Patient-specific cell communication networks associate with disease progression in cancer

Abstract

The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling 'axes' with clinical outcomes. Using cell-sorted gene expression data, we generated a scaffold of cell-cell interactions and define a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for TCGA patient samples, each representing likely channels of intercellular communication in the tumor microenvironment. It was shown that particular edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

Analyzing network diversity of cell–cell interactions in COVID-19 using single-cell transcriptomics

“…The majority of CCI inference methods are designed to analyze CCI for a single condition. As characterizing differences in CCI activity across multiple conditions-for example, between healthy and diseased tissue-helps elucidate the diverse mechanisms of CCI-mediated responses to disease, more so than analyzing condition-specific CCI networks in isolation, recent efforts have focused on developing methodologies to systematically study the differences in CCI between multiple conditions (Xiong et al, 2019;Gibbs et al, 2021;Wang et al, 2022). However, the majority of previous CCI analyses that analyze differences between conditions tend to analyze CCI networks inferred from a single scRNA-seq sample from each condition or analyze CCI networks constructed by aggregating several samples.…”