Systems immune monitoring in cancer therapy

Abstract

Treatments that successfully modulate anti-cancer immunity have significantly improved outcomes for advanced stage malignancies and sparked intense study of the cellular mechanisms governing therapy response and resistance. These responses are governed by an evolving milieu of cancer and immune cell subpopulations that can be a rich source of biomarkers and biological insight, but it is only recently that research tools have developed to comprehensively characterize this level of cellular complexity. Mass cytometry is particularly well suited to tracking cells in complex tissues because >35 measurements can be made on each of hundreds of thousands of cells per sample, allowing all cells detected in a sample to be characterized for cell type, signalling activity, and functional outcome. This review focuses on mass cytometry as an example of systems level characterization of cancer and immune cells in human tissues, including blood, bone marrow, lymph nodes, and primary tumours. This review also discusses the state of the art in single cell tumour immunology, including tissue collection, technical and biological quality controls, computational analysis, and integration of different experimental and clinical data types. Ex vivo analysis of human tumour cells complements both in vivo monitoring, which generally measures far fewer features or lacks single cell resolution, and laboratory models, which incur cell type losses, signalling alterations, and genomic changes during establishment. Mass cytometry is on the leading edge of a new generation of cytomic tools that work with small tissue samples, such as a fine needle aspirates or blood draws, to monitor changes in rare or unexpected cell subsets during cancer therapy. This approach holds great promise for dissecting cellular microenvironments, monitoring how treatments affect tissues, revealing cellular biomarkers and effector mechanisms, and creating new treatments that productively engage the immune system to fight cancer and other diseases.

Keywords: Human immune monitoring; Immunotherapy; Mass cytometry; Systems immunology; Tumour immunology.

Figure 1. Focal single cell areas in systems cancer immunology

Mass cytometry and other multidimensional single cell tools can be focused to resolve key biomarkers and mechanisms at different layers of cellular interaction. Most commonly, mass cytometry is used to provide cytomic resolution, meaning that all the different cell types present in a tissue are quantified and phenotyped. As this can generally be achieved with 10 markers on a typical mass cytometry panel, this leaves at least 25 mass channels available for detection of cell interaction markers, immunophenotype, and intracellular signaling (4). As nearly any cellular property can now be quantified at the single cell level (5), multidimensional cytometry enables biomarkers with complex expression patterns that can vary with cell type and activation state – such as PD-L1 (6) – to be broadly monitored. Another advantage of cytomic approaches is that cells with unusual and unexpected phenotypes present in a patient’s tissue sample do not escape detection due to expert bias or overly focused analysis strategies. These advantages of mass cytometry address ongoing needs in cancer and immune biomarker development (7).

Figure 2. Systems immune monitoring in cancer therapy

1) Acquisition of high quality tissue samples pre- and post-treatment is a critical element of human immune monitoring. Following processing into single cell suspension, smaller samples of 100,000 to 2 million cells are generally best analyzed immediately, whereas larger samples are typically cryopreserved as aliquots (58). 2) Next, single cells must be detected using a quantitative technique. This review focuses on mass cytometry, but many other flow, imaging, and sequencing based approaches now yield quantitative single cell information with sufficient cellular throughput. Critical to the analysis are software tools that cope with high dimensional data and provide human-readable single cell views (44, 59). 3) Finally, statistical models are derived that correlate cell subsets and biomarkers with clinical outcomes. This information can be used to develop new mechanistic models of cell to cell interactions and the impact of treatment on signaling networks within and between cells.