SIO: A Spatioimageomics Pipeline to Identify Prognostic Biomarkers Associated with the Ovarian Tumor Microenvironment.
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Cited by: 4


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.


cancer microenvironment
deep learning
high-grade serous ovarian cancer
imaging mass cytometry
survival prediction
transcriptomic profiling
tumor biomarkers


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

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