PDF(Pubmed)
Abstract：
Patient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of >1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image-based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of >1,000 patient-derived xenograft hematoxylin and eosin-stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories.
摘要：
在免疫受损小鼠的完整组织的背景下，患者来源的异种移植物（PDX）模型人类肿瘤内和肿瘤间异质性。通过苏木精和伊红（H&E）染色的组织学成像常规对PDX样本进行，可以用于计算分析。大型临床H&E图像存储库的先前研究表明，深度学习分析可以识别与疾病表型和治疗反应相关的细胞间和形态学信号。在这项研究中,我们开发了一个广泛的，泛癌症存储库>1,000PDX和配对的亲本肿瘤H&E图像。这些图像,由PDX开发和试验中心研究网络联盟策划，有一系列相关的基因组和转录组数据，临床元数据，细胞组成的病理评估，and,在一些情况下，肿瘤的详细病理注释，基质,和坏死区域。通过三个应用强调了这些图像对深度学习的适应性：(i)开发肿瘤分类器，基质,和坏死区域；（ii）异种移植淋巴增生性疾病的预测因子的发展；（iii）已发表的微卫星不稳定性预测因子的应用。一起,这个PDX开发和试验中心研究网络图像存储库为受控数字病理分析提供了宝贵的资源，用于评估技术问题和开发基于计算图像的方法，这些方法基于PDX治疗研究进行临床预测。意义：>1,000个患者来源的异种移植苏木精和伊红染色图像的泛癌症存储库将通过组织病理学分析促进癌症生物学研究，并提供重要的模型系统数据，扩展现有的人类组织学存储库。
