Dataset Information

Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer.

ABSTRACT: Triple negative breast cancer (TNBC) is characterized by an aggressive biological behavior in the absence of a specific target agent. Nicotinamide has recently been proven to be a novel therapeutic agent for skin tumors in an ONTRAC trial. We performed combinatory transcriptomic and in-depth proteomic analyses to characterize the network of molecular interactions in TNBC cells treated with nicotinamide. The multi-omic profiles revealed that nicotinamide drives significant functional alterations related to major cellular pathways, including the cell cycle, DNA replication, apoptosis and DNA damage repair. We further elaborated the global interaction networks of molecular events via nicotinamide-inducible expression changes at the mRNA and functional protein levels. This approach indicated that nicotinamide treatment rewires interaction networks toward dysfunction in DNA damage repair and away from a pro-growth state in TNBC. To our knowledge, the high-resolution network interactions identified in the present study provide the first evidence to comprehensively support the hypothesis of nicotinamide as a novel therapeutic agent in TNBC.

SUBMITTER: Kim JY

PROVIDER: S-EPMC5471278 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer.

Kim Ji Young JY Lee Hyebin H Woo Jongmin J Yue Wang W Kim Kwangsoo K Choi Seongmin S Jang Ja-June JJ Kim Youngsoo Y Park In Ae IA Han Dohyun D Ryu Han Suk HS

Scientific reports 20170614 1

Triple negative breast cancer (TNBC) is characterized by an aggressive biological behavior in the absence of a specific target agent. Nicotinamide has recently been proven to be a novel therapeutic agent for skin tumors in an ONTRAC trial. We performed combinatory transcriptomic and in-depth proteomic analyses to characterize the network of molecular interactions in TNBC cells treated with nicotinamide. The multi-omic profiles revealed that nicotinamide drives significant functional alterations ...[more]

PMID: 28615672

Similar Datasets

Project description:To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

Project description:ClpP activators ONC201 and related small molecules (TR compounds, Madera Therapeutics), have demonstrated significant anti-cancer potential in vitro and in vivo studies, including clinical trials for refractory solid tumors. Though progress has been made in identifying specific phenotypic outcomes following ClpP activation, the exact mechanism by which ClpP activation leads to broad anti-cancer activity has yet to be fully elucidated. In this study, we utilized a multi-omics approach to identify the ClpP-dependent proteomic, transcriptomic, and metabolomic changes resulting from ONC201 or the TR compound TR-57 in triple-negative breast cancer cells. Applying mass spectrometry-based methods of proteomics and metabolomics, we identified ∼8,000 proteins and 588 metabolites, respectively. From proteomics data, 113 (ONC201) and 191 (TR-57) proteins significantly increased and 572 (ONC201) and 686 (TR-57) proteins significantly decreased in this study. Gene ontological (GO) analysis revealed strong similarities between proteins up- or downregulated by ONC201 or TR-57 treatment. Notably, this included the downregulation of many mitochondrial processes and proteins, including mitochondrial translation and mitochondrial matrix proteins. We performed a large-scale transcriptomic analysis of WT SUM159 cells, identifying ∼7,700 transcripts (746 and 1,100 significantly increasing, 795 and 1,013 significantly decreasing in ONC201 and TR-57 treated cells, respectively). Less than 21% of these genes were affected by these compounds in ClpP null cells. GO analysis of these data demonstrated additional similarity of response to ONC201 and TR-57, including a decrease in transcripts related to the mitochondrial inner membrane and matrix, cell cycle, and nucleus, and increases in other nuclear transcripts and transcripts related to metal-ion binding. Comparison of response between both compounds demonstrated a highly similar response in all -omics datasets. Analysis of metabolites also revealed significant similarities between ONC201 and TR-57 with increases in α-ketoglutarate and 2-hydroxyglutaric acid and decreased ureidosuccinic acid, L-ascorbic acid, L-serine, and cytidine observed following ClpP activation in TNBC cells. Further analysis identified multiple pathways that were specifically impacted by ClpP activation, including ATF4 activation, heme biosynthesis, and the citrulline/urea cycle. In summary the results of our studies demonstrate that ONC201 and TR-57 induce highly similar and broad effects against multiple mitochondrial processes required for cell proliferation.

Project description:BackgroundOne goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be "ome aware." Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs.MethodsWe present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting "top table" of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements).ResultsWe applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10-5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User's Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/ .ConclusionCANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

Dataset Information

Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer.

Publications

Reconstruction of pathway modification induced by nicotinamide using multi-omic network analyses in triple negative breast cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets