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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 
 CANCERS, Vol.13(8) : 1777, 2021-04 
Journal Title
Issue Date
cancer microenvironment ; deep learning ; high-grade serous ovarian cancer ; imaging mass cytometry ; survival prediction ; transcriptomic profiling ; tumor biomarkers
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
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Obstetrics and Gynecology (산부인과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jae Hoon(김재훈) ORCID logo https://orcid.org/0000-0001-6599-7065
Han, Gwan Hee(한관희) ORCID logo https://orcid.org/0000-0001-5263-4855
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