Dataset Information

Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations.

ABSTRACT: Glioblastoma multiforme (GBM), a deadly cancer, is the most lethal and common malignant brain tumor, and the leading cause of death in adult brain tumors. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in the TCGA network offers a window to generate novel clinical hypotheses. We hypothesized that a TCGA-derived gene-classifier can be applied across different gene profiling platforms and population groups. This gene-classifier validated three robust GBM-subtypes across six different platforms, among Caucasian, Korean and Chinese populations: Three Caucasian-predominant TCGA-cohorts (Affymetrix U133A?=?548, Agilent Custom-Array?=?588, RNA-seq?=?168), and three Asian-cohorts (Affymetrix Human Gene 1.0ST-Array?=?61, Illumina?=?52, Agilent 4?×?44?K?=?60). To understand subtype-relevance in patient therapy, we investigated retrospective TCGA patient clinical sets. Subtype-specific patient survival outcome was similarly poor and reflected the net result of a mixture of treatment regimens with/without surgical resection. As a proof-of-concept, in subtype-specific patient-derived orthotopic xenograft (PDOX) mice, Classical-subtype demonstrated no survival difference comparing radiation-therapy versus temozolomide monotherapies. Though preliminary, a PDOX model of Proneural/Neural-subtype demonstrated significantly improved survival with temozolomide compared to radiation-therapy. A larger scale study using this gene-classifier may be useful in clinical outcome prediction and patient selection for trials based on subtyping.

SUBMITTER: Teo WY

PROVIDER: S-EPMC6520485 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Publications

Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations.

Teo Wan-Yee WY Sekar Karthik K Seshachalam Pratap P Shen Jianhe J Chow Wing-Yuk WY Lau Ching C CC Yang HeeKyoung H Park Junseong J Kang Seok-Gu SG Li Xiaonan X Nam Do-Hyun DH Hui Kam M KM

Scientific reports 20190515 1

Glioblastoma multiforme (GBM), a deadly cancer, is the most lethal and common malignant brain tumor, and the leading cause of death in adult brain tumors. While genomic data continues to rocket, clinical application and translation to patient care are lagging behind. Big data now deposited in the TCGA network offers a window to generate novel clinical hypotheses. We hypothesized that a TCGA-derived gene-classifier can be applied across different gene profiling platforms and population groups. Th ...[more]

PMID: 31092847

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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

Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations.

Publications

Relevance of a TCGA-derived Glioblastoma Subtype Gene-Classifier among Patient Populations.

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