Multiplexed Immuno‐Profiling of the Colorectal Carcinoma Microenvironment Using Archival Human Tissue
Authors: Borowsky Jennifer , Nowak Jonathan A. , da Silva Annacarolina , Hamada Tsuyoshi , Morikawa Teppei , Twombly Tyler S. , Nosho Katsuhiko , Nishihara Reiko , Lennerz Jochen K. , Giannakis Marios , Chan Andrew T. , Meyerhardt Jeffrey A. , Fuchs Charles S. , Ogino Shuji 
Published: June 2021

Colorectal carcinoma remains a major cause of cancer‐related morbidity and mortality. Clinical therapeutics targeting the tumor immune microenvironment have been employed with remarkable success in subset of tumor types, including some colorectal carcinomas. However, there are limited options to interrogate the immune cell components of the tumor microenvironment that might best predict response to these therapies. We therefore sought to develop a quantitative and high‐throughput method for profiling the T cell populations composing the colorectal tumor immune microenvironment.To this end, we have developed a multiplexed immunofluorescence (mIF) assay relying upon antibodies targeting five immune cell markers (CD3, CD4, CD8, FOXP3 and CD45RO) known to be important in tumor immunity. In addition, a pan‐cytokeratin antibody enables identification of epithelial/tumor cells, and a nuclear counterstain (DAPI) aids in cell enumeration. This multiplexed approach allows for simultaneous evaluation of all seven markers in a single section of formalin‐fixed paraffin‐embedded tumor tissue. Each marker was first separately validated by standard immunohistochemistry performed on samples of lymph node and normal and neoplastic colon tissue. Antibodies were then tested in a single‐plex immunofluorescence format and subsequently optimized in an iterative fashion to ensure equivalent performance in the full multiplexed protocol. The final assay has been successfully applied to both whole slide sections of tumor and tissue microarrays (TMAs) (see Figures 1 and 2).Slides stained using the multiplex panel are imaged using an automated, whole slide scanning platform with calibrated quantification of imaging regions to generate a precise measure of cell density for any defined population per square mm of tissue. Combinatorial marker analysis allows for sensitive detection and quantification of multiple T cell populations, including all T cells (CD3+), cytotoxic T cells (CD3+CD8+CD4−), T helper cells (CD3+CD4+CD8−), regulatory T cells (CD3+CD8+FOXP3+), and memory T cells (CD3+CD45RO+). Additionally, under pathologist supervision, a machine learning algorithm is employed to automatically segment tissue into regions of tumor epithelium and surrounding stroma. Quantification of immune cells within separate tissue compartments will allow enumeration of true tumor‐infiltrating lymphocytes, periglandular immune cells within the stroma surrounding tumor glands/nests, and the immune microenvironment at the leading invasive edge of the tumor compared to the microenvironment within the center of the tumor.This multiplexed, integrated analysis platform will be used to profile T cell populations composing the immune microenvironment of a cohort of over 1000 colorectal cancer cases from the Nurses' Health Study and Health Professionals Follow‐up Study. This dataset has accumulated clinical, prognostic epidemiological, and tumor molecular data, including whole exome sequencing and tumor neoantigen load. Overall, this approach yields substantially better quality and more quantitative data than conventional single marker immunohistochemistry, while consuming less tissue. Most importantly, it enables biologically relevant analyses that are not possible with single marker approaches and will yield a deeper and more nuanced view of the tumor microenvironment.Support or Funding InformationThis work was supported by U.S. National Institutes of Health (NIH) grants (P01 CA87969 to M.J. Stampfer; UM1 CA186107 to M.J. Stampfer; P01 CA55075 to W.C. Willett; UM1 CA167552 to W.C. Willett; U01 CA167552 to W.C. Willett and L.A. Mucci; P50 CA127003 to C.S.F.; R01 CA118553 to C.S.F.; R01 CA169141 to C.S.F.; R01 CA137178 to A.T.C.; K24 DK098311 to A.T.C.; R35 CA197735 to S.O.; R01 CA151993 to S.O.; K07 CA190673 to R.N.; and K07 CA188126 to X.Z.); by Nodal Award from the Dana‐Farber Harvard Cancer Center (to S.O.); and by grants from the Project P Fund, The Friends of the Dana‐Farber Cancer Institute, Bennett Family Fund, and the Entertainment Industry Foundation through National Colorectal Cancer Research Alliance, and by Stand Up to Cancer Colorectal Cancer Dream Team Translational Research Grant (to C.S.F. and M.Gi.). J.B. was supported by the Australia Awards‐Endeavour Scholarships and Fellowships Program. K.K. was supported by grants from Overseas Research Fellowship (JP2017‐775) from Japan Society for the Promotion of Science. T.H. was supported by a fellowship grant from the Mitsukoshi Health and Welfare Foundation.T.H. was supported by a fellowship grant from the Mitsukoshi Health and Welfare Foundation. L.L. was supported by a scholarship grant from Chinese Scholarship Council and a fellowship grant from Huazhong University of Science and Technology. The content is solely the responsibility of the authors and does not necessarily represent the official views of NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.