Group additive regression models for genomic data analysis

One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group features that are related to the phenotypes. In addition, the prediction mean square errors are also smaller than the component-wise boosting procedure. We demonstrate the application of the methods to pathway-based analysis of microarray gene expression data of breast cancer. Results from analysis of a breast cancer microarray gene expression data set indicate that the pathways of metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer-specific survival.     

Exploring metabolic pathway disruption in the subchronic phencyclidine model of schizophrenia with the Generalized Singular Value Decomposition

Background

One of the major goals in gene and protein expression profiling of cancer is to identify biomarkers and build classification models for prediction of disease prognosis or treatment response. Many traditional statistical methods, based on microarray gene expression data alone and individual genes' discriminatory power, often fail to identify biologically meaningful biomarkers thus resulting in poor prediction performance across data sets. Nonetheless, the variables in multivariable classifiers should synergistically interact to produce more effective classifiers than individual biomarkers.

Results

We developed an integrated approach, namely network-constrained support vector machine (netSVM), for cancer biomarker identification with an improved prediction performance. The netSVM approach is specifically designed for network biomarker identification by integrating gene expression data and protein-protein interaction data. We first evaluated the effectiveness of netSVM using simulation studies, demonstrating its improved performance over state-of-the-art network-based methods and gene-based methods for network biomarker identification. We then applied the netSVM approach to two breast cancer data sets to identify prognostic signatures for prediction of breast cancer metastasis. The experimental results show that: (1) network biomarkers identified by netSVM are highly enriched in biological pathways associated with cancer progression; (2) prediction performance is much improved when tested across different data sets. Specifically, many genes related to apoptosis, cell cycle, and cell proliferation, which are hallmark signatures of breast cancer metastasis, were identified by the netSVM approach. More importantly, several novel hub genes, biologically important with many interactions in PPI network but often showing little change in expression as compared with their downstream genes, were also identified as network biomarkers; the genes were enriched in signaling pathways such as TGF-beta signaling pathway, MAPK signaling pathway, and JAK-STAT signaling pathway. These signaling pathways may provide new insight to the underlying mechanism of breast cancer metastasis.

Conclusions

We have developed a network-based approach for cancer biomarker identification, netSVM, resulting in an improved prediction performance with network biomarkers. We have applied the netSVM approach to breast cancer gene expression data to predict metastasis in patients. Network biomarkers identified by netSVM reveal potential signaling pathways associated with breast cancer metastasis, and help improve the prediction performance across independent data sets.     

乳腺癌转移相关基因表达调控网络的线性微分方程模型的初步研究

初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,并分析模型的可靠性和生物学意义. 采用基因芯片技术,分别对30例伴有淋巴结转移的乳腺癌组织及其相应淋巴结转移癌组织进行基因表达谱的比较,选择差异基因通过线性微分数学方法构建表达调控网络模型. 差异表达基因共27个,其中Ratio > 3的明显上调基因14个,而Ratio < 0.33的明显下调基因13个. 比较伴有淋巴结转移的乳腺癌组织和其相应淋巴结转移癌组织,分析筛选了27个表达差异基因,应用数学线性微分方程方法初步构建乳腺癌转移相关基因表达调控网络的线性微分方程模型,通过分析模型中重要节点、通路的生物学意义,判定网络的数学特性,初步表明,调控网络的可靠性和乳腺癌转移的形成是与多基因、多通路异常引起的细胞恶性转化相关.     

Association of Protein Translation and Extracellular Matrix Gene Sets with Breast Cancer Metastasis: Findings Uncovered on Analysis of Multiple Publicly Available Datasets Using Individual Patient Data Approach

Introduction

Microarray analysis has revolutionized the role of genomic prognostication in breast cancer. However, most studies are single series studies, and suffer from methodological problems. We sought to use a meta-analytic approach in combining multiple publicly available datasets, while correcting for batch effects, to reach a more robust oncogenomic analysis.

Aim

The aim of the present study was to find gene sets associated with distant metastasis free survival (DMFS) in systemically untreated, node-negative breast cancer patients, from publicly available genomic microarray datasets.

Methods

Four microarray series (having 742 patients) were selected after a systematic search and combined. Cox regression for each gene was done for the combined dataset (univariate, as well as multivariate – adjusted for expression of Cell cycle related genes) and for the 4 major molecular subtypes. The centre and microarray batch effects were adjusted by including them as random effects variables. The Cox regression coefficients for each analysis were then ranked and subjected to a Gene Set Enrichment Analysis (GSEA).

Results

Gene sets representing protein translation were independently negatively associated with metastasis in the Luminal A and Luminal B subtypes, but positively associated with metastasis in Basal tumors. Proteinaceous extracellular matrix (ECM) gene set expression was positively associated with metastasis, after adjustment for expression of cell cycle related genes on the combined dataset. Finally, the positive association of the proliferation-related genes with metastases was confirmed.

Conclusion

To the best of our knowledge, the results depicting mixed prognostic significance of protein translation in breast cancer subtypes are being reported for the first time. We attribute this to our study combining multiple series and performing a more robust meta-analytic Cox regression modeling on the combined dataset, thus discovering ''hidden'' associations. This methodology seems to yield new and interesting results and may be used as a tool to guide new research.     

miR-139-5p is a regulator of metastatic pathways in breast cancer

Metastasis is a complex, multistep process involved in the progression of cancer from a localized primary tissue to distant sites, often characteristic of the more aggressive forms of this disease. Despite being studied in great detail in recent years, the mechanisms that govern this process remain poorly understood. In this study, we identify a novel role for miR-139-5p in the inhibition of breast cancer progression. We highlight its clinical relevance by reviewing miR-139-5p expression across a wide variety of breast cancer subtypes using in-house generated and online data sets to show that it is most frequently lost in invasive tumors. A biotin pull-down approach was then used to identify the mRNA targets of miR-139-5p in the breast cancer cell line MCF7. Functional enrichment analysis of the pulled-down targets showed significant enrichment of genes in pathways previously implicated in breast cancer metastasis (P < 0.05). Further bioinformatic analysis revealed a predicted disruption to the TGFβ, Wnt, Rho, and MAPK/PI3K signaling cascades, implying a potential role for miR-139-5p in regulating the ability of cells to invade and migrate. To corroborate this finding, using the MDA-MB-231 breast cancer cell line, we show that overexpression of miR-139-5p results in suppression of these cellular phenotypes. Furthermore, we validate the interaction between miR-139-5p and predicted targets involved in these pathways. Collectively, these results suggest a significant functional role for miR-139-5p in breast cancer cell motility and invasion and its potential to be used as a prognostic marker for the aggressive forms of breast cancer.     

Integrated Genomic and Epigenomic Analysis of Breast Cancer Brain Metastasis

The brain is a common site of metastatic disease in patients with breast cancer, which has few therapeutic options and dismal outcomes. The purpose of our study was to identify common and rare events that underlie breast cancer brain metastasis. We performed deep genomic profiling, which integrated gene copy number, gene expression and DNA methylation datasets on a collection of breast brain metastases. We identified frequent large chromosomal gains in 1q, 5p, 8q, 11q, and 20q and frequent broad-level deletions involving 8p, 17p, 21p and Xq. Frequently amplified and overexpressed genes included ATAD2, BRAF, DERL1, DNMTRB and NEK2A. The ATM, CRYAB and HSPB2 genes were commonly deleted and underexpressed. Knowledge mining revealed enrichment in cell cycle and G2/M transition pathways, which contained AURKA, AURKB and FOXM1. Using the PAM50 breast cancer intrinsic classifier, Luminal B, Her2+/ER negative, and basal-like tumors were identified as the most commonly represented breast cancer subtypes in our brain metastasis cohort. While overall methylation levels were increased in breast cancer brain metastasis, basal-like brain metastases were associated with significantly lower levels of methylation. Integrating DNA methylation data with gene expression revealed defects in cell migration and adhesion due to hypermethylation and downregulation of PENK, EDN3, and ITGAM. Hypomethylation and upregulation of KRT8 likely affects adhesion and permeability. Genomic and epigenomic profiling of breast brain metastasis has provided insight into the somatic events underlying this disease, which have potential in forming the basis of future therapeutic strategies.     

Pathway-based classification of cancer subtypes

ABSTRACT: BACKGROUND: Molecular markers based on gene expression profiles have been used in experimental and clinical settings to distinguish cancerous tumors in stage, grade, survival time, metastasis, and drug sensitivity. However, most significant gene markers are unstable (not reproducible) among data sets. We introduce a standardized method for representing cancer markers as 2-level hierarchical feature vectors, with a basic gene level as well as a second level of (more stable) pathway markers, for the purpose of discriminating cancer subtypes. This extends standard gene expression arrays with new pathway-level activation features obtained directly from off-the-shelf gene set enrichment algorithms such as GSEA. Such so-called pathway-based expression arrays are significantly more reproducible across datasets. Such reproducibility will be important for clinical usefulness of genomic markers, and augment currently accepted cancer classification protocols. RESULTS: The present method produced more stable (reproducible) pathway-based markers for discriminating breast cancer metastasis and ovarian cancer survival time. Between two datasets for breast cancer metastasis, the intersection of standard significant gene biomarkers totaled 7.47% of selected genes, compared to 17.65% using pathway-based markers; the corresponding percentages for ovarian cancer datasets were 20.65% and 33.33% respectively. Three pathways, consisting of Type_1_diabetes mellitus, Cytokine-cytokine_receptor_interaction and Hedgehog_signaling (all previously implicated in cancer), are enriched in both the ovarian long survival and breast non-metastasis groups. In addition, integrating pathway and gene information, we identified five (ID4, ANXA4, CXCL9, MYLK, FBXL7) and six (SQLE, E2F1, PTTG1, TSTA3, BUB1B, MAD2L1) known cancer genes significant for ovarian and breast cancer respectively. CONCLUSIONS: Standardizing the analysis of genomic data in the process of cancer staging, classification and analysis is important as it has implications for both pre-clinical as well as clinical studies. The paradigm of diagnosis and prediction using pathway-based biomarkers as features can be an important part of the process of biomarker-based cancer analysis, and the resulting canonical (clinically reproducible) biomarkers can be important in standardizing genomic data. We expect that identification of such canonical biomarkers will improve clinical utility of high-throughput datasets for diagnostic and prognostic applications. Reviewers This article was reviewed by John McDonald (nominated by I. King Jordon), Eugene Koonin, Nathan Bowen (nominated by I, King Jordon), and Ekaterina Kotelnikova (nominated by Mikhail Gelfand).     

A taxonomy of organ-specific breast cancer metastases based on a protein-protein interaction network

We carried out a systems-level study of the mechanisms underlying organ-specific metastases of breast cancer. We followed a network-based approach using microarray expression data from human breast cancer metastases to select organ-specific proteins that exert a range of functions allowing cell survival and growth in the microenvironment of distant organs. MinerProt, a home-made software application, was used to group organ-specific signatures of brain (1191 genes), bone (1623 genes), liver (977 genes) and lung (254 genes) metastases by function and select the most differentially expressed gene in each function. As a result, we obtained 19 functional representative proteins in brain, 23 in bone, 15 in liver and 9 in lung, with which we constructed four organ-specific protein-protein interaction networks. The network taxonomy included seven proteins that interacted in brain metastasis, which were mainly associated with signal transduction. Proteins related to immune response functions were bone specific, while those involved in proteolysis, signal transduction and hepatic glucose metabolism were found in liver metastasis. No experimental protein-protein interaction was found in lung metastasis; thus, computationally determined interactions were included in this network. Moreover, three of these selected genes (CXCL12, DSC2 and TFDP2) were associated with progression to specific organs when tested in an independent dataset. In conclusion, we present a network-based approach to filter information by selecting key protein functions as metastatic markers or therapeutic targets.     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.     

Identification of novel metastasis suppressor signaling pathways for breast cancer

Cancer lethality is mainly caused by metastasis. Therefore, understanding the nature of the genes involved in this process has become a priority. Given the heterogeneity of mutations in cancer cells, considerable focus has been directed toward characterizing metastasis genes in the context of relevant signaling pathways rather than treating genes as independent and equal entities. One signaling cascade implicated in the regulation of cell growth, invasion and metastasis is the MAP kinase pathway. Raf kinase inhibitory protein (RKIP) functions as an inhibitor of the MAP kinase pathway and is a metastasis suppressor in different cancer models. By utilizing statistical analysis of clinical data integrated with experimental validation, we recently identified components of the RKIP signaling pathway relevant to breast cancer metastasis. Using the RKIP pathway as an example, we show how prior biological knowledge can be efficiently combined with genome-wide patient data to identify gene regulatory mechanisms that control metastasis.     

Primary Tumor and MEF Cell Isolation to Study Lung Metastasis

In breast tumorigenesis, the metastatic stage of the disease poses the greatest threat to the affected individual. Normal breast cells with altered genotypes now possess the ability to invade and survive in other tissues. In this protocol, mouse mammary tumors are removed and primary cells are prepared from tumors. The cells isolated from this procedure are then available for gene profiling experiments. For successful metastasis, these cells must be able to intravasate, survive in circulation, extravasate to distant organs, and survive in that new organ system. The lungs are the typical target of breast cancer metastasis. A set of genes have been discovered that mediates the selectivity of metastasis to the lung. Here we describe a method of studying lung metastasis from a genetically engineered mouse model.. Furthermore, another protocol for analyzing mouse embryonic fibroblasts (MEFs) from the mouse embryo is included. MEF cells from the same animal type provide a clue of non-cancer cell gene expression. Together, these techniques are useful in studying mouse mammary tumorigenesis, its associated signaling mechanisms and pathways of the abnormalities in embryos.     

Identifying gene pathways associated with cancer characteristics via sparse statistical methods

We propose a statistical method for uncovering gene pathways that characterize cancer heterogeneity. To incorporate knowledge of the pathways into the model, we define a set of activities of pathways from microarray gene expression data based on the Sparse Probabilistic Principal Component Analysis (SPPCA). A pathway activity logistic regression model is then formulated for cancer phenotype. To select pathway activities related to binary cancer phenotypes, we use the elastic net for the parameter estimation and derive a model selection criterion for selecting tuning parameters included in the model estimation. Our proposed method can also reverse-engineer gene networks based on the identified multiple pathways that enables us to discover novel gene-gene associations relating with the cancer phenotypes. We illustrate the whole process of the proposed method through the analysis of breast cancer gene expression data.     

U-statistics in genetic association studies

Many common human diseases are complex and are expected to be highly heterogeneous, with multiple causative loci and multiple rare and common variants at some of the causative loci contributing to the risk of these diseases. Data from the genome-wide association studies (GWAS) and metadata such as known gene functions and pathways provide the possibility of identifying genetic variants, genes and pathways that are associated with complex phenotypes. Single-marker-based tests have been very successful in identifying thousands of genetic variants for hundreds of complex phenotypes. However, these variants only explain very small percentages of the heritabilities. To account for the locus- and allelic-heterogeneity, gene-based and pathway-based tests can be very useful in the next stage of the analysis of GWAS data. U-statistics, which summarize the genomic similarity between pair of individuals and link the genomic similarity to phenotype similarity, have proved to be very useful for testing the associations between a set of single nucleotide polymorphisms and the phenotypes. Compared to single marker analysis, the advantages afforded by the U-statistics-based methods is large when the number of markers involved is large. We review several formulations of U-statistics in genetic association studies and point out the links of these statistics with other similarity-based tests of genetic association. Finally, potential application of U-statistics in analysis of the next-generation sequencing data and rare variants association studies are discussed.     

C2ORF40 suppresses breast cancer cell proliferation and invasion through modulating expression of M phase cell cycle genes

Recently, it has been suggested that C2ORF40 is a candidate tumor suppressor gene in breast cancer. However, the mechanism for reduced expression of C2ORF40 and its functional role in breast cancers remain unclear. Here we show that C2ORF40 is frequently silenced in human primary breast cancers and cell lines through promoter hypermethylation. C2ORF40 mRNA level is significantly associated with patient disease-free survival and distant cancer metastasis. Overexpression of C2ORF40 inhibits breast cancer cell proliferation, migration and invasion. By contrast, silencing C2ORF40 expression promotes these biological phenotypes. Bioinformatics and FACS analysis reveal C2ORF40 functions at G2/M phase by downregulation of mitotic genes expression, including UBE2C. Our results suggest that C2ORF40 acts as a tumor suppressor gene in breast cancer pathogenesis and progression and is a candidate prognostic marker for this disease.     

MicroRNA-224 targets RKIP to control cell invasion and expression of metastasis genes in human breast cancer cells

The Raf kinase inhibitor protein (RKIP) is a tumor suppressor that protects against metastasis and genomic instability. RKIP is downregulated in many types of tumors, although the mechanism for this remains unknown. MicroRNAs silence target genes via translational inhibition or target mRNA degradation, and are thus important regulators of gene expression. In the current study, we found that miR-224 expression is significantly upregulated in breast cancer cell lines, and especially in highly invasive MDA-MB-231 cells, compared to human normal breast epithelial cells. In addition, miR-224 inhibits RKIP gene expression by directly targeting its 3'-untranslated region (3'-UTR). Moreover, metastasis, as assayed by Transwell migration, 3D growth in Matrigel, and wound healing, was enhanced by ectopic expression of miR-224 and inhibited by miR-224 downregulation. Promotion of metastasis in response to miR-224 downregulation was associated with derepression of the stroma-associated RKIP target genes, CXCR4, MMP1, and OPN, which are involved in breast tumor metastasis to the bone. Taken together, our data indicate that miR-224 play an important role in metastasis of human breast cancer cells to the bone by directly suppressing the RKIP tumor suppressor.