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Summary: Thymus and spleen, in contrast to liver, are radiosensitive tissues in which p53-dependent apoptosis is triggered after whole-body radiation in vivo. Combined RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing (ChIP-seq) analyses of radiation-treated mouse organs identifies both shared and tissue-specific p53 transcriptional responses. As expected, the p53 targets shared among thymus and spleen are enriched in apoptotic targets. The inability to upregulate these genes in the liver is not due to reduced gene occupancy. Use of an engineered mouse model shows that deletion of the C terminus of p53 can confer radiation-induced expression of p53 apoptotic targets in the liver with concomitant increased cell death. Global RNA-seq analysis reveals that an additional role of the C terminus is also needed for transcriptional activation of liver-specific p53 targets. It is hypothesized that both suppression of apoptotic gene expression combined with enhanced activation of liver-specific targets confers tissue-specific radio-resistance.

Abstract A promotional role for androgen receptor (AR) signaling in hepatocellular carcinogenesis is emerging. In pre-clinical models, including diethylnitrosamine- (DEN-) induced hepatocellular carcinoma (HCC), anti-androgen therapies delay hepatocarcinogenesis. However, pharmacologic anti-androgen therapy in advanced HCC patients fails, suggesting that AR plays a role in HCC onset. This study aims to characterize AR expression and function throughout DEN-induced liver inflammation and carcinogenesis and evaluate the efficacy of prophylactic AR antagonism to prevent hepatocarcinogenesis. We demonstrate that pharmacologic AR antagonism with enzalutamide inhibits hepatocellular carcinogenesis. With enzalutamide treatment, we observe decreased CYP2E1 expression, reducing DEN-induced hepatocyte death and DNA ethyl-adducts. AR protein expression analyses show that DEN causes an initial upregulation of AR in portal fibroblasts and leukocytes, but not hepatocytes, suggesting that hepatocyte-autonomous AR signaling is not essential for DEN-induced carcinogenesis. Ablating androgen signaling by surgical castration reduced pre-carcinogen Kupffer cell populations but did not alter DEN-mediated immune cell recruitment nor AR expression. In this study, we identified that anti-androgen interventions modulate mutagenic DNA adducts, tumour initiation, and immune cell composition. Additionally, we find that AR expression in hepatocytes is not present during nor required for early DEN-mediated carcinogenesis.

Summary: The involvement of membrane-bound solute carriers (SLCs) in neoplastic transdifferentiation processes is poorly defined. Here, we examined changes in the SLC landscape during epithelial-mesenchymal transition (EMT) of pancreatic cancer cells. We show that two SLCs from the organic anion/cation transporter family, SLC22A10 and SLC22A15, favor EMT via interferon (IFN) α and γ signaling activation of receptor tyrosine kinase-like orphan receptor 1 (ROR1) expression. In addition, SLC22A10 and SLC22A15 allow tumor cell accumulation of glutathione to support EMT via the IFNα/γ-ROR1 axis. Moreover, a pan-SLC22A inhibitor lesinurad reduces EMT-induced metastasis and gemcitabine chemoresistance to prolong survival in mouse models of pancreatic cancer, thus identifying new vulnerabilities for human PDAC.

Abstract In prostate cancer (PCa), and many other hormone-dependent cancers, there is clear evidence for distorted transcriptional control as disease driver mechanisms. Defining which transcription factor (TF) and coregulators are altered and combine to become oncogenic drivers remains a challenge, in part because of the multitude of TFs and coregulators and the diverse genomic space on which they function. The current study was undertaken to identify which TFs and coregulators are commonly altered in PCa. We generated unique lists of TFs (n = 2662), coactivators (COA; n = 766); corepressors (COR; n = 599); mixed function coregulators (MIXED; n = 511), and to address the challenge of defining how these genes are altered we tested how expression, copy number alterations and mutation status varied across seven prostate cancer (PCa) cohorts (three of localized and four advanced disease). Testing of significant changes was undertaken by bootstrapping approaches and the most significant changes were identified. For one commonly and significantly altered gene were stably knocked-down expression and undertook cell biology experiments and RNA-Seq to identify differentially altered gene networks and their association with PCa progression risks. COAS, CORS, MIXED and TFs all displayed significant down-regulated expression (q.value 1.5), but only modestly impacted PPARγ responses. GSEA analyses of the PGC1α transcriptome revealed that it significantly altered the AR-dependent transcriptome, and was enriched for epigenetic modifiers. PGC1α-dependent genes were overlapped with PGC1α-ChIP-Seq genes and significantly associated in TCGA with higher grade tumors and worse disease-free survival. These methods and data demonstrate an approach to identify cancer-driver coregulators in cancer, and that PGC1α expression is clinically significant yet underexplored coregulator in aggressive early stage PCa.

Protein kinases have emerged as critical regulators of disease pathogenesis. Here, the authors have utilized kinome-wide screening approaches to reveal a pathogenic role of CDKL5 kinase in acute kidney injury, which is dependent on suppression of a SOX9-associated transcriptional network.

Summary: This study addresses the roles of nuclear receptor corepressor 2 (NCOR2) in prostate cancer (PC) progression in response to androgen deprivation therapy (ADT). Reduced NCOR2 expression significantly associates with shorter disease-free survival in patients with PC receiving adjuvant ADT. Utilizing the CWR22 xenograft model, we demonstrate that stably reduced NCOR2 expression accelerates disease recurrence following ADT, associates with gene expression patterns that include neuroendocrine features, and induces DNA hypermethylation. Stably reduced NCOR2 expression in isogenic LNCaP (androgen-sensitive) and LNCaP-C4-2 (androgen-independent) cells revealed that NCOR2 reduction phenocopies the impact of androgen treatment and induces global DNA hypermethylation patterns. NCOR2 genomic binding is greatest in LNCaP-C4-2 cells and most clearly associates with forkhead box (FOX) transcription factor FOXA1 binding. NCOR2 binding significantly associates with transcriptional regulation most when in active enhancer regions. These studies reveal robust roles for NCOR2 in regulating the PC transcriptome and epigenome and underscore recent mutational studies linking NCOR2 loss of function to PC disease progression.

Abstract Background Virtually all patients with metastatic prostate cancer (PCa) will relapse and develop lethal castration-resistant prostate cancer (CRPC). Long noncoding RNAs (lncRNAs) are emerging as critical regulatory elements of many cellular biological processes, and may serve as therapeutic targets for combating PCa progression. Here, we have discovered in a high-throughput RNAi screen a novel lncRNA in PCa, and assessed the oncogenic effects of this lncRNA. Methods Rapid amplification of cDNA ends and sequencing was utilized to identify a previously unannotated lncRNA lying within exon six and the 3’UTR of the lymphocyte-specific protein tyrosine kinase (LCK) gene. The levels of HULLK in the presence or absence of hormone and/or enzalutamide or coregulator inhibitors were measured by quantitative PCR (qPCR). The determination of HULLK transcription and localization were characterized by strand-specific qPCR and cellular fractionation followed by qPCR, respectively. The correlation between HULLK expression and prostate cancer Gleason score was analyzed by droplet digital PCR. CyQuant assays were conducted to evaluate the effects of knocking down HULLK with shRNAs or overexpressing HULLK on cell growth. Results In this study, a previously unannotated lncRNA lying within exon six and 3’UTR of the LCK gene was dramatically upregulated by androgen in a dose-dependent manner, and the anti-androgen enzalutamide completely blocked this hormone-induced increase. Therefore, we labeled this lncRNA “HULLK” for Hormone-Upregulated lncRNA within LCK. Binding sites for two AR coregulators p300 and Brd4 reside near the HULLK transcriptional start site (TSS), and inhibitors of these coregulators downregulated HULLK. HULLK is transcribed from the sense strand of DNA, and predominantly localizes to the cytoplasm. HULLK transcripts are not only expressed in prostate cancer cell lines, but also prostate cancer patient tissue. Remarkably, there was a significant positive correlation between HULLK expression and high-grade PCa in multiple cohorts. shRNAs targeting HULLK significantly decreased PCa cell growth. Moreover, cells overexpressing HULLK were hypersensitive to androgen stimulation. Conclusions HULLK is a novel lncRNA situated within the LCK gene that may serve as an oncogene in PCa. Our data enhances our understanding of lncRNA biology and may assist in the development of additional biomarkers or more effective therapeutic targets for advanced PCa.

The mammalian master circadian pacemaker within the suprachiasmatic nucleus (SCN) maintains tight entrainment to the 24 hr light/dark cycle via a sophisticated clock-gated rhythm in the responsiveness of the oscillator to light. A central event in this light entrainment process appears to be the rapid induction of gene expression via the ERK/MAPK pathway. Here, we used RNA array-based profiling in combination with pharmacological disruption methods to examine the contribution of ERK/MAPK signaling to light-evoked gene expression. Transient photic stimulation during the circadian night, but not during the circadian day, triggered marked changes in gene expression, with early-night light predominately leading to increased gene expression and late-night light predominately leading to gene downregulation. Functional analysis revealed that light-regulated genes are involved in a diversity of physiological processes, including DNA transcription, RNA translation, mRNA processing, synaptic plasticity and circadian timing. The disruption of MAPK signaling led to a marked reduction in light-evoked gene regulation during the early night (32/52 genes) and late night (190/191 genes); further, MAPK signaling was found to gate gene expression across the circadian cycle. Together, these experiments reveal potentially important insights into the transcriptional-based mechanisms by which the ERK/MAPK pathway regulates circadian clock timing and light-evoked clock entrainment.

Retinoblastoma is a rare pediatric tumor of the retina, caused by the homozygous loss of the Retinoblastoma 1 (RB1) tumor suppressor gene. Previous microarray studies have identified changes in the expression profiles of coding genes; however, our understanding of how non-coding genes change in this tumor is absent. This is an important area of research, as in many adult malignancies, non-coding genes including LNC-RNAs are used as biomarkers to predict outcome and/or relapse. To establish a complete and in-depth RNA profile, of both coding and non-coding genes, in Retinoblastoma tumors, we conducted RNA-seq from a cohort of tumors and normal retina controls. This analysis identified widespread transcriptional changes in the levels of both coding and non-coding genes. Unexpectedly, we also found rare RNA fusion products resulting from genomic alterations, specific to Retinoblastoma tumor samples. We then determined whether these gene expression changes, of both coding and non-coding genes, were also found in a completely independent Retinoblastoma cohort. Using our dataset, we then profiled the potential effects of deregulated LNC-RNAs on the expression of neighboring genes, the entire genome, and on mRNAs that contain a putative area of homology. This analysis showed that most deregulated LNC-RNAs do not act locally to change the transcriptional environment, but potentially function to modulate genes at distant sites. From this analysis, we selected a strongly down-regulated LNC-RNA in Retinoblastoma, DRAIC, and found that restoring DRAIC RNA levels significantly slowed the growth of the Y79 Retinoblastoma cell line. Collectively, our work has generated the first non-coding RNA profile of Retinoblastoma tumors and has found that these tumors show widespread transcriptional deregulation.

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

Abstract The topology of nuclear receptor (NR) signaling is captured in a systems biological graphical notation. This enables us to identify a number of ‘design’ aspects of the topology of these networks that might appear unnecessarily complex or even functionally paradoxical. In realistic kinetic models of increasing complexity, calculations show how these features correspond to potentially important design principles, e.g.: (i) cytosolic ‘nuclear’ receptor may shuttle signal molecules to the nucleus, (ii) the active export of NRs may ensure that there is sufficient receptor protein to capture ligand at the cytoplasmic membrane, (iii) a three conveyor belts design dissipating GTP‐free energy, greatly aids response, (iv) the active export of importins may prevent sequestration of NRs by importins in the nucleus and (v) the unspecific nature of the nuclear pore may ensure signal‐flux robustness. In addition, the models developed are suitable for implementation in specific cases of NR‐mediated signaling, to predict individual receptor functions and differential sensitivity toward physiological and pharmacological ligands.

The process of DNA CpG methylation has been extensively investigated for over 50 years and revealed associations between changing methylation status of CpG islands and gene expression. As a result, DNA CpG methylation is implicated in the control of gene expression in developmental and homeostasis processes, as well as being a cancer-driver mechanism. The development of genome-wide technologies and sophisticated statistical analytical approaches has ushered in an era of widespread analyses, for example in the cancer arena, of the relationships between altered DNA CpG methylation, gene expression, and tumor status. The remarkable increase in the volume of such genomic data, for example, through investigators from the Cancer Genome Atlas (TCGA), has allowed dissection of the relationships between DNA CpG methylation density and distribution, gene expression, and tumor outcome. In this manner, it is now possible to test that the genome-wide correlations are measurable between changes in DNA CpG methylation and gene expression. Perhaps surprisingly is that these associations can only be detected for hundreds, but not thousands, of genes, and the direction of the correlations are both positive and negative. This, perhaps, suggests that CpG methylation events in cancer systems can act as disease drivers but the effects are possibly more restricted than suspected. Additionally, the positive and negative correlations suggest direct and indirect events and an incomplete understanding. Within the prostate cancer TCGA cohort, we examined the relationships between expression of genes that control DNA methylation, known targets of DNA methylation and tumor status. This revealed that genes that control the synthesis of S-adenosyl-l-methionine (SAM) associate with altered expression of DNA methylation targets in a subset of aggressive tumors.

The GRCh37.p13 primary assembly of the human genome contains 20805 protein coding mRNA, and 37147 non-protein coding genes and pseudogenes that as a result of RNA processing and editing generate 196501 gene transcripts. Given the size and diversity of the human transcriptome, it is timely to revisit what is known of VDR function in the regulation and targeting of transcription.Early transcriptomic studies using microarray approaches focused on the protein coding mRNA that were regulated by the VDR, usually following treatment with ligand. These studies quickly established the approxamte size, and surprising diversity of the VDR transcriptome, revealing it to be highly heterogenous and cell type and time dependent. With the discovery of microRNA, investigators also considered VDR regulation of these non-protein coding RNA. Again, cell and time dependency has emerged. Attempts to integrate mRNA and miRNA regulation patterns are beginning to reveal patterns of co-regulation and interaction that allow for greater control of mRNA expression, and the capacity to govern more complex cellular events. As the awareness of the diversity of non-coding RNA increases, it is evident that VDR actions are mediated through these molecules also. Key knowledge gaps remain over the VDR transcriptome. The causes for the cell and type dependent transcriptional heterogenetiy remain enigmatic. ChIP-Seq approaches have confirmed that VDR binding choices differ very significantly by cell type, but as yet the underlying causes distilling VDR binding choices are unclear. Similarly, it is clear that many of the VDR binding sites are non-canonical in nature but again the mechanisms underlying these interactions are unclear. Finally, although alternative splicing is clearly a very significant process in cellular transcriptional control, the lack of RNA-Seq data centered on VDR function are currently limiting the global assessment of the VDR transcriptome. VDR focused research that complements

Statistical modelling, in combination with genome-wide expression profiling techniques, has demonstrated that the molecular state of the tumour is sufficient to infer its pathological state. These studies have been extremely important in diagnostics and have contributed to improving our understanding of tumour biology. However, their importance in in-depth understanding of cancer patho-physiology may be limited since they do not explicitly take into consideration the fundamental role of the tissue microenvironment in specifying tumour physiology. Because of the importance of normal cells in shaping the tissue microenvironment we formulate the hypothesis that molecular components of the profile of normal epithelial cells adjacent the tumour are predictive of tumour physiology. We addressed this hypothesis by developing statistical models that link gene expression profiles representing the molecular state of adjacent normal epithelial cells to tumour features in prostate cancer. Furthermore, network analysis showed that predictive genes are linked to the activity of important secreted factors, which have the potential to influence tumor biology, such as IL1, IGF1, PDGF BB, AGT, and TGFβ.

Retinoblastoma is a rare pediatric tumor of the retina, caused by the homozygous loss of the Retinoblastoma 1 (RB1) tumor suppressor gene. Previous microarray studies have identified changes in the expression profiles of coding genes; however, our understanding of how non-coding genes change in this tumor is absent. This is an important area of research, as in many adult malignancies, non-coding genes including LNC-RNAs are used as biomarkers to predict outcome and/or relapse. To establish a complete and in-depth RNA profile, of both coding and non-coding genes, in Retinoblastoma tumors, we conducted RNA-seq from a cohort of tumors and normal retina controls. This analysis identified widespread transcriptional changes in the levels of both coding and non-coding genes. Unexpectedly, we also found rare RNA fusion products resulting from genomic alterations, specific to Retinoblastoma tumor samples. We then determined whether these gene expression changes, of both coding and non-coding genes, were also found in a completely independent Retinoblastoma cohort. Using our dataset, we then profiled the potential effects of deregulated LNC-RNAs on the expression of neighboring genes, the entire genome, and on mRNAs that contain a putative area of homology. This analysis showed that most deregulated LNC-RNAs do not act locally to change the transcriptional environment, but potentially function to modulate genes at distant sites. From this analysis, we selected a strongly down-regulated LNC-RNA in Retinoblastoma, DRAIC, and found that restoring DRAIC RNA levels significantly slowed the growth of the Y79 Retinoblastoma cell line. Collectively, our work has generated the first non-coding RNA profile of Retinoblastoma tumors and has found that these tumors show widespread transcriptional deregulation.

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

In 1997, the first published reports highlighted PPARgamma as a novel cancer therapeutic target regulating differentiation of cancer cells. A subsequent flurry of papers described these activities more widely and fuelled further enthusiasm for differentiation therapy, as the ligands for the PPARgamma were seen as well tolerated and in several cases well-established in other therapeutic contexts. This initial enthusiasm and promise was somewhat tempered by contradictory findings in several murine cancer models and equivocal trial findings. As more understanding has emerged in recent years, a renaissance has occurred in targeting PPARgamma within the context of either chemoprevention or chemotherapy. This clarity has arisen in part through a clearer understanding of PPARgamma biology, how the receptor interacts with other proteins and signaling events, and the mechanisms that modulate its transcriptional actions. Equally greater translational understanding of this target has arisen from a clearer understanding of in vivo murine cancer models. Clinical exploitation will most likely require precise and quantifiable description of PPARgamma actions, and resolution of which targets are the most beneficial to target combined with an understanding of the mechanisms that limits its anticancer effectiveness.

In 1997, the first published reports highlighted PPARgamma as a novel cancer therapeutic target regulating differentiation of cancer cells. A subsequent flurry of papers described these activities more widely and fuelled further enthusiasm for differentiation therapy, as the ligands for the PPARgamma were seen as well tolerated and in several cases well-established in other therapeutic contexts. This initial enthusiasm and promise was somewhat tempered by contradictory findings in several murine cancer models and equivocal trial findings. As more understanding has emerged in recent years, a renaissance has occurred in targeting PPARgamma within the context of either chemoprevention or chemotherapy. This clarity has arisen in part through a clearer understanding of PPARgamma biology, how the receptor interacts with other proteins and signaling events, and the mechanisms that modulate its transcriptional actions. Equally greater translational understanding of this target has arisen from a clearer understanding of in vivo murine cancer models. Clinical exploitation will most likely require precise and quantifiable description of PPARgamma actions, and resolution of which targets are the most beneficial to target combined with an understanding of the mechanisms that limits its anticancer effectiveness.