Tumor And Immune Cell Profiling In Breast Cancer Using Highly Multiplexed Imaging Mass Cytometry Single-Cell Technology Demonstrates Tumor Heterogeneity And Immune Phenotypic Abnormality In Ethiopian Women.

Background: Tumor heterogeneity represents a complex challenge to cancer treatment, disease recurrence, and patient survival. Imaging mass cytometry (IMC) is an emerging proteomic tool for cancer profiling in tumor tissue samples. IMC enables digital image analysis by multiplexed immunostaining of cells and proteins within tissue and preserves spatial relations within tumor environment. We have applied IMC based approach to study the heterogeneity of invasive breast carcinoma protein expression pattern in formalin fixed paraffin embedded tissues. Methods: A total of 10 region of interest (ROI) derived from 5 patients with primary invasive breast carcinoma representing three molecular subclasses (HR+/HER2-,HER2+/HR- and TNBC) were stained with a 30-marker IMC metal labeled antibody panel (α-SMA, EGFR, p53, CD33, CD16, CD163, CD11b, PDL1, CD31, CD45, D44,Vimentin, FoxP3, CD4, ECadherin, CD68, CD20, CD8a, Cytokeratin7, PD1, GranzymeB, Ki67, ColTypeI, CD3, CD45RO, HLADR, DARC \u0026 CD11c). Tissue imaging was done by quantifying the abundance of bound antibody with a Hyperion IMC. MCD Viewer was used for visualization purpose and to export raw 16-bit tiff images for segmentation on CellProfiler. Segmentation masks were combined with the individual tiff files to extract single-cell information from each individual image. HistoCAT was applied to perform unbiased clustering of cell populations using the PhenoGraph algorithm and clustered cell populations was overlaid on t-SNE plot. The relative marker expression was used to generate heat-maps and each cluster was manually assigned a phenotype based on its expression profile. Results: The t-SNE generated from each ROI revealed different distinct cell populations and we report the presence of diverse tumor and immune cell populations in our samples. The (min, max) number of PhenoGraph clustered tumor cell populations in HR+/HER2-, HER2+ and TNBC Cases were (5,8) (7,9) and (5,7) respectively. Similarly, the (min, max) number of PhenoGraph clustered immune cell populations in HR+/HER2-, HER2+ and TNBC Cases were (5,8) (7,9) and (5,7) respectively. We also document the presence of inter and intra-tumor heterogeniety in expression of PD1 and PDL1 in all the tumor subtypes studied. Additionally, we report a phenotypic abnormality in the immune cell populations identified with dual or triple markers expression of the canonical CD antigens of T-Cells, B-Cells and macrophages. Conclusion: The current study demonstrates high-dimensional visualization with the simultaneous analysis of epithelial, immune, and stromal components using IMC can be used to explore cell populations in tumor tissue to quantify tumor heterogeneity or identification of novel clustering patterns that has potential for translational research and clinical practice. Significance: This study presents the potential of Imaging Mass Cytometry and single cell analysis algorithms in multiplex high throughput tumor tissue studies. Citation Format: Maheteme Bekele, Aisha Jibril, Daniel Seifu, Markos Abebe, Abebe Bekele, Wondemagegnhu Tigneh, Yonas Bokretsion, Christina Karlsson, Mats G. Karlsson, Rachel Martini, Olivier Elemento, Clayton Yates, Paula Ginter, Lisa Newman, Melissa Davis, Endale Hadgu Gebregzabher. Tumor and immune cell profiling in breast cancer using highly multiplexed imaging mass cytometry single-cell technology demonstrates tumor heterogeneity and immune phenotypic abnormality in Ethiopian women [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-087.