a small-molecule LSD1 inhibitor-SP-2509-blocks important demethylase-independent functions and suppresses castration-resistant prostate cancer cell viability demonstrates the potential of LSD1 inhibition in this disease.

Prognostic value of FOXM1 in solid tumors: a systematic review and meta-analysis

Accumulated studies have provided controversial evidences of the association between Forkhead Box M1 (FOXM1) expression and survival of human solid tumors. To address this inconsistency, we performed a meta-analysis with 23 studies identified from PubMed and Medline. We found elevated FOXM1-protein expression was significantly associated with worse 3-year overall survival (OS) (OR = 3.30, 95% CI = 2.56 to 4.25, P < 0.00001) 5-year OS (OR =3.35, 95% CI = 2.64 to 4.26, P < 0.00001) and 10-year OS (OR = 5.24, 95% CI = 2.61 to 10.52, P < 0.00001) of human solid tumors. Similar results were observed when disease free survival (DFS) were analyzed. Subgroup analysis showed that FOXM1 overexpression was associated with poor prognosis of colorectal cancer, gastric cancer, hepatic cancer, lung cancer and ovarian cancer. High expression level of FOXM1 was also associated with advanced tumor stage. In conclusion, elevated FOXM1 expression is associated with poor survival in most solid tumors. FOXM1 is a potential biomarker for prognosis prediction and a promising therapeutic target in human solid tumors.

Identification of potential therapeutic targets in prostate cancer through a cross-species approach

Genetically engineered mouse models of cancer can be used to filter genome-wide expression datasets generated from human tumours and to identify gene expression alterations that are functionally important to cancer development and progression. In this study, we have generated RNAseq data from tumours arising in two established mouse models of prostate cancer, PB-Cre/PtenloxP/loxP and p53loxP/loxPRbloxP/loxP, and integrated this with published human prostate cancer expression data to pinpoint cancer-associated gene expression changes that are conserved between the two species. To identify potential therapeutic targets, we then filtered this information for genes that are either known or predicted to be druggable. Using this approach, we revealed a functional role for the kinase MELK as a driver and potential therapeutic target in prostate cancer. We found that MELK expression was required for cell survival, affected the expression of genes associated with prostate cancer progression and was associated with biochemical recurrence.

Genes involved in prostate cancer progression determine MRI visibility

MRI is used to image prostate cancer and target tumors for biopsy or therapeutic ablation. The objective was to understand the biology of tumors not visible on MRI that may go undiagnosed and untreated. Methods: Prostate cancers visible or invisible on multiparametric MRI were macrodissected and examined by RNAseq. Differentially expressed genes (DEGs) based on MRI visibility status were cross-referenced with publicly available gene expression databases to identify genes associated with disease progression. Genes with potential roles in determining MRI visibility and disease progression were knocked down in murine prostate cancer xenografts, and imaged by MRI. Results: RNAseq identified 1,654 DEGs based on MRI visibility status. Comparison of DEGs based on MRI visibility and tumor characteristics revealed that Gleason score (dissimilarity test, p<0.0001) and tumor size (dissimilarity test, p<0.039) did not completely determine MRI visibility. Genes in previously reported prognostic signatures significantly correlated with MRI visibility suggesting that MRI visibility was prognostic. Cross-referencing DEGs with external datasets identified four genes (PHYHD1, CENPF, ALDH2, GDF15) that predict MRI visibility, progression free survival and metastatic deposits. Genetic modification of a human prostate cancer cell line to induce miR-101 and suppress CENPF decreased cell migration and invasion. As prostate cancer xenografts in mice, these cells had decreased visibility on diffusion weighted MRI and decreased perfusion, which correlated with immunostaining showing decreased cell density and proliferation. Conclusions: Genes involved in prostate cancer prognosis and metastasis determine MRI visibility, indicating that MRI visibility has prognostic significance. MRI visibility was associated with genetic features linked to poor prognosis.

MicroRNA-27a-5p regulation by promoter methylation and MYC signaling in prostate carcinogenesis

Upregulation of MYC and miRNAs deregulation are common in prostate cancer (PCa). Overactive MYC may cause miRNAs’ expression deregulation through transcriptional and post-transcriptional mechanisms and epigenetic alterations are also involved in miRNAs dysregulation. Herein, we aimed to elucidate the role of regulatory network between MYC and miRNAs in prostate carcinogenesis. MYC expression was found upregulated in PCa cases and matched precursor lesions. MicroRNA’s microarray analysis of PCa samples with opposed MYC levels identified miRNAs significantly overexpressed in high-MYC PCa. However, validation of miR-27a-5p in primary prostate tissues disclosed downregulation in PCa, instead, correlating with aberrant promoter methylation. In a series of castration-resistant PCa (CRPC) cases, miR-27a-5p was upregulated, along with promoter hypomethylation. MYC and miR-27a-5p expression levels in LNCaP and PC3 cells mirrored those observed in hormone-naíve PCa and CRPC, respectively. ChIP analysis showed that miR-27a-5p expression is only regulated by c-Myc in the absence of aberrant promoter methylation. MiR-27a-5p knockdown in PC3 cells promoted cell growth, whereas miRNA forced expression in LNCaP and stable MYC-knockdown PC3 cells attenuated the malignant phenotype, suggesting a tumor suppressive role for miR-27a-5p. Furthermore, miR-27a-5p upregulation decreased EGFR/Akt1/mTOR signaling. We concluded that miR-27a-5p is positively regulated by MYC, and its silencing due to aberrant promoter methylation occurs early in prostate carcinogenesis, concomitantly with loss of MYC regulatory activity. Our results further suggest that along PCa progression, miR-27a-5p promoter becomes hypomethylated, allowing for MYC to resume its regulatory activity. However, the altered cellular context averts miR-27a-5p from successfully accomplishing its tumor suppressive function at this stage of disease.

GRK3 is a direct target of CREB activation and regulates neuroendocrine differentiation of prostate cancer cells

Neuroendocrine prostate cancer (NEPC) is an aggressive subtype of prostate cancer that commonly arises through neuroendocrine differentiation (NED) of prostate adenocarcinoma (PAC) after therapy, such as radiation therapy and androgen deprivation treatment (ADT). No effective therapeutic is available for NEPC and its molecular mechanisms remain poorly understood. We have reported that G protein-coupled receptor kinase 3 (GRK3, also called ADRBK2) promotes prostate cancer progression. In this study, we demonstrate that the ADT-activated cAMP response element binding protein (CREB) directly targets and induces GRK3. We show GRK3 expression is higher in NEPC than in PAC cells and mouse models, and it positively correlates with the expression and activity of CREB in human prostate cancers. Notably, overexpression of GRK3 in PAC cells increased the expression of NE markers in a kinase activity dependent manner. Conversely, silencing GRK3 blocked CREB-induced NED in PAC cells, reversed NE phenotypes and inhibited proliferation of NEPC cells. Taken together, these results indicate that GRK3 is a new critical activator of NE phenotypes and mediator of CREB activation in promoting NED of prostate cancer cells.

Treatment with docetaxel in combination with Aneustat leads to potent inhibition of metastasis in a patient-derived xenograft model of advanced prostate cancer

Background:

Docetaxel used for first-line treatment of advanced prostate cancer (PCa) is only marginally effective. We previously showed, using the LTL-313H subrenal capsule patient-derived metastatic PCa xenograft model, that docetaxel combined with Aneustat (OMN54), a multivalent plant-derived therapeutic, led to marked synergistic tumour growth inhibition. Here, we investigated the effect of docetaxel+Aneustat on metastasis.

Methods:

C4-2 cells were incubated with docetaxel, Aneustat and docetaxel+Aneustat to assess effects on cell migration. The LTL-313H model, similarly treated, was analysed for effects on lung micro-metastasis and kidney invasion. The LTL-313H gene expression profile was compared with profiles of PCa patients (obtained from Oncomine) and subjected to IPA to determine involvement of cancer driver genes.

Results:

Docetaxel+Aneustat markedly inhibited C4-2 cell migration and LTL-313H lung micro-metastasis/kidney invasion. Oncomine analysis indicated that treatment with docetaxel+Aneustat was associated with improved patient outcome. The drug combination markedly downregulated expression of cancer driver genes such as FOXM1 (and FOXM1-target genes). FOXM1 overexpression reduced the anti-metastatic activity of docetaxel+Aneustat.

Conclusions:

Docetaxel+Aneustat can inhibit PCa tissue invasion and metastasis. This activity appears to be based on reduced expression of cancer driver genes such as FOXM1. Use of docetaxel+Aneustat may provide a new, more effective regimen for therapy of metastatic PCa.

Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

Since the five-year survival rate is less than 5%, pancreatic ductal adenocarcinoma (PDAC) remains the 4th cause of cancer-related death. Although PDAC has been repeatedly researched in recent years, it is still predicted to be the second leading cause of cancer death by year 2030. In our study, the differentially expressed genes in dataset GSE62452 were used to construct a co-expression network by WGCNA. The yellow module related to grade of PDAC was screened. Combined with co-expression network and PPI network, 36 candidates were screened. After survival and regression analysis by using GSE62452 and TCGA dataset, we identified 10 real hub genes (CCNA2, CCNB1, CENPF, DLGAP5, KIF14, KIF23, NEK2, RACGAP1, TPX2 and UBE2C) tightly related to progression of PDAC. According to Oncomine database and The Human Protein Atlas (HPA), we found that all real hub genes were overexpressed in pancreatic carcinoma compared with normal tissues on transcriptional and translational level. ROC curve was plotted and AUC was calculated to distinguish recurrent and non-recurrent PDAC and every AUC of the real hub gene was greater than 0.5. Finally, functional enrichment analysis and gene set enrichment (GSEA) was performed and both of them showed the cell cycle played a vital role in PDAC.

Regulation of UHRF1 by dual-strand tumor-suppressor microRNA-145 (miR-145-5p and miR-145-3p): Inhibition of bladder cancer cell aggressiveness

In microRNA (miRNA) biogenesis, the guide-strand of miRNA integrates into the RNA induced silencing complex (RISC), whereas the passenger-strand is inactivated through degradation. Analysis of our miRNA expression signature of bladder cancer (BC) by deep-sequencing revealed that microRNA (miR)-145-5p (guide-strand) and miR-145-3p (passenger-strand) were significantly downregulated in BC tissues. It is well known that miR-145-5p functions as a tumor suppressor in several types of cancer. However, the impact of miR-145-3p on cancer cells is still ambiguous. The aim of the present study was to investigate the functional significance of miR-145-3p and BC oncogenic pathways and targets regulated by miR-145-5p/miR-145-3p. Ectopic expression of either miR-145-5p or miR-145-3p in BC cells significantly suppressed cancer cell growth, migration and invasion and it also induced apoptosis. The gene encoding ubiquitin-like with PHD and ring finger domains 1 (UHRF1) was a direct target of these miRNAs. Silencing of UHRF1 induced apoptosis and inhibited cancer cell proliferation, migration, and invasion in BC cells. In addition, overexpressed UHRF1 was confirmed in BC clinical specimens, and the high UHRF1 expression group showed a significantly poorer cause specific survival rate in comparison with the low expression group. Taken together, our present data demonstrated that both strands of miR-145 (miR-145-5p: guide-strand and miR-145-3p: passenger-strand) play pivotal roles in BC cells by regulating UHRF1. The identification of the molecular target of a tumor suppressive miRNAs provides novel insights into the potential mechanisms of BC oncogenesis and suggests novel therapeutic strategies.

TumorMap: Exploring the Molecular Similarities of Cancer Samples in an Interactive Portal

Vast amounts of molecular data are being collected on tumor samples, which provide unique opportunities for discovering trends within and between cancer subtypes. Such cross-cancer analyses require computational methods that enable intuitive and interactive browsing of thousands of samples based on their molecular similarity. We created a portal called TumorMap to assist in exploration and statistical interrogation of high-dimensional complex "omics" data in an interactive and easily interpretable way. In the TumorMap, samples are arranged on a hexagonal grid based on their similarity to one another in the original genomic space and are rendered with Google's Map technology. While the important feature of this public portal is the ability for the users to build maps from their own data, we pre-built genomic maps from several previously published projects. We demonstrate the utility of this portal by presenting results obtained from The Cancer Genome Atlas project data. Cancer Res; 77(21); e111-4. ©2017 AACR.

Deconvolution of Transcriptional Networks in Post-Traumatic Stress Disorder Uncovers Master Regulators Driving Innate Immune System Function

Post-Traumatic Stress Disorder (PTSD) is a psychiatric disorder that develops in individuals experiencing a shocking incident, but the underlying disease susceptibility gene networks remain poorly understood. Breen et al. conducted a Weighted Gene Co-expression Network Analysis on PTSD, and identified a dysregulated innate immune module associated with PTSD development. To further identify the Master Regulators (MRs) driving the network function, here we deconvoluted the transcriptional networks on the same datasets using ARACNe (Algorithm for Reconstruction of Accurate Cellular Networks) followed by protein activity analysis. We successfully identified several MRs including SOX3, TNFAIP3, TRAFD1, POU3F3, STAT2, and PML that govern the expression of a large collection of genes. Transcription factor binding site enrichment analysis verified the binding of these MRs to their predicted targets. Notably, the sub-networks regulated by TNFAIP3, TRAFD1 and PML are involved in innate immune response, suggesting that these MRs may correlate with the innate immune module identified by Breen et al. These findings were replicated in an independent dataset generated on expression microarrays. In conclusion, our analysis corroborated previous findings that innate immunity may be involved in the progression of PTSD, yet also identified candidate MRs driving the disease progression in the innate immunity pathways.

Dysregulation of AKT3 along with a small panel of mRNAs stratifies high-grade serous ovarian cancer from both normal epithelia and benign tumor tissues

Screening methods of High-Grade Serous Ovarian Cancer (HGSOC) lack specificity and sensitivity, partly due to benign tumors producing false-positive findings. We utilized a differential expression analysis pipeline on malignant tumor (MT) and normal epithelial (NE) samples, and also filtered the results to discriminate between MT and benign tumor (BT). We report that a panel of 26 dysregulated genes stratifies MT from both BT and NE. We further validated our findings by utilizing unsupervised clustering methods on two independent datasets. We show that the 26-genes panel completely distinguishes HGSOC from NE, and produces a more accurate classification between HGSOC and BT. Pathway analysis reveals that AKT3 is of particular significance, because of its high fold change and appearance in the majority of the dysregulated pathways. mRNA patterns of AKT3 suggest essential connections with tumor growth and metastasis, as well as a strong biomarker potential when used with 3 other genes (PTTG1, MND1, CENPF). Our results show that dysregulation of the 26-mRNA signature panel provides an evidence of malignancy and contribute to the design of a high specificity biomarker panel for detection of HGSOC, potentially in an early more curable stage.

An integrated approach to identify critical transcription factors in the protection against hydrogen peroxide-induced oxidative stress by Danhong injection

Oxidative stress plays a vital role in many pathological processes of the cardiovascular diseases. However, the underlying mechanism remains unclear, especially on a transcription factor (TF) level. In this study, a new method, concatenated tandem array of consensus transcription factor response elements (catTFREs), and an Illumina-based RNA-seq technology were integrated to systematically investigate the role of TFs in hydrogen peroxide (H₂O₂)-induced oxidative stress in cardiomyocytes; the damage was then rescued by Danhong injection (DHI), a Chinese standardized product approved for cardiovascular diseases treatment. The overall gene expression revealed cell apoptosis and DNA repair were vital for cardiomyocytes in resisting oxidative stress. By comprehensively integrating the transcription activity of TFs and their downstream target genes, an important TFs-target network were constructed and 13 TFs were identified as critical TFs in DHI-mediated protection in H₂O₂-induced oxidative stress. By using the integrated approach, seven TFs of these 13 TFs were also identified in melatonin-mediated protection in H₂O₂-induced damage. Furthermore, the transcription activity of DNA-(apurinic or apyrimidinic site) lyase (Apex1), Myocyte-specific enhancer factor 2D (Mef2d) and Pre B-cell leukemia transcription factor 3 (Pbx3) was further verified in pluripotent stem cell-derived cardiomyocytes. This research offers a new understanding of cardiomyocytes in response to H₂O₂-induced oxidative stress and reveals additional potential therapeutic targets. The combination of two parallel omics datasets (corresponding to the transcriptome and proteome) can reduce the noise in high-throughput data and reveal the fundamental changes of the biological process, making it suitable and reliable for investigation of critical targets in many other complicated pathological processes.

Screening and identification of critical transcription factors involved in the protection of cardiomyocytes against hydrogen peroxide-induced damage by Yixin-shu

Oxidative stress initiates harmful cellular responses, such as DNA damage and protein denaturation, triggering a series of cardiovascular disorders. Systematic investigations of the transcription factors (TFs) involved in oxidative stress can help reveal the underlying molecular mechanisms and facilitate the discovery of effective therapeutic targets in related diseases. In this study, an integrated strategy which integrated RNA-seq-based transcriptomics techniques and a newly developed concatenated tandem array of consensus TF response elements (catTFREs)-based proteomics approach and then combined with a network pharmacology analysis, was developed and this integrated strategy was used to investigate critical TFs in the protection of Yixin-shu (YXS), a standardized medical product used for ischaemic heart disease, against hydrogen peroxide (H₂O₂)-induced damage in cardiomyocytes. Importantly, YXS initiated biological process such as anti-apoptosis and DNA repair to protect cardiomyocytes from H₂O₂-induced damage. By using the integrated strategy, DNA-(apurinic or apyrimidinic site) lyase (Apex1), pre B-cell leukemia transcription factor 3 (Pbx3), and five other TFs with their functions involved in anti-oxidation, anti-apoptosis and DNA repair were identified. This study offers a new understanding of the mechanism underlying YXS-mediated protection against H₂O₂-induced oxidative stress in cardiomyocytes and reveals novel targets for oxidative stress-related diseases.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

Systematic, network-based characterization of therapeutic target inhibitors

A large fraction of the proteins that are being identified as key tumor dependencies represent poor pharmacological targets or lack clinically-relevant small-molecule inhibitors. Availability of fully generalizable approaches for the systematic and efficient prioritization of tumor-context specific protein activity inhibitors would thus have significant translational value. Unfortunately, inhibitor effects on protein activity cannot be directly measured in systematic and proteome-wide fashion by conventional biochemical assays. We introduce OncoLead, a novel network based approach for the systematic prioritization of candidate inhibitors for arbitrary targets of therapeutic interest. In vitro and in vivo validation confirmed that OncoLead analysis can recapitulate known inhibitors as well as prioritize novel, context-specific inhibitors of difficult targets, such as MYC and STAT3. We used OncoLead to generate the first unbiased drug/regulator interaction map, representing compounds modulating the activity of cancer-relevant transcription factors, with potential in precision medicine.

Accelerated parallel algorithm for gene network reverse engineering

Background

The Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE) represents one of the most effective tools to reconstruct gene regulatory networks from large-scale molecular profile datasets. However, previous implementations require intensive computing resources and, in some cases, restrict the number of samples that can be used. These issues can be addressed elegantly in a GPU computing framework, where repeated mathematical computation can be done efficiently, but requires extensive redesign to apply parallel computing techniques to the original serial algorithm, involving detailed optimization efforts based on a deep understanding of both hardware and software architecture.

Result

Here, we present an accelerated parallel implementation of ARACNE (GPU-ARACNE). By taking advantage of multi-level parallelism and the Compute Unified Device Architecture (CUDA) parallel kernel-call library, GPU-ARACNE successfully parallelizes a serial algorithm and simplifies the user experience from multi-step operations to one step. Using public datasets on comparable hardware configurations, we showed that GPU-ARACNE is faster than previous implementations and is able to reconstruct equally valid gene regulatory networks.

Conclusion

Given that previous versions of ARACNE are extremely resource demanding, either in computational time or in hardware investment, GPU-ARACNE is remarkably valuable for researchers who need to build complex regulatory networks from large expression datasets, but with limited budget on computational resources. In addition, our GPU-centered optimization of adaptive partitioning for Mutual Information (MI) estimation provides lessons that are applicable to other domains.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0458-5) contains supplementary material, which is available to authorized users.

Targeting Prostate Cancer Subtype 1 by Forkhead Box M1 Pathway Inhibition

Purpose: Prostate cancer was recently classified to three clinically relevant subtypes (PCS) demarcated by unique pathway activation and clinical aggressiveness. In this preclinical study, we investigated molecular targets and therapeutics for PCS1, the most aggressive and lethal subtype, with no treatment options available in the clinic.Experimental Design: We utilized the PCS1 gene set and our model of enzalutamide (ENZ^R) castration-resistant prostate cancer (CRPC) to identify targetable pathways and inhibitors for PCS1. The findings were evaluated in vitro and in the ENZ^R CRPC xenograft model in vivoResults: The results revealed that ENZ^R CRPC cells are enriched with PCS1 signature and that Forkhead box M1 (FOXM1) pathway is the central driver of this subtype. Notably, we identified Monensin as a novel FOXM1-binding agent that selectively targets FOXM1 to reverse the PCS1 signature and its associated stem-like features and reduces the growth of ENZ^R CRPC cells and xenograft tumors.Conclusions: Our preclinical data indicate FOXM1 pathway as a master regulator of PCS1 tumors, namely in ENZ^R CRPC, and targeting FOXM1 reduces cell growth and stemness in ENZ^R CRPC in vitro and in vivo These preclinical results may guide clinical evaluation of targeting FOXM1 to eradicate highly aggressive and lethal PCS1 prostate cancer tumors. Clin Cancer Res; 23(22); 6923-33. ©2017 AACR.

FoxM1: Repurposing an oncogene as a biomarker

The past few decades have witnessed a tremendous progress in understanding the biology of cancer, which has led to more comprehensive approaches for global gene expression profiling and genome-wide analysis. This has helped to determine more sophisticated prognostic and predictive signature markers for the prompt diagnosis and precise screening of cancer patients. In the search for novel biomarkers, there has been increased interest in FoxM1, an extensively studied transcription factor that encompasses most of the hallmarks of malignancy. Considering the attractive potential of this multifarious oncogene, FoxM1 has emerged as an important molecule implicated in initiation, development and progression of cancer. Bolstered with the skill to maneuver the proliferation signals, FoxM1 bestows resistance to contemporary anti-cancer therapy as well. This review sheds light on the large body of literature that has accumulated in recent years that implies that FoxM1 neoplastic functions can be used as a novel predictive, prognostic and therapeutic marker for different cancers. This assessment also highlights the key features of FoxM1 that can be effectively harnessed to establish FoxM1 as a strong biomarker in diagnosis and treatment of cancer.

Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

The identification of functional non-coding mutations is a key challenge in the field of genomics. Here we introduce μ-cisTarget to filter, annotate and prioritize cis-regulatory mutations based on their putative effect on the underlying "personal" gene regulatory network. We validated μ-cisTarget by re-analyzing the TAL1 and LMO1 enhancer mutations in T-ALL, and the TERT promoter mutation in melanoma. Next, we re-sequenced the full genomes of ten cancer cell lines and used matched transcriptome data and motif discovery to identify master regulators with de novo binding sites that result in the up-regulation of nearby oncogenic drivers. μ-cisTarget is available from http://mucistarget.aertslab.org .

Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma

Comprehensive multiplatform analysis of 80 uveal melanomas (UM) identifies four molecularly distinct, clinically relevant subtypes: two associated with poor-prognosis monosomy 3 (M3) and two with better-prognosis disomy 3 (D3). We show that BAP1 loss follows M3 occurrence and correlates with a global DNA methylation state that is distinct from D3-UM. Poor-prognosis M3-UM divide into subsets with divergent genomic aberrations, transcriptional features, and clinical outcomes. We report change-of-function SRSF2 mutations. Within D3-UM, EIF1AX- and SRSF2/SF3B1-mutant tumors have distinct somatic copy number alterations and DNA methylation profiles, providing insight into the biology of these low- versus intermediate-risk clinical mutation subtypes.

Identification and Validation of PCAT14 as Prognostic Biomarker in Prostate Cancer

Rapid advances in the discovery of long noncoding RNAs (lncRNAs) have identified lineage- and cancer-specific biomarkers that may be relevant in the clinical management of prostate cancer (PCa). Here we assembled and analyzed a large RNA-seq dataset, from 585 patient samples, including benign prostate tissue and both localized and metastatic PCa to discover and validate differentially expressed genes associated with disease aggressiveness. We performed Sample Set Enrichment Analysis (SSEA) and identified genes associated with low versus high Gleason score in the RNA-seq database. Comparing Gleason 6 versus 9+ PCa samples, we identified 99 differentially expressed genes with variable association to Gleason grade as well as robust expression in prostate cancer. The top-ranked novel lncRNA PCAT14, exhibits both cancer and lineage specificity. On multivariate analysis, low PCAT14 expression independently predicts for BPFS (P = .00126), PSS (P = .0385), and MFS (P = .000609), with trends for OS as well (P = .056). An RNA in-situ hybridization (ISH) assay for PCAT14 distinguished benign vs malignant cases, as well as high vs low Gleason disease. PCAT14 is transcriptionally regulated by AR, and endogenous PCAT14 overexpression suppresses cell invasion. Thus, Using RNA-sequencing data we identify PCAT14, a novel prostate cancer and lineage-specific lncRNA. PCAT14 is highly expressed in low grade disease and loss of PCAT14 predicts for disease aggressiveness and recurrence.

Forkhead Box M1 positively regulates UBE2C and protects glioma cells from autophagic death

Ubiquitin-conjugating enzyme E2C (UBE2C) is characterized as a crucial molecule in cancer cell growth that plays an essential role in the development of gliomas, but the detailed mechanisms have not been fully elucidated. In this study, we found that Forkhead box transcription factor M1 (FoxM1) overexpression increased UBE2C expression, whereas FoxM1 suppression inhibited UBE2C expression in glioma cells. In addition, high FoxM1/UBE2C expression was significantly correlated with poor prognosis in glioma. We subsequently demonstrated that UBE2C was a direct transcriptional target of FoxM1, and site-directed mutations markedly down-regulated UBE2C promoter activity. Moreover, UBE2C siRNA (si-UBE2C) significantly induced glioma cell autophagy and increased both mCherry-LC3 punctate fluorescence and LC3B-II/LC3-I expression. Notably, the si-UBE2C-induced decrease in cell viability was markedly inhibited by the autophagy inhibitor bafilomycin A1. The silencing of UBE2C resulted in a distinct inhibition of the PI3K-Akt-mTOR pathway, which functions in the negative modulation of autophagy. Collectively, our findings provide clinical and molecular evidence that FoxM1 promotes glioma progression by enhancing UBE2C transcription and that the inhibition of UBE2C partially induces autophagic glioma cell death. Thus, targeting the FoxM1-UBE2C axis has therapeutic potential in the treatment of gliomas.

FOXM1 promotes the progression of prostate cancer by regulating PSA gene transcription

Androgen/AR is the primary contributor to prostate cancer (PCa) progression by regulating Prostate Specific Antigen (PSA) gene transcription. The disease inevitably evolves to androgen-independent (AI) status. Other mechanisms by which PSA is regulated and develops to AI have not yet been fully determined. FOXM1 is a cell proliferation-specific transcription factor highly expressed in PCa cells compared to non-malignant prostate epithelial cells, suggesting that the aberrant overexpression of FOXM1 contributes to PCa development. In addition to regulating AR gene transcription and cell cycle-regulatory genes, FOXM1 selectively regulates the gene transcription of KLK2 and PSA, typical androgen responsive genes. Screening the potential FOXM1-binding sites by ChIP-PCR, we found that FOXM1 directly binds to the FHK binding motifs in the PSA promoter/enhancer regions. AI C4-2 cells have more FOXM1 binding sites than androgen dependent LNCaP cells. The depletion of FOXM1 by small molecular inhibitors significantly improves the suppression of PSA gene transcription by the anti-AR agent Cadosax. This is the first report showing that FOXM1 promotes PCa progression by regulating PSA gene transcription, particularly in AI PCa cells. The combination of anti-AR agents and FOXM1 inhibitors has the potential to greatly improve therapy for late-stage PCa patients by suppressing PSA levels.

Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network

We report a novel computational method, RegNetDriver, to identify tumorigenic drivers using the combined effects of coding and non-coding single nucleotide variants, structural variants, and DNA methylation changes in the DNase I hypersensitivity based regulatory network. Integration of multi-omics data from 521 prostate tumor samples indicated a stronger regulatory impact of structural variants, as they affect more transcription factor hubs in the tissue-specific network. Moreover, crosstalk between transcription factor hub expression modulated by structural variants and methylation levels likely leads to the differential expression of target genes. We report known prostate tumor regulatory drivers and nominate novel transcription factors (ERF, CREB3L1, and POU2F2), which are supported by functional validation.

Impact of novel miR-145-3p regulatory networks on survival in patients with castration-resistant prostate cancer

BACKGROUND

Despite recent advancements, metastatic castration-resistant prostate cancer (CRPC) is not considered curative. Novel approaches for identification of therapeutic targets of CRPC are needed.

METHODS

Next-generation sequencing revealed 945-1248 miRNAs from each lethal mCRPC sample. We constructed miRNA expression signatures of CRPC by comparing the expression of miRNAs between CRPC and normal prostate tissue or hormone-sensitive prostate cancer (HSPC). Genome-wide gene expression studies and in silico analyses were carried out to predict miRNA regulation and investigate the functional significance and clinical utility of the novel oncogenic pathways regulated by these miRNAs in prostate cancer (PCa).

RESULTS

Based on the novel miRNA expression signature of CRPC, miR-145-5p and miR-145-3p were downregulated in CRPC. By focusing on miR-145-3p, which is a passenger strand and has not been well studied in previous reports, we showed that miR-145-3p targeted 4 key molecules, i.e., MELK, NCAPG, BUB1, and CDK1, in CPRC. These 4 genes significantly predicted survival in patients with PCa.

CONCLUSIONS

Small RNA sequencing for lethal CRPC and in silico analyses provided novel therapeutic targets for CRPC.

PLATE-Seq for genome-wide regulatory network analysis of high-throughput screens

Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.

Despite the importance of pharmacological and functional genomic screens the readouts are of low complexity. Here the authors introduce PLATE-Seq, a low-cost genome-wide mRNA profiling method to complement high-throughput screening.

G2/M checkpoint plays a vital role at the early stage of HCC by analysis of key pathways and genes

The present study was designed to explore the molecular mechanism at the early stage of hepatocarcinoma (HCC) and identify the candidate genes and pathways changed significantly. We downloaded the gene expression file dataset GSE6764 from GEO, adopted the Robust Multi-array Average (RMA) algorithm to preprocess the raw file. 797 differentially expressed genes (DEGs) were screened out based on the SAM method using R language. Ingenuity Pathway Analysis (IPA) was used to perform canonical pathway analysis in order to calculate the most significantly changed pathways and predict the upstream regulators. In order to confirm the results from the DEGs which based on the individual gene level, the gene set enrichment analysis (GSEA) was done from the gene set level and the leading edge analysis was performed to find out the most appeared genes in several gene sets. The PPI network was built using GeneMANIA and the key genes were calculated using cytoHubba plugin based on cytoscape 3.4.0. We found that the Cell Cycle: G2/M DNA damage checkpoint regulation is the top-ranked pathways at the early stage of HCC by IPA. The high expression of several genes including CCNB1, CDC25B, XPO1, GMPS, KPNA2 and MELK is correlated with high risk, poor prognosis and shorter overall survival time in HCC patients by use of Kaplan-Meier Survival analysis. Taken together, our study showed that the G2/M checkpoint plays a vital role at the early HCC and the genes participate in the process may serve as biomarkers for the diagnosis and prognosis.

Oxidative Stress Gene Expression Profile Correlates with Cancer Patient Poor Prognosis: Identification of Crucial Pathways Might Select Novel Therapeutic Approaches

The role of altered redox status and high reactive oxygen species (ROS) is still controversial in cancer development and progression. Intracellular levels of ROS are elevated in cancer cells suggesting a role in cancer initiation and progression; on the contrary, ROS elevated levels may induce programmed cell death and have been associated with cancer suppression. Thus, it is crucial to consider the double-face of ROS, for novel therapeutic strategies targeting redox regulatory mechanisms. In this review, in order to derive cancer-type specific oxidative stress genes' profile and their potential prognostic role, we integrated a publicly available oxidative stress gene signature with patient survival data from the Cancer Genome Atlas database. Overall, we found several genes statistically significant associated with poor prognosis in the examined six tumor types. Among them, FoxM1 and thioredoxin reductase1 expression showed the same pattern in four out of six cancers, suggesting their specific critical role in cancer-related oxidative stress adaptation. Our analysis also unveiled an enriched cellular network, highlighting specific pathways, in which many genes are strictly correlated. Finally, we discussed novel findings on the correlation between oxidative stress and cancer stem cells in order to define those pathways to be prioritized in drug development.

Transdifferentiation as a Mechanism of Treatment Resistance in a Mouse Model of Castration-Resistant Prostate Cancer

Current treatments for castration-resistant prostate cancer (CRPC) that target androgen receptor (AR) signaling improve patient survival, yet ultimately fail. Here, we provide novel insights into treatment response for the antiandrogen abiraterone by analyses of a genetically engineered mouse (GEM) model with combined inactivation of Trp53 and Pten, which are frequently comutated in human CRPC. These NPp53 mice fail to respond to abiraterone and display accelerated progression to tumors resembling treatment-related CRPC with neuroendocrine differentiation (CRPC-NE) in humans. Cross-species computational analyses identify master regulators of adverse response that are conserved with human CRPC-NE, including the neural differentiation factor SOX11, which promotes neuroendocrine differentiation in cells derived from NPp53 tumors. Furthermore, abiraterone-treated NPp53 prostate tumors contain regions of focal and/or overt neuroendocrine differentiation, distinguished by their proliferative potential. Notably, lineage tracing in vivo provides definitive and quantitative evidence that focal and overt neuroendocrine regions arise by transdifferentiation of luminal adenocarcinoma cells. These findings underscore principal roles for TP53 and PTEN inactivation in abiraterone resistance and progression from adenocarcinoma to CRPC-NE by transdifferentiation.Significance: Understanding adverse treatment response and identifying patients likely to fail treatment represent fundamental clinical challenges. By integrating analyses of GEM models and human clinical data, we provide direct genetic evidence for transdifferentiation as a mechanism of drug resistance as well as for stratifying patients for treatment with antiandrogens. Cancer Discov; 7(7); 736-49. ©2017 AACR.See related commentary by Sinha and Nelson, p. 673This article is highlighted in the In This Issue feature, p. 653.

FOXM1 in Cancer: Interactions and Vulnerabilities

FOXM1 is a transcription factor of the Forkhead family that is required for cell proliferation of normal cells. However, FOXM1 is repeatedly overexpressed in a variety of human cancers, and it has been implicated in all major hallmarks of cancer delineated by Hanahan and Weinberg. It has been postulated that the oncogenic potential of FOXM1 is determined by its capacity to transactivate target genes that are implicated in different phases of cancer development. However, FOXM1 may also play an oncogenic role by interacting with other proteins, such as β-catenin or SMAD3 to induce oncogenic WNT and TGFβ signaling pathways, respectively. In this review, I will discuss the protein-protein interactions of FOXM1 that are critical for cancer development and may represent novel targets for anticancer drugs. Cancer Res; 77(12); 3135-9. ©2017 AACR.

The Landscape of microRNA Targeting in Prostate Cancer Defined by AGO-PAR-CLIP

MicroRNA (miRNA) deregulation in prostate cancer (PCa) contributes to PCa initiation and metastatic progression. To comprehensively define the cancer-associated changes in miRNA targeting and function in commonly studied models of PCa, we performed photoactivatable ribonucleoside-enhanced cross-linking immunoprecipitation of the Argonaute protein in a panel of PCa cell lines modeling different stages of PCa progression. Using this comprehensive catalogue of miRNA targets, we analyzed miRNA targeting on known drivers of PCa and examined tissue-specific and stage-specific pathway targeting by miRNAs. We found that androgen receptor is the most frequently targeted PCa oncogene and that miR-148a targets the largest number of known PCa drivers. Globally, tissue-specific and stage-specific changes in miRNA targeting are driven by homeostatic response to active oncogenic pathways. Our findings indicate that, even in advanced PCa, the miRNA pool adapts to regulate continuing alterations in the cancer genome to balance oncogenic molecular changes. These findings are important because they are the first to globally characterize miRNA changes in PCa and demonstrate how the miRNA target spectrum responds to staged tumorigenesis.

FOXM1 expression is significantly associated with chemotherapy resistance and adverse prognosis in non-serous epithelial ovarian cancer patients

BACKGROUND

Epithelial ovarian cancer (EOC) is a spectrum of different diseases, which makes their treatment a challenge. Forkhead box M1 (FOXM1) is an oncogene aberrantly expressed in many solid cancers including serous EOC, but its role in non-serous EOCs remains undefined. We examined FOXM1 expression and its correlation to prognosis across the three major EOC subtypes, and its role in tumorigenesis and chemo-resistance in vitro.

METHODS

Gene signatures were generated by microarray for 14 clear-cell and 26 endometrioid EOCs, and 15 normal endometrium snap-frozen biopsies. Validation of FOXM1 expression was performed by RT-qPCR and immunohistochemistry in the same samples and additionally in 50 high-grade serous EOCs and in their most adequate normal controls (10 luminal fallopian tube and 20 ovarian surface epithelial brushings). Correlations of FOXM1 expression to clinic-pathological parameters and patients' prognosis were evaluated by Kaplan-Meier and Cox proportional-hazards analyses. OVCAR-3 and two novel deeply characterized EOC cell lines (EOC-CC1 and OSPC2, with clear-cell and serous subtype, respectively) were employed for in vitro studies. Effects of FOXM1 inhibition by transient siRNA transfection were evaluated on cell-proliferation, cell-cycle, colony formation, invasion, and response to conventional first- and second-line anticancer agents, and to the PARP-inhibitor olaparib. Gene signatures of FOXM1-silenced cell lines were generated by microarray and confirmed by RT-qPCR.

RESULTS

A significant FOXM1 mRNA up-regulation was found in EOCs compared to normal controls. FOXM1 protein overexpression significantly correlated to serous histology (p = 0.001) and advanced FIGO stage (p = 0.004). Multivariate analyses confirmed FOXM1 protein overexpression as an independent indicator of worse disease specific survival in non-serous EOCs, and of shorter time to progression in platinum-resistant cases. FOXM1 downregulation in EOC cell lines inhibited cell growth and clonogenicity, and promoted the cytotoxic effects of platinum compounds, doxorubicin hydrochloride and olaparib. Upon FOXM1 knock-down in EOC-CC1 and OSPC2 cells, microarray and RT-qPCR analyses revealed the deregulation of several common and other unique subtype-specific FOXM1 putative targets involved in cell cycle, metastasis, DNA repair and drug response.

CONCLUSIONS

FOXM1 is up-regulated in all three major EOCs subtypes, and is a prognostic biomarker and a potential combinatorial therapeutic target in platinum resistant disease, irrespective of tumor histology.

lncRNA HOXD-AS1 Regulates Proliferation and Chemo-Resistance of Castration-Resistant Prostate Cancer via Recruiting WDR5

Castration-resistant prostate cancer (CRPC) that occurs after the failure of androgen deprivation therapy is the leading cause of deaths in prostate cancer patients. Thus, there is an obvious and urgent need to fully understand the mechanism of CRPC and discover novel therapeutic targets. Long noncoding RNAs (lncRNAs) are crucial regulators in many human cancers, yet their potential roles and molecular mechanisms in CRPC are poorly understood. In this study, we discovered that an lncRNA HOXD-AS1 is highly expressed in CRPC cells and correlated closely with Gleason score, T stage, lymph nodes metastasis, and progression-free survival. Knockdown of HOXD-AS1 inhibited the proliferation and chemo-resistance of CRPC cells in vitro and in vivo. Furthermore, we identified several cell cycle, chemo-resistance, and castration-resistance-related genes, including PLK1, AURKA, CDC25C, FOXM1, and UBE2C, that were activated transcriptionally by HOXD-AS1. Further investigation revealed that HOXD-AS1 recruited WDR5 to directly regulate the expression of target genes by mediating histone H3 lysine 4 tri-methylation (H3K4me3). In conclusion, our findings indicate that HOXD-AS1 promotes proliferation, castration resistance, and chemo-resistance in prostate cancer by recruiting WDR5. This sheds a new insight into the regulation of CRPC by lncRNA and provides a potential approach for the treatment of CRPC.

A computational systems approach identifies synergistic specification genes that facilitate lineage conversion to prostate tissue

To date, reprogramming strategies for generating cell types of interest have been facilitated by detailed understanding of relevant developmental regulatory factors. However, identification of such regulatory drivers often represents a major challenge, as specific gene combinations may be required for reprogramming. Here we show that a computational systems approach can identify cell type specification genes (master regulators) that act synergistically, and demonstrate its application for reprogramming of fibroblasts to prostate tissue. We use three such master regulators (FOXA1, NKX3.1 and androgen receptor, AR) in a primed conversion strategy starting from mouse fibroblasts, resulting in prostate tissue grafts with appropriate histological and molecular properties that respond to androgen-deprivation. Moreover, generation of reprogrammed prostate does not require traversal of a pluripotent state. Thus, we describe a general strategy by which cell types and tissues can be generated even with limited knowledge of the developmental pathways required for their specification in vivo.

Master Transcriptional Regulators in Cancer: Discovery via Reverse Engineering Approaches and Subsequent Validation

Reverse engineering of transcriptional networks using gene expression data enables identification of genes that underpin the development and progression of different cancers. Methods to this end have been available for over a decade and, with a critical mass of transcriptomic data in the oncology arena having been reached, they are ever more applicable. Extensive and complex networks can be distilled into a small set of key master transcriptional regulators (MTR), genes that are very highly connected and have been shown to be involved in processes of known importance in disease. Interpreting and validating the results of standardized bioinformatic methods is of crucial importance in determining the inherent value of MTRs. In this review, we briefly describe how MTRs are identified and focus on providing an overview of how MTRs can and have been validated for use in clinical decision making in malignant diseases, along with serving as tractable therapeutic targets. Cancer Res; 77(9); 2186-90. ©2017 AACR.

Gene Regulatory Elements, Major Drivers of Human Disease

Gene expression changes, the driving forces for cellular diversity in multicellular organisms, are regulated by a diverse set of gene regulatory elements that direct transcription in specific cells. Mutations in these elements, ranging from chromosomal aberrations to single-nucleotide polymorphisms, are a major cause of human disease. However, we currently have a very limited understanding of how regulatory element genotypes lead to specific phenotypes. In this review, we discuss the various methods of regulatory element identification, the different types of mutations they harbor, and their impact on human disease. We highlight how these variations can affect transcription of multiple genes in gene regulatory networks. In addition, we describe how novel technologies, such as massively parallel reporter assays and CRISPR/Cas9 genome editing, are beginning to provide a better understanding of the functional roles that these elements have and how their alteration can lead to specific phenotypes.

Co-clinical Analysis of a Genetically Engineered Mouse Model and Human Prostate Cancer Reveals Significance of NKX3.1 Expression for Response to 5α-reductase Inhibition

BACKGROUND

Although men on active surveillance for prostate cancer (PCa) may benefit from intervention with 5α-reductase inhibitors (5-ARIs), it has not been resolved whether 5-ARIs are effective for delaying disease progression and, if so, whether specific patients are more likely to benefit.

OBJECTIVE

To identify molecular features predictive of patient response to 5-ARIs.

DESIGN, SETTING, AND PARTICIPANTS

Nkx3.1 mutant mice, a model of early-stage PCa, were treated with the 5-ARI finasteride, and histopathological and molecular analyses were performed. Cross-species computational analyses were used to compare expression profiles for treated mice with those of patients who had received 5-ARIs before prostatectomy.

INTERVENTION

Finasteride administered to Nkx3.1 mutant mice. 5-ARI-treated patient specimens obtained retrospectively.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Endpoints in mice included histopathology, immunohistochemistry, and molecular profiling. GraphPad Prism software, R-studio, and Matlab were used for statistical and data analyses.

RESULTS AND LIMITATIONS

Finasteride treatment of Nkx3.1 mutant mice resulted in a significant reduction in prostatic intraepithelial neoplasia (PIN), as evident from histopathological and expression profiling analyses. Cross-species computational analysis comparing finasteride-treated mice with two independent 5-ARI-treated patient cohorts showed that reduced NKX3.1 expression is predictive of response to 5-ARI. A limitation of the study is that these retrospective human cohorts have relatively few patients with limited clinical outcome data. Future prospective clinical trials are needed to validate whether stratifying patients on the basis of NKX3.1 expression improves the benefit of 5-ARIs during active surveillance.

CONCLUSIONS

This co-clinical study implicates NKX3.1 status as a predictor of response to 5-ARIs, and suggests that molecular features, including NKX3.1 expression, may help to identify PCa patients most likely to benefit from 5-ARIs during active surveillance.

PATIENT SUMMARY

The aim of precision cancer prevention is to tailor interventions on the basis of individualized patient characteristics. We propose that patients with low NKX3.1 expression are optimal candidates for intervention with 5α-reductase inhibitors as an adjunct to active surveillance.

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.