Sio: A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment

Ying Zhu, Sammy Ferri-Borgogno, Jianting Sheng, Tsz Lun Yeung, Jared K. Burks, Paola Cappello, Amir A. Jazaeri, Jae Hoon Kim, Gwan Hee Han, Michael J. Birrer, Samuel C. Mok, Stephen T.C. Wong

Research output: Contribution to journalArticle

Abstract

Stromal and immune cells in the tumor microenvironment (TME) have been shown to directly affect high-grade serous ovarian cancer (HGSC) malignant phenotypes, however, how these cells interact to influence HGSC patients’ survival remains largely unknown. To investigate the cell-cell communication in such a complex TME, we developed a SpatioImageOmics (SIO) pipeline that combines imaging mass cytometry (IMC), location-specific transcriptomics, and deep learning to identify the distribution of various stromal, tumor and immune cells as well as their spatial relationship in TME. The SIO pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among tumor, immune, and stromal cells that coordinate to influence overall survival rates in HGSC patients. In addition, SIO integrates IMC data with microdissected tumor and stromal transcriptomes from the same patients to identify novel signaling networks, which would lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.

Original languageEnglish (US)
Article number1777
JournalCancers
Volume13
Issue number8
DOIs
StatePublished - Apr 2 2021

Keywords

  • Cancer microenvironment
  • Deep learning
  • High-grade serous ovarian cancer
  • Imaging mass cytometry
  • Survival prediction
  • Transcriptomic profiling
  • Tumor biomarkers

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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