iEDGE: integration of Epi-DNA and Gene Expression and applications to the discovery of somatic copy number-associated drivers in cancer

AbstractThe emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively.First, iEDGE identifies the cis and trans genes associated with the presence/absence of a particular epi-DNA alteration across samples. Tests of statistical mediation are then performed to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks.We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible athttps://montilab.bu.edu/iEDGE.

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Identification of candidate cancer drivers by integrative Epi-DNA and Gene Expression (iEDGE) data analysis

Scientific Reports ◽

10.1038/s41598-019-52886-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Amy Li ◽

Bjoern Chapuy ◽

Xaralabos Varelas ◽

Paola Sebastiani ◽

Stefano Monti

Keyword(s):

Gene Expression ◽

Method Development ◽

Expression Profiles ◽

Integrative Analysis ◽

The Cancer Genome Atlas ◽

Pathway Enrichment Analysis ◽

Driver Genes ◽

Cancer Types ◽

Cis And Trans ◽

Trans Gene

AbstractThe emergence of large-scale multi-omics data warrants method development for data integration. Genomic studies from cancer patients have identified epigenetic and genetic regulators – such as methylation marks, somatic mutations, and somatic copy number alterations (SCNAs), among others – as predictive features of cancer outcome. However, identification of “driver genes” associated with a given alteration remains a challenge. To this end, we developed a computational tool, iEDGE, to model cis and trans effects of (epi-)DNA alterations and identify potential cis driver genes, where cis and trans genes denote those genes falling within and outside the genomic boundaries of a given (epi-)genetic alteration, respectively. iEDGE first identifies the cis and trans gene expression signatures associated with the presence/absence of a particular epi-DNA alteration across samples. It then applies tests of statistical mediation to determine the cis genes predictive of the trans gene expression. Finally, cis and trans effects are annotated by pathway enrichment analysis to gain insights into the underlying regulatory networks. We used iEDGE to perform integrative analysis of SCNAs and gene expression data from breast cancer and 18 additional cancer types included in The Cancer Genome Atlas (TCGA). Notably, cis gene drivers identified by iEDGE were found to be significantly enriched for known driver genes from multiple compendia of validated oncogenes and tumor suppressors, suggesting that the remainder are of equal importance. Furthermore, predicted drivers were enriched for functionally relevant cancer genes with amplification-driven dependencies, which are of potential prognostic and therapeutic value. All the analyses results are accessible at https://montilab.bu.edu/iEDGE. In summary, integrative analysis of SCNAs and gene expression using iEDGE successfully identified known cancer driver genes and putative cancer therapeutic targets across 19 cancer types in the TCGA. The proposed method can easily be applied to the integration of gene expression profiles with other epi-DNA assays in a variety of disease contexts.

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Data Fusion Techniques for the Integration of Multi-Domain Genomic Data from Uveal Melanoma

Cancers ◽

10.3390/cancers11101434 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1434 ◽

Cited By ~ 1

Author(s):

Max Pfeffer ◽

André Uschmajew ◽

Adriana Amaro ◽

Ulrich Pfeffer

Keyword(s):

Gene Expression ◽

Data Fusion ◽

Uveal Melanoma ◽

Copy Number ◽

Somatic Mutations ◽

Genomic Data ◽

The Cancer Genome Atlas ◽

Driver Genes ◽

Discrimination Power ◽

Value Decomposition

Uveal melanoma (UM) is a rare cancer that is well characterized at the molecular level. Two to four classes have been identified by the analyses of gene expression (mRNA, ncRNA), DNA copy number, DNA-methylation and somatic mutations yet no factual integration of these data has been reported. We therefore applied novel algorithms for data fusion, joint Singular Value Decomposition (jSVD) and joint Constrained Matrix Factorization (jCMF), as well as similarity network fusion (SNF), for the integration of gene expression, methylation and copy number data that we applied to the Cancer Genome Atlas (TCGA) UM dataset. Variant features that most strongly impact on definition of classes were extracted for biological interpretation of the classes. Data fusion allows for the identification of the two to four classes previously described. Not all of these classes are evident at all levels indicating that integrative analyses add to genomic discrimination power. The classes are also characterized by different frequencies of somatic mutations in putative driver genes (GNAQ, GNA11, SF3B1, BAP1). Innovative data fusion techniques confirm, as expected, the existence of two main types of uveal melanoma mainly characterized by copy number alterations. Subtypes were also confirmed but are somewhat less defined. Data fusion allows for real integration of multi-domain genomic data.

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Discovering novel driver mutations from pan-cancer analysis of mutational and gene expression profiles

PLoS ONE ◽

10.1371/journal.pone.0242780 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242780

Author(s):

Houriiyah Tegally ◽

Kevin H. Kensler ◽

Zahra Mungloo-Dilmohamud ◽

Anisah W. Ghoorah ◽

Timothy R. Rebbeck ◽

...

Keyword(s):

Gene Expression ◽

Large Scale ◽

Cancer Genomics ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Driver Mutations ◽

Literature Mining ◽

Cancer Genes ◽

Multiple Cancer ◽

Driver Genes

As the genomic profile across cancers varies from person to person, patient prognosis and treatment may differ based on the mutational signature of each tumour. Thus, it is critical to understand genomic drivers of cancer and identify potential mutational commonalities across tumors originating at diverse anatomical sites. Large-scale cancer genomics initiatives, such as TCGA, ICGC and GENIE have enabled the analysis of thousands of tumour genomes. Our goal was to identify new cancer-causing mutations that may be common across tumour sites using mutational and gene expression profiles. Genomic and transcriptomic data from breast, ovarian, and prostate cancers were aggregated and analysed using differential gene expression methods to identify the effect of specific mutations on the expression of multiple genes. Mutated genes associated with the most differentially expressed genes were considered to be novel candidates for driver mutations, and were validated through literature mining, pathway analysis and clinical data investigation. Our driver selection method successfully identified 116 probable novel cancer-causing genes, with 4 discovered in patients having no alterations in any known driver genes: MXRA5, OBSCN, RYR1, and TG. The candidate genes previously not officially classified as cancer-causing showed enrichment in cancer pathways and in cancer diseases. They also matched expectations pertaining to properties of cancer genes, for instance, showing larger gene and protein lengths, and having mutation patterns suggesting oncogenic or tumor suppressor properties. Our approach allows for the identification of novel putative driver genes that are common across cancer sites using an unbiased approach without any a priori knowledge on pathways or gene interactions and is therefore an agnostic approach to the identification of putative common driver genes acting at multiple cancer sites.

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Integrated DNA Copy Number and Gene Expression Regulatory Network Analysis of Non-small Cell Lung Cancer Metastasis

Cancer Informatics ◽

10.4137/cin.s14055 ◽

2014 ◽

Vol 13s5 ◽

pp. CIN.S14055 ◽

Cited By ~ 1

Author(s):

Seyed M. Iranmanesh ◽

Nancy L. Guo

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Copy Number ◽

Regulatory Networks ◽

Cancer Metastasis ◽

Integrative Analysis ◽

Transcriptional Level ◽

Dna Copy Number ◽

Driver Genes ◽

Lung Cancer Metastasis

Integrative analysis of multi-level molecular profiles can distinguish interactions that cannot be revealed based on one kind of data in the analysis of cancer susceptibility and metastasis. DNA copy number variations (CNVs) are common in cancer cells, and their role in cell behaviors and relationship to gene expression (GE) is poorly understood. An integrative analysis of CNV and genome-wide mRNA expression can discover copy number alterations and their possible regulatory effects on GE. This study presents a novel framework to identify important genes and construct potential regulatory networks based on these genes. Using this approach, DNA copy number aberrations and their effects on GE in lung cancer progression were revealed. Specifically, this approach contains the following steps: (1) select a pool of candidate driver genes, which have significant CNV in lung cancer patient tumors or have a significant association with the clinical outcome at the transcriptional level; (2) rank important driver genes in lung cancer patients with good prognosis and poor prognosis, respectively, and use top-ranked driver genes to construct regulatory networks with the COpy Number and Expression In Cancer (CONEXIC) method; (3) identify experimentally confirmed molecular interactions in the constructed regulatory networks using Ingenuity Pathway Analysis (IPA); and (4) visualize the refined regulatory networks with the software package Genatomy. The constructed CNV/mRNA regulatory networks provide important insights into potential CNV-regulated transcriptional mechanisms in lung cancer metastasis.

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GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions

Nucleic Acids Research ◽

10.1093/nar/gkaa995 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D97-D103

Author(s):

Li Fang ◽

Yunjin Li ◽

Lu Ma ◽

Qiyue Xu ◽

Fei Tan ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Large Scale ◽

Gene Expression Regulation ◽

Expression Regulation ◽

The Cancer Genome Atlas ◽

Gene Regulatory ◽

Human And Mouse

Abstract Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

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A multiomics comparison between endometrial cancer and serous ovarian cancer

PeerJ ◽

10.7717/peerj.8347 ◽

2019 ◽

Vol 8 ◽

pp. e8347 ◽

Cited By ~ 2

Author(s):

Hui Zhong ◽

Huiyu Chen ◽

Huahong Qiu ◽

Chen Huang ◽

Zhihui Wu

Keyword(s):

Gene Expression ◽

Copy Number ◽

Large Scale ◽

Copy Number Alteration ◽

Immune Cell ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cancer Type ◽

Tumor Microenvironments ◽

Molecular Phenotypes

Background Endometrial carcinoma (EC) and serous ovarian carcinoma (OvCa) are both among the common cancer types in women. EC can be divided into two subtypes, endometroid EC and serous-like EC, with distinct histological characterizations and molecular phenotypes. There is an increasing awareness that serous-like EC resembles serous OvCa in genetic landscape, but a clear relationship between them is still lacking. Methods Here, we took advantage of the large-scale molecular profiling of The Cancer Genome Atlas(TCGA) to compare the two EC subtypes and serous OvCa. We used bioinformatics data analytic methods to systematically examine the somatic mutation (SM) and copy number alteration (SCNA), gene expression, pathway activities, survival gene signatures and immune infiltration. Based on these quantifiable molecular characterizations, we asked whether serous-like EC should be grouped more closely to serous OvCa, based on the context of being serous-like; or if should be grouped more closely to endometroid EC, based on the same organ origin. Results We found that although serous-like EC and serous OvCa share some common genotypes, including mutation and copy number alteration, they differ in molecular phenotypes such as gene expression and signaling pathway activity. Moreover, no shared prognostic gene signature was found, indicating that they use unique genes governing tumor progression. Finally, although the endometrioid EC and serous OvCa are both highly immune infiltrated, the immune cell composition in serous OvCa is mostly immune suppressive, whereas endometrioid EC has a higher level of cytotoxic immune cells. Overall, our genetic aberration and molecular phenotype characterizations indicated that serous-like EC and serous OvCa cannot be simply treated as a simple “serous” cancer type. In particular, additional attention should be paid to their unique gene activities and tumor microenvironments for novel targeted therapy development.

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Integrating Genetic and Transcriptomic Data to Reveal Pathogenesis and Prognostic Markers of Pancreatic Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.747270 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kaisong Bai ◽

Tong Zhao ◽

Yilong Li ◽

Xinjian Li ◽

Zhantian Zhang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Adenocarcinoma ◽

Somatic Mutation ◽

Somatic Mutations ◽

Prognostic Markers ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Genomic Variation ◽

The Cancer Genome Atlas ◽

Driver Genes

Pancreatic adenocarcinoma (PAAD) is one of the deadliest malignancies and mortality for PAAD have remained increasing under the conditions of substantial improvements in mortality for other major cancers. Although multiple of studies exists on PAAD, few studies have dissected the oncogenic mechanisms of PAAD based on genomic variation. In this study, we integrated somatic mutation data and gene expression profiles obtained by high-throughput sequencing to characterize the pathogenesis of PAAD. The mutation profile containing 182 samples with 25,470 somatic mutations was obtained from The Cancer Genome Atlas (TCGA). The mutation landscape was generated and somatic mutations in PAAD were found to have preference for mutation location. The combination of mutation matrix and gene expression profiles identified 31 driver genes that were closely associated with tumor cell invasion and apoptosis. Co-expression networks were constructed based on 461 genes significantly associated with driver genes and the hub gene FAM133A in the network was identified to be associated with tumor metastasis. Further, the cascade relationship of somatic mutation-Long non-coding RNA (lncRNA)-microRNA (miRNA) was constructed to reveal a new mechanism for the involvement of mutations in post-transcriptional regulation. We have also identified prognostic markers that are significantly associated with overall survival (OS) of PAAD patients and constructed a risk score model to identify patients’ survival risk. In summary, our study revealed the pathogenic mechanisms and prognostic markers of PAAD providing theoretical support for the development of precision medicine.

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Pervasive cis effects of variation in copy number of large tandem repeats on local epigenetics and gene expression

10.1101/2020.12.16.423078 ◽

2020 ◽

Author(s):

Paras Garg ◽

Alejandro Martin-Trujillo ◽

Oscar L. Rodriguez ◽

Scott J. Gies ◽

Bharati Jadhav ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Copy Number ◽

Large Scale ◽

Tandem Repeats ◽

Local Variation ◽

Read Depth ◽

Variable Number ◽

Human Populations ◽

Functional Variation

ABSTRACTVariable Number Tandem Repeats (VNTRs) are composed of large tandemly repeated motifs, many of which are highly polymorphic in copy number. However, due to their large size and repetitive nature, they remain poorly studied. To investigate the regulatory potential of VNTRs, we used read-depth data from Illumina whole genome sequencing to perform association analysis between copy number of ~70,000 VNTRs (motif size ≥10bp) with both gene expression (404 samples in 48 tissues) and DNA methylation (235 samples in peripheral blood), identifying thousands of VNTRs that are associated with local gene expression (eVNTRs) and DNA methylation levels (mVNTRs). Using large-scale replication analysis in an independent cohort we validated 73-80% of signals observed in the two discovery cohorts, providing robust evidence to support that these represent genuine associations. Further, conditional analysis indicated that many eVNTRs and mVNTRs act as QTLs independently of other local variation. We also observed strong enrichments of eVNTRs and mVNTRs for regulatory features such as enhancers and promoters. Using the Human Genome Diversity Panel, we defined sets of VNTRs that show highly divergent copy numbers among human populations, show that these are enriched for regulatory effects on gene expression and epigenetics, and preferentially associate with genes that have been linked with human phenotypes through GWAS. Our study provides strong evidence supporting functional variation at thousands of VNTRs, and defines candidate sets of VNTRs, copy number variation of which potentially plays a role in numerous human phenotypes.

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Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1901156116 ◽

2019 ◽

Vol 116 (38) ◽

pp. 18962-18970 ◽

Cited By ~ 5

Author(s):

Sushant Kumar ◽

Declan Clarke ◽

Mark B. Gerstein

Keyword(s):

Protein Dynamics ◽

Protein Function ◽

Large Scale ◽

Protein Structures ◽

Structural Data ◽

The Cancer Genome Atlas ◽

Detection Methods ◽

Hotspot Detection ◽

Driver Genes ◽

Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

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OncoVar: an integrated database and analysis platform for oncogenic driver variants in cancers

Nucleic Acids Research ◽

10.1093/nar/gkaa1033 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D1289-D1301 ◽

Cited By ~ 2

Author(s):

Tao Wang ◽

Shasha Ruan ◽

Xiaolu Zhao ◽

Xiaohui Shi ◽

Huajing Teng ◽

...

Keyword(s):

Cancer Genome ◽

The Cancer Genome Atlas ◽

Driver Mutations ◽

Cancer Genes ◽

Driver Genes ◽

Cancer Driver ◽

Cancer Cell Population ◽

Cancer Types ◽

Neutral Mutations ◽

Analysis Platform

Abstract The prevalence of neutral mutations in cancer cell population impedes the distinguishing of cancer-causing driver mutations from passenger mutations. To systematically prioritize the oncogenic ability of somatic mutations and cancer genes, we constructed a useful platform, OncoVar (https://oncovar.org/), which employed published bioinformatics algorithms and incorporated known driver events to identify driver mutations and driver genes. We identified 20 162 cancer driver mutations, 814 driver genes and 2360 pathogenic pathways with high-confidence by reanalyzing 10 769 exomes from 33 cancer types in The Cancer Genome Atlas (TCGA) and 1942 genomes from 18 cancer types in International Cancer Genome Consortium (ICGC). OncoVar provides four points of view, ‘Mutation’, ‘Gene’, ‘Pathway’ and ‘Cancer’, to help researchers to visualize the relationships between cancers and driver variants. Importantly, identification of actionable driver alterations provides promising druggable targets and repurposing opportunities of combinational therapies. OncoVar provides a user-friendly interface for browsing, searching and downloading somatic driver mutations, driver genes and pathogenic pathways in various cancer types. This platform will facilitate the identification of cancer drivers across individual cancer cohorts and helps to rank mutations or genes for better decision-making among clinical oncologists, cancer researchers and the broad scientific community interested in cancer precision medicine.

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