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.

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.

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.

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.

SparseIso: a novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data.

Motivation: Recent advances in high-throughput RNA sequencing (RNA-seq) technologies have made it possible to reconstruct the full transcriptome of various types of cells. It is important to accurately assemble transcripts or identify isoforms for an improved understanding of molecular mechanisms in biological systems. Results: We have developed a novel Bayesian method, SparseIso, to reliably identify spliced isoforms from RNA-seq data. A spike-and-slab prior is incorporated into the Bayesian model to enforce the sparsity for isoform identification, effectively alleviating the problem of overfitting. A Gibbs sampling procedure is further developed to simultaneously identify and quantify transcripts from RNA-seq data. With the sampling approach, SparseIso estimates the joint distribution of all candidate transcripts, resulting in a significantly improved performance in detecting lowly expressed transcripts and multiple expressed isoforms of genes. Both simulation study and real data analysis have demonstrated that the proposed SparseIso method significantly outperforms existing methods for improved transcript assembly and isoform identification. Availability and implementation: The SparseIso package is available at http://github.com/henryxushi/SparseIso. Contact: xuan@vt.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

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.

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.

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

PURPOSE: This project was undertaken to examine the association between dietary intake of soy or cruciferous vegetables and breast cancer treatment-related symptoms among Chinese-American (CA) and Non-Hispanic White (NHW) breast cancer survivors. METHODS: This cross-sectional study included 192 CA and 173 NHW female breast cancer survivors (stages 0-III, diagnosed between 2006 and 2012) recruited from two California cancer registries, who had completed primary treatment. Patient-reported data on treatment-related symptoms and potential covariates were collected via telephone interviews. Dietary data were ascertained by mailed questionnaires. The outcomes evaluated were menopausal symptoms (hot flashes, night sweats, vaginal dryness, vaginal discharge), joint problems, fatigue, hair thinning/loss, and memory problems. Associations between soy and cruciferous vegetables and symptoms were assessed using logistic regression. Analyses were further stratified by race/ethnicity and endocrine therapy usage (non-user, tamoxifen, aromatase inhibitors). RESULTS: Soy food and cruciferous vegetable intake ranged from no intake to 431 and 865 g/day, respectively, and was higher in CA survivors. Higher soy food intake was associated with lower odds of menopausal symptoms (≥ 24.0 vs. 0 g/day, OR 0.51, 95% CI 0.25, 1.03), and fatigue (≥ 24.0 vs. 0 g/day, OR 0.43, 95% CI 0.22, 0.84). However, when stratified by race/ethnicity, associations were statistically significant in NHW survivors only. Compared with low intake, higher cruciferous vegetable intake was associated with lower odds of experiencing menopausal symptoms (≥ 70.8 vs. < 33.0 g/day, OR 0.50, 95% CI 0.25, 0.97) in the overall population. CONCLUSIONS: In this population of breast cancer survivors, higher soy and cruciferous vegetable intake was associated with less treatment-related menopausal symptoms and fatigue.

DM-BLD: differential methylation detection using a hierarchical Bayesian model exploiting local dependency.

MOTIVATION: The advent of high-throughput DNA methylation profiling techniques has enabled the possibility of accurate identification of differentially methylated genes for cancer research. The large number of measured loci facilitates whole genome methylation study, yet posing great challenges for differential methylation detection due to the high variability in tumor samples. RESULTS: We have developed a novel probabilistic approach, D: ifferential M: ethylation detection using a hierarchical B: ayesian model exploiting L: ocal D: ependency (DM-BLD), to detect differentially methylated genes based on a Bayesian framework. The DM-BLD approach features a joint model to capture both the local dependency of measured loci and the dependency of methylation change in samples. Specifically, the local dependency is modeled by Leroux conditional autoregressive structure; the dependency of methylation changes is modeled by a discrete Markov random field. A hierarchical Bayesian model is developed to fully take into account the local dependency for differential analysis, in which differential states are embedded as hidden variables. Simulation studies demonstrate that DM-BLD outperforms existing methods for differential methylation detection, particularly when the methylation change is moderate and the variability of methylation in samples is high. DM-BLD has been applied to breast cancer data to identify important methylated genes (such as polycomb target genes and genes involved in transcription factor activity) associated with breast cancer recurrence. AVAILABILITY AND IMPLEMENTATION: A Matlab package of DM-BLD is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: Xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

PSSV: a novel pattern-based probabilistic approach for somatic structural variation identification.

MOTIVATION: Whole genome DNA-sequencing (WGS) of paired tumor and normal samples has enabled the identification of somatic DNA changes in an unprecedented detail. Large-scale identification of somatic structural variations (SVs) for a specific cancer type will deepen our understanding of driver mechanisms in cancer progression. However, the limited number of WGS samples, insufficient read coverage, and the impurity of tumor samples that contain normal and neoplastic cells, limit reliable and accurate detection of somatic SVs. RESULTS: We present a novel pattern-based probabilistic approach, PSSV, to identify somatic structural variations from WGS data. PSSV features a mixture model with hidden states representing different mutation patterns; PSSV can thus differentiate heterozygous and homozygous SVs in each sample, enabling the identification of those somatic SVs with heterozygous mutations in normal samples and homozygous mutations in tumor samples. Simulation studies demonstrate that PSSV outperforms existing tools. PSSV has been successfully applied to breast cancer data to identify somatic SVs of key factors associated with breast cancer development. AVAILABILITY AND IMPLEMENTATION: An R package of PSSV is available at http://www.cbil.ece.vt.edu/software.htm CONTACT: xuan@vt.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Maternal intake of high n-6 polyunsaturated fatty acid diet during pregnancy causes transgenerational increase in mammary cancer risk in mice.

BACKGROUND: Maternal and paternal high-fat (HF) diet intake before and/or during pregnancy increases mammary cancer risk in several preclinical models. We studied if maternal consumption of a HF diet that began at a time when the fetal primordial germ cells travel to the genital ridge and start differentiating into germ cells would result in a transgenerational inheritance of increased mammary cancer risk. METHODS: Pregnant C57BL/6NTac mouse dams were fed either a control AIN93G or isocaloric HF diet composed of corn oil high in n-6 polyunsaturated fatty acids between gestational days 10 and 20. Offspring in subsequent F1-F3 generations were fed only the control diet. RESULTS: Mammary tumor incidence induced by 7,12-dimethylbenz[a]anthracene was significantly higher in F1 (p < 0.016) and F3 generation offspring of HF diet-fed dams (p < 0.040) than in the control offspring. Further, tumor latency was significantly shorter (p < 0.028) and burden higher (p < 0.027) in F1 generation HF offspring, and similar trends were seen in F3 generation HF offspring. RNA sequencing was done on normal mammary glands to identify signaling differences that may predispose to increased breast cancer risk by maternal HF intake. Analysis revealed 1587 and 4423 differentially expressed genes between HF and control offspring in F1 and F3 generations, respectively, of which 48 genes were similarly altered in both generations. Quantitative real-time polymerase chain reaction analysis validated 13 chosen up- and downregulated genes in F3 HF offspring, but only downregulated genes in F1 HF offspring. Ingenuity Pathway Analysis identified upregulation of Notch signaling as a key alteration in HF offspring. Further, knowledge-fused differential dependency network analysis identified ten node genes that in the HF offspring were uniquely connected to genes linked to increased cancer risk (ANKEF1, IGFBP6, SEMA5B), increased resistance to cancer treatments (SLC26A3), poor prognosis (ID4, JAM3, TBX2), and impaired anticancer immunity (EGR3, ZBP1). CONCLUSIONS: We conclude that maternal HF diet intake during pregnancy induces a transgenerational increase in offspring mammary cancer risk in mice. The mechanisms of inheritance in the F3 generation may be different from the F1 generation because significantly more changes were seen in the transcriptome.

Paternal programming of breast cancer risk in daughters in a rat model: opposing effects of animal- and plant-based high-fat diets.

BACKGROUND: Although males contribute half of the embryo's genome, only recently has interest begun to be directed toward the potential impact of paternal experiences on the health of offspring. While there is evidence that paternal malnutrition may increase offspring susceptibility to metabolic diseases, the influence of paternal factors on a daughter's breast cancer risk has been examined in few studies. METHODS: Male Sprague-Dawley rats were fed, before and during puberty, either a lard-based (high in saturated fats) or a corn oil-based (high in n-6 polyunsaturated fats) high-fat diet (60 % of fat-derived energy). Control animals were fed an AIN-93G control diet (16 % of fat-derived energy). Their 50-day-old female offspring fed only a commercial diet were subjected to the classical model of mammary carcinogenesis based on 7,12-dimethylbenz[a]anthracene initiation, and mammary tumor development was evaluated. Sperm cells and mammary gland tissue were subjected to cellular and molecular analysis. RESULTS: Compared with female offspring of control diet-fed male rats, offspring of lard-fed male rats did not differ in tumor latency, growth, or multiplicity. However, female offspring of lard-fed male rats had increased elongation of the mammary epithelial tree, number of terminal end buds, and tumor incidence compared with both female offspring of control diet-fed and corn oil-fed male rats. Compared with female offspring of control diet-fed male rats, female offspring of corn oil-fed male rats showed decreased tumor growth but no difference regarding tumor incidence, latency, or multiplicity. Additionally, female offspring of corn oil-fed male rats had longer tumor latency as well as decreased tumor growth and multiplicity compared with female offspring of lard-fed male rats. Paternal consumption of animal- or plant-based high-fat diets elicited opposing effects, with lard rich in saturated fatty acids increasing breast cancer risk in offspring and corn oil rich in n-6 polyunsaturated fatty acids decreasing it. These effects could be linked to alterations in microRNA expression in fathers' sperm and their daughters' mammary glands, and to modifications in breast cancer-related protein expression in this tissue. CONCLUSIONS: Our findings highlight the importance of paternal nutrition in affecting future generations' risk of developing breast cancer.

BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method.

UNLABELLED: Identification of protein interaction subnetworks is an important step to help us understand complex molecular mechanisms in cancer. In this paper, we develop a BMRF-Net package, implemented in Java and C++, to identify protein interaction subnetworks based on a bagging Markov random field (BMRF) framework. By integrating gene expression data and protein-protein interaction data, this software tool can be used to identify biologically meaningful subnetworks. A user friendly graphic user interface is developed as a Cytoscape plugin for the BMRF-Net software to deal with the input/output interface. The detailed structure of the identified networks can be visualized in Cytoscape conveniently. The BMRF-Net package has been applied to breast cancer data to identify significant subnetworks related to breast cancer recurrence. AVAILABILITY AND IMPLEMENTATION: The BMRF-Net package is available at http://sourceforge.net/projects/bmrfcjava/. The package is tested under Ubuntu 12.04 (64-bit), Java 7, glibc 2.15 and Cytoscape 3.1.0.

BMRF-MI: integrative identification of protein interaction network by modeling the gene dependency.

BACKGROUND: Identification of protein interaction network is a very important step for understanding the molecular mechanisms in cancer. Several methods have been developed to integrate protein-protein interaction (PPI) data with gene expression data for network identification. However, they often fail to model the dependency between genes in the network, which makes many important genes, especially the upstream genes, unidentified. It is necessary to develop a method to improve the network identification performance by incorporating the dependency between genes. RESULTS: We proposed an approach for identifying protein interaction network by incorporating mutual information (MI) into a Markov random field (MRF) based framework to model the dependency between genes. MI is widely used in information theory to measure the uncertainty between random variables. Different from traditional Pearson correlation test, MI is capable of capturing both linear and non-linear relationship between random variables. Among all the existing MI estimators, we choose to use k-nearest neighbor MI (kNN-MI) estimator which is proved to have minimum bias. The estimated MI is integrated with an MRF framework to model the gene dependency in the context of network. The maximum a posterior (MAP) estimation is applied on the MRF-based model to estimate the network score. In order to reduce the computational complexity of finding the optimal network, a probabilistic searching algorithm is implemented. We further increase the robustness and reproducibility of the results by applying a non-parametric bootstrapping method to measure the confidence level of the identified genes. To evaluate the performance of the proposed method, we test the method on simulation data under different conditions. The experimental results show an improved accuracy in terms of subnetwork identification compared to existing methods. Furthermore, we applied our method onto real breast cancer patient data; the identified protein interaction network shows a close association with the recurrence of breast cancer, which is supported by functional annotation. We also show that the identified subnetworks can be used to predict the recurrence status of cancer patients by survival analysis. CONCLUSIONS: We have developed an integrated approach for protein interaction network identification, which combines Markov random field framework and mutual information to model the gene dependency in PPI network. Improvements in subnetwork identification have been demonstrated with simulation datasets compared to existing methods. We then apply our method onto breast cancer patient data to identify recurrence related subnetworks. The experiment results show that the identified genes are enriched in the pathway and functional categories relevant to progression and recurrence of breast cancer. Finally, the survival analysis based on identified subnetworks achieves a good result of classifying the recurrence status of cancer patients.

Maternal exposure to diethylstilbestrol during pregnancy and increased breast cancer risk in daughters.

The idea that susceptibility to breast cancer is determined not only through inherited germline mutations but also by epigenetic changes induced by alterations in hormonal environment during fetal development is gaining increasing support. Using findings obtained in human and animal studies, this review addresses the mechanisms that may explain why daughters of mothers who took synthetic estrogen diethylstilbestrol (DES) during pregnancy have two times higher breast cancer risk than women who were not exposed to it. The mechanisms likely involve epigenetic alterations, such as increased DNA methylation and modifications in histones and microRNA expression.Further, these alterations may target genes that regulate stem cells and prevent differentiation of their daughter cells. Recent findings in a preclinical model suggest that not only are women exposed to DES in utero at an increased risk of developing breast cancer, but this risk may extend to their daughters and granddaughters as well. It is critical, therefore, to determine if the increased risk is driven by epigenetic alterations in genes that increase susceptibility to breast cancer and if these alterations are reversible.

MYC regulates the unfolded protein response and glucose and glutamine uptake in endocrine resistant breast cancer.

BACKGROUND: About 70% of all breast cancers are estrogen receptor alpha positive (ER+) and are treated with antiestrogens. However, 50% of ER + tumors develop resistance to these drugs (endocrine resistance). In endocrine resistant cells, an adaptive pathway called the unfolded protein response (UPR) is elevated that allows cells to tolerate stress more efficiently than in sensitive cells. While the precise mechanism remains unclear, the UPR can trigger both pro-survival and pro-death outcomes that depend on the nature and magnitude of the stress. In this study, we identified MYC, an oncoprotein that is upregulated in endocrine resistant breast cancer, as a regulator of the UPR in glucose-deprived conditions. METHODS: ER+ human breast cancer cell lines (LCC1, LCC1, LY2 and LCC9) and rat mammary tumors were used to confirm upregulation of MYC in endocrine resistance. To evaluate functional relevance of proteins, siRNA-mediated inhibition or small molecule inhibitors were used. Cell density/number was evaluated with crystal violet assay; cell cycle and apoptosis were measured by flow cytometry. Relative quantification of glutamine metabolites were determined by mass spectrometry. Signaling molecules of the UPR, apoptosis or autophagy pathways were investigated by western blotting. RESULTS: Increased MYC function in resistant cells correlated with increased dependency on glutamine and glucose for survival. Inhibition of MYC reduced cell growth and uptake of both glucose and glutamine in resistant cells. Interestingly, in glucose-deprived conditions, glutamine induced apoptosis and necrosis, arrested autophagy, and triggered the unfolded protein response (UPR) though GRP78-IRE1α with two possible outcomes: (i) inhibition of cell growth by JNK activation in most cells and, (ii) promotion of cell growth by spliced XBP1 in the minority of cells. These disparate effects are regulated, at different signaling junctions, by MYC more robustly in resistant cells. CONCLUSIONS: Endocrine resistant cells overexpress MYC and are better adapted to withstand periods of glucose deprivation and can use glutamine in the short term to maintain adequate metabolism to support cell survival. Our findings reveal a unique role for MYC in regulating cell fate through the UPR, and suggest that targeting glutamine metabolism may be a novel strategy in endocrine resistant breast cancer.

Exposures to synthetic estrogens at different times during the life, and their effect on breast cancer risk.

Women are using estrogens for many purposes, such as to prevent pregnancy or miscarriage, or to treat menopausal symptoms. Estrogens also have been used to treat breast cancer which seems puzzling, since there is convincing evidence to support a link between high lifetime estrogen exposure and increased breast cancer risk. In this review, we discuss the findings that maternal exposure to the synthetic estrogen diethylstilbestrol during pregnancy increases breast cancer risk in both exposed mothers and their daughters. In addition, we review data regarding the use of estrogens in oral contraceptives and as postmenopausal hormone therapy and discuss the opposing effects on breast cancer risk based upon timing of exposure. We place particular emphasis on studies investigating how maternal estrogenic exposures during pregnancy increase breast cancer risk among daughters. New data suggest that these exposures induce epigenetic modifications in the mammary gland and germ cells, thereby causing an inheritable increase in breast cancer risk for multiple generations.

Effects of maternal dietary exposure to cadmium during pregnancy on mammary cancer risk among female offspring.

BACKGROUND: Since heavy metal cadmium is an endocrine disrupting chemical, we investigated whether maternal exposure to cadmium during the pregnancy alters mammary tumorigenesis among female offspring. METHODS: From gestation day 10 to day 19, pregnant rat dams were fed modified American Institute of Nutrition (AIN93G) diet containing 39% energy from fat (baseline diet), or the baseline diet containing moderate (75 µg/kg of feed) or high (150 µg/kg) cadmium levels. Some dams were injected with 10 µg 17ß-estradiol (E2) daily between gestation days 10 and 19. RESULTS: Rats exposed to a moderate cadmium dose in utero were heavier and exhibited accelerated puberty onset. Both moderate and high cadmium dose led to increased circulating testosterone levels and reduced the expression of androgen receptor in the mammary gland. The moderate cadmium dose mimicked the effects of in utero E2 exposure on mammary gland morphology and increased both the number of terminal end buds and pre-malignant hyperplastic alveolar nodules (HANs), but in contrast to the E2, it did not increase 7, 12-dimethylbenz (a) anthracene-induced mammary tumorigenesis. CONCLUSIONS: The effects of in utero cadmium exposure were dependent on the dose given to pregnant dams: Moderate, but not high, cadmium dose mimicked some of the effects seen in the in utero E2 exposed rats, such as increased HANs in the mammary gland.

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.