Cristina Mitrea

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.

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.

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.

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 Fishers, Stouffers, 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 (Fishers, Stouffers 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 Alzheimers 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.

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 to 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, Alzheimers 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 survey of the approaches for identifying differential methylation using bisulfite sequencing data

&NA; 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.

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.

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.

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.