From bench to bedside: Single-cell analysis for cancer immunotherapy

Abstract

Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

Keywords: computational biology; single-cell proteomics; single-cell transcriptomics; spatial proteomics; spatial transcriptomics; translational medicine; tumor immunology.

Figure 1 -

High dimensional transcriptomics and proteomics approaches for cancer profiling. Several high dimensional approaches are currently available to understand cancers cellular composition and inter-cellular interactions. A. Single-cell proteomics (CyTOF) provides cell composition and cell state information. B. Single-cell transcriptomics allows the same type of analysis, but its genome-wide coverage can also deliver cell trajectory predictions and T and B cell repertoires. In order to correlate cell composition and states to cellular interactions, spatial technologies are more informative than single-cell suspension analysis. C. With spatial proteomics and its single-cell resolution, it is possible to identify individual cell types and determine specific cell-to-cell interactions. D. Although it lacks single-cell resolution, spatial transcriptomics can predict cell interactions based on the molecular expression of receptors and ligands between different cell neighbors and discover driving oncogenic pathways among the different cell niches because it is not restricted to previously selected markers. The selection of which approach to apply will depend on what samples are available, how they are preserved, and what biological questions need answered.

Figure 2 -

Computational workflow and methods for single-cell and spatial analysis. Several open source benchmarked computational tools are available for high dimensional datasets analysis. Independent of the tools of choice, analytical steps are required in order to obtain reproducible results and identify markers to predict response and targets for new therapeutics. A. Single-cell and spatial data analysis will start with raw data preprocessing for (1) data clean-up to remove poor quality cells and normalization to correct for low or high numbers of reads associated with experimental artifacts; (2) batch correction to remove unwanted variation among samples due to experimental discrepancies; (3) and data imputation to correct for the real data dropouts (zeros in the data). B. Subsequently, dimensionality reduction will allow data visualization and cell type annotation using clusterization tools that assign annotations based on specific markers expressed by each cluster. From there, the data is ready for downstream analysis depending on the methodology applied and biological questions. C. Molecular alterations can be identified using (1) differential expression analysis. In the case of transcriptomics data, it is also possible (2) to perform pathway analysis to identify drivers of cancer progression and responses to therapies and (3) to predict cell fate trajectories to understand tumor and TME modulation across time. D. From proteomics and transcriptomics data, it is possible to take a snapshot of the (1) molecular (e.g.: protein markers expression, cytokine genes expression, receptor-ligand expression), and (2) cellular interactions (e.g.: cell proximity analysis) that potentially drive the different features associated with cancer progression and response to therapies. E. Finally, multi-omics approaches allowing (1) protein and gene expression analysis from the same samples (CITE-seq) or (2) T and B cell repertoire analysis in combination with transcriptional profile add an additional layer of information that increases accuracy for cell types annotation and investigation of their role in cancer evolution and therapeutic responses.

Figure 3 -

Mouse to human studies using high dimensional analysis will drive the next generation of precision cancer immunotherapies. Single-cell and spatial technologies have the power to drive discoveries based on the cell types that are commonly affected by immunotherapies in preclinical and human tumors. Following identification of the commonalities between models, studies can focus on identifying molecular and cellular markers of response using multi-omics approaches. The combination of different layers of data will drive patient selection for the most adequate therapy, better clinical trial designs, and development of new immunotherapies.