SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment

• Main Authors: 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
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Cancers
• Subjects: cancer microenvironment; imaging mass cytometry; deep learning; transcriptomic profiling; high-grade serous ovarian cancer; tumor biomarkers
• Online Access: https://www.mdpi.com/2072-6694/13/8/1777

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.