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Cancer Res. 2017 Nov 1;77(21):e71-e74. doi: 10.1158/0008-5472.CAN-17-0676.

Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.

Author information

1
Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania.
2
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
3
Software Science and Analytics Organization, GE Global Research Center, Niskayuna, New York.
4
Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
5
University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.
6
Department of Pharmacology and Chemical Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
7
Biosciences Organization, GE Global Research Center, Niskayuna, New York.
8
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania. chakracs@pitt.edu.

Abstract

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.

PMID:
29092944
PMCID:
PMC5683175
DOI:
10.1158/0008-5472.CAN-17-0676
[Indexed for MEDLINE]
Free PMC Article

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