Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates.
Machine-based morphologic analysis of glioblastoma using whole-slide pathology images uncovers clinically relevant molecular correlates.
复制标题
DOI：
10.1371/journal.pone.0081049
复制
发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Brat DJ
中科院分区：
文献类型：
--
作者：
Kong J;Cooper LA;Wang F;Gao J;Teodoro G;Scarpace L;Mikkelsen T;Schniederjan MJ;Moreno CS;Saltz JH;Brat DJ
组织切片中肿瘤形态的病理检查是癌症分类和分级的传统方法，但人类检查具有局限性，可能导致重复性低和观察者之间的一致性。计算机化的图像分析可以部分克服这些缺点，因为它能够定量和重复性地测量大规模的组织结构。在这篇文章中，我们提出了一种端到端的图像分析和数据集成管道，用于大规模的病理图像的形态分析，并展示了将表型组与分子数据和临床结果相关联的能力。我们在胶质母细胞瘤(GBM)的背景下展示了我们的方法，特别关注少突胶质瘤成分的程度。对来自癌症基因组图谱中117个GBM的数字化病理切片中的超过2亿个核进行了定量分析，随后进行了核特征与分子和临床数据的多平台关联。对于每个核，使用从最佳特征集训练的回归模型，根据少突胶质瘤的出现程度计算核分数(NS)。利用肿瘤核在低NS间期和高NS间期的频率，我们能够将患者分为三组，分别包含低、中、高少突胶质瘤成分(OC)。我们发现，基于机器分类的具有高少突胶质细胞瘤成分的GBM发现了一组与PDGFRA扩增、神经转录类别以及少突胶质细胞特征基因MBP、HOXD1、PLP1、MOBP和PDGFRA表达密切相关的肿瘤。基底节内与少突胶质细胞基因表达相关性最强的定量形态特征是高核环性和低偏心率。这些发现突出了高通量形态分析的潜力，以补充和通知以人类为基础的病理审查。
Pathologic review of tumor morphology in histologic sections is the traditional method for cancer classification and grading, yet human review has limitations that can result in low reproducibility and inter-observer agreement. Computerized image analysis can partially overcome these shortcomings due to its capacity to quantitatively and reproducibly measure histologic structures on a large-scale. In this paper, we present an end-to-end image analysis and data integration pipeline for large-scale morphologic analysis of pathology images and demonstrate the ability to correlate phenotypic groups with molecular data and clinical outcomes. We demonstrate our method in the context of glioblastoma (GBM), with specific focus on the degree of the oligodendroglioma component. Over 200 million nuclei in digitized pathology slides from 117 GBMs in the Cancer Genome Atlas were quantitatively analyzed, followed by multiplatform correlation of nuclear features with molecular and clinical data. For each nucleus, a Nuclear Score (NS) was calculated based on the degree of oligodendroglioma appearance, using a regression model trained from the optimal feature set. Using the frequencies of neoplastic nuclei in low and high NS intervals, we were able to cluster patients into three well-separated disease groups that contained low, medium, or high Oligodendroglioma Component (OC). We showed that machine-based classification of GBMs with high oligodendroglioma component uncovered a set of tumors with strong associations with PDGFRA amplification, proneural transcriptional class, and expression of the oligodendrocyte signature genes MBP, HOXD1, PLP1, MOBP and PDGFRA. Quantitative morphologic features within the GBMs that correlated most strongly with oligodendrocyte gene expression were high nuclear circularity and low eccentricity. These findings highlight the potential of high throughput morphologic analysis to complement and inform human-based pathologic review.
登录
查看更多内容
影响因子：
7.4
作者：
De Pascalis, V;Chiaradia, C;Carotenuto, E
通讯作者：
Carotenuto, E
影响因子：
5.3
作者：
Kong, J;Gollub, RL;Kaptchuk, TJ
通讯作者：
Kaptchuk, TJ
影响因子：
7.4
作者：
Bingel, U;Lorenz, J;Büchel, C
通讯作者：
Büchel, C
影响因子：
7.4
作者：
GRACELY, RH;MCGRATH, P;DUBNER, R
通讯作者：
DUBNER, R
影响因子：
4.7
作者：
Geers, AL;Helfer, SG;Landry, SJ
通讯作者：
Landry, SJ