Social Isolation and Breast Cancer.

Although the role of life stressors in breast cancer remains unclear, social isolation is consistently associated with increased breast cancer risk and mortality. Social isolation can be defined as loneliness or an absence of perceived social connections. In female mice and rats, social isolation is mimicked by housing animals 1 per cage. Social isolation causes many biological changes, of which an increase in inflammatory markers and disruptions in mitochondrial and cellular metabolism are commonly reported. It is not clear how the 2 traditional stress-induced pathways, namely, the hypothalamic-pituitary-adrenocortical axis (HPA), resulting in a release of glucocorticoids from the adrenal cortex, and autonomic nervous system (ANS), resulting in a release of catecholamines from the adrenal medulla and postganglionic neurons, could explain the increased breast cancer risk in socially isolated individuals. For instance, glucocorticoid receptor activation in estrogen receptor positive breast cancer cells inhibits their proliferation, and activation of ß-adrenergic receptor in immature immune cells promotes their differentiation toward antitumorigenic T cells. However, activation of HPA and ANS pathways may cause a disruption in the brain-gut-microbiome axis, resulting in gut dysbiosis. Gut dysbiosis, in turn, leads to an alteration in the production of bacterial metabolites, such as short chain fatty acids, causing a systemic low-grade inflammation and inducing dysfunction in mitochondrial and cellular metabolism. A possible causal link between social isolation-induced increased breast cancer risk and mortality and gut dysbiosis should be investigated, as it offers new tools to prevent breast cancer.

Social Isolation Activates Dormant Mammary Tumors, and Modifies Inflammatory and Mitochondrial Metabolic Pathways in the Rat Mammary Gland.

Although multifactorial in origin, one of the most impactful consequences of social isolation is an increase in breast cancer mortality. How this happens is unknown, but many studies have shown that social isolation increases circulating inflammatory cytokines and impairs mitochondrial metabolism. Using a preclinical Sprague Dawley rat model of estrogen receptor-positive breast cancer, we investigated whether social isolation impairs the response to tamoxifen therapy and increases the risk of tumors emerging from dormancy, and thus their recurrence. We also studied which signaling pathways in the mammary glands may be affected by social isolation in tamoxifen treated rats, and whether an anti-inflammatory herbal mixture blocks the effects of social isolation. Social isolation increased the risk of dormant mammary tumor recurrence after tamoxifen therapy. The elevated recurrence risk was associated with changes in multiple signaling pathways including an upregulation of IL6/JAK/STAT3 signaling in the mammary glands and tumors and suppression of the mitochondrial oxidative phosphorylation (OXPHOS) pathway. In addition, social isolation increased the expression of receptor for advanced glycation end-products (RAGE), consistent with impaired insulin sensitivity and weight gain linked to social isolation. In socially isolated animals, the herbal product inhibited IL6/JAK/STAT3 signaling, upregulated OXPHOS signaling, suppressed the expression of RAGE ligands S100a8 and S100a9, and prevented the increase in recurrence of dormant mammary tumors. Increased breast cancer mortality among socially isolated survivors may be most effectively prevented by focusing on the period following the completion of hormone therapy using interventions that simultaneously target several different pathways including inflammatory and mitochondrial metabolism pathways.

Metabolic reprogramming and its clinical implication for liver cancer.

Cancer cells often encounter hypoxic and hypo-nutrient conditions, which force them to make adaptive changes to meet their high demands for energy and various biomaterials for biomass synthesis. As a result, enhanced catabolism (breakdown of macromolecules for energy production) and anabolism (macromolecule synthesis from bio-precursors) are induced in cancer. This phenomenon is called "metabolic reprogramming," a cancer hallmark contributing to cancer development, metastasis, and drug resistance. HCC and cholangiocarcinoma (CCA) are 2 different liver cancers with high intertumoral heterogeneity in terms of etiologies, mutational landscapes, transcriptomes, and histological representations. In agreement, metabolism in HCC or CCA is remarkably heterogeneous, although changes in the glycolytic pathways and an increase in the generation of lactate (the Warburg effect) have been frequently detected in those tumors. For example, HCC tumors with activated ß-catenin are addicted to fatty acid catabolism, whereas HCC tumors derived from fatty liver avoid using fatty acids. In this review, we describe common metabolic alterations in HCC and CCA as well as metabolic features unique for their subsets. We discuss metabolism of NAFLD as well, because NAFLD will likely become a leading etiology of liver cancer in the coming years due to the obesity epidemic in the Western world. Furthermore, we outline the clinical implication of liver cancer metabolism and highlight the computation and systems biology approaches, such as genome-wide metabolic models, as a valuable tool allowing us to identify therapeutic targets and develop personalized treatments for liver cancer patients.

Maternal obesity and resistance to breast cancer treatments among offspring: Link to gut dysbiosis.

BACKGROUND: About 50 000 new cases of cancer in the United States are attributed to obesity. The adverse effects of obesity on breast cancer may be most profound when affecting the early development; that is, in the womb of a pregnant obese mother. Maternal obesity has several long-lasting adverse health effects on the offspring, including increasing offspring's breast cancer risk and mortality. Gut microbiota is a player in obesity as well as may impact breast carcinogenesis. Gut microbiota is established early in life and the microbial composition of an infant's gut becomes permanently dysregulated because of maternal obesity. Metabolites from the microbiota, especially short chain fatty acids (SCFAs), play a critical role in mediating the effect of gut bacteria on multiple biological functions, such as immune system, including tumor immune responses. RECENT FINDINGS: Maternal obesity can pre-program daughter's breast cancer to be more aggressive, less responsive to treatments and consequently more likely to cause breast cancer related death. Maternal obesity may also induce poor response to immune checkpoint inhibitor (ICB) therapy through increased abundance of inflammation associated microbiome and decreased abundance of bacteria that are linked to production of SCFAs. Dietary interventions that increase the abundance of bacteria producing SCFAs potentially reverses offspring's resistance to breast cancer therapy. CONCLUSION: Since immunotherapies have emerged as highly effective treatments for many cancers, albeit there is an urgent need to enlarge the patient population who will be responsive to these treatments. One of the factors which may cause ICB refractoriness could be maternal obesity, based on its effects on the microbiota markers of ICB therapy response among the offspring. Since about 40% of children are born to obese mothers in the Western societies, it is important to determine if maternal obesity impairs offspring's response to cancer immunotherapies.

Experimental models of endocrine responsive breast cancer: strengths, limitations, and use.

Breast cancers characterized by expression of estrogen receptor-alpha; ESR1) represent approximately 70% of all new cases and comprise the largest molecular subtype of this disease. Despite this high prevalence, the number of adequate experimental models of ER+ breast cancer is relatively limited. Nonetheless, these models have proved very useful in advancing understanding of how cells respond to and resist endocrine therapies, and how the ER acts as a transcription factor to regulate cell fate signaling. We discuss the primary experimental models of ER+ breast cancer including 2D and 3D cultures of established cell lines, cell line- and patient-derived xenografts, and chemically induced rodent models, with a consideration of their respective general strengths and limitations. What can and cannot be learned easily from these models is also discussed, and some observations on how these models may be used more effectively are provided. Overall, despite their limitations, the panel of models currently available has enabled major advances in the field, and these models remain central to the ability to study mechanisms of therapy action and resistance and for hypothesis testing that would otherwise be intractable or unethical in human subjects.

ChIP-GSM: Inferring active transcription factor modules to predict functional regulatory elements.

Transcription factors (TFs) often function as a module including both master factors and mediators binding at cis-regulatory regions to modulate nearby gene transcription. ChIP-seq profiling of multiple TFs makes it feasible to infer functional TF modules. However, when inferring TF modules based on co-localization of ChIP-seq peaks, often many weak binding events are missed, especially for mediators, resulting in incomplete identification of modules. To address this problem, we develop a ChIP-seq data-driven Gibbs Sampler to infer Modules (ChIP-GSM) using a Bayesian framework that integrates ChIP-seq profiles of multiple TFs. ChIP-GSM samples read counts of module TFs iteratively to estimate the binding potential of a module to each region and, across all regions, estimates the module abundance. Using inferred module-region probabilistic bindings as feature units, ChIP-GSM then employs logistic regression to predict active regulatory elements. Validation of ChIP-GSM predicted regulatory regions on multiple independent datasets sharing the same context confirms the advantage of using TF modules for predicting regulatory activity. In a case study of K562 cells, we demonstrate that the ChIP-GSM inferred modules form as groups, activate gene expression at different time points, and mediate diverse functional cellular processes. Hence, ChIP-GSM infers biologically meaningful TF modules and improves the prediction accuracy of regulatory region activities.

Inhibition of Antiestrogen-Promoted Pro-Survival Autophagy and Tamoxifen Resistance in Breast Cancer through Vitamin D Receptor.

We determined how vitamin D receptor (VDR) is linked to disease outcome in estrogen receptor-positive (ER+) breast cancer patients treated with tamoxifen (TAM). Breast cancer patients (n = 581) in four different datasets were divided into those expressing higher (above median) and lower levels of VDR in pretreatment ER+ tumors. Across all datasets, TAM-treated patients with higher pretreatment tumor VDR expression exhibited significantly longer recurrence-free survival. Ingenuity pathway analysis identified autophagy and unfolded protein response (UPR) as top differentially expressed pathways between high and low VDR-expressing ER+ cancers. Activation of VDR with vitamin D (VitD), either calcitriol or its synthetic analog EB1089, sensitized MCF-7-derived, antiestrogen-resistant LCC9 human breast cancer cells to TAM, and attenuated increased UPR and pro-survival autophagy. Silencing of VDR blocked these effects through the IRE1α-JNK pathway. Further, silencing of VDR impaired sensitivity to TAM in antiestrogen-responsive LCC1 cells, and prevented the effects of calcitriol and EB1089 on UPR and autophagy. In a preclinical mouse model, dietary VitD supplementation induced VDR activation and reduced carcinogen-induced ER+ mammary tumor incidence. In addition, IRE1α-JNK signaling was downregulated and survival autophagy was inhibited in mammary tumors of VitD-supplemented mice. Thus, activation of VDR is predictive of reduced risk of breast cancer recurrence in ER+ patients, possibly by inhibiting antiestrogen-promoted pro-survival autophagy.

ChIP-BIT2: a software tool to detect weak binding events using a Bayesian integration approach.

BACKGROUND: ChIP-seq combines chromatin immunoprecipitation assays with sequencing and identifies genome-wide binding sites for DNA binding proteins. While many binding sites have strong ChIP-seq 'peak' observations and are well captured, there are still regions bound by proteins weakly, with a relatively low ChIP-seq signal enrichment. These weak binding sites, especially those at promoters and enhancers, are functionally important because they also regulate nearby gene expression. Yet, it remains a challenge to accurately identify weak binding sites in ChIP-seq data due to the ambiguity in differentiating these weak binding sites from the amplified background DNAs. RESULTS: ChIP-BIT2 ( http://sourceforge.net/projects/chipbitc/ ) is a software package for ChIP-seq peak detection. ChIP-BIT2 employs a mixture model integrating protein and control ChIP-seq data and predicts strong or weak protein binding sites at promoters, enhancers, or other genomic locations. For binding sites at gene promoters, ChIP-BIT2 simultaneously predicts their target genes. ChIP-BIT2 has been validated on benchmark regions and tested using large-scale ENCODE ChIP-seq data, demonstrating its high accuracy and wide applicability. CONCLUSION: ChIP-BIT2 is an efficient ChIP-seq peak caller. It provides a better lens to examine weak binding sites and can refine or extend the existing binding site collection, providing additional regulatory regions for decoding the mechanism of gene expression regulation.

Genistein Reduces the Risk of Local Mammary Cancer Recurrence and Ameliorates Alterations in the Gut Microbiota in the Offspring of Obese Dams.

The risk of recurrence of estrogen receptor-positive breast cancer remains constant, even 20 years after diagnosis. Recurrence may be more likely in patients pre-programmed for it already in the womb, such as in the daughters born to obese mothers. Maternal obesity persistently alters offspring's gut microbiota and impairs tumor immune responses. To investigate if the gut dysbiosis is linked to increased risk of mammary cancer recurrence in the offspring of obese rat dams, we fed adult offspring genistein which is known to have beneficial effects on the gut bacteria. However, the effects of genistein on breast cancer remain controversial. We found that genistein intake after tamoxifen response prevented the increased risk of local recurrence in the offspring of obese dams but had no effect on the control offspring. A significant increase in the abundance of inflammatory Prevotellaceae and Enterobacteriaceae, and a reduction in short-chain fatty acid producing Clostridiaceae was observed in the offspring of obese dams. Genistein supplementation reversed these changes as well as reversed increased gut metabolite N-acetylvaline levels which are linked to increased all-cause mortality. Genistein supplementation also reduced genotoxic tyramine levels, increased metabolites improving pro-resolving phase of inflammation, and reversed the elevated tumor mRNA expression of multiple immunosuppressive genes in the offspring of obese dams. If translatable to breast cancer patients, attempts to prevent breast cancer recurrences might need to focus on dietary modifications which beneficially modify the gut microbiota.

Identifying intracellular signaling modules and exploring pathways associated with breast cancer recurrence.

Exploring complex modularization of intracellular signal transduction pathways is critical to understanding aberrant cellular responses during disease development and drug treatment. IMPALA (Inferred Modularization of PAthway LAndscapes) integrates information from high throughput gene expression experiments and genome-scale knowledge databases to identify aberrant pathway modules, thereby providing a powerful sampling strategy to reconstruct and explore pathway landscapes. Here IMPALA identifies pathway modules associated with breast cancer recurrence and Tamoxifen resistance. Focusing on estrogen-receptor (ER) signaling, IMPALA identifies alternative pathways from gene expression data of Tamoxifen treated ER positive breast cancer patient samples. These pathways were often interconnected through cytoplasmic genes such as IRS1/2, JAK1, YWHAZ, CSNK2A1, MAPK1 and HSP90AA1 and significantly enriched with ErbB, MAPK, and JAK-STAT signaling components. Characterization of the pathway landscape revealed key modules associated with ER signaling and with cell cycle and apoptosis signaling. We validated IMPALA-identified pathway modules using data from four different breast cancer cell lines including sensitive and resistant models to Tamoxifen. Results showed that a majority of genes in cell cycle/apoptosis modules that were up-regulated in breast cancer patients with short survivals (< 5 years) were also over-expressed in drug resistant cell lines, whereas the transcription factors JUN, FOS, and STAT3 were down-regulated in both patient and drug resistant cell lines. Hence, IMPALA identified pathways were associated with Tamoxifen resistance and an increased risk of breast cancer recurrence. The IMPALA package is available at https://dlrl.ece.vt.edu/software/ .

IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.

MOTIVATION: High-throughput RNA sequencing has revolutionized the scope and depth of transcriptome analysis. Accurate reconstruction of a phenotype-specific transcriptome is challenging due to the noise and variability of RNA-seq data. This requires computational identification of transcripts from multiple samples of the same phenotype, given the underlying consensus transcript structure. RESULTS: We present a Bayesian method, integrated assembly of phenotype-specific transcripts (IntAPT), that identifies phenotype-specific isoforms from multiple RNA-seq profiles. IntAPT features a novel two-layer Bayesian model to capture the presence of isoforms at the group layer and to quantify the abundance of isoforms at the sample layer. A spike-and-slab prior is used to model the isoform expression and to enforce the sparsity of expressed isoforms. Dependencies between the existence of isoforms and their expression are modeled explicitly to facilitate parameter estimation. Model parameters are estimated iteratively using Gibbs sampling to infer the joint posterior distribution, from which the presence and abundance of isoforms can reliably be determined. Studies using both simulations and real datasets show that IntAPT consistently outperforms existing methods for the IntAPT. Experimental results demonstrate that, despite sequencing errors, IntAPT exhibits a robust performance among multiple samples, resulting in notably improved identification of expressed isoforms of low abundance. AVAILABILITY AND IMPLEMENTATION: The IntAPT package is available at http://github.com/henryxushi/IntAPT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Author Correction: BICORN: An R package for integrative inference of de novo cis-regulatory modules.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Effects of Maternal Grape Juice Intake on Unfolded Protein Response in the Mammary Glands of Offspring of High Fat Diet Fed Rat Dams.

Maternal high fat diet (HFD) and obesity during pregnancy increase female offspring's mammary cancer risk in animal studies. We aimed to observe whether the consumption of grape juice during pregnancy can reverse this risk. During pregnancy and lactation, female Wistar rats were fed either a control or HFD and also received grape juice or tap water. At the age of 50 days, female offspring were euthanized, and mammary glands were collected to assess changes in biomarkers of increased mammary cancer risk. Maternal HFD increased the number of terminal end buds in offspring's mammary glands and promoted cell proliferation (ki67). Maternal grape consumption blocked these effects. Apoptosis marker caspase 7, but not caspase 3, was reduced in the HFD offspring. HFD offspring also exhibited a reduction in the indicators of cell cycle regulation (p27, p21) and an ability to maintain DNA integrity (reduced p53). Maternal grape juice did not have any effect on these endpoints in the HFD offspring but reduced caspase 7 and p53 levels in the control offspring, perhaps reflecting reduced cellular stress. Maternal HFD increased oxidative stress marker GPx1 mRNA expression, and grape juice increased the levels of GPx2 in both the control and HFD offspring. HFD increased XBP1/Xbp1s, Atf4 and Atf6 mRNA expression and reduced ATF6 and CHOP protein levels. Maternal grape juice reversed the increase in XBP1/Xbp1s, Atf4 and Atf6 in the HFD offspring. PPAR was downregulated in the HFD group, and grape juice reversed this effect. Grape juice also reduced the levels of HER2 and IRS, both in the control and HFD offspring. In conclusion, maternal grape juice supplementation reversed some of the biomarkers that are indicative of increased breast cancer risk in the HFD offspring.

Maternal obesity increases offspring's mammary cancer recurrence and impairs tumor immune response.

Over 50% of women at a childbearing age in the United States are overweight or obese, and this can adversely affect their offspring. We studied if maternal obesity-inducing high fat diet (HFD) not only increases offspring's mammary cancer risk but also impairs response to antiestrogen tamoxifen. Female rat offspring of HFD and control diet-fed dams, in which estrogen receptor-positive (ER+) mammary tumors were induced with the carcinogen 7,12-dimethylbenz[a]anthracene (DMBA), exhibited similar initial responses to antiestrogen tamoxifen. However, after tamoxifen therapy was completed, almost all (91%) tumors recurred in HFD offspring, compared with only 29% in control offspring. The increase in local mammary tumor recurrence in HFD offspring was linked to an increase in the markers of immunosuppression (Il17f, Tgfß1, VEGFR2) in the tumor microenvironment (TME). Protein and mRNA levels of the major histocompatibility complex II (MHC-II), but not MHC-I, were reduced in the recurring DMBA tumors of HFD offspring. Further, infiltration of CD8+ effector T cells and granzyme B+ (GZMB+) cells were lower in their recurring tumors. To determine if maternal HFD can pre-program similar changes in the TME of allografted E0771 mammary tumors in offspring of syngeneic mice, flow cytometry analysis was performed. E0771 mammary tumor growth was significantly accelerated in the HFD offspring, and a reduction in the numbers of GZMB and non-significant reduction of interferon γ (IFNγ) secreting CD8+ T cells in the TME was seen. Thus, consumption of a HFD during pregnancy increases susceptibility of the female rat and mouse offspring to tumor immune suppression and mammary tumor growth and recurrence.

BICORN: An R package for integrative inference of de novo cis-regulatory modules.

Genome-wide transcription factor (TF) binding signal analyses reveal co-localization of TF binding sites based on inferred cis-regulatory modules (CRMs). CRMs play a key role in understanding the cooperation of multiple TFs under specific conditions. However, the functions of CRMs and their effects on nearby gene transcription are highly dynamic and context-specific and therefore are challenging to characterize. BICORN (Bayesian Inference of COoperative Regulatory Network) builds a hierarchical Bayesian model and infers context-specific CRMs based on TF-gene binding events and gene expression data for a particular cell type. BICORN automatically searches for a list of candidate CRMs based on the input TF bindings at regulatory regions associated with genes of interest. Applying Gibbs sampling, BICORN iteratively estimates model parameters of CRMs, TF activities, and corresponding regulation on gene transcription, which it models as a sparse network of functional CRMs regulating target genes. The BICORN package is implemented in R (version 3.4 or later) and is publicly available on the CRAN server at https://cran.r-project.org/web/packages/BICORN/index.html.

Effects of Jaeumkanghwa-tang on tamoxifen responsiveness in preclinical ER+ breast cancer model.

Resistance to endocrine therapy remains a clinical challenge in the treatment of estrogen receptor-positive (ER+) breast cancer. We investigated if adding a traditional Asian herbal mixture consisting of 12 herbs, called Jaeumkanghwa-tang (JEKHT), to tamoxifen (TAM) therapy might prevent resistance and recurrence in the ER+ breast cancer model of 7,12-dimethylbenz[a]anthracene (DMBA)-exposed Sprague-Dawley rats. Rats were divided into four groups treated as follows: 15 mg/kg TAM administered via diet as TAM citrate (TAM only); 500 mg/kg JEKHT administered via drinking water (JEKHT only group); TAM + JEKHT and no treatment control group. The study was replicated using two different batches of JEKHT. In both studies, a significantly higher proportion of ER+ mammary tumors responded to TAM if animals also were treated with JEKHT (experiment 1: 47% vs 65%, P = 0.015; experiment 2: 43% vs 77%, P < 0.001). The risk of local recurrence also was reduced (31% vs 12%, P = 0.002). JEKHT alone was mostly ineffective. In addition, JEKHT prevented the development of premalignant endometrial lesions in TAM-treated rats (20% in TAM only vs 0% in TAM + JEKHT). Co-treatment of antiestrogen-resistant LCC9 human breast cancer cells with 1.6 mg/mL JEKHT reversed their TAM resistance in dose-response studies in vitro. Several traditional herbal medicine preparations can exhibit anti-inflammatory properties and may increase anti-tumor immune activities in the tumor microenvironment. In the tumors of rats treated with both JEKHT and TAM, expression of Il-6 (P = 0.03), Foxp3/T regulatory cell (Treg) marker (P = 0.033) and Tgfß1 that activates Tregs (P < 0.001) were significantly downregulated compared with TAM only group. These findings indicate that JEKHT may prevent TAM-induced evasion of tumor immune responses.

Developmental Origins of Breast Cancer: A Paternal Perspective.

The developmental origins of breast cancer have been considered predominantly from a maternal perspective. Although accumulating evidence suggests a paternal programming effect on metabolic diseases, the potential impact of fathers' experiences on their daughters' breast cancer risk has received less attention. In this chapter, we focus on the developmental origins of breast cancer and examine the emerging evidence for a role of fathers' experiences.

Investigation of Paternal Programming of Breast Cancer Risk in Female Offspring in Rodent Models.

Emerging experimental evidence show that fathers' experiences during preconception can influence their daughters' risk of developing breast cancer. Here we describe detailed protocols for investigation in rats and mice of paternally mediated breast cancer risk programming effects.

Correction to: Dietary intake of soy and cruciferous vegetables and treatment-related symptoms in Chinese-American and non-Hispanic White breast cancer survivors.

In the original publication, the values provided for the isoflavone and glucosinolate intake variables were incorrectly labeled in Table 1. The correct values of 6.3 mg/day for isoflavone intake, and 20.4 mg/day and 50.1 mg/day for glucosinolate intake are provided in this erratum.

CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data.

Motivation: NGS techniques have been widely applied in genetic and epigenetic studies. Multiple ChIP-seq and RNA-seq profiles can now be jointly used to infer functional regulatory networks (FRNs). However, existing methods suffer from either oversimplified assumption on transcription factor (TF) regulation or slow convergence of sampling for FRN inference from large-scale ChIP-seq and time-course RNA-seq data. Results: We developed an efficient Bayesian integration method (CRNET) for FRN inference using a two-stage Gibbs sampler to estimate iteratively hidden TF activities and the posterior probabilities of binding events. A novel statistic measure that jointly considers regulation strength and regression error enables the sampling process of CRNET to converge quickly, thus making CRNET very efficient for large-scale FRN inference. Experiments on synthetic and benchmark data showed a significantly improved performance of CRNET when compared with existing methods. CRNET was applied to breast cancer data to identify FRNs functional at promoter or enhancer regions in breast cancer MCF-7 cells. Transcription factor MYC is predicted as a key functional factor in both promoter and enhancer FRNs. We experimentally validated the regulation effects of MYC on CRNET-predicted target genes using appropriate RNAi approaches in MCF-7 cells. Availability and implementation: R scripts of CRNET are available at http://www.cbil.ece.vt.edu/software.htm. Contact: xuan@vt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.