A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer

Exploring the Spatial Landscape of the Estrogen Receptor Proximal Proteome With Antibody-Based Proximity Labeling

Estrogen receptor α (ERα) drives the transcription of genes involved in breast cancer (BC) progression, relying on coregulatory protein recruitment for its transcriptional and biological activities. Mutation of ERα as well as aberrant recruitment of its regulatory proteins contribute to tumor adaptation and drug resistance. Therefore, understanding the dynamic changes in ERα protein interaction networks is crucial for elucidating drug resistance mechanisms in BC. Despite progress in studying ERα-associated proteins, capturing subcellular transient interactions remains challenging and, as a result, significant number of important interactions remain undiscovered. In this study, we employed biotinylation by antibody recognition (BAR), an innovative antibody-based proximity labeling (PL) approach, coupled with mass spectrometry to investigate the ERα proximal proteome and its changes associated with resistance to aromatase inhibition, a key therapy used in the treatment of ERα-positive BC. We show that BAR successfully detected most of the known ERα interactors and mainly identified nuclear proteins, using either an epitope tag or endogenous antibody to target ERα. We further describe the ERα proximal proteome rewiring associated with resistance applying BAR to a panel of isogenic cell lines modeling tumor adaptation in the clinic. Interestingly, we find that ERα associates with some of the canonical cofactors in resistant cells and several proximal proteome changes are due to increased expression of ERα. Resistant models also show decreased levels of estrogen-regulated genes. Sensitive and resistant cells harboring a mutation in the ERα (Y537C) revealed a similar proximal proteome. We provide an ERα proximal protein network covering several novel ERα-proximal partners. These include proteins involved in highly dynamic processes such as sumoylation and ubiquitination difficult to detect with traditional protein interaction approaches. Overall, we present BAR as an effective approach to investigate the ERα proximal proteome in a spatial context and demonstrate its application in different experimental conditions.

Gene co-regulation and co-expression in the aryl hydrocarbon receptor-mediated transcriptional regulatory network in the mouse liver

Four decades after its discovery, the aryl hydrocarbon receptor (AHR), a ligand-inducible transcription factor (TF) activated by the persistent environmental contaminant 2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD), remains an enigmatic molecule with a controversial endogenous role. Here, we have assembled a global map of the AHR gene regulatory network in female C57BL/6 mice orally gavaged with 30 µg/kg of TCDD from a combination of previously published gene expression and genome-wide TF-binding data sets. Using Kohonen self-organizing maps and subspace clustering, we show that genes co-regulated by common upstream TFs in the AHR network exhibit a pattern of co-expression. Directly bound, indirectly bound, and non-genomic AHR target genes exhibit distinct expression patterns, with the directly bound targets associated with highest median expression. Interestingly, among the directly bound AHR target genes, the expression level increases with the number of AHR-binding sites in the proximal promoter regions. Finally, we show that co-regulated genes in the AHR network activate distinct groups of downstream biological processes. Although the specific findings described here are restricted to hepatic effects under short-term TCDD exposure, this work describes a generalizable approach to the reconstruction and analysis of transcriptional regulatory cascades underlying cellular stress response, revealing network hierarchy and the nature of information flow from the initial signaling events to phenotypic outcomes. Such reconstructed networks can form the basis of a new generation of quantitative adverse outcome pathways.

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer

Background

With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking.

Results

We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions.

Conclusions

Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

EGR1 regulates cellular metabolism and survival in endocrine resistant breast cancer

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

BACKGROUND

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

METHODS

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

RESULTS

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

CONCLUSIONS

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Organ-specific gene modulation: Principles and applications in cancer research

Microarray and next generation sequencing has led to the exploration of correlated gene patterns and their shared functions. Gene modulators are proteins that alter the activity of transcription factors and influence the expression of their target genes. It is assumed that modulators are dependent on transcription factors. Several algorithms have been developed for the detection of gene modulators. On the other hand, it is becoming increasingly evident that modulators play a crucial role in carcinogenesis by interfering with fundamental biologic processes. Therapeutic gene modulation that is based on artificial modification of endogenous gene functions by designer molecules is an exciting new field of investigation.

Antiestrogen Resistant Cell Lines Expressing Estrogen Receptor α Mutations Upregulate the Unfolded Protein Response and are Killed by BHPI

Outgrowth of metastases expressing ERα mutations Y537S and D538G is common after endocrine therapy for estrogen receptor α (ERα) positive breast cancer. The effect of replacing wild type ERα in breast cancer cells with these mutations was unclear. We used the CRISPR-Cas9 genome editing system and homology directed repair to isolate and characterize 14 T47D cell lines in which ERαY537S or ERαD538G replace one or both wild-type ERα genes. In 2-dimensional, and in quantitative anchorage-independent 3-dimensional cell culture, ERαY537S and ERαD538G cells exhibited estrogen-independent growth. A progestin further increased their already substantial proliferation in micromolar 4-hydroxytamoxifen and fulvestrant/ICI 182,780 (ICI). Our recently described ERα biomodulator, BHPI, which hyperactivates the unfolded protein response (UPR), completely blocked proliferation. In ERαY537S and ERαD538G cells, estrogen-ERα target genes were constitutively active and partially antiestrogen resistant. The UPR marker sp-XBP1 was constitutively activated in ERαY537S cells and further induced by progesterone in both cell lines. UPR-regulated genes associated with tamoxifen resistance, including the oncogenic chaperone BiP/GRP78, were upregulated. ICI displayed a greater than 2 fold reduction in its ability to induce ERαY537S and ERαD538G degradation. Progestins, UPR activation and perhaps reduced ICI-stimulated ERα degradation likely contribute to antiestrogen resistance seen in ERαY537S and ERαD538G cells.

Information-dependent enrichment analysis reveals time-dependent transcriptional regulation of the estrogen pathway of toxicity

The twenty-first century vision for toxicology involves a transition away from high-dose animal studies to in vitro and computational models (NRC in Toxicity testing in the 21st century: a vision and a strategy, The National Academies Press, Washington, DC, 2007). This transition requires mapping pathways of toxicity by understanding how in vitro systems respond to chemical perturbation. Uncovering transcription factors/signaling networks responsible for gene expression patterns is essential for defining pathways of toxicity, and ultimately, for determining the chemical modes of action through which a toxicant acts. Traditionally, transcription factor identification is achieved via chromatin immunoprecipitation studies and summarized by calculating which transcription factors are statistically associated with up- and downregulated genes. These lists are commonly determined via statistical or fold-change cutoffs, a procedure that is sensitive to statistical power and may not be as useful for determining transcription factor associations. To move away from an arbitrary statistical or fold-change-based cutoff, we developed, in the context of the Mapping the Human Toxome project, an enrichment paradigm called information-dependent enrichment analysis (IDEA) to guide identification of the transcription factor network. We used a test case of activation in MCF-7 cells by 17β estradiol (E2). Using this new approach, we established a time course for transcriptional and functional responses to E2. ERα and ERβ were associated with short-term transcriptional changes in response to E2. Sustained exposure led to recruitment of additional transcription factors and alteration of cell cycle machinery. TFAP2C and SOX2 were the transcription factors most highly correlated with dose. E2F7, E2F1, and Foxm1, which are involved in cell proliferation, were enriched only at 24 h. IDEA should be useful for identifying candidate pathways of toxicity. IDEA outperforms gene set enrichment analysis (GSEA) and provides similar results to weighted gene correlation network analysis, a platform that helps to identify genes not annotated to pathways.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.

Co-modulation analysis of gene regulation in breast cancer reveals complex interplay between ESR1 and ERBB2 genes

BACKGROUND

Gene regulation is dynamic across cellular conditions and disease subtypes. From the aspect of regulation under modulation, regulation strength between a pair of genes can be modulated by (dependent on) expression abundance of another gene (modulator gene). Previous studies have demonstrated the involvement of genes modulated by single modulator genes in cancers, including breast cancer. However, analysis of multi-modulator co-modulation that can further delineate the landscape of complex gene regulation is, to our knowledge, unexplored previously. In the present study we aim to explore the joint effects of multiple modulator genes in modulating global gene regulation and dissect the biological functions in breast cancer.

RESULTS

To carry out the analysis, we proposed the Covariability-based Multiple Regression (CoMRe) method. The method is mainly built on a multiple regression model that takes expression levels of multiple modulators as inputs and regulation strength between genes as output. Pairs of genes were divided into groups based on their co-modulation patterns. Analyzing gene expression profiles from 286 breast cancer patients, CoMRe investigated ten candidate modulator genes that interacted and jointly determined global gene regulation. Among the candidate modulators, ESR1, ERBB2, and ADAM12 were found modulating the most numbers of gene pairs. The largest group of gene pairs was composed of ones that were modulated by merely ESR1. Functional annotation revealed that the group was significantly related to tumorigenesis and estrogen signaling in breast cancer. ESR1-ERBB2 co-modulation was the largest group modulated by more than one modulators. Similarly, the group was functionally associated with hormone stimulus, suggesting that functions of the two modulators are performed, at least partially, through modulation. The findings were validated in majorities of patients (> 99%) of two independent breast cancer datasets.

CONCLUSIONS

We have showed CoMRe is a robust method to discover critical modulators in gene regulatory networks, and it is capable of achieving reproducible and biologically meaningful results. Our data reveal that gene regulatory networks modulated by single modulator or co-modulated by multiple modulators play important roles in breast cancer. Findings of this report illuminate complex and dynamic gene regulation under modulation and its involvement in breast cancer.

Network-based inference framework for identifying cancer genes from gene expression data

Great efforts have been devoted to alleviate uncertainty of detected cancer genes as accurate identification of oncogenes is of tremendous significance and helps unravel the biological behavior of tumors. In this paper, we present a differential network-based framework to detect biologically meaningful cancer-related genes. Firstly, a gene regulatory network construction algorithm is proposed, in which a boosting regression based on likelihood score and informative prior is employed for improving accuracy of identification. Secondly, with the algorithm, two gene regulatory networks are constructed from case and control samples independently. Thirdly, by subtracting the two networks, a differential-network model is obtained and then used to rank differentially expressed hub genes for identification of cancer biomarkers. Compared with two existing gene-based methods (t-test and lasso), the method has a significant improvement in accuracy both on synthetic datasets and two real breast cancer datasets. Furthermore, identified six genes (TSPYL5, CD55, CCNE2, DCK, BBC3, and MUC1) susceptible to breast cancer were verified through the literature mining, GO analysis, and pathway functional enrichment analysis. Among these oncogenes, TSPYL5 and CCNE2 have been already known as prognostic biomarkers in breast cancer, CD55 has been suspected of playing an important role in breast cancer prognosis from literature evidence, and other three genes are newly discovered breast cancer biomarkers. More generally, the differential-network schema can be extended to other complex diseases for detection of disease associated-genes.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

This review encompasses the most important advances in liver functions and hepatotoxicity and analyzes which mechanisms can be studied in vitro. In a complex architecture of nested, zonated lobules, the liver consists of approximately 80 % hepatocytes and 20 % non-parenchymal cells, the latter being involved in a secondary phase that may dramatically aggravate the initial damage. Hepatotoxicity, as well as hepatic metabolism, is controlled by a set of nuclear receptors (including PXR, CAR, HNF-4α, FXR, LXR, SHP, VDR and PPAR) and signaling pathways. When isolating liver cells, some pathways are activated, e.g., the RAS/MEK/ERK pathway, whereas others are silenced (e.g. HNF-4α), resulting in up- and downregulation of hundreds of genes. An understanding of these changes is crucial for a correct interpretation of in vitro data. The possibilities and limitations of the most useful liver in vitro systems are summarized, including three-dimensional culture techniques, co-cultures with non-parenchymal cells, hepatospheres, precision cut liver slices and the isolated perfused liver. Also discussed is how closely hepatoma, stem cell and iPS cell-derived hepatocyte-like-cells resemble real hepatocytes. Finally, a summary is given of the state of the art of liver in vitro and mathematical modeling systems that are currently used in the pharmaceutical industry with an emphasis on drug metabolism, prediction of clearance, drug interaction, transporter studies and hepatotoxicity. One key message is that despite our enthusiasm for in vitro systems, we must never lose sight of the in vivo situation. Although hepatocytes have been isolated for decades, the hunt for relevant alternative systems has only just begun.

Dystonia, facial dysmorphism, intellectual disability and breast cancer associated with a chromosome 13q34 duplication and overexpression of TFDP1: case report

Background

Dystonia is a movement disorder characterized by involuntary sustained muscle contractions causing twisting and repetitive movements or abnormal postures. Some cases of primary and neurodegenerative dystonia have been associated with mutations in individual genes critical to the G₁-S checkpoint pathway (THAP1, ATM, CIZ1 and TAF1). Secondary dystonia is also a relatively common clinical sign in many neurogenetic disorders. However, the contribution of structural variation in the genome to the etiopathogenesis of dystonia remains largely unexplored.

Case presentation

Cytogenetic analyses with the Affymetrix Genome-Wide Human SNP Array 6.0 identified a chromosome 13q34 duplication in a 36 year-old female with global developmental delay, facial dysmorphism, tall stature, breast cancer and dystonia, and her neurologically-normal father. Dystonia improved with bilateral globus pallidus interna (GPi) deep brain stimulation (DBS). Genomic breakpoint analysis, quantitative PCR (qPCR) and leukocyte gene expression were used to characterize the structural variant. The 218,345 bp duplication was found to include ADPRHL1, DCUN1D2, and TMCO3, and a 69 bp fragment from a long terminal repeat (LTR) located within Intron 3 of TFDP1. The 3' breakpoint was located within Exon 1 of a TFDP1 long non-coding RNA (NR_026580.1). In the affected subject and her father, gene expression was higher for all three genes located within the duplication. However, in comparison to her father, mother and neurologically-normal controls, the affected subject also showed marked overexpression (2×) of the transcription factor TFDP1 (NM_007111.4). Whole-exome sequencing identified an SGCE variant (c.1295G > A, p.Ser432His) that could possibly have contributed to the development of dystonia in the proband. No pathogenic mutations were identified in BRCA1 or BRCA2.

Conclusion

Overexpression of TFDP1 has been associated with breast cancer and may also be linked to the tall stature, dysmorphism and dystonia seen in our patient.

LOcating non-unique matched tags (LONUT) to improve the detection of the enriched regions for ChIP-seq data

One big limitation of computational tools for analyzing ChIP-seq data is that most of them ignore non-unique tags (NUTs) that match the human genome even though NUTs comprise up to 60% of all raw tags in ChIP-seq data. Effectively utilizing these NUTs would increase the sequencing depth and allow a more accurate detection of enriched binding sites, which in turn could lead to more precise and significant biological interpretations. In this study, we have developed a computational tool, LOcating Non-Unique matched Tags (LONUT), to improve the detection of enriched regions from ChIP-seq data. Our LONUT algorithm applies a linear and polynomial regression model to establish an empirical score (ES) formula by considering two influential factors, the distance of NUTs to peaks identified using uniquely matched tags (UMTs) and the enrichment score for those peaks resulting in each NUT being assigned to a unique location on the reference genome. The newly located tags from the set of NUTs are combined with the original UMTs to produce a final set of combined matched tags (CMTs). LONUT was tested on many different datasets representing three different characteristics of biological data types. The detected sites were validated using de novo motif discovery and ChIP-PCR. We demonstrate the specificity and accuracy of LONUT and show that our program not only improves the detection of binding sites for ChIP-seq, but also identifies additional binding sites.

Gene regulation, modulation, and their applications in gene expression data analysis

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of “modulation” can adapt to various biological mechanisms to discover the novel gene regulation mechanisms.

Endogenous purification reveals GREB1 as a key estrogen receptor regulatory factor

Estrogen receptor-α (ER) is the driving transcription factor in most breast cancers, and its associated proteins can influence drug response, but direct methods for identifying interacting proteins have been limited. We purified endogenous ER using an approach termed RIME (rapid immunoprecipitation mass spectrometry of endogenous proteins) and discovered the interactome under agonist- and antagonist-liganded conditions in breast cancer cells, revealing transcriptional networks in breast cancer. The most estrogen-enriched ER interactor is GREB1, a potential clinical biomarker with no known function. GREB1 is shown to be a chromatin-bound ER coactivator and is essential for ER-mediated transcription, because it stabilizes interactions between ER and additional cofactors. We show a GREB1-ER interaction in three xenograft tumors, and using a directed protein-protein approach, we find GREB1-ER interactions in half of ER(+) primary breast cancers. This finding is supported by histological expression of GREB1, which shows that GREB1 is expressed in half of ER(+) cancers, and predicts good clinical outcome. These findings reveal an unexpected role for GREB1 as an estrogen-specific ER cofactor that is expressed in drug-sensitive contexts.

Whole transcriptome analysis of the ERα synthetic fragment P295-T311 (ERα17p) identifies specific ERα-isoform (ERα, ERα36)-dependent and -independent actions in breast cancer cells

ERα17p is a peptide corresponding to the sequence P295LMIKRSKKNSLALSLT311 of the estrogen receptor alpha (ERα) and initially found to interfere with ERα-related calmodulin binding. ERα17p was subsequently found to elicit estrogenic responses in E2-deprived ERα-positive breast cancer cells, increasing proliferation and ERE-dependent gene transcription. Surprisingly, in E2-supplemented media, ERα17p-induced apoptosis and modified the actin network, influencing cell motility. Here, we report that ERα17p internalizes in breast cancer cells (T47D, MDA-MB-231, SKBR3) and induces a massive early (3 h) transcriptional activity. Remarkably, about 75% of significantly modified transcripts were also modified by E2, confirming the pro-estrogenic profile of ERα17p. The different ER spectra of the used cell lines allowed us to identify a specific ERα17p signature related to ERα as well as its variant ERα36. With respect to ERα, the peptide activates nuclear (cell cycle, cell proliferation, nucleic acid and protein synthesis) and extranuclear signaling pathways. In contrast, through ERα36, it mainly triggers inhibitory actions on inflammation. This is the first work reporting a detailed ERα36-specific transcriptional signature. In addition, we report that ERα17p-induced transcripts related to apoptosis and actin modifying effects of the peptide are independent from its estrogen receptor(s)-related actions. We discuss our findings in view of the potential use of ERα17p as a selective peptidomimetic estrogen receptor modulator (PERM).

Antiestrogen Resistance and the Application of Systems Biology

Understanding the molecular changes that drive an acquired antiestrogen resistance phenotype is of major clinical relevance. Previous methodologies for addressing this question have taken a single gene/pathway approach and the resulting gains have been limited in terms of their clinical impact. Recent systems biology approaches allow for the integration of data from high throughput "-omics" technologies. We highlight recent advances in the field of antiestrogen resistance with a focus on transcriptomics, proteomics and methylomics.

A modulator based regulatory network for ERα signaling pathway

Background

Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ERα requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERα on breast cancer.

Methods

To investigate the regulatory network of ERα and discover novel modulators of ERα functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes.

Results

Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two cancer cells showed 1% overlap for the ERα genomic regulatory network, but 4% overlap for the non-genomic regulatory network.

Conclusions

We proposed a novel approach to infer the ERα/modulator/target relationships, and construct the genomic/non-genomic regulatory networks in two cancer cells. We found that the non-genomic regulatory network is more reliable than the genomic regulatory network.

A hidden Markov model to identify combinatorial epigenetic regulation patterns for estrogen receptor α target genes

MOTIVATION

Many studies have shown that epigenetic changes, such as altered DNA methylation and histone modifications, are linked to estrogen receptor α (ERα)-positive tumors and disease prognoses. Several recent studies have applied high-throughput technologies such as ChIP-seq and MBD-seq to interrogate the altered architectures of ERα regulation in tamoxifen (Tam)-resistant breast cancer cells. However, the details of combinatorial epigenetic regulation of ERα target genes in breast cancers with acquired Tam resistance have not yet been fully examined.

RESULTS

We developed a computational approach to identify and analyze epigenetic patterns associated with Tam resistance in the MCF7-T cell line as opposed to the Tam-sensitive MCF7 cell line, with the goal of understanding the underlying mechanisms of epigenetic regulatory influence on resistance to Tam treatment in breast cancer. In this study, we used ChIP-seq of ERα, RNA polymerase II, three histone modifications and MBD-seq data of DNA methylation in MCF7 and MCF7-T cells to train hidden Markov models (HMMs). We applied the Bayesian information criterion to determine that a 20-state HMM was best, which was reduced to a 14-state HMM with a Bayesian information criterion score of 1.21291 × 10(7). We further identified four classes of biologically meaningful states in this breast cancer cell model system, and a set of ERα combinatorial epigenetic regulated target genes. The correlated gene expression level and gene ontology analyses showed that different gene ontology terms were enriched with Tam-resistant versus sensitive breast cancer cells. Our study illustrates the applicability of HMM-based analysis of genome-wide high-throughput genomic data to study epigenetic influences on E2/ERα regulation in breast cancer.

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.

Can case study approaches speed implementation of the NRC report: "toxicity testing in the 21st century: a vision and a strategy?"

The 2007 report "Toxicity Testing in the 21st Century: A Vision and a Strategy" argued for a change in toxicity testing for environmental agents and discussed federal funding mechanisms that could be used to support this transformation within the USA. The new approach would test for in vitro perturbations of toxicity pathways using human cells with high-throughput testing platforms. The NRC report proposed a deliberate timeline, spanning about 20 years, to implement a wholesale replacement of current in-life toxicity test approaches focused on apical responses with in vitro assays. One approach to accelerating implementation is to focus on well-studied prototype compounds with known toxicity pathway targets. Through a series of carefully executed case studies with four or five pathway prototypes, the various steps required for implementation of an in vitro toxicity pathway approach to risk assessment could be developed and refined. In this article, we discuss alternative approaches for implementation and also outline advantages of a case study approach and the manner in which the case studies could be pursued using current methodologies. A case study approach would be complementary to recently proposed efforts to map the human toxome, while representing a significant extension toward more formal risk assessment compared to the profiling and prioritization approaches inherent in programs such as the EPA's ToxCast effort.