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Diffuse large B-cell lymphoma, not-otherwise-specified, (DLBCL), is the most common aggressive non-Hodgkin lymphoma and a biologically heterogeneous disease. Despite the development of effective immunotherapies, the organization of the DLBCL tumor-immune microenvironment (TIME) remains poorly understood. We interrogated the intact TIME of 51 de novo DLBCLs with triplicate sampling to characterize 337,995 tumor and immune cells using a 27-plex antibody panel that captured cell lineage, architectural, and functional markers. We spatially assigned individual cells, identified local cell neighborhoods, and established their topographical organization in situ. We found that the organization of local tumor and immune cells can be modeled by six composite cell neighborhood types (CNTs). Differential CNT representation divided cases into three aggregate TIME categories: immune-deficient, dendritic-cell enriched (DC-enriched), and macrophage-enriched (Mac-enriched). Cases with immune-deficient TIMEs have tumor cell-rich CNTs wherein the few infiltrating immune cells are enriched near CD31-positive vessels in keeping with limited immune activity. Cases with DC-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD11c-positive dendritic cells and antigen-experienced T cells also enriched near CD31-positive vessels in keeping with increased immune activity. Cases with Mac-enriched TIMEs selectively include tumor cell-poor/immune cell-rich CNTs with high numbers of CD163-positive macrophages and CD8 T cells throughout the microenvironment, accompanied by increased IDO-1 and LAG-3 and decreased HLA-DR expression, and genetic signatures in keeping with immune evasion. Our findings reveal that the heterogenous cellular components of DLBCL are not randomly distributed but organized into CNTs that define aggregate TIMEs with distinct cellular, spatial, and functional features.

An ability to characterize the cellular composition and spatial organization of the tumor microenvironment (TME) using multiplexed IHC has been limited by the techniques available. Here we show the applicability of multiplexed ion beam imaging (MIBI) for cell phenotype identification and analysis of spatial relationships across numerous tumor types. Formalin-fixed paraffin-embedded (FFPE) samples from tumor biopsies were simultaneously stained with a panel of 15 antibodies, each labeled with a specific metal isotope. Multi-step processing produced images of the TME that were further segmented into single cells. Frequencies of different cell subsets and the distributions of nearest neighbor distances between them were calculated using this data. A total of 50 tumor specimens from 15 tumor types were characterized for their immune profile and spatial organization. Most samples showed infiltrating cytotoxic T cells and macrophages present amongst tumor cells. Spatial analysis of the TME in two ovarian serous carcinoma images highlighted differences in the degree of mixing between tumor and immune cells across samples. Identification of admixed PD-L1+ macrophages and PD-1+ T cells in an urothelial carcinoma sample allowed for the detailed observations of immune cell subset spatial arrangement. These results illustrate the high-parameter capability of MIBI at a sensitivity and resolution uniquely suited to understanding the complex tumor immune landscape including the spatial relationships of immune and tumor cells and expression of immunoregulatory proteins.

The growing application of immunotherapy and its ability, or inability, to modulate the tumor microenvironment (TME) has exposed a need for techniques capable of simultaneously tracking large numbers of biomarkers in complex tissue at sub-cellular resolution. To address this, MIBI uses tissue processed similar to standard immunohistochemistry (IHC) methods but with the advantage of being able to stain the tissue simultaneously with a panel of up to 40 metal labeled antibodies. The expression of proteins is imaged by MIBI in a single scan at subcellular spatial resolution by combining the high resolution of an ion beam and the sensitivity of time-of-flight secondary ion mass spectrometry (ToF-SIMS). To characterize the immune infiltrates present in a set of 10 non-small-cell lung carcinoma (NSCLC) samples, a panel of 22 metal labeled antibodies was used to stain the tissue. The stained tissue was then analyzed by MIBI and the masses of detected species were assigned to target biomolecules given the unique label of each antibody. Multi-step processing and segmentation utilizing highly expressed nuclear, membrane, and cytoplasmic markers were performed to accurately determine cell boundaries and enable quantitative analyses of marker expression, cell classification, and the spatial relationships between cell types. Results comparing the abundance of tumor cells, vascular cells, and immune cell subsets between samples identified “cold” tumors with few immune cells and inflamed samples that varied in the type of immune populations present. Immune checkpoint markers (LAG3, PD-1, PD-L1, TIM-3) were also quantified at single cell resolution. In conclusion, this study shows that MIBI offers high-parameter capability necessary to (1) identify the wide range of cell types present within the complex tumor immune landscape, (2) the spatial relationships between these different cell populations, and (3) the expression status of key immunoregulatory proteins. Citation Format: Jason Ptacek, Srimoyee Ghosh, Lourdes Pablo, Bin Feng, Hailei Zhang, Yari Sigal, Jay G. Tarolli, Murat Aksoy, Yi Zhang, Rachel Finck, Jessica Finn. Immune profiling of the tumor microenvironment using multiplexed ion beam imaging (MIBI) [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-092.

Understanding the cell types present in the tumor microenvironment and their activation state is at the forefront of immunotherapy research. To address this, MIBI has been developed to simultaneously image more than 40 markers at single cell resolution. Staining of 10 NSCLC formalin-fixed paraffin embedded tissue sections was performed with a panel of 20 metal labeled antibodies stained together. The tissue was imaged at subcellular resolution using an ion beam and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Masses of detected species were assigned to target biomolecules given the unique label of each antibody and multi-step processing and segmentation were performed to create images of the TME. Each tumor sample was imaged across 10 regions of interest (ROIs) to assess heterogeneity of the TME. The resulting single cell segmentation enabled quantitative analyses of both marker expression and the spatial relationships between cells of different types. At the highest level, cells were classified as immune (CD45) and tumor cells (keratin) based on measured intensities of marker expression. Co-expression of markers were used to classify B cells, macrophages, and T cells subsets. Markers associated with immune suppression such as PD-L1 were also quantified. Cell types and their frequency were compared within the 10 ROIs collected per sample as well as between samples. Finally, distances between tumor and the closest immune cell were measured as a means for describing the spatial organization of the TME, which has been linked to patient survival. In conclusion, MIBI offers high-parameter capability, at a sensitivity and resolution uniquely suited to understanding the complex tumor immune landscape.