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Platform for Quantitative Evaluation of Spatial Intratumoral Heterogeneity in Multiplexed Fluorescence Images.
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.
Affiliation
  • Spagnolo DM; Program in Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Al-Kofahi Y; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Zhu P; Software Science and Analytics Organization, GE Global Research Center, Niskayuna, New York.
  • Lezon TR; Software Science and Analytics Organization, GE Global Research Center, Niskayuna, New York.
  • Gough A; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Stern AM; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Lee AV; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Ginty F; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Sarachan B; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Taylor DL; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, Pennsylvania.
  • Chennubhotla SC; University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania.
Cancer Res ; 77(21): e71-e74, 2017 11 01.
Article in En | MEDLINE | ID: mdl-29092944
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.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Genetic Heterogeneity / Optical Imaging / Neoplasms Limits: Humans Language: En Journal: Cancer Res Year: 2017 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Software / Genetic Heterogeneity / Optical Imaging / Neoplasms Limits: Humans Language: En Journal: Cancer Res Year: 2017 Document type: Article