Inferring pathway dysregulation in cancers from multiple types of omic data

MacNeil, Shelley M.; Johnson, W. Evan; Li, Dean Y.; Piccolo, Stephen R.; Bild, Andrea

doi:10.1186/s13073-015-0189-4

Cited by 13 publications

(13 citation statements)

References 70 publications

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“…Furthermore, Macneil et al . performed gene set analysis based on a Support Vector Machine and found a correlation between gene set size and classification ability 36 . In this study, we observed this phenomenon in the full TCGA and GTEx datasets where the OCP of a Hallmark or random gene set tends to increase with the number of genes used in classification (Fig.…”

Section: Discussionmentioning

confidence: 99%

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Targonski

Shearer

Shealy

et al. 2019

Sci Rep

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Given the complex relationship between gene expression and phenotypic outcomes, computationally efficient approaches are needed to sift through large high-dimensional datasets in order to identify biologically relevant biomarkers. In this report, we describe a method of identifying the most salient biomarker genes in a dataset, which we call “candidate genes”, by evaluating the ability of gene combinations to classify samples from a dataset, which we call “classification potential”. Our algorithm, Gene Oracle, uses a neural network to test user defined gene sets for polygenic classification potential and then uses a combinatorial approach to further decompose selected gene sets into candidate and non-candidate biomarker genes. We tested this algorithm on curated gene sets from the Molecular Signatures Database (MSigDB) quantified in RNAseq gene expression matrices obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) data repositories. First, we identified which MSigDB Hallmark subsets have significant classification potential for both the TCGA and GTEx datasets. Then, we identified the most discriminatory candidate biomarker genes in each Hallmark gene set and provide evidence that the improved biomarker potential of these genes may be due to reduced functional complexity.

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Section: Discussionmentioning

confidence: 99%

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Targonski

Shearer

Shealy

et al. 2019

Sci Rep

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“… 3 In the tumor samples before and after VPA treatment in the VAST trial, we examined the gene expression patterns using gene set omic analysis (GSOA) and generally applicable gene set enrichment (GAGE). 34 , 35 By using either the C6 data sets ( P = .022 by GSOA; P = 4.47 × 10 −7 by GAGE) or the Hallmark pathway gene sets ( P = .001 by GSOA; P = 4.42 × 10 −61 by GAGE) from MSigDB, 36 genes regulated by MYC were downregulated in the post-treatment samples compared with the pretreatment samples.…”

Section: Resultsmentioning

confidence: 99%

Window-of-Opportunity Study of Valproic Acid in Breast Cancer Testing a Gene Expression Biomarker

Cohen

Neumayer

Boucher

et al. 2017

JCO Precision Oncology

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Purpose The anticancer activity of valproic acid (VPA) is attributed to the inhibition of histone deacetylase. We previously published the genomically derived sensitivity signature for VPA (GDSS-VPA), a gene expression biomarker that predicts breast cancer sensitivity to VPA in vitro and in vivo. We conducted a window-of-opportunity study that examined the tolerability of VPA and the ability of the GDSS-VPA to predict biologic changes in breast tumors after treatment with VPA. Patients and Methods Eligible women had untreated breast cancer with breast tumors larger than 1.5 cm. After a biopsy, women were given VPA for 7 to 12 days, increasing from 30 mg/kg/d orally divided into two doses per day to a maximum of 50 mg/kg/d. After VPA treatment, serum VPA level was measured and then breast surgery or biopsy was performed. Tumor proliferation was assessed by using Ki-67 immunohistochemistry. Histone acetylation of peripheral blood mononuclear cells was assessed by Western blot. Dynamic contrast-enhanced magnetic resonance imaging scans were performed before and after VPA treatment. Results Thirty women were evaluable. The median age was 54 years (range, 31-73 years). Fifty-two percent of women tolerated VPA at 50 mg/kg/d, but 10% missed more than two doses as a result of adverse events. Grade 3 adverse events included vomiting and diarrhea (one patient) and fatigue (one patient). The end serum VPA level correlated with a change in histone acetylation of peripheral blood mononuclear cells (ρ = 0.451; P = .024). Fifty percent of women (three of six) with triple-negative breast cancer had a Ki-67 reduction of at least 10% compared with 17% of other women. Women whose tumors had higher GDSS-VPA were more likely to have a Ki-67 decrease of at least 10% (area under the curve, 0.66). Conclusion VPA was well tolerated and there was a significant correlation between serum VPA levels and histone acetylation. VPA treatment caused a decrease in proliferation of breast tumors. The genomic biomarker correlated with decreased proliferation. Inhibition of histone deacetylase is a valid strategy for drug development in triple-negative breast cancer using gene expression biomarkers.

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“…A key decision in multi-omics pathway analyses is how to integrate different types of 'omics data. Methods such as the Gene Set Omic Analysis (GSOA) 34 or the PAthway Recognition Algorithm using Data Integration on Genomic Models (PARADIGM) 35,36 merge different 'omics measurements into a single result. Thereby, only data from the same or highly similar samples can be integrated.…”

Section: Discussionmentioning

confidence: 99%

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Griss

Viteri

Sidiropoulos

et al. 2020

Preprint

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Pathway analyses are key methods to analyse ‘omics experiments. Nevertheless, integrating data from different ‘omics technologies and different species still requires considerable bioinformatics knowledge.Here we present the novel ReactomeGSA resource for comparative pathway analyses of multi-omics datasets. ReactomeGSA can be used through Reactome’s existing web interface and the novel ReactomeGSA R Bioconductor package with explicit support for scRNA-seq data. Data from different species is automatically mapped to a common pathway space. Public data from ExpressionAtlas and Single Cell ExpressionAtlas can be directly integrated in the analysis. ReactomeGSA thereby greatly reduces the technical barrier for multi-omics, cross-species, comparative pathway analyses.We used ReactomeGSA to characterise the role of B cells in anti-tumour immunity. We compared B cell rich and poor human cancer samples from five TCGA transcriptomics and two CPTAC proteomics studies. There, B cell-rich lung adenocarcinoma samples lack the otherwise present activation through NFkappaB. This may be linked to the presence of a specific subset of tumour associated IgG+ plasma cells that lack NFkappaB activation in scRNA-seq data from human melanoma. This showcases how ReactomeGSA can derive novel biomedical insights by integrating large multi-omics datasets.

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Inferring pathway dysregulation in cancers from multiple types of omic data

Cited by 13 publications

References 70 publications

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Uncovering biomarker genes with enriched classification potential from Hallmark gene sets

Window-of-Opportunity Study of Valproic Acid in Breast Cancer Testing a Gene Expression Biomarker

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

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