TY - JOUR
T1 - Sio
T2 - A spatioimageomics pipeline to identify prognostic biomarkers associated with the ovarian tumor microenvironment
AU - Zhu, Ying
AU - Ferri-Borgogno, Sammy
AU - Sheng, Jianting
AU - Yeung, Tsz Lun
AU - Burks, Jared K.
AU - Cappello, Paola
AU - Jazaeri, Amir A.
AU - Kim, Jae Hoon
AU - Han, Gwan Hee
AU - Birrer, Michael J.
AU - Mok, Samuel C.
AU - Wong, Stephen T.C.
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/4/2
Y1 - 2021/4/2
N2 - 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.
AB - 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.
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U2 - 10.3390/cancers13081777
DO - 10.3390/cancers13081777
M3 - Article
AN - SCOPUS:85103843641
SN - 2072-6694
VL - 13
JO - Cancers
JF - Cancers
IS - 8
M1 - 1777
ER -
