Steroid Hormone Receptors as Prognostic Markers in Breast Cancer

Despite the existence of many promising anti-cancer therapies, not all breast cancers are equally treatable, due partly to the fact that focus has been primarily on a few select breast cancer biomarkers- notably ERα, PR and HER2. In cases like triple negative breast cancer (ERα-, PR-, and HER2-), there is a complete lack of available biomarkers for prognosis and therapeutic purposes. The goal of this review is to determine if other steroid receptors, like ERβ and AR, could play a prognostic and/or therapeutic role. Data from various in vitro, in vivo, and clinical breast cancer studies were examined to analyze the presence and function of ERβ, PR, and AR in the presence and absence of ERα. Additionally, we focused on studies that examined how expression of the various steroid receptor isoforms affects breast cancer progression. Our findings suggest that while we have a solid understanding of how these receptors work individually, how they interact and behave in the presence and absence of other receptors requires further research. Furthermore, there is an incomplete understanding of how the various steroid receptor isoforms interact and impact receptor function and breast cancer progression, partly due to the difficulty in detecting all the various isoforms. More large-scale clinical studies must be made to analyze systematically the expression of steroid hormone receptors and their respective isoforms in breast cancer patients in order to determine how these receptors interact with each other and in turn affect cancer progression

Direct Control of Cell Cycle Gene Expression by Proto-oncogene Product ACTR, and Its Autoregulation Underlies Its Transforming Activity

ACTR (also called AIB1 and SRC-3) was identified as a coactivator for nuclear receptors and is linked to multiple types of human cancer due to its frequent overexpression. However, the molecular mechanism of ACTR oncogenicity and its function independent of nuclear receptors remain to be defined. We demonstrate here that ACTR is required for both normal and malignant human cells to effectively enter S phase. RNA interference-mediated depletion and chromatin immunoprecipitation assays show that endogenous ACTR directly controls the expression of genes important for initiation of DNA replication, which include cdc6, cdc25A, MCM7, cyclin E, and Cdk2. Moreover, consistent with its critical role in cell cycle control, ACTR expression appears to be cell cycle regulated, which involves E2F. Interestingly, ACTR is recruited to its own promoter at the G1/S transition and activates its own expression, suggesting a positive feedback mechanism for ACTR action in the control of cell cycle progression and for its aberrant expression in cancers. Importantly, overexpression of ACTR alone transforms human mammary epithelial cells, which requires its association with E2F. These findings reveal a novel role for ACTR in cell cycle control and support the notion that the ability of aberrant ACTR to deregulate the cell cycle through E2F underlies its oncogenicity in human cancers

ROR-γ drives androgen receptor expression and represents a therapeutic target in castration-resistant prostate cancer.

The androgen receptor (AR) is overexpressed and hyperactivated in human castration-resistant prostate cancer (CRPC). However, the determinants of AR overexpression in CRPC are poorly defined. Here we show that retinoic acid receptor-related orphan receptor γ (ROR-γ) is overexpressed and amplified in metastatic CRPC tumors, and that ROR-γ drives AR expression in the tumors. ROR-γ recruits nuclear receptor coactivator 1 and 3 (NCOA1 and NCOA3, also known as SRC-1 and SRC-3) to an AR-ROR response element (RORE) to stimulate AR gene transcription. ROR-γ antagonists suppress the expression of both AR and its variant AR-V7 in prostate cancer (PCa) cell lines and tumors. ROR-γ antagonists also markedly diminish genome-wide AR binding, H3K27ac abundance and expression of the AR target gene network. Finally, ROR-γ antagonists suppressed tumor growth in multiple AR-expressing, but not AR-negative, xenograft PCa models, and they effectively sensitized CRPC tumors to enzalutamide, without overt toxicity, in mice. Taken together, these results establish ROR-γ as a key player in CRPC by acting upstream of AR and as a potential therapeutic target for advanced PCa

A Cost-Effectiveness Analysis of “Test” versus “Treat” Patients Hospitalized with Suspected Influenza in Hong Kong

BACKGROUND: Seasonal and 2009 H1N1 influenza viruses may cause severe diseases and result in excess hospitalization and mortality in the older and younger adults, respectively. Early antiviral treatment may improve clinical outcomes. We examined potential outcomes and costs of test-guided versus empirical treatment in patients hospitalized for suspected influenza in Hong Kong. METHODS: We designed a decision tree to simulate potential outcomes of four management strategies in adults hospitalized for severe respiratory infection suspected of influenza: "immunofluorescence-assay" (IFA) or "polymerase-chain-reaction" (PCR)-guided oseltamivir treatment, "empirical treatment plus PCR" and "empirical treatment alone". Model inputs were derived from literature. The average prevalence (11%) of influenza in 2010-2011 (58% being 2009 H1N1) among cases of respiratory infections was used in the base-case analysis. Primary outcome simulated was cost per quality-adjusted life-year (QALY) expected (ICER) from the Hong Kong healthcare providers' perspective. RESULTS: In base-case analysis, "empirical treatment alone" was shown to be the most cost-effective strategy and dominated the other three options. Sensitivity analyses showed that "PCR-guided treatment" would dominate "empirical treatment alone" when the daily cost of oseltamivir exceeded USD18, or when influenza prevalence was <2.5% and the predominant circulating viruses were not 2009 H1N1. Using USD50,000 as the threshold of willingness-to-pay, "empirical treatment alone" and "PCR-guided treatment" were cost-effective 97% and 3% of time, respectively, in 10,000 Monte-Carlo simulations. CONCLUSIONS: During influenza epidemics, empirical antiviral treatment appears to be a cost-effective strategy in managing patients hospitalized with severe respiratory infection suspected of influenza, from the perspective of healthcare providers in Hong Kong

The Role of Cadmium and Nickel in Estrogen Receptor Signaling and Breast Cancer: Metalloestrogens or Not?

During the last half-century, incidences of breast cancer have increased globally. Various factors —genetic and environmental— have been implicated in the initiation and progression of this disease. One potential environmental risk factor that has not received a lot of attention is the exposure to heavy metals. While several mechanisms have been put forth describing how high concentrations of heavy metals play a role in carcinogenesis, it is unclear whether chronic, lowlevel exposure to certain heavy metals (i.e. cadmium and nickel), can directly result in the development and progression of cancer. Cadmium and nickel have been hypothesized to play a role in breast cancer development by acting as metalloestrogens— metals that bind to estrogen receptors and mimic the actions of estrogen. Since the lifetime exposure to estrogen is a wellestablished risk factor for breast cancer, anything that mimics its activity will likely contribute to the etiology of the disease. However, heavy metals, depending on their concentration, are capable of binding to a variety of proteins and may exert their toxicities by disrupting multiple cellular functions, complicating the analysis of whether heavy metal-induced carcinogenesis is mediated by the estrogen receptor. The purpose of this review is to discuss the various epidemiological, in vivo, and in vitro studies that show a link between the heavy metals, cadmium and nickel, and breast cancer development. We will particularly focus on the studies that test whether or not these two metals act as metalloestrogens in order to assess the strength of the data supporting this hypothesis

Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P \u3c 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P &lt; 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.</p

Defining the causes of sporadic Parkinson's disease in the global Parkinson's genetics program (GP2)

The Global Parkinson’s Genetics Program (GP2) will genotype over 150,000 participants from around the world, and integrate genetic and clinical data for use in large-scale analyses to dramatically expand our understanding of the genetic architecture of PD. This report details the workflow for cohort integration into the complex arm of GP2, and together with our outline of the monogenic hub in a companion paper, provides a generalizable blueprint for establishing large scale collaborative research consortia

Multi-ancestry genome-wide association meta-analysis of Parkinson?s disease

Although over 90 independent risk variants have been identified for Parkinson’s disease using genome-wide association studies, most studies have been performed in just one population at a time. Here we performed a large-scale multi-ancestry meta-analysis of Parkinson’s disease with 49,049 cases, 18,785 proxy cases and 2,458,063 controls including individuals of European, East Asian, Latin American and African ancestry. In a meta-analysis, we identified 78 independent genome-wide significant loci, including 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300 and PPP6R2) and fine-mapped 6 putative causal variants at 6 known PD loci. By combining our results with publicly available eQTL data, we identified 25 putative risk genes in these novel loci whose expression is associated with PD risk. This work lays the groundwork for future efforts aimed at identifying PD loci in non-European populations