Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

Abstract

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Keywords: Immunoscore; TIL; multispectral immunofluorescence; sTIL score; spatial heterogeneity; spatial statistics; tumor infiltrating lymphocytes.

Figure 1.

Raster versus vector spatial data structure. (A) An example of a TNBC specimen imaged by H&E, with high-resolution multi-color images obtained using mIF (Vectra platform). High-resolution images are used to obtain cell coordinates and phenotypes. (B) In raster-based spatial analysis, the tumor is divided into small subregions (usually by a rectangular grid) and spatial metrics are calculated independently across each subregion, allowing for analysis of spatial metrics such as average cell count, deviation/skewness, and hotspot analysis. (C and D) In vector-based spatial analysis, cells are annotated by their phenotype, (X,Y) geographic location, and other attributes, such as PD-L1 expression. These data can then be analyzed using statistical software to calculate a variety of metrics.

Figure 2.

Illustration of various hotspot metrics. Various methods of calculating IC hotspots have been described in the literature, and include methods based upon rank-ordering of IC density across subregions, or based upon inferential testing. (A) Rank-order-based approaches, which define hotspots as either ‘top 3’ (the three most densely infiltrated subregions) or ‘top 30%’ (the top 30% most densely infiltrated subregions). (B) The Getis-Ord Gi* method, which uses inferential statistical testing to estimate p values indicating the likelihood of each subregion being a hotspot or a coldspot. The Getis-Ord test statistic follows a normal distribution and can be thought of as a measure of local IC density in neighboring subregions, relative to overall IC density. IC, immune cell; TC, tumor cell.

Figure 3.

G and cross-G function for describing cellular colocalization. (A) The (X,Y) locations of ICs in relation to cancer cells of an early-stage breast cancer specimen. Colocalization of cells at specified distances can be illustrated using (B and C) the G function (colocalization of the same cell type) and (D) the cross-G function (colocalization of two distinct cell types). The blue lines illustrate the observed colocalization patterns of the sample, whereas the red lines illustrate the expected colocalization under the assumption of randomness/homogeneous point pattern. In (D), the AUC is illustrated in green and is used to provide a global metric of colocalization of two cell types within a certain proximity range (<50 pixels in this example). TC, tumor cell.

Figure 4.

sTILs score and Immunoscore: methodology and opportunities for spatial applications. (A) Segmentation step: for the sTILs score, the intraepithelial versus stromal tumor compartment are visually determined by a pathologist, whereas for Immunoscore, the IM versus tumor center is determined using an automated ML platform. (B) Sampling and density estimation steps: for the sTIL score, several representative subregions are visually selected and sTIL counts are estimated across each subregion, whereas for Immunoscore, the entirety of the tumor area is divided by a rectangular raster grid, and CD3⁺ and CD8⁺ ICs are counted for each raster cell. (C) Calculation step: for the sTIL score, the arithmetic mean of sTIL densities for each subregion is calculated, whereas for Immunoscore, the arithmetic mean of cohort-level percentile scores across the four cellular compartments is calculated (CD3⁺ IM, CD3⁺ TC, CD8⁺ IM, CD8⁺ TC). (D) Advanced ML, histologic imaging, and spatial analytic approaches can be applied to the sTIL score and Immunoscore to potentially improve predictive/prognostic utility.