A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples

TL;DR

This paper introduces a graph neural network framework that integrates tissue morphology and protein expression data to analyze the tumor microenvironment across different stages, providing a novel approach to complex multiplexed tissue analysis.

Contribution

It presents a new graph-based neural network method for analyzing multiplexed tissue data, overcoming key challenges and enabling biologically meaningful interaction insights.

Findings

Effective profiling of tumor microenvironment stages

Integration of morphology and protein data using GNNs

Potential for improved cancer diagnostics

Abstract

Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated with different tumour stages. Our framework presents a new approach to analysing and processing these complex multi-dimensional datasets that overcomes some of the key challenges in analysing these data and opens up the opportunity to abstract biologically meaningful interactions.