Role of TET1 and 5hmC in an Obesity-Linked Pathway Driving Cancer Stem Cells in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that lacks expression of estrogen receptor, progesterone receptor, and the HER2 but is enriched with cancer stem cell-like cells (CSC). CSCs are the fraction of cancer cells recognized as the source of primary malignant tumors that also give rise to metastatic recurrence. 5-Hydroxymethylcytosine (5hmC) is a DNA epigenetic feature derived from 5-methylcytosine by action of tet methylcytosine dioxygenase enzymes (e.g., TET1); and although TET1 and 5hmC are required to maintain embryonic stem cells, the mechanism and role in CSCs remain unknown. Data presented in this report support the conclusion that TET1 and TET1-dependent 5hmC mediate hydrogen peroxide (H2O2)-dependent activation of a novel gene expression cascade driving self-renewal and expansion of CSCs in TNBC. Evidence presented also supports that the H2O2 affecting this pathway arises due to endogenous mechanisms-including downregulation of antioxidant enzyme catalase in TNBC cells-and by exogenous routes, such as systemic inflammation and oxidative stress coupled with obesity, a known risk factor for TNBC incidence and recurrence. IMPLICATIONS: This study elucidates a pathway dependent on H2O2 and linked to obesity-driven TNBC tumor-initiating CSCs; thus, it provides new understanding that may advance TNBC prevention and treatment strategies.

Detecting qualitative changes in biological systems.

Currently, most diseases are diagnosed only after significant disease-associated transformations have taken place. Here, we propose an approach able to identify when systemic qualitative changes in biological systems happen, thus opening the possibility for therapeutic interventions before the occurrence of symptoms. The proposed method exploits knowledge from biological networks and longitudinal data using a system impact analysis. The method is validated on eight biological phenomena, three synthetic datasets and five real datasets, for seven organisms. Most importantly, the method accurately detected the transition from the control stage (benign) to the early stage of hepatocellular carcinoma on an eight-stage disease dataset.

Obesity-induced MBD2_v2 expression promotes tumor-initiating triple-negative breast cancer stem cells.

Obesity is a risk factor for triple-negative breast cancer (TNBC) incidence and poor outcomes, but the underlying molecular biology remains unknown. We previously identified in TNBC cell cultures that expression of epigenetic reader methyl-CpG-binding domain protein 2 (MBD2), specifically the alternative mRNA splicing variant MBD variant 2 (MBD2_v2), is dependent on reactive oxygen species (ROS) and is crucial for maintenance and expansion of cancer stem cell-like cells (CSCs). Because obesity is coupled with inflammation and ROS, we hypothesized that obesity can fuel an increase in MBD2_v2 expression to promote the tumor-initiating CSC phenotype in TNBC cells in vivo. Analysis of TNBC patient datasets revealed associations between high tumor MBD2_v2 expression and high relapse rates and high body mass index (BMI). Stable gene knockdown/overexpression methods were applied to TNBC cell lines to elucidate that MBD2_v2 expression is governed by ROS-dependent expression of serine- and arginine-rich splicing factor 2 (SRSF2). We employed a diet-induced obesity (DIO) mouse model that mimics human obesity to investigate whether obesity causes increased MBD2_v2 expression and increased tumor initiation capacity in inoculated TNBC cell lines. MBD2_v2 and SRSF2 levels were increased in TNBC cell line-derived tumors that formed more frequently in DIO mice relative to tumors in lean control mice. Stable MBD2_v2 overexpression increased the CSC fraction in culture and increased TNBC cell line tumor initiation capacity in vivo. SRSF2 knockdown resulted in decreased MBD2_v2 expression, decreased CSCs in TNBC cell cultures, and hindered tumor formation in vivo. This report describes evidence to support the conclusion that MBD2_v2 expression is induced by obesity and drives TNBC cell tumorigenicity, and thus provides molecular insights into support of the epidemiological evidence that obesity is a risk factor for TNBC. The majority of TNBC patients are obese and rising obesity rates threaten to further increase the burden of obesity-linked cancers, which reinforces the relevance of this report.

Significance Testing and Multivariate Analysis of Datasets from Surface Plasmon Resonance and Surface Acoustic Wave Biosensors: Prediction and Assay Validation for Surface Binding of Large Analytes.

In this study, we performed uni- and multivariate data analysis on the extended binding curves of several affinity pairs: immobilized acetylcholinesterase (AChE)/bioconjugates of aflatoxin B1(AFB1) and immobilized anti-AFB1 monoclonal antibody/AFB1-protein carriers. The binding curves were recorded on three mass sensitive cells operating in batch configurations: one commercial surface plasmon resonance (SPR) sensor and two custom-made Love wave surface-acoustic wave (LW-SAW) sensors. We obtained 3D plots depicting the time-evolution of the sensor response as a function of analyte concentration using real-time SPR binding sensograms. These "calibration" surfaces exploited the transient periods of the extended kinetic curves, prior to equilibrium, creating a "fingerprint" for each analyte, in considerably shortened time frames compared to the conventional 2D calibration plots. The custom-made SAW sensors operating in different experimental conditions allowed the detection of AFB1-protein carrier in the nanomolar range. Subsequent statistical significance tests were performed on unpaired data sets to validate the custom-made LW-SAW sensors.

Network-Based Approaches for Pathway Level Analysis.

Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.

Exogenous IL-6 induces mRNA splice variant MBD2_v2 to promote stemness in TP53 wild-type, African American PCa cells.

African American men (AAM) are at higher risk of being diagnosed with prostate cancer (PCa) and are at higher risk of dying from the disease compared to European American men (EAM). We sought to better understand PCa molecular diversity that may be underlying these disparities. We performed RNA-sequencing analysis on high-grade PCa to identify genes showing differential tumor versus noncancer adjacent tissue expression patterns unique to AAM or EAM. We observed that interleukin-6 (IL-6) was upregulated in the nonmalignant adjacent tissue in AAM, but in EAM IL-6 expression was higher in PCa tissue. Enrichment analysis identified that genes linked to the function of TP53 were overrepresented and downregulated in PCa tissue from AAM. These RNA-sequencing results informed our subsequent investigation of a diverse PCa cell line panel. We observed that PCa cell lines that are TP53 wild-type, which includes cell lines derived from AAM (MDA-PCa-2b and RC77T), did not express detectable IL-6 mRNA. IL-6 treatment of these cells downregulated wild-type TP53 protein and induced mRNA and protein expression of the epigenetic reader methyl CpG binding domain protein 2 (MBD2), specifically the alternative mRNA splicing variant MBD2_v2. Further investigation validated that upregulation of this short isoform promotes self-renewal and expansion of PCa cancer stem-like cells (CSCs). In conclusion, this report contributes to characterizing gene expression patterns in high-grade PCa and adjacent noncancer tissues from EAM and AAM. The results we describe here advance what is known about the biology associated with PCa race disparities and the molecular signaling of CSCs.

A survey of the approaches for identifying differential methylation using bisulfite sequencing data.

DNA methylation is an important epigenetic mechanism that plays a crucial role in cellular regulatory systems. Recent advancements in sequencing technologies now enable us to generate high-throughput methylation data and to measure methylation up to single-base resolution. This wealth of data does not come without challenges, and one of the key challenges in DNA methylation studies is to identify the significant differences in the methylation levels of the base pairs across distinct biological conditions. Several computational methods have been developed to identify differential methylation using bisulfite sequencing data; however, there is no clear consensus among existing approaches. A comprehensive survey of these approaches would be of great benefit to potential users and researchers to get a complete picture of the available resources. In this article, we present a detailed survey of 22 such approaches focusing on their underlying statistical models, primary features, key advantages and major limitations. Importantly, the intrinsic drawbacks of the approaches pointed out in this survey could potentially be addressed by future research.

Integrating 5hmC and gene expression data to infer regulatory mechanisms.

Motivation: Epigenetic mechanisms are known to play a major role in breast cancer. However, the role of 5-hydroxymethylcytosine (5hmC) remains understudied. We hypothesize that 5hmC mediates redox regulation of gene expression in an aggressive subtype known as triple negative breast cancer (TNBC). To address this, our objective was to highlight genes that may be the target of this process by identifying redox-regulated, antioxidant-sensitive, gene-localized 5hmC changes associated with mRNA changes in TNBC cells. Results: We proceeded to develop an approach to integrate novel Pvu-sequencing and RNA-sequencing data. The result of our approach to merge genome-wide, high-throughput TNBC cell line datasets to identify significant, concordant 5hmC and mRNA changes in response to antioxidant treatment produced a gene set with relevance to cancer stem cell function. Moreover, we have established a method that will be useful for continued research of 5hmC in TNBC cells and tissue samples. Availability and implementation: Data are available at Gene Expression Omnibus (GEO) under accession number GSE103850. Contact: bollig@karmanos.org.

Treating triple negative breast cancer cells with erlotinib plus a select antioxidant overcomes drug resistance by targeting cancer cell heterogeneity.

Among breast cancer patients, those diagnosed with the triple-negative breast cancer (TNBC) subtype have the worst prog-nosis. TNBC does not express estrogen receptor-alpha, progesterone receptor, or the HER2 oncogene; therefore, TNBC lacks targets for molecularly-guided therapies. The concept that EGFR oncogene inhibitor drugs could be used as targeted treatment against TNBC has been put forth based on estimates that 30-60% of TNBC express high levels of EGFR. However, results from clinical trials testing EGFR inhibitors, alone or in combination with cytotoxic chemotherapy, did not improve patient outcomes. Results herein offer an explanation as to why EGFR inhibitors failed TNBC patients and support how combining a select antioxidant and an EGFR-specific small molecule kinase inhibitor (SMKI) could be an effective, novel therapeutic strategy. Treatment with CAT-SKL-a re-engineered protein form of the antioxidant enzyme catalase-inhibited cancer stem-like cells (CSCs), and treatment with the EGFR-specific SMKI erlotinib inhibited non-CSCs. Thus, combining the antioxidant CAT-SKL with erlotinib targeted both CSCs and bulk cancer cells in cultures of EGFR-expressing TNBC-derived cells. We also report evidence that the mechanism for CAT-SKL inhibition of CSCs may depend on antioxidant-induced downregulation of a short alternative mRNA splicing variant of the methyl-CpG binding domain 2 gene, isoform MBD2c.

DANUBE: Data-driven meta-ANalysis using UnBiased Empirical distributions-applied to biological pathway analysis.

Identifying the pathways and mechanisms that are significantly impacted in a given phenotype is challenging. Issues include patient heterogeneity and noise. Many experiments do not have a large enough sample size to achieve the statistical power necessary to identify significantly impacted pathways. Meta-analysis based on combining p-values from individual experiments has been used to improve power. However, all classical meta-analysis approaches work under the assumption that the p-values produced by experiment-level statistical tests follow a uniform distribution under the null hypothesis. Here we show that this assumption does not hold for three mainstream pathway analysis methods, and significant bias is likely to affect many, if not all such meta-analysis studies. We introduce DANUBE, a novel and unbiased approach to combine statistics computed from individual studies. Our framework uses control samples to construct empirical null distributions, from which empirical p-values of individual studies are calculated and combined using either a Central Limit Theorem approach or the additive method. We assess the performance of DANUBE using four different pathway analysis methods. DANUBE is compared with five meta-analysis approaches, as well as with a pathway analysis approach that employs multiple datasets (MetaPath). The 25 approaches have been tested on 16 different datasets related to two human diseases, Alzheimer's disease (7 datasets) and acute myeloid leukemia (9 datasets). We demonstrate that DANUBE overcomes bias in order to consistently identify relevant pathways. We also show how the framework improves results in more general cases, compared to classical meta-analysis performed with common experiment-level statistical tests such as Wilcoxon and t-test.

A novel bi-level meta-analysis approach: applied to biological pathway analysis.

MOTIVATION: The accumulation of high-throughput data in public repositories creates a pressing need for integrative analysis of multiple datasets from independent experiments. However, study heterogeneity, study bias, outliers and the lack of power of available methods present real challenge in integrating genomic data. One practical drawback of many P-value-based meta-analysis methods, including Fisher's, Stouffer's, minP and maxP, is that they are sensitive to outliers. Another drawback is that, because they perform just one statistical test for each individual experiment, they may not fully exploit the potentially large number of samples within each study. RESULTS: We propose a novel bi-level meta-analysis approach that employs the additive method and the Central Limit Theorem within each individual experiment and also across multiple experiments. We prove that the bi-level framework is robust against bias, less sensitive to outliers than other methods, and more sensitive to small changes in signal. For comparative analysis, we demonstrate that the intra-experiment analysis has more power than the equivalent statistical test performed on a single large experiment. For pathway analysis, we compare the proposed framework versus classical meta-analysis approaches (Fisher's, Stouffer's and the additive method) as well as against a dedicated pathway meta-analysis package (MetaPath), using 1252 samples from 21 datasets related to three human diseases, acute myeloid leukemia (9 datasets), type II diabetes (5 datasets) and Alzheimer's disease (7 datasets). Our framework outperforms its competitors to correctly identify pathways relevant to the phenotypes. The framework is sufficiently general to be applied to any type of statistical meta-analysis. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. CONTACT: sorin@wayne.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion.

MOTIVATION: Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies; however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes. RESULTS: Our method highlighted regulatory interactions previously identified in the cell line and a novel finding that the HER2 oncogene, as opposed to the proto-oncogene, upregulates expression of the E2F2 transcription factor. By targeted gene knockdown we show the significance of this, demonstrating that cancer cell-matrix adhesion and outgrowth were markedly inhibited when E2F2 levels were reduced. Thus, validating in this context that upregulation of E2F2 represents a key intermediate event in a HER2 oncogene-directed gene expression-based signaling circuit. This work demonstrates how predictive modeling of longitudinal gene expression data combined with multiple systems-level analyses can be used to accurately predict downstream signaling pathways. Here, our integrated method was applied to reveal insights as to how the HER2 oncogene drives a specific cancer cell phenotype, but it is adaptable to investigate other oncogenes and model systems. AVAILABILITY AND IMPLEMENTATION: Accessibility of various tools is listed in methods; the Log-Gain Stoichiometric Stepwise algorithm is accessible at http://www.cbmc.it/software/Software.php.

Methods and approaches in the topology-based analysis of biological pathways.

The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as "third generation," have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.