Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images
| Autor: | Fiona Ginty, Adrian V. Lee, Yousef Al-Kofahi, Chakra Chennubhotla, Andrew M. Stern, Peihong Zhu, Albert Gough, D. Lansing Taylor, Daniel Spagnolo, Timothy R. Lezon, Brion Daryl Sarachan |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Cancer Research Computational biology Biology Bioinformatics Multiplexing Tumor heterogeneity Article Genetic Heterogeneity 03 medical and health sciences 0302 clinical medicine Optical imaging Immune infiltration Neoplasms Image Processing Computer-Assisted Humans Segmentation Interactive visualization Extramural Optical Imaging ComputingMethodologies_PATTERNRECOGNITION 030104 developmental biology Workflow Oncology Tissue Array Analysis 030220 oncology & carcinogenesis Algorithms Software |
| Zdroj: | Cancer Research. 77:e71-e74 |
| ISSN: | 1538-7445 0008-5472 |
| DOI: | 10.1158/0008-5472.can-17-0676 |
| Popis: | 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. Cancer Res; 77(21); e71–74. ©2017 AACR. |
| Databáze: | OpenAIRE |
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