Analysing spatial point patterns in digital pathology: immune cells in high-grade serous ovarian carcinomas

TL;DR

This paper reviews methods for analyzing spatial point patterns of immune cells in multiplex immunofluorescence images of ovarian cancer, revealing complex spatial relationships that correlate with patient outcomes and can inform targeted therapies.

Contribution

It introduces a comprehensive approach to analyze spatial distributions of immune cells in digital pathology, emphasizing functional descriptors and inter-patient variation analysis.

Findings

Immune cell spatial patterns vary across patients.

Spatial relationships correlate with clinical features.

Reproducible software supports analysis.

Abstract

Multiplex immunofluorescence (mIF) imaging technology facilitates the study of the tumour microenvironment in cancer patients. Due to the capabilities of this emerging bioimaging technique, it is possible to statistically analyse, 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 realisation 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 tumours and additional information such as cell classification (tumour or stroma) and other patient clinical characteristics. Our analyses, supported by reproducible software, apply to other digital pathology datasets.