Dataset Information

Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.

ABSTRACT: Recent development in high throughput proteomics and genomics profiling enable one to study regulations of genome alterations on protein activities in a systematic manner. In this article, we propose a new statistical method, ProMAP, to systematically characterize the regulatory relationships between proteins and DNA copy number alterations (CNA) in breast and ovarian tumors based on proteogenomic data from the CPTAC-TCGA studies. Because of the dynamic nature of mass spectrometry instruments, proteomics data from labeled mass spectrometry experiments usually have non-ignorable batch effects. Moreover, mass spectrometry based proteomic data often possesses high percentages of missing values and non-ignorable missing-data patterns. Thus, we use a linear mixed effects model to account for the batch structure and explicitly incorporate the abundance-dependent-missing-data mechanism of proteomic data in ProMAP. In addition, we employ a multivariate regression framework to characterize the multiple-to-multiple regulatory relationships between CNA and proteins. Further, we use proper statistical regularization to facilitate the detection of master genetic regulators, which affect the activities of many proteins and often play important roles in genetic regulatory networks. Improved performance of ProMAP over existing methods were illustrated through extensive simulation studies and real data examples. Applying ProMAP to the CPTAC-TCGA breast and ovarian cancer data sets, we identified many genome regions, including a few novel ones, whose CNA were associated with protein and or phosphoprotein abundances. For example, in breast tumors, a small region in 8p11.21 was recognized as the second biggest hub in the CNA-phosphoprotein regulatory map, and further investigation of the regulatory targets suggests the potential role of 8p11.21 CNA in perturbing oxygen binding and transport activities in tumor cells. This and other findings from our analyses help to characterize the impacts of CNAs on protein activity landscapes and cast light on the genetic regulation mechanisms underlying these tumors.

SUBMITTER: Ma W

PROVIDER: S-EPMC6692778 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.

Ma Weiping W Chen Lin S LS Özbek Umut U Han Sung Won SW Lin Chenwei C Paulovich Amanda G AG Zhong Hua H Wang Pei P

Molecular & cellular proteomics : MCP 20190707 8 suppl 1

Recent development in high throughput proteomics and genomics profiling enable one to study regulations of genome alterations on protein activities in a systematic manner. In this article, we propose a new statistical method, ProMAP, to systematically characterize the regulatory relationships between proteins and DNA copy number alterations (CNA) in breast and ovarian tumors based on proteogenomic data from the CPTAC-TCGA studies. Because of the dynamic nature of mass spectrometry instruments, p ...[more]

PMID: 31281117

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Project description:BackgroundThe survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).MethodsThe proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.ResultsWe identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.ConclusionThese identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

Project description:Previous genome-wide association studies (GWAS) have identified several common genetic variants that may significantly modulate cancer susceptibility. However, the precise molecular mechanisms behind these associations remain largely unknown; it is often not clear whether discovered variants are themselves functional or merely genetically linked to other functional variants. Here, we provide an integrated method for identifying functional regulatory variants associated with cancer and their target genes by combining analyses of expression quantitative trait loci, a modified version of allele-specific expression that systematically utilizes haplotype information, transcription factor (TF)-binding preference, and epigenetic information. Application of our method to a breast cancer susceptibility region in 5p12 demonstrates that the risk allele rs4415084-T correlates with higher expression levels of the protein-coding gene mitochondrial ribosomal protein S30 (MRPS30) and lncRNA RP11-53O19.1 We propose an intergenic SNP rs4321755, in linkage disequilibrium (LD) with the GWAS SNP rs4415084 (r2 = 0.988), to be the predicted functional SNP. The risk allele rs4321755-T, in phase with the GWAS rs4415084-T, created a GATA3-binding motif within an enhancer, resulting in differential GATA3 binding and chromatin accessibility, thereby promoting transcription of MRPS30 and RP11-53O19.1. MRPS30 encodes a member of the mitochondrial ribosomal proteins, implicating the role of risk SNP in modulating mitochondrial activities in breast cancer. Our computational framework provides an effective means to integrate GWAS results with high-throughput genomic and epigenomic data and can be extended to facilitate rapid functional characterization of other genetic variants modulating cancer susceptibility.Significance: Unification of GWAS results with information from high-throughput genomic and epigenomic profiles provides a direct link between common genetic variants and measurable molecular perturbations. Cancer Res; 78(7); 1579-91. ©2018 AACR.

Dataset Information

Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.

Publications

Integrative Proteo-genomic Analysis to Construct CNA-protein Regulatory Map in Breast and Ovarian Tumors.

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