Dataset Information

Proteogenomic characterization and integrative analysis of glioblastoma multiforme.

ABSTRACT: Glioblastoma multiforme (GBM), the most aggressive and lethal primary brain tumor, is characterized by very low life expectancy. Understanding the genomic and proteogenomic characteristics of GBM is essential for devising better therapeutic approaches.Here, we performed proteomic profiling of 8 GBM and paired normal brain tissues. In parallel, comprehensive integrative genomic analysis of GBM was performed in silico using mRNA microarray and sequencing data. Two whole transcript expression profiling cohorts were used - a set of 3 normal brain tissues and 22 glioma tissue samples and a cohort of 5 normal brain tissues and 49 glioma tissue samples. A validation cohort included 529 GBM patients from The Cancer Genome Atlas datasets. We identified 36 molecules commonly changed at the level of the gene and protein, including up-regulated TGFBI and NES and down-regulated SNCA and HSPA12A. Single amino acid variant analysis identified 200 proteins with high mutation rates in GBM samples. We further identified 14 differentially expressed genes with high-level protein modification, among which NES and TNC showed differential expression at the protein level. Moreover, higher expression of NES and TNC mRNAs correlated with shorter overall survival, suggesting that these genes constitute potential biomarkers for GBM.

SUBMITTER: Song YC

PROVIDER: S-EPMC5722563 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Proteogenomic characterization and integrative analysis of glioblastoma multiforme.

Song Ying-Chun YC Lu Gai-Xia GX Zhang Hong-Wei HW Zhong Xiao-Ming XM Cong Xian-Ling XL Xue Shao-Bo SB Kong Rui R Li Dan D Chang Zheng-Yan ZY Wang Xiao-Feng XF Zhang Yun-Jie YJ Sun Ran R Chai Li L Xie Ru-Ting RT Cai Ming-Xiang MX Sun Ming M Mao Wei-Qing WQ Yang Hui-Qiong HQ Shao Yun-Chao YC Fan Su-Yun SY Wu Ting-Miao TM Xia Qing Q Lv Zhong-Wei ZW Fu David A DA Ma Yu-Shui YS

Oncotarget 20171019 57

Glioblastoma multiforme (GBM), the most aggressive and lethal primary brain tumor, is characterized by very low life expectancy. Understanding the genomic and proteogenomic characteristics of GBM is essential for devising better therapeutic approaches.Here, we performed proteomic profiling of 8 GBM and paired normal brain tissues. In parallel, comprehensive integrative genomic analysis of GBM was performed <i>in silico</i> using mRNA microarray and sequencing data. Two whole transcript expressio ...[more]

PMID: 29228611

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Project description:Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data.A computational pipeline has been designed to continuously update a local image database, with limited clinical information, from an NIH repository. Each image is partitioned into blocks, where each cell in the block is characterized through a multidimensional representation (e.g., nuclear size, cellularity). A subset of morphometric indices, representing potential underlying biological processes, can then be selected for subtyping and genomic association. Simultaneously, these subtypes can also be predictive of the outcome as a result of clinical treatments. Using the cellularity index and nuclear size, the computational pipeline has revealed five subtypes, and one subtype, corresponding to the extreme high cellularity, has shown to be a predictor of survival as a result of a more aggressive therapeutic regime. Further association of this subtype with the corresponding gene expression data has identified enrichment of (i) the immune response and AP-1 signaling pathways, and (ii) IFNG, TGFB1, PKC, Cytokine, and MAPK14 hubs.While subtyping is often performed with genome-wide molecular data, we have shown that it can also be applied to categorizing histology sections. Accordingly, we have identified a subtype that is a predictor of the outcome as a result of a therapeutic regime. Computed representation has become publicly available through our Web site.

Dataset Information

Proteogenomic characterization and integrative analysis of glioblastoma multiforme.

Publications

Proteogenomic characterization and integrative analysis of glioblastoma multiforme.

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