Quantitative, Architectural Analysis of Immune Cell Subsets in Tumor-Draining Lymph Nodes from Breast Cancer Patients and Healthy Lymph Nodes

Abstract

BackgroundTo date, pathological examination of specimens remains largely qualitative. Quantitative measures of tissue spatial features are generally not captured. To gain additional mechanistic and prognostic insights, a need for quantitative architectural analysis arises in studying immune cell-cancer interactions within the tumor microenvironment and tumor-draining lymph nodes (TDLNs).Methodology/Principal FindingsWe present a novel, quantitative image analysis approach incorporating 1) multi-color tissue staining, 2) high-resolution, automated whole-section imaging, 3) custom image analysis software that identifies cell types and locations, and 4) spatial statistical analysis. As a proof of concept, we applied this approach to study the architectural patterns of T and B cells within tumor-draining lymph nodes from breast cancer patients versus healthy lymph nodes. We found that the spatial grouping patterns of T and B cells differed between healthy and breast cancer lymph nodes, and this could be attributed to the lack of B cell localization in the extrafollicular region of the TDLNs.Conclusions/SignificanceOur integrative approach has made quantitative analysis of complex visual data possible. Our results highlight spatial alterations of immune cells within lymph nodes from breast cancer patients as an independent variable from numerical changes. This opens up new areas of investigations in research and medicine. Future application of this approach will lead to a better understanding of immune changes in the tumor microenvironment and TDLNs, and how they affect clinical outcomes.

Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology

“…One of the first studies to apply rigorous spatial statistics on data from fully automated image analysis investigated clustering patterns of B cells and T cells in healthy and tumor-draining lymph nodes in breast cancer patients. 40 Specifically, the L-function, 41 which is a statistical test of spatial homogeneity, was applied to the location data of B cells and T cells in immunohistochemically (IHC) stained sections generated from the image analysis software GemIdent (Stanford University, USA) developed by the same group. 42 The L-and K-functions 43 are estimators of spatial homogeneity in a set of points, and can be used to gauge the extent of spatial clustering or dispersion in the data across any scale length (Figure 2a) in two or three dimensions.…”

“…36,40,60 In ecology, spatial patterns of different species are extensively studied and there exist many well-established tools to aid these investigations. These tools have been widely applied to robustly analyze spatial variability and capture patterns of environmental interaction, eg, the influence of hurricanes on dolphin foraging in marine ecology.…”

Comparison of the spatial organization in colorectal tumors using second-order statistics and functional ANOVA

“…All these functions consider the distribution of distances between pair of detected points representing the biological objects. They have been used in various biomedical contexts such as the description of the organisation of cancerous cells in breast [5], [6] or brain [7] tumors, or diabetic and non-diabetic epidermal nerve fibers [8]. However, in all these papers, these statistics are generally used in the simple context of comparing the organization of diseased and healthy data, which lead to studying images containing very different spatial organisations.…”

“…In addition, the object extraction in these works is either done manually [5], [8] or semi-automatically [7], [6]. A manual intervention leads to inter-and intra-operator variability and, being costly, limits the possible size of the study.…”

“…A manual intervention leads to inter-and intra-operator variability and, being costly, limits the possible size of the study. Semi-supervised methods include fine-tuning thresholding method [7] and pixel-classification using intensive user feedback over many classification-correction iterations [6].…”