Lymphocytes form the core of the adaptive immune response. By expressing unique receptors that can recognize specific proteins, B and T cells sense if the body is out of order. B cells recognize foreign (e.g., pathogen-derived) proteins and are able to distinguish them from the body’s own cells. Using their T cell receptor (TCR), T cells scan proteins on the cell surface that present peptides from the inside of the cell. If a body cell is infected or has undergone malignant transformation (i.e., turns into a cancerous cell), the T cell recognises and eliminates this cell. T cell receptors provide information on what particular immune responses are ongoing, and the changes in these receptors can be used to monitor the activity of the adaptive immune system.
The diversity of B and T lymphocytes within a host is called the immune repertoire and represents the total set of receptors that can recognize antigens. Since these B and T cell receptors are enormously diverse, the nucleotide sequence of the antigen-recognizing part of these receptors (the ‘CDR3 region’) can be used as a barcode to identify B and T cell clones (1,2). As a result of the high diversity of the immune repertoire, the limited output of Sanger sequencing provides only limited visualization of this variability. Next generation sequencing (NGS) platforms are ideally suited to extensively characterize and visualize the complexity and plasticity of the TCR and BCR repertoires. By applying this technology, sequences of millions of receptors can easily be obtained. This data can be used to monitor lymphocyte clonotypes with specific sequences and to profile the immune repertoire as a whole (3,4).
Immune repertoire profiling is a generic and excellent method for monitoring the adaptive immune response. The high-throughput sequencing of immune repertoires is increasingly applied in the development of (tumor) biomarkers and immunotherapeutics.
However, the available tools are not designed to track and examine the dynamic nature of the TCR/BCR repertoire across multiple time points or between different biological compartments in a clinical context. By integrating the identity and magnitude of clones over time within an individual, a detailed picture of repertoire dynamics can be obtained.
The emergence of cancer immunotherapies initiated a significant change in the clinical management of melanoma and several other cancer types (5). Treatment with immune checkpoint inhibitors results in superior median and long-term survivals compared to standard chemotherapy. However, only a small group of patients respond to immune checkpoint blockade. Therefore, researchers are now focusing on identifying biomarkers which could predict a patient’s response prior treatment initiation.
Postow et al. (6) showed that patients who did not respond to anti-CTLA-4 treatment have low richness or evenness of the TCR repertoire as shown in Figure 1. That is, a TCR repertoire composed of less unique clones (less diverse) or skewed toward a few specific clones (very clonal) is predictive of a non-response to the treatment.
Figure 1 The study of Postow et al. (6) shows that melanoma patients who had Clinical Benefit (CB) from anti-CTLA-4 therapy had a higher degree of TCR repertoire richness (p = 0.033) and evenness (p = 0.028) at baseline.
This publication, as several others (5), show how immune repertoire sequencing is an informative analysis for the monitoring and stratification of patients treated with immunotherapies. Specifically, the exact quantification of richness and evenness is crucial to use TCR repertoire sequencing in the clinic as biomarker.
The ImmunoGenomiX (IGX) platform will be modularly expanded to become a comprehensive end-to-end immunogenomics data analysis platform designed to analyze, monitor, and compare the immune repertoires in the context of immunotherapy development and at all stages of treatment and over time. Starting from high-throughput sequencing data, it will deliver an easy-to-read report depending on the specific application, be it research, diagnosis, patient stratification, or treatment monitoring.
The IGX platform will allow customers to use their own sample preparation protocols and the next-generation sequencing technology of choice. It requires no programming skills as the interface is intuitive and flexible. Need something specific for your application? The IGX platform can be expanded with additional functionality and applications, including your own tools.
Currently available is the basic module and main building block of the platform, IGX Explore (TCR). With IGX Explore you can reliably perform clonality analysis of the TCR repertoire. From raw sequencing data, the module analyses and counts the individual receptor clones and interactively visualizes them in a user-friendly way.
With IGX Explore you can boost your immunotherapy target discovery, research and development processes:
- Characterize and identify TILs (to identify TCRs that are associated with tumor response)
- Tracking TILs in blood (to check the effect of therapy being developed)
- Identify the sequence of an isolated TCR (identifying the sequence of the TCR with high affinity to antigens)
- Monitor the status of the immune system before, during and after treatment in (pre-)clinical drug development phases
Do you need TCR repertoire for a specific application or do you want to visualize results in a different way? We can custom build it for you. Besides, the IGX platform can be expanded with additional functionality and visualization, including your own tools.
Using a recently published and independent benchmark, we show that IGX Explore offers superior accuracy. The independent benchmark was published in a recent paper by Afzal et al (8), in which the authors benchmarked ’10 state-of-the-art’ T cell receptor repertoire analysis tools (Figure 2). Using simulated sequencing data, the authors created a ‘clonal plane analysis’ that evaluates the performance of the different tools. The clonal plane analysis evaluates repertoire-specific parameters, i.e., the number of different clones (‘richness, x axis’) and the size distribution of the clones (‘evenness’, y axis).
Figure 2 Clonoplot generated using synthetic data by Afzal et al. (8). The intersection of the grey lines mark the correct value of richness and evenness of the repertoire generated for this benchmark by Afzal and colleagues. Each point represents the repertoire richness and evenness inferred by different repertoire analysis tools as presented in (8). The total richness and evenness computed using IGX Explore has been added to the original plot in red (while in gray using only functional sequences).
An important step in repertoire analysis is the correction of sequencing errors. If sequencing errors were left uncorrected, the richness of a sample would be vastly overestimated; correcting too many errors would lead to under-estimating richness. A critical challenge in immune repertoire analysis is to correct the sequencing errors in the data without losing repertoire-specific information in the process.
IGX Explore uses an Adaptive Error Correction algorithm that learns sample-specific mutation rates from the data. By using sample-specific error estimations, erroneous sequences can then be corrected to provide accurate clone sequences, sizes, and frequencies, as demonstrated in (7).
Figure 3 Three benchmark datasets have been generated for each of the four different error profiles shown in the figure. Error profile 0 is the expected error profile of an Illumina MiSeq machine. Negative error profiles increase the expected error rates, while positive profiles decrease error rates. On the y-axis is shown the mean percentage of reads with errors generated at each error profile and the respective correction performed by IGX Explore. IGX Explore Adaptive Error Correction appropriately estimate the error level in each sample and corrects the majority of the sequences under different sequencing error profiles.
Your data is at the core of your business and IGX Explore unlocks the potential of repertoire analysis leaving you in full control of your data. All source data and analysis results are available for follow up investigations and exploration.
The ImmunoGenomiX platform will implement a full workflow management system, assuring full connectivity with LIMS and/or other data management environments at the customer site. A fine control of software versions guarantees that the workflows are completely reproducible.
To ensure scientific soundness and reliability, the IGX platform, including the IGX Explore module is built from the grounds up using software engineering principles. This makes IGX Explore so robust. Besides, it is thoroughly tested at the unit, component and system level and well documented.
IGX Explore does not require programming or other computer skills. An easy-to-use graphical user interface allows you to explore the immune repertoire of your sample with a few clicks. Most plots for the analysis of immune repertoires will be available in interactive formats for easy exploration of your results.
If you require specific analysis or visualization, the interface can be easily expanded with new analysis modules.
Figure 3 IGX Explore extendible user interface.
- Liu et al, Cell 2017, 10.1016/j.cell.2017.01.014
- Liu et al, Cell Biol Tox 2018, 10.1007/s10565-018-9426-0
- Kurz et al, Blood 2015, 10.1182/blood-2015-03-635169
- Roschewsky et al, Lan Oncol 2015, 10.1016/s1470-2045(15)70106-3
- Hogan et al, Front Oncol 2018, 10.3389/fonc.2018.00178
- Postow et al, J Immunother Cancer 2015, 10.1186/s40425-015-0070-4
- Gerritsen et al, Bioinf 2016, 10.1093/bioinformatics/btw339
- Afzal et al, Brief Bioinf 2017, 10.1093/bib/bbx111