Morphometic analysis of TCGA glioblastoma multiforme.
Morphometic analysis of TCGA glioblastoma multiforme.
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DOI：
10.1186/1471-2105-12-484
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发表时间：
2011-12-20
影响因子：
3
通讯作者：
Parvin B
中科院分区：
文献类型：
--
作者：
Chang H;Fontenay GV;Han J;Cong G;Baehner FL;Gray JW;Spellman PT;Parvin B
我们的目标是开发一个计算组织病理学管道，用于表征癌症基因组图谱（TCGA）生成的肿瘤类型，以进行基因组关联。TCGA是一个国家合作计划，收集不同的肿瘤类型，并使用各种全基因组平台对每种肿瘤进行表征。在这里，我们开发了一个以肿瘤为中心的分析管道，用于处理用苏木精和伊红（H&E）染色的组织切片，以进行可视化和逐细胞定量分析。到目前为止，分析仅限于多形性胶质母细胞瘤（GBM）和肾透明细胞癌组织切片。最终结果正在分发，用于分型，并将组织学切片与基因组数据联系起来。已经设计了一个计算管道，以不断更新本地图像数据库，有限的临床信息，从美国国立卫生研究院存储库。每个图像被划分成块，其中块中的每个单元通过多维表示（例如，核大小、细胞性）。然后，可以选择代表潜在的潜在生物过程的形态指标的子集用于亚型分型和基因组关联。同时，这些亚型也可以预测临床治疗的结果。使用细胞性指数和核大小，计算管道已经揭示了五种亚型，并且一种亚型对应于极高的细胞性，已经显示出作为更积极的治疗方案的结果的生存预测因子。该亚型与相应基因表达数据的进一步关联已经鉴定了（i）免疫应答和AP-1信号传导途径，以及（ii）IFNG、TGFB 1、PKC、细胞因子和MAPK 14枢纽的富集。虽然亚型通常是与全基因组的分子数据进行，我们已经表明，它也可以应用于分类组织学切片。因此，我们已经确定了一种亚型，它是治疗方案结果的预测因子。计算表示已通过我们的网站公开提供。
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.
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影响因子：
1.3
作者：
Dey, P;Mohanty, SK
通讯作者：
Mohanty, SK
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
影响因子：
10.9
作者：
Ballaro, Benedetto;Florena, Ada Maria;Valenti, Cesare
通讯作者：
Valenti, Cesare
影响因子：
5.1
作者：
Chang, Hang;Yang, Qing;Parvin, Bahram
通讯作者：
Parvin, Bahram
DOI：
10.1109/isbi.2010.5490168
发表时间：
2010-04-14
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
--
作者：
Bhagavatula R;Fickus M;Kelly W;Guo C;Ozolek JA;Castro CA;Kovačević J
通讯作者：
Kovačević J