Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

A New View of Activating Mutations in Cancer

A vast effort has been invested in the identification of driver mutations of cancer. However, recent studies and observations call into question whether the activating mutations or the signal strength are the major determinant of tumor development. The data argue that signal strength determines cell fate, not the mutation that initiated it. In addition to activating mutations, factors that can impact signaling strength include (i) homeostatic mechanisms that can block or enhance the signal, (ii) the types and locations of additional mutations, and (iii) the expression levels of specific isoforms of genes and regulators of proteins in the pathway. Because signal levels are largely decided by chromatin structure, they vary across cell types, states, and time windows. A strong activating mutation can be restricted by low expression, whereas a weaker mutation can be strengthened by high expression. Strong signals can be associated with cell proliferation, but too strong a signal may result in oncogene-induced senescence. Beyond cancer, moderate signal strength in embryonic neural cells may be associated with neurodevelopmental disorders, and moderate signals in aging may be associated with neurodegenerative diseases, like Alzheimer's disease. The challenge for improving patient outcomes therefore lies in determining signaling thresholds and predicting signal strength.

Technological and computational advances driving high-throughput oncology

Engineering and computational advances have opened many new avenues in cancer research, particularly when being exploited in interdisciplinary approaches. For example, the combination of microfluidics, novel sequencing technologies, and computational analyses has been crucial to enable single-cell assays, giving a detailed picture of tumor heterogeneity for the very first time. In a similar way, these 'tech' disciplines have been elementary for generating large data sets in multidimensional cancer 'omics' approaches, cell-cell interaction screens, 3D tumor models, and tissue level analyses. In this review we summarize the most important technology and computational developments that have been or will be instrumental for transitioning classical cancer research to a large data-driven, high-throughput, high-content discipline across all biological scales.

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

Cell–cell interactions (CCI) play significant roles in manipulating biological functions of cells. Analyzing the differences in CCI between healthy and diseased conditions of a biological system yields greater insight than analyzing either conditions alone. There has been a recent and rapid growth of methods to infer CCI from single-cell RNA-sequencing (scRNA-seq), revealing complex CCI networks at a previously inaccessible scale. However, the majority of current CCI analyses from scRNA-seq data focus on direct comparisons between individual CCI networks of individual samples from patients, rather than “group-level” comparisons between sample groups of patients comprising different conditions. To illustrate new biological features among different disease statuses, we investigated the diversity of key network features on groups of CCI networks, as defined by different disease statuses. We considered three levels of network features: node level, as defined by cell type; node-to-node level; and network level. By applying these analysis to a large-scale single-cell RNA-sequencing dataset of coronavirus disease 2019 (COVID-19), we observe biologically meaningful patterns aligned with the progression and subsequent convalescence of COVID-19.

Intercellular communication in the tumour microecosystem: Mediators and therapeutic approaches for hepatocellular carcinoma

Hepatocellular carcinoma (HCC), one of the most common tumours worldwide, is one of the main causes of mortality in cancer patients. There are still numerous problems hindering its early diagnosis, which lead to late patients receiving treatment, and these problems need to be solved urgently. The tumour microecosystem is a complex network system comprising seven parts: the hypoxia niche, immune microenvironment, metabolic microenvironment, acidic niche, innervated niche, mechanical microenvironment, and microbial microenvironment. Intercellular communication is divided into direct contact and indirect communication. Direct contact communication includes gap junctions, tunneling nanotubes, and receptor-ligand interactions, whereas indirect communication includes exosomes, apoptotic vesicles, and soluble factors. Mechanical communication and cytoplasmic exchange are further means of intercellular communication. Intercellular communication mediates the crosstalk between the tumour microecosystem and the host as well as that between cells and cell-free components in the tumour microecosystem, causing changes in the tumour hallmarks of the HCC microecosystem such as changes in tumour proliferation, invasion, apoptosis, angiogenesis, metastasis, inflammatory response, gene mutation, immune escape, metabolic reprogramming, and therapeutic resistance. Here, we review the role of the above-mentioned intercellular communication in the HCC microecosystem and discuss the advantages of targeted intercellular communication in the clinical diagnosis and treatment of HCC. Finally, the current problems and prospects are discussed.