A modular master regulator landscape controls cancer transcriptional identity.

Despite considerable efforts, the mechanisms linking genomic alterations to the transcriptional identity of cancer cells remain elusive. Integrative genomic analysis, using a network-based approach, identified 407 master regulator (MR) proteins responsible for canalizing the genetics of individual samples from 20 cohorts in The Cancer Genome Atlas (TCGA) into 112 transcriptionally distinct tumor subtypes. MR proteins could be further organized into 24 pan-cancer, master regulator block modules (MRBs), each regulating key cancer hallmarks and predictive of patient outcome in multiple cohorts. Of all somatic alterations detected in each individual sample, >50% were predicted to induce aberrant MR activity, yielding insight into mechanisms linking tumor genetics and transcriptional identity and establishing non-oncogene dependencies. Genetic and pharmacological validation assays confirmed the predicted effect of upstream mutations and MR activity on downstream cellular identity and phenotype. Thus, co-analysis of mutational and gene expression profiles identified elusive subtypes and provided testable hypothesis for mechanisms mediating the effect of genetic alterations.

Advances in integrative African genomics.

There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine.

Integrative genomic analyses reveal putative cell type-specific targets of the Drosophila ets transcription factor Pointed.

The Ets domain transcription factors direct diverse biological processes throughout all metazoans and are implicated in development as well as in tumor initiation, progression and metastasis. The Drosophila Ets transcription factor Pointed (Pnt) is the downstream effector of the Epidermal growth factor receptor (Egfr) pathway and is required for cell cycle progression, specification, and differentiation of most cell types in the larval eye disc. Despite its critical role in development, very few targets of Pnt have been reported previously. Here, we employed an integrated approach by combining genome-wide single cell and bulk data to identify putative cell type-specific Pnt targets. First, we used chromatin immunoprecipitation with high-throughput sequencing (ChIP-seq) to determine the genome-wide occupancy of Pnt in late larval eye discs. We identified enriched regions that mapped to an average of 6,941 genes, the vast majority of which are novel putative Pnt targets. Next, we integrated ChIP-seq data with two other larval eye single cell genomics datasets (scRNA-seq and snATAC-seq) to reveal 157 putative cell type-specific Pnt targets that may help mediate unique cell type responses upon Egfr-induced differentiation. Finally, our integrated data also predicts cell type-specific functional enhancers that were not reported previously. Together, our study provides a greatly expanded list of putative cell type-specific Pnt targets in the eye and is a resource for future studies that will allow mechanistic insights into complex developmental processes regulated by Egfr signaling.

Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics.

Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.

Genomic correlates of clinical outcome in advanced prostate cancer.

Heterogeneity in the genomic landscape of metastatic prostate cancer has become apparent through several comprehensive profiling efforts, but little is known about the impact of this heterogeneity on clinical outcome. Here, we report comprehensive genomic and transcriptomic analysis of 429 patients with metastatic castration-resistant prostate cancer (mCRPC) linked with longitudinal clinical outcomes, integrating findings from whole-exome, transcriptome, and histologic analysis. For 128 patients treated with a first-line next-generation androgen receptor signaling inhibitor (ARSI; abiraterone or enzalutamide), we examined the association of 18 recurrent DNA- and RNA-based genomic alterations, including androgen receptor (AR) variant expression, AR transcriptional output, and neuroendocrine expression signatures, with clinical outcomes. Of these, only RB1 alteration was significantly associated with poor survival, whereas alterations in RB1, AR, and TP53 were associated with shorter time on treatment with an ARSI. This large analysis integrating mCRPC genomics with histology and clinical outcomes identifies RB1 genomic alteration as a potent predictor of poor outcome, and is a community resource for further interrogation of clinical and molecular associations.

Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis.

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Integrative genomics of the mammalian alveolar macrophage response to intracellular mycobacteria.

BACKGROUND: Bovine TB (bTB), caused by infection with Mycobacterium bovis, is a major endemic disease affecting global cattle production. The key innate immune cell that first encounters the pathogen is the alveolar macrophage, previously shown to be substantially reprogrammed during intracellular infection by the pathogen. Here we use differential expression, and correlation- and interaction-based network approaches to analyse the host response to infection with M. bovis at the transcriptome level to identify core infection response pathways and gene modules. These outputs were then integrated with genome-wide association study (GWAS) data sets to enhance detection of genomic variants for susceptibility/resistance to M. bovis infection. RESULTS: The host gene expression data consisted of RNA-seq data from bovine alveolar macrophages (bAM) infected with M. bovis at 24 and 48 h post-infection (hpi) compared to non-infected control bAM. These RNA-seq data were analysed using three distinct computational pipelines to produce six separate gene sets: 1) DE genes filtered using stringent fold-change and P-value thresholds (DEG-24: 378 genes, DEG-48: 390 genes); 2) genes obtained from expression correlation networks (CON-24: 460 genes, CON-48: 416 genes); and 3) genes obtained from differential expression networks (DEN-24: 339 genes, DEN-48: 495 genes). These six gene sets were integrated with three bTB breed GWAS data sets by employing a new genomics data integration tool-gwinteR. Using GWAS summary statistics, this methodology enabled detection of 36, 102 and 921 prioritised SNPs for Charolais, Limousin and Holstein-Friesian, respectively. CONCLUSIONS: The results from the three parallel analyses showed that the three computational approaches could identify genes significantly enriched for SNPs associated with susceptibility/resistance to M. bovis infection. Results indicate distinct and significant overlap in SNP discovery, demonstrating that network-based integration of biologically relevant transcriptomics data can leverage substantial additional information from GWAS data sets. These analyses also demonstrated significant differences among breeds, with the Holstein-Friesian breed GWAS proving most useful for prioritising SNPS through data integration. Because the functional genomics data were generated using bAM from this population, this suggests that the genomic architecture of bTB resilience traits may be more breed-specific than previously assumed.

PathwayPCA: an R/Bioconductor Package for Pathway Based Integrative Analysis of Multi-Omics Data.

The authors present pathwayPCA, an R/Bioconductor package for integrative pathway analysis that utilizes modern statistical methodology, including supervised and adaptive, elastic-net, sparse principal component analysis. pathwayPCA can be applied to continuous, binary, and survival outcomes in studies with multiple covariates and/or interaction effects. It outperforms several alternative methods at identifying disease-associated pathways in integrative analysis using both simulated and real datasets. In addition, several case studies are provided to illustrate pathwayPCA analysis with gene selection, estimating, and visualizing sample-specific pathway activities, identifying sex-specific pathway effects in kidney cancer, and building integrative models for predicting patient prognosis. pathwayPCA is an open-source R package, freely available through the Bioconductor repository. pathwayPCA is expected to be a useful tool for empowering the wider scientific community to analyze and interpret the wealth of available proteomics data, along with other types of molecular data recently made available by Clinical Proteomic Tumor Analysis Consortium and other large consortiums.

Integrative analysis of rare copy number variants and gene expression data in alopecia areata implicates an aetiological role for autophagy.

Alopecia areata (AA) is a highly prevalent autoimmune disease that attacks the hair follicle and leads to hair loss that can range from small patches to complete loss of scalp and body hair. Our previous linkage and genome-wide association studies (GWAS) generated strong evidence for aetiological contributions from inherited genetic variants at different population frequencies, including both rare mutations and common polymorphisms. Additionally, we conducted gene expression (GE) studies on scalp biopsies of 96 patients and controls to establish signatures of active disease. In this study, we performed an integrative analysis on these two datasets to test the hypothesis that rare CNVs in patients with AA could be leveraged to identify drivers of disease in our AA GE signatures. We analysed copy number variants (CNVs) in a case-control cohort of 673 patients with AA and 16 311 controls independent of the case-control cohort of 96 research participants used in our GE study. Using an integrative computational analysis, we identified 14 genes whose expression levels were altered by CNVs in a consistent direction of effect, corresponding to gene expression changes in lesional skin of patients. Four of these genes were affected by CNVs in three or more unrelated patients with AA, including ATG4B and SMARCA2, which are involved in autophagy and chromatin remodelling, respectively. Our findings identified new classes of genes with potential contributions to AA pathogenesis.

GPS: Identification of disease genes by rank aggregation of multi-genomic scoring schemes.

In solving the gene prioritization problem, ranking candidate genes from most to least promising is attempted before further experimental validation. Integrating the results of various data sources and methods tends to result in a better performance when solving the gene prioritization problem. Therefore, a wide range of datasets and algorithms was investigated; these included topological features of protein networks, physicochemical characteristics and blast similarity scores of protein sequences, gene ontology, biological pathways, and tissue-based data sources. The novelty of this study lies in how the best-performing methods and reliable multi-genomic data sources were applied in an efficient two-step approach. In the first step, various multi-genomic data sources and algorithms were evaluated and seven best-performing rankers were then applied to prioritize candidate genes in different ways. In the second step, global prioritization was obtained by aggregating several scoring schemes. The results showed that protein networks, functional linkage networks, gene ontology, and biological pathway data sources have a significant impact on the quality of the gene prioritization approach. The findings also demonstrated a direct relationship between the degree of genes and the ranking quality of the evaluated tools. This approach outperformed previously published algorithms (e.g., DIR, GPEC, GeneDistiller, and Endeavour) in all evaluation metrices and led to the development of GPS software. Its user-friendly interface and accuracy makes GPS a powerful tool for the identification of human disease genes. GPS is available at http://gpsranker.com and http://LBB.ut.ac.ir.

Integrative Genomics Analysis Identifies ACVR1B as a Candidate Causal Gene of Emphysema Distribution.

Genome-wide association studies (GWAS) have identified multiple associations with emphysema apicobasal distribution (EABD), but the biological functions of these variants are unknown. To characterize the functions of EABD-associated variants, we integrated GWAS results with 1) expression quantitative trait loci (eQTL) from the Genotype Tissue Expression (GTEx) project and subjects in the COPDGene (Genetic Epidemiology of COPD) study and 2) cell type epigenomic marks from the Roadmap Epigenomics project. On the basis of these analyses, we selected a variant near ACVR1B (activin A receptor type 1B) for functional validation. SNPs from 168 loci with P values less than 5 × 10-5 in the largest GWAS meta-analysis of EABD were analyzed. Eighty-four loci overlapped eQTL, with 12 of these loci showing greater than 80% likelihood of harboring a single, shared GWAS and eQTL causal variant. Seventeen cell types were enriched for overlap between EABD loci and Roadmap Epigenomics marks (permutation P < 0.05), with the strongest enrichment observed in CD4+, CD8+, and regulatory T cells. We selected a putative causal variant, rs7962469, associated with ACVR1B expression in lung tissue for additional functional investigation, and reporter assays confirmed allele-specific regulatory activity for this variant in human bronchial epithelial and Jurkat immune cell lines. ACVR1B expression levels exhibit a nominally significant association with emphysema distribution. EABD-associated loci are preferentially enriched in regulatory elements of multiple cell types, most notably T-cell subsets. Multiple EABD loci colocalize to regulatory elements that are active across multiple tissues and cell types, and functional analyses confirm the presence of an EABD-associated functional variant that regulates ACVR1B expression, indicating that transforming growth factor-ß signaling plays a role in the EABD phenotype. Clinical trial registered with www.clinicaltrials.gov (NCT00608764).

Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic obstructive pulmonary disease (COPD). However, many genetic variants show suggestive evidence for association but do not meet the strict threshold for genome-wide significance. Integrative analysis of multiple omics datasets has the potential to identify novel genes involved in disease pathogenesis by leveraging these variants in a functional, regulatory context. RESULTS: We performed expression quantitative trait locus (eQTL) analysis using genome-wide SNP genotyping and gene expression profiling of lung tissue samples from 86 COPD cases and 31 controls, testing for SNPs associated with gene expression levels. These results were integrated with a prior COPD GWAS using an ensemble statistical and network methods approach to identify relevant genes and observe them in the context of overall genetic control of gene expression to highlight co-regulated genes and disease pathways. We identified 250,312 unique SNPs and 4997 genes in the cis(local)-eQTL analysis (5% false discovery rate). The top gene from the integrative analysis was MAPT, a gene recently identified in an independent GWAS of lung function. The genes HNRNPAB and PCBP2 with RNA binding activity and the gene ACVR1B were identified in network communities with validated disease relevance. CONCLUSIONS: The integration of lung tissue gene expression with genome-wide SNP genotyping and subsequent intersection with prior GWAS and omics studies highlighted candidate genes within COPD loci and in communities harboring known COPD genes. This integration also identified novel disease genes in sub-threshold regions that would otherwise have been missed through GWAS.

RPPA: Origins, Transition to a Validated Clinical Research Tool, and Next Generations of the Technology.

RPPA technology has graduated from a research tool to an essential component of clinical drug discovery research and personalized medicine. Next generations of RPPA technology will be a single clinical instrument that integrates all the steps of the workflow.

Genetic-Epigenetic Interactions in Asthma Revealed by a Genome-Wide Gene-Centric Search.

OBJECTIVES: There is evidence to suggest that asthma pathogenesis is affected by both genetic and epigenetic variation independently, and there is some evidence to suggest that genetic-epigenetic interactions affect risk of asthma. However, little research has been done to identify such interactions on a genome-wide scale. The aim of this studies was to identify genes with genetic-epigenetic interactions associated with asthma. METHODS: Using asthma case-control data, we applied a novel nonparametric gene-centric approach to test for interactions between multiple SNPs and CpG sites simultaneously in the vicinities of 18,178 genes across the genome. RESULTS: Twelve genes, PF4, ATF3, TPRA1, HOPX, SCARNA18, STC1, OR10K1, UPK1B, LOC101928523, LHX6, CHMP4B, and LANCL1, exhibited statistically significant SNP-CpG interactions (false discovery rate = 0.05). Of these, three have previously been implicated in asthma risk (PF4, ATF3, and TPRA1). Follow-up analysis revealed statistically significant pairwise SNP-CpG interactions for several of these genes, including SCARNA18, LHX6, and LOC101928523 (p = 1.33E-04, 8.21E-04, 1.11E-03, respectively). CONCLUSIONS: Joint effects of genetic and epigenetic variation may play an important role in asthma pathogenesis. Statistical methods that simultaneously account for multiple variations across chromosomal regions may be needed to detect these types of effects on a genome-wide scale.

RRAD, IL4I1, CDKN1A, and SERPINE1 genes are potentially co-regulated by NF-κB and p53 transcription factors in cells exposed to high doses of ionizing radiation.

BACKGROUND: The cellular response to ionizing radiation involves activation of p53-dependent pathways and activation of the atypical NF-κB pathway. The crosstalk between these two transcriptional networks include (co)regulation of common gene targets. Here we looked for novel genes potentially (co)regulated by p53 and NF-κB using integrative genomics screening in human osteosarcoma U2-OS cells irradiated with a high dose (4 and 10 Gy). Radiation-induced expression in cells with silenced TP53 or RELA (coding the p65 NF-κB subunit) genes was analyzed by RNA-Seq while radiation-enhanced binding of p53 and RelA in putative regulatory regions was analyzed by ChIP-Seq, then selected candidates were validated by qPCR. RESULTS: We identified a subset of radiation-modulated genes whose expression was affected by silencing of both TP53 and RELA, and a subset of radiation-upregulated genes where radiation stimulated binding of both p53 and RelA. For three genes, namely IL4I1, SERPINE1, and CDKN1A, an antagonistic effect of the TP53 and RELA silencing was consistent with radiation-enhanced binding of both p53 and RelA. This suggested the possibility of a direct antagonistic (co)regulation by both factors: activation by NF-κB and inhibition by p53 of IL4I1, and activation by p53 and inhibition by NF-κB of CDKN1A and SERPINE1. On the other hand, radiation-enhanced binding of both p53 and RelA was observed in a putative regulatory region of the RRAD gene whose expression was downregulated both by TP53 and RELA silencing, which suggested a possibility of direct (co)activation by both factors. CONCLUSIONS: Four new candidates for genes directly co-regulated by NF-κB and p53 were revealed.

Dimension reduction techniques for the integrative analysis of multi-omics data.

State-of-the-art next-generation sequencing, transcriptomics, proteomics and other high-throughput 'omics' technologies enable the efficient generation of large experimental data sets. These data may yield unprecedented knowledge about molecular pathways in cells and their role in disease. Dimension reduction approaches have been widely used in exploratory analysis of single omics data sets. This review will focus on dimension reduction approaches for simultaneous exploratory analyses of multiple data sets. These methods extract the linear relationships that best explain the correlated structure across data sets, the variability both within and between variables (or observations) and may highlight data issues such as batch effects or outliers. We explore dimension reduction techniques as one of the emerging approaches for data integration, and how these can be applied to increase our understanding of biological systems in normal physiological function and disease.

Public data and open source tools for multi-assay genomic investigation of disease.

Molecular interrogation of a biological sample through DNA sequencing, RNA and microRNA profiling, proteomics and other assays, has the potential to provide a systems level approach to predicting treatment response and disease progression, and to developing precision therapies. Large publicly funded projects have generated extensive and freely available multi-assay data resources; however, bioinformatic and statistical methods for the analysis of such experiments are still nascent. We review multi-assay genomic data resources in the areas of clinical oncology, pharmacogenomics and other perturbation experiments, population genomics and regulatory genomics and other areas, and tools for data acquisition. Finally, we review bioinformatic tools that are explicitly geared toward integrative genomic data visualization and analysis. This review provides starting points for accessing publicly available data and tools to support development of needed integrative methods.

Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis.

BACKGROUND: Burgeoning interest in integrative analyses has produced a rise in studies which incorporate data from multiple genomic platforms. Literature for conducting formal hypothesis testing on an integrative gene set level is considerably sparse. This paper is biologically motivated by our interest in the joint effects of epigenetic methylation loci and their associated mRNA gene expressions on lung cancer survival status. RESULTS: We provide an efficient screening approach across multiplatform genomic data on the level of biologically related sets of genes, and our methods are applicable to various disease models regardless whether the underlying true model is known (iTEGS) or unknown (iNOTE). Our proposed testing procedure dominated two competing methods. Using our methods, we identified a total of 28 gene sets with significant joint epigenomic and transcriptomic effects on one-year lung cancer survival. CONCLUSIONS: We propose efficient variance component-based testing procedures to facilitate the joint testing of multiplatform genomic data across an entire gene set. The testing procedure for the gene set is self-contained, and can easily be extended to include more or different genetic platforms. iTEGS and iNOTE implemented in R are freely available through the inote package at https://cran.r-project.org// .

Integrative clustering of high-dimensional data with joint and individual clusters.

When measuring a range of genomic, epigenomic, and transcriptomic variables for the same tissue sample, an integrative approach to analysis can strengthen inference and lead to new insights. This is also the case when clustering patient samples, and several integrative cluster procedures have been proposed. Common for these methodologies is the restriction to a joint cluster structure, equal in all data layers. We instead present a clustering extension of the Joint and Individual Variance Explained algorithm (JIVE), Joint and Individual Clustering (JIC), enabling the construction of both joint and data type-specific clusters simultaneously. The procedure builds on the connection between k-means clustering and principal component analysis, and hence, the number of clusters can be determined by the number of relevant principal components. The proposed procedure is compared with iCluster, a method restricted to only joint clusters, and simulations show that JIC is clearly advantageous when both individual and joint clusters are present. The procedure is illustrated using gene expression and miRNA levels measured in breast cancer tissue from The Cancer Genome Atlas. The analysis suggests a division into three joint clusters common for both data types and two expression-specific clusters.

Integrative genetic risk prediction using non-parametric empirical Bayes classification.

Genetic risk prediction is an important component of individualized medicine, but prediction accuracies remain low for many complex diseases. A fundamental limitation is the sample sizes of the studies on which the prediction algorithms are trained. One way to increase the effective sample size is to integrate information from previously existing studies. However, it can be difficult to find existing data that examine the target disease of interest, especially if that disease is rare or poorly studied. Furthermore, individual-level genotype data from these auxiliary studies are typically difficult to obtain. This article proposes a new approach to integrative genetic risk prediction of complex diseases with binary phenotypes. It accommodates possible heterogeneity in the genetic etiologies of the target and auxiliary diseases using a tuning parameter-free non-parametric empirical Bayes procedure, and can be trained using only auxiliary summary statistics. Simulation studies show that the proposed method can provide superior predictive accuracy relative to non-integrative as well as integrative classifiers. The method is applied to a recent study of pediatric autoimmune diseases, where it substantially reduces prediction error for certain target/auxiliary disease combinations. The proposed method is implemented in the R package ssa.