Abstract
Simple SummaryHigh-grade serous ovarian cancer (HGSC) caused more than 13,000 deaths annually in the United States. A critically important component that influences the HGSC patient survival is the tumor microenvironment. However, how different cells interact to influence HGSC patients’ survival remains largely unknown. To investigate this, we developed a 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. Our pipeline automatically and accurately segments cells and extracts salient cellular features to identify biomarkers, and multiple nearest-neighbor interactions among different cells that coordinate to influence overall survival rates in HGSC patients. In addition, we integrated IMC data with microdissected tumor and stromal transcriptomes to identify novel signaling networks. These results may lead to the discovery of novel survival rate-modulating mechanisms in HGSC patients.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.
Highlights
Advanced high-grade serous ovarian cancer (HGSC) is the most lethal gynecologic malignancy, causing more than 13,000 deaths annually in the United States [1]
Cell segmentation was performed using the deep learning method, MRCNN, followed by phenograph clusterings to identify and annotate different cell subtypes used for cell density and neighborhood analyses
We focused on the cell densities of the six imaging mass cytometry (IMC) features significantly different between long-term survivors (LTS) and short-term survivors (STS) (Figure 5A), four of which were selected by our machine learning model for survival prediction (Figure 6B, bottom)
Summary
Advanced high-grade serous ovarian cancer (HGSC) is the most lethal gynecologic malignancy, causing more than 13,000 deaths annually in the United States [1]. Most tumors (>75–80%) recur within 12 to 24 months after treatment, and many patients die of progressively chemotherapy-resistant disease [4,5,6]. A critically important component that influences the patient survival is the tumor microenvironment [7,8], which is primarily composed of fibroblasts, extracellular matrix proteins, endothelial cells, lymphocytic infiltrates, and cancer cells. The tumor microenvironment has been shown to directly affect cancer cell growth, migration, invasion, chemoresistance, cell-cell interactions, and matrix remodeling [9,10]. Spatially resolved, single-cell analysis that can identify tumor and stromal cell phenotypes, characterize their heterogeneity and cell-cell interactions, and biomarkers for predicting survival of HGSC patients, are lacking
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