Analyzing Spatial Point Patterns in Digital Pathology: Immune Cells in High-Grade Serous Ovarian Carcinomas

Jonatan A. González, Julia Wrobel, Simon Vandekar and Paula Moraga

The American Statistician, 2025, vol. 79, issue 3, 355-370

Abstract: Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumor microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyze, for example, the co-varying location and functions of multiple different types of immune cells. Complex spatial relationships between different immune cells have been shown to correlate with patient outcomes and may reveal new pathways for targeted immunotherapy treatments. This tutorial reviews methods and procedures relating to spatial point patterns for complex data analysis. We consider tissue cells as a realization of a spatial point process for each patient. We focus on proper functional descriptors for each observation and techniques that allow us to obtain information about inter-patient variation. Ovarian cancer is the deadliest gynaecological malignancy and can resist chemotherapy treatment effective in cancers. We use a dataset of high-grade serous ovarian cancer samples from 51 patients. We examine the immune cell composition (T cells, B cells, macrophages) within tumors and additional information such as cell classification (tumor or stroma) and other patient clinical characteristics. Our analyses, supported by reproducible software, apply to other digital pathology datasets.

Date: 2025
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DOI: 10.1080/00031305.2025.2459280

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