Proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this "guilt-by-association" (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies

Mapping genomic and transcriptomic alterations spatially in epithelial cells adjacent to human breast carcinoma.

Almost all genomic studies of breast cancer have focused on well-established tumours because it is technically challenging to study the earliest mutational events occurring in human breast epithelial cells. To address this we created a unique dataset of epithelial samples ductoscopically obtained from ducts leading to breast carcinomas and matched samples from ducts on the opposite side of the nipple. Here, we demonstrate that perturbations in mRNA abundance, with increasing proximity to tumour, cannot be explained by copy number aberrations. Rather, we find a possibility of field cancerization surrounding the primary tumour by constructing a classifier that evaluates where epithelial samples were obtained relative to a tumour (cross-validated micro-averaged AUC = 0.74). We implement a spectral co-clustering algorithm to define biclusters. Relating to over-represented bicluster pathways, we further validate two genes with tissue microarrays and in vitro experiments. We highlight evidence suggesting that bicluster perturbation occurs early in tumour development

Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor

<p>Abstract</p> <p>Background</p> <p>Elucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways.</p> <p>Results</p> <p>We propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function.</p> <p>Conclusions</p> <p>Understanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.</p

A Densely Interconnected Genome-Wide Network of MicroRNAs and Oncogenic Pathways Revealed Using Gene Expression Signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA–pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA–pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA–pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes (“hubs”), most nodes in the miRNA–pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA–pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available

Attractor Metafeatures and Their Application in Biomolecular Data Analysis

This dissertation proposes a family of algorithms for deriving signatures of mutually associated features, to which we refer as attractor metafeatures, or simply attractors. Specifically, we present multi-cancer attractor derivation algorithms, identifying correlated features in signatures from multiple biological data sets in one analysis, as well as the groups of samples or cells that exclusively express these signatures. Our results demonstrate that these signatures can be used, in proper combinations, as biomarkers that predict a patient’s survival rate, based on the transcriptome of the tumor sample. They can also be used as features to analyze the composition of the tumor. Through analyzing large data sets of 18 cancer types and three high-throughput platforms from The Cancer Genome Atlas (TCGA) PanCanAtlas Project and multiple single-cell RNA-seq data sets, we identified novel cancer attractor signatures and elucidated the identity of the cells that express these signatures. Using these signatures, we developed a prognostic biomarker for breast cancer called the Breast Cancer Attractor Metagenes (BCAM) biomarker as well as a software platform to analyze the tumor sample, called Analysis of the Single-Cell Omics for Tumor (ASCOT)

Weighted gene co-expression network analysis of colorectal patients to identify right drug-right target for potent efficacy of targeted therapy

Indiana University-Purdue University Indianapolis (IUPUI)Colon rectal cancer (CRC) is one of the most common cancers worldwide. It is characterized by the successive accumulation of mutations in genes controlling epithelial cell growth and differentiation leading to genomic in-stability. This results in the activation of proto-oncogene(K-ras), loss of tumor suppressor gene activity and ab-normality in DNA repair genes. Targeted therapy is a new generation of cancer treatment in which drugs attack targets which are specific for the cancer cell and are critical for its survival or for its malignant behavior. Survival of metastatic CRC patients has approximately doubled due to the development of new combinations of stan-dard chemotherapy, and the innovative targeted therapies, such as monoclonal antibodies against epidermal growth factor receptor (EGFR) or monoclonal antibodies against vascular endothelial growth factor (VEGFR).The study is to exhibit the need for right drug-right target and provides a proof of principle for potent efficacy of molecular targeted therapy for CRC. We have performed the weighted gene co-expression network analysis for three different patient cohort treated with different targeted therapy drugs. The results demonstrates the variation across different treatment regime in context of transcription factor networks. New significant tran-scription factors have been identified as potential biomarker for CRC cancer including EP300, STAT6, ATF3, ELK1, HNF4A, JUN, TAF1, IRF1, TP53, ELF1 and YY1. The results provides guidance for future omic study on CRC and additional validation work for potent biomarker for CRC

Statistical approaches to harness high throughput sequencing data in diverse biological systems

The development of novel statistical approaches to questions specific to biological systems of interest is becoming more valuable as we tackle increasingly complex problems. This thesis explores three distinct biological systems in which high throughput sequencing data is utilised, varying in research area, organism, number of sequencing platforms and datasets integrated, and structure such as matched samples; showcasing the variety of study designs and thus the need for tailored statistical approaches. First, we characterise allelic imbalance from RNA-Seq data including stringent filtering criteria and a count based likelihood ratio test. This work identified genes of particular importance in livestock genomics such as those related to energy use. Second, we outline a novel methodology to identify highly expressed genes and cells for single cell RNA-Seq data. We derive a gamma-normal mixture model to identify lowly and highly expressed components, and use this to identify novel markers for olfactory sensory neuron (OSN) maturity across publicly available mouse neuron datasets. In addition we estimate single cell networks and find that mature OSN single cell networks are more centralised than immature OSN single cell networks. Third, we develop two novel frameworks for relating information from Whole Exome DNA-Seq and RNA-Seq data when i) samples are matched and when ii) samples are not necessary matched between platforms. In the latter case, we relate functional somatic mutation driver gene scores to transcriptional network correlation disturbance using a permutation testing framework, identifying potential candidate genes for targeted therapies. In the former case, we estimate directed mutation-expression networks for each cancer using linear models, providing a useful exploratory tool for identifying novel relationships among genes. This thesis demonstrates the importance of tailored statistical approaches to further understanding across many biological systems