Figure 1. Heat map of TRBV allele profiles for 55 Caucasians who developed adverse events following checkpoint blockade immunotherapy (6). TCRB repertoire was determined and used to construct variable gene allele profiles for each individual. Each row in the heat map represents an individual and each column is a different variable gene allele. Red tiles indicate that an allele was detected, while blue tiles indicate allele absence. Individuals are arranged by haplotype group classification with four groups identified. There are six groups identified as indicated by the cluster column. In this retrospective study, Group 2 appears to be protected against severe adverse events, illustrating how accurate polymorphism studies of the TCR repertoire may be used to improve immunotherapy.
What is the immune repertoire?
Your immune system is comprised of two subsystems that work together, providing both innate and adaptive immunity. Innate immunity refers to the non-specific defense mechanism that protects the body from a toxin or a foreign object, called an antigen. The rapid response of your innate immune system also activates the adaptive system, which is the body’s antigen specific response to protect itself.
The more complex adaptive immune system takes days to respond to an infection and is composed of two major types of lymphocytes called B cells and T cells. These lymphocytes have unique antigen receptors acquired through encounters with foreign bodies over a person’s lifetime. This means the adaptive immune system is capable of creating an immunological memory once someone is exposed to an infection, resulting in a more efficient and stronger response to future exposures.
Each unique antigen receptor only recognizes a single antigen and, incredibly, this range of specificity is encoded by a fixed number of gene segments. Through a mechanism called V(D)J recombination, irreversible somatic DNA recombination of these genetic regions during cellular development results in a mature lymphocyte having a single specificity. Immune repertoire refers to all of the unique T-cell receptor (TCR) and B-cell receptor (BCR) genetic rearrangements within the adaptive immune system. Researchers also refer to the TCR repertoire and BCR repertoire when studying T cells and B cells, respectively. At any given point in time, a person’s immune repertoire is made up of ~108 lymphocytes with different specificities (1).
Why the immune repertoire is important
Each mature lymphocyte differs from the others in the repertoire in its specificity, and it is ‘clonal’. Since T-cell and B-cell lymphocytes are constantly monitoring the body for antigens from foreign bodies, they undergo a process similar to natural selection. According to natural selection, organisms that are better suited for an environment thrive and produce offspring. In the immune repertoire, only lymphocytes that encounter an antigen with the right receptor to bind to it will be activated and proliferate during an immune response, forming a clone of cells with identical antigen receptors.
Understanding the immune repertoire is important with the advent of precision medicine and immunotherapy, where treatments are being developed that are tailored to an individual for greater efficacy. Numerous treatments are in development and coming into use that affect the immune system. Measuring the immune repertoire may help to predict therapeutic outcome and be used to monitor the effects of treatment. Treatments are also in development to increase the immune repertoire, such as the use of cytokines as drugs.
A low immune repertoire after therapy, such as cancer treatment, may be a potential side effect of treatment, indicating a weakening of the immune system and resulting greater susceptibility to disease. Immune-mediated adverse events (imAEs) are indeed one of the limiting factors for development of cancer treatments, resulting in patients needing to be nursed back to health in carefully monitored environments. imAEs are also called immune-related adverse events (irAEs) and measuring the immune repertoire may help indicate if a patient will have a greater propensity for them. Identifying methods to monitor and speed up the recovery of the immune repertoire would also be beneficial.
Measuring the immune repertoire with NGS
Accurately determining the immune repertoire is challenging on several fronts. High enough throughput is required to be able to identify all the genetic rearrangements that make up the immune repertoire. High sensitivity is also required to detect the rare clones within the repertoire. The massively parallel sequencing of NGS provides the high throughput and quantitative capabilities to examine the immune repertoire at an unprecedented level.
Hypervariable regions such as the TCR gene are incredibly hard to sequence. The loci are also repetitive in nature and structurally complex. These challenges result in existing databases and reference genomes that are neither complete nor accurate (2). Due to the high number of variants, the sequencing reads will look very different compared to the reference genome and may not be mapped appropriately.
Targeted sequencing using an amplicon-based approach is ideal for immune repertoire sequencing. PCR primers can be carefully designed to flank and amplify the hypervariable target region of interest, allowing researchers to be confident that the TCR gene is being accurately targeted for sequencing and capture of the somatic information.
Targeted sequencing also opens up an opportunity to examine the immune repertoire with RNA. Targeted RNA sequencing provides access to more abundant genetic material and directly correlates with the clonal genetic information. This allows the possibility of sequencing the genetic material in peripheral blood to enable liquid biopsy applications.
To learn more about targeted sequencing approaches and the advantages of amplicon-based enrichment, explore the basics of NGS.
TCR immune repertoire as a biomarker for immunotherapy
T cells play a direct role in the cancer-immunity cycle and so TCR profiling may provide the most direct measurement of tumor immunogenicity (i.e., ability to induce an immune response). A single sample of pre-treatment peripheral blood may potentially allow one to predict both an objective clinical response and immune-mediated adverse events following immunotherapy.
Measurements such as evenness, richness, and diversity are typically used to describe ecological communities and their interactions with their environment. The same profiling can also be used to describe the clonal population of T cells and may be a biomarker for immunotherapy response (Table 1).
Term | Definition |
|---|---|
| Clonal evenness | Distribution of TCR to see if there is clonal expansion |
| Clonal richness | Measurement of the number of clones with unique TCRs |
| Clonal diversity | Distribution of TCR, taking into account both evenness and richness |
Table 1. TCR immune repertoire measurements and the definitions
In the cancer-immunity cycle, neoantigens and antigens are released by tumor cells that may or may not be recognized by T cells. Checkpoint proteins, such as PD-L1 on tumor cells, may be suppressing immune response. High clonal evenness may indicate an improved clinical response to checkpoint inhibitors, which block checkpoint proteins and remove the “brakes” being applied on the immune system. It stands to reason that with greater evenness, there is an increased probability that a T cell will be able to recognize a tumor antigen and activate a response to begin attacking the tumor.
Evenness can also be applied to T-cell therapy by providing manufacturing quality control information on engineered T cells in production. In adoptive cell therapy (ACT), a patient’s own T cells are genetically modified to target antigens selectively expressed on cancer cells (3, 4). High T-cell evenness is desired to ensure there is an unbiased growth of the polyclonal population and to maximize efficacy. Measuring the TCR immune repertoire of the product would provide manufacturers and even physicians a quality control that could potentially result in a better outcome for the patient.
Polymorphisms within the TCR beta variable gene (TRBV) have been implicated in autoimmune disease and irAEs (5). Sequencing the TCR repertoire and accurately identifying polymorphisms may be a promising method to stratify patients and predict if the patient will have an adverse event from immunotherapy (Figure 1). For accurate polymorphism detection in TCR, a low substitution error rate with the sequencing assay is imperative. This low error rate also enables the ability to measure convergent TCRs, which may be another promising biomarker to predict clinical response.
What is TCR convergence?
The genetic code is the set of rules used to translate the information encoded in genetic material into proteins and defines how sequences of three nucleotides specify an amino acid for protein synthesis. These sequences are called codons and because there are four nucleotides in both DNA and RNA, there are 64 triplet sequences as seen in the RNA codon table (Table 2). As a result, codons are degenerate and different genetic sequences can code for the same protein (Figure 2).
Table 2. RNA codon table
As noted previously, lymphocytes that encounter an antigen with the right receptor to bind to it will be activated, resulting in clonal expansion during an immune response. Given a mature T cell has a unique genetic sequence, this means that different clones with the same TCR would proliferate for a response. This concept is called TCR convergence and has important implications in immunotherapy as it provides further indication beyond looking at clonal expansion to potentially predict therapeutic outcome.
Figure 2. Illustration of codon degeneracy and TCR convergence. In this example, three different clones each have a unique genetic sequence, with differences in the sequence highlighted in grey. Although each sequence is unique, the resulting amino acid sequence for the protein receptor is the same due to codon degeneracy. Within the T-cell immune repertoire, this would be an example of TCR convergence, which may help predict outcome for immunotherapy.
The frequency of convergent TCRs within a repertoire may provide an indication of the immunogenicity of a tumor and thus its sensitivity to checkpoint blockade therapy. Convergent TCRs may preferentially occur due to chronic antigen stimulation rather than an acute, but transient, event. Unlike biomarkers that rely on the quantification of tumor genetic alterations, TCR convergence may detect T-cell responses to tumor neoantigens beyond those arising from non-synonymous mutations (Table 3). Using peripheral blood, convergence and clonal expansion may predict response to anti-CTLA-4 (Figure 3) (7). It may also predict response to vaccine-based immunotherapy as seen in advanced-stage melanoma patients (8).
Table 3. Types of antigens measured by tumor mutation burden and TCR convergence
Figure 3. Prediction of anti-CTLA-4 outcome via TCR evenness and convergence (7). A logistic regression classifier was trained using TCR evenness and convergence as features to predict response to immunotherapy. Model scoring is shown on the left plot for responders (objective clinical response, OCR) versus non-responders (progressive disease, PD). A clear delineation is seen between the two groups of individuals. Receiver operator characteristics (ROC) curves on the right are derived from convergence, evenness, and the combination of both measurements. The combination of TCR evenness and convergence improves prediction of response to immunotherapy.
NGS sequencing enables in-depth analysis of the TCR repertoire at an unprecedented level. A greater understanding of the cancer-immunity cycle is now possible, leveraging a deeper understanding of evenness, convergence, and haplotyping to potentially improve patient outcome. Given the infancy of immuno-oncology and TCR repertoire sequencing, more research is required to maximize the potential for TCR repertoire to be used in multiple applications beyond what we have highlighted here (Table 4).
Measurement | Potential application |
|---|---|
TCR convergence and evenness | Predict response to immunotherapy/improved vs evenness alone |
| Epitope spreading | |
| Dendritic cell-based immunotherapy (vaccine based) | |
| ID autoreactive TCR | |
| Analyzing samples deriving from autoimmune lesions | |
Polymorphisms | Predict adverse effects |
| Study checkpoint blockade agents in combination | |
| Use checkpoint blockade in neoadjuvant settings | |
Haplotyping | Patient stratification |
| Population stratification | |
| Dosage studies | |
| Pharmacogenomics |
Table 4. TCR repertoire measurements and their potential applications.
References
- Janeway’s Immunobiology, 9th Edition.
- Watson CT, et al. J Immunol 198:3371-3373 (2017)
- Wesolowski R, et al. J Immunother Cancer 1:10 (2013)
- Rosenberg SA, et al. Science 348:62 (2015)
- Looney T, et al. bioRxiv 406157; doi: (2018) https://doi.org/10.1101/406157
- Looney T, et al. 377T. Presented at the 68th Annual Meeting of The American Society of Human Genetics (2018)
- Zhang L et al. J Immunother Cancer 6(Suppl 1):P82 (2018)
- 8. Storkus WJ, et al. J Immunother Cancer 6(Suppl 2):P709 (2018)