Physics Approaches to the Spatial Distribution of Immune Cells in Tumors

TL;DR

This paper introduces a maximum entropy method to analyze the spatial distribution of immune cells in tumor tissues, revealing its significance in predicting cancer recurrence and improving immunotherapy strategies.

Contribution

The study applies a novel maximum entropy approach to quantify immune cell spatial distribution, linking it to clinical outcomes in breast cancer patients.

Findings

Distinct spatial distributions correlate with clinical outcomes

Spatial distribution analysis improves recurrence prediction

Immune cell arrangement impacts immunotherapy effectiveness

Abstract

The goal of immunotherapy is to enhance the ability of the immune system to kill cancer cells. Immunotherapy is more effective and, in general, the prognosis is better, when more immune cells infiltrate the tumor. We explore the question of whether the spatial distribution rather than just the density of immune cells in the tumor is important in forecasting whether cancer recurs. After reviewing previous work on this issue, we introduce a novel application of maximum entropy to quantify the spatial distribution of discrete point-like objects. We apply our approach to B and T cells in images of tumor tissue taken from triple negative breast cancer (TBNC) patients. We find that there is a distinct difference in the spatial distribution of immune cells between good clinical outcome (no recurrence of cancer within at least 5 years of diagnosis) and poor clinical outcome (recurrence within 3 years of diagnosis). Our results highlight the importance of spatial distribution of immune cells within tumors with regard to clinical outcome, and raise new questions on their role in cancer recurrence.