665 Spatial single-cell analysis of colorectal cancer tumour using multiplexed imaging mass cytometry. (9th November 2020) - Tran, Minh; Su, Andrew; Lee, HoJoon; Cruz, Richard; Pflieger, Lance; Dean, Ashely; Nguyen, Quan; Ji, Hanlee; Rhodes, Terence
Abstract : Background: Cancer research experiments often require the dissociation of cells from their native tissue before molecular profiling, leading to the loss of spatial tissue context. The cancer genomics research has shifted from mostly profiling tumour DNA mutations towards the current frontier of investigating individual genes and gene products in single cells and their immediate microenvironments. Information at this level with the spatial context enables us to link cancer–causing mutations and environmental factors to outcomes in cell signalling, responses and survival that will lead to solutions for diagnosing, predicting progression and treating cancers in different individuals. In this project we aim to capture tissue morphology, cancer cell types, multi-parameter protein contents of single cells in within morphologically intact tissue sections of colorectal tumours from 52 patients. Methods: Using Hyperion Imaging Mass Cytometry (IMC), we simultaneously profiled 16 protein markers for each tissue section, capturing molecular signatures of tissue architecture, cancer cells, and immune cells. IMC uses laser beam to accurately ablate every 1µm 2 of tissue region, generating data at subcellular resolution for FFPE tissue sections on a glass microscopy slide. We selected 2–8 regions of interest (ROI), each containing approximately 2098 cells. The ROI sizes range from 141µm x 500µm to 1121µm x 1309µm. We developed an analysis pipeline to process raw Hyperion imaging data (IMCtools), define cellular masks with information about nuclei, membrane, cytoplasm (using CellProfiler and Ilastick), and analyses cellular communities (HistoCAT). We also generated whole exome sequencing data and histopathological mages from sections of the same tissue blocks. Results: By measuring 16 multiplexed proteins, for each tissue region we were able to identify up to seven cell types and preserved their spatial location within the tissue (figure 1 A). Through the spatial map of the cell types to the tissue, we showed the heterogeneity of the tumour microenvironment, such as the infiltration of macrophages and B-cells to the cancer regions (figure 1 A). We found cancer cells consistently marked as positive for p53 and Ki67 proteins. Moreover, we could measure the level of p53 in every individual cell within each tissue section (figure 1 B). The quantitative measurement of p53 by imaging mass cytometry was correlated with the result from traditional genomic sequencing of p53 mutations and with the histopathological annotation. Conclusions: Applying the Hyperion technology, we could acquire rich information from each of the precious cancer samples. The spatial data at single-cell resolution enabled us to assess the heterogeneity of tumour tissue by defining cell types, immune infiltration, and cancer-immune cell interaction within an undissociated tissue section. Future analysis and application of Hyperion data would allow us to find better predictors for colorectal cancer tissue with more accurate diagnosis and prognosis. Ethics Approval: This study was approved by the Institutional Review Board (#1050191) at Intermountain Healthcare (Salt Lake City, UT USA)