Abstract 854: An artificial intelligence pipeline for imaging mass cytometry data analysis and its application in ovarian cancer prognostic biomarker discovery
Authors: Ying Zhu, Jianting Sheng, Sammy Ferri-Borgogno, Tsz-Lun Yeung, Jared K. Burks, Samuel C. Mok, Stephen T. Wong
Journal: Cancer Research (2020)

Abstract Imaging mass cytometry (IMC) is a powerful platform which enables high-dimensional, single-cell analysis of cell type and state. However, reliable methods used to analyze the IMC data remain to be developed. We seek to build an artificial intelligence (AI)-based analytics pipeline for imaging mass cytometry (IMC) to increase the accuracy of cell segmentation and spatial information extraction and apply the AI pipeline to analyze the IMC data derived from high-grade serous ovarian cancer samples for patient overall survival prediction. Multiplexed spatial analysis of the tumor microenvironment by IMC was performed on 41 formalin fixed paraffin embedded (FFPE) tissue samples obtained from treatment naïve high- grade serous ovarian cancer patients using a panel of 24 metal-tagged antibodies that are specific to tumor, immune and stromal cell markers. IMC data was collected using a a Helios CyTOF instrument equipped with Hyperion Imaging System (Fluidigm). Mask Region-Convolution Neural Network (Mask R-CNN) model was used for cell segmentation. Cell subtypes were derived by iterative phenograph clustering with different marker combinations and different subsets of cells in each iteration. Tumor area was calculated as a thresholded Gaussian blurred image of the density map of the center-of-mass of Keratin positive tumor cells. All cells with center-of-mass located within the tumor region were classified as intratumoral cells. The average cell composition in the nearest neighborhood of each cell type (distance between the center of mass of two cells &amp;lt; 20 μm) in the tumor cell compartment was computed. The results showed that cell segmentation by Mask R-CNN has a higher accuracy than traditional watershed segmentation. Significantly more granzymeB+ CD8+ T cells and CD11b+ Vista+ cells were found in long-term survivors comparing to short-term survivors in both tumor and stromal cell compartments of the tumor microenvironment. Additionally, CD196+, CD45RO+ and CD73+ cell densities in the tumor cell compartment were significantly lower in long-term than short-term survivors. Our results also showed that the mean numbers of CD73+ cells adjacent to Vista- CD4+ T cells, macrophages and B cells were significantly lower in long-term than short-term survivors. The mean number of granzymeB+ CD8+ T cells adjacent to Vista- CD4+ T cells was significantly higher in long-term than short-term survivors. These data demonstrated that the deep learning-based cell segmentation method achieved higher accuracy than the conventional watershed segmentation method. Furthermore, our AI pipeline can automatically extract cell count and cell neighborhood information in tumor. The cell count and neighborhood information could be further employed as features for machine learning to generate predictive biomarkers for ovarian cancer patient survival. Citation Format: Ying Zhu, Jianting Sheng, Sammy Ferri-Borgogno, Tsz-Lun Yeung, Jared K. Burks, Samuel C. Mok, Stephen T. Wong. An artificial intelligence pipeline for imaging mass cytometry data analysis and its application in ovarian cancer prognostic biomarker discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 854.