Tertiary Lymphoid Structures Generation through Graph-based Diffusion

TL;DR

This paper introduces a novel application of graph-based diffusion models to generate biologically meaningful cell-graphs, specifically capturing tertiary lymphoid structures, and demonstrates their utility in data augmentation for cancer research.

Contribution

It is the first to apply graph diffusion models for generating realistic biological cell structures, enhancing understanding of tumor microenvironments and aiding in cancer biomarker analysis.

Findings

Accurately learned the distribution of cells in tertiary lymphoid structures

Generated biologically meaningful cell-graphs for oncology research

Improved TLS classification through data augmentation

Abstract

Graph-based representation approaches have been proven to be successful in the analysis of biomedical data, due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in a tumor tissue. However, to further enhance our understanding of the underlying governing biological mechanisms, it is important to accurately capture the actual distributions of such complex data. Graph-based deep generative models are specifically tailored to accomplish that. In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. In particular, we show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content, a well-established biomarker for evaluating the cancer progression in oncology research. Additionally, we further illustrate the utility of the learned generative models for data augmentation in a TLS classification task. To the best of our knowledge, this is the first work that leverages the power of graph diffusion models in generating meaningful biological cell structures.