T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

Abstract

T cells are integral components of the adaptive immune system, and their responses are mediated by unique T-cell receptors (TCR) that recognize specific antigens from a variety of biological contexts. As a result, analyzing the T-cell repertoire offers a better understanding of immune responses and of diseases like cancer. Next-generation sequencing technologies have greatly enabled the high-throughput analysis of the TCR repertoire. On the basis of our extensive experience in the field from the past decade, we provide an overview of TCR sequencing, from the initial library preparation steps to sequencing and analysis methods and finally to functional validation techniques. With regards to data analysis, we detail important TCR repertoire metrics and present several computational tools for predicting antigen specificity. Finally, we highlight important applications of TCR sequencing and repertoire analysis to understanding tumor biology and developing cancer immunotherapies.

Figure 1.

Schematic representation of TCR, MHC, and V(D)J recombination. A, TCR with paired alpha and beta chains at the cell surface. In the variable region of the TCR, variable (V), diversity (D), and joining (J) gene segments are shown in dark blue, purple, and green, respectively. The constant (C) region is shown in light blue. The commonly sequenced CDR3 region of the TCRβ chain is shown in the antigen-binding site. B, V(D)J recombination of the beta chain. Initially, a D segment randomly recombines with a J segment, then the D-J segment recombines with a random V segment. The complete variable region joins with the constant region to encode a functioning beta chain. C, Interaction of TCR and pMHC. MHC class I molecule is represented with alpha chains in yellow and beta chain in orange. The peptide is shown in dark blue.

Figure 2.

Visualization of metrics to characterize T-cell repertoires. A, Density, diversity, and clonality metrics used to characterize T-cell repertoires. High metrics are contrasted with low metrics, and colors represent different T-cell clonotypes. Density refers to the proportion of T cells in a given area, diversity refers to the number of unique clonotypes in a sample, and clonality refers to expansion of clonotypes in a sample. B, Jaccard index and MOI used to compare different T-cell repertoires. High overlap is contrasted with low overlap, and colors represent different T-cell clonotypes. Jaccard index considers only clonotype, while MOI considers both clonotype and frequency. Colored spheres represent T-cell clonotypes, and gray units in the background represent tumor cells. S1, sample 1; S2, sample 2.

Figure 3.

Common representations of TCR-related data. Stacked bar graphs showing proportion of top 10 clonotypes (A), clone tracking graphs (B), Venn diagrams (C), differential abundance plots (D), heatmaps (E), phylogenetic trees (F), alluvial plots (G), and network diagrams (H); all are used to present TCR data. Such graphs offer information about the frequency and evolution of T-cell clonotypes in different patients, in different tissue types, and through different disease or therapeutic conditions.

Figure 4.

Applications of TCR sequencing and T-cell repertoire analysis. TCR sequencing is a valuable tool with fundamental applications to cancer biology and translational implications with cancer immunotherapy. Its utility extends to other contexts as well, including organ transplantation and pathogen immunity.