Spagnolo Daniel M, Al-Kofahi Yousef, Zhu Peihong, Lezon Timothy R, Gough Albert, Stern Andrew M, Lee Adrian V, Ginty Fiona, Sarachan Brion, Taylor D Lansing, Chennubhotla S Chakra
Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania.
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
Cancer Res. 2017 Nov 1;77(21):e71-e74. doi: 10.1158/0008-5472.CAN-17-0676.
We introduce THRIVE (Tumor Heterogeneity Research Interactive Visualization Environment), an open-source tool developed to assist cancer researchers in interactive hypothesis testing. The focus of this tool is to quantify spatial intratumoral heterogeneity (ITH), and the interactions between different cell phenotypes and noncellular constituents. Specifically, we foresee applications in phenotyping cells within tumor microenvironments, recognizing tumor boundaries, identifying degrees of immune infiltration and epithelial/stromal separation, and identification of heterotypic signaling networks underlying microdomains. The THRIVE platform provides an integrated workflow for analyzing whole-slide immunofluorescence images and tissue microarrays, including algorithms for segmentation, quantification, and heterogeneity analysis. THRIVE promotes flexible deployment, a maintainable code base using open-source libraries, and an extensible framework for customizing algorithms with ease. THRIVE was designed with highly multiplexed immunofluorescence images in mind, and, by providing a platform to efficiently analyze high-dimensional immunofluorescence signals, we hope to advance these data toward mainstream adoption in cancer research. .
我们推出了THRIVE(肿瘤异质性研究交互式可视化环境),这是一个为帮助癌症研究人员进行交互式假设检验而开发的开源工具。该工具的重点是量化肿瘤内空间异质性(ITH)以及不同细胞表型与非细胞成分之间的相互作用。具体而言,我们预见到它在肿瘤微环境中细胞表型分析、识别肿瘤边界、确定免疫浸润程度和上皮/基质分离情况以及识别微域潜在的异型信号网络等方面的应用。THRIVE平台提供了一个用于分析全切片免疫荧光图像和组织微阵列的集成工作流程,包括分割、量化和异质性分析算法。THRIVE促进灵活部署,使用开源库构建可维护的代码库,并提供一个易于定制算法的可扩展框架。THRIVE的设计考虑到了高度多重免疫荧光图像,通过提供一个有效分析高维免疫荧光信号的平台,我们希望推动这些数据在癌症研究中得到更广泛的应用。