Tools for fundamental analysis functions of TCR repertoires: a systematic comparison

Immunosequencing identifies signatures of T cell responses for early detection of nasopharyngeal carcinoma

To identify nasopharyngeal carcinoma (NPC)-relevant T cell receptors (TCRs), we profile the repertoires of peripheral blood TCRβ chains from 228 NPC patients, 241 at-risk controls positive for serum Epstein-Barr virus (EBV) VCA-IgA antibody, and 251 seronegative controls. We develop a TCR-based signature (T-score) based on 208 NPC-enriched CDR3β sequences, which accurately diagnoses NPC in both the original and independent validation cohorts. Notably, a higher T-score, associated with a shorter time interval to NPC diagnosis, effectively identifies early-stage NPC among EBV-seropositive at-risk individuals prior to clinical diagnosis. These NPC-enriched TCRs react against not only EBV-specific antigens but also non-EBV antigens expressed by NPC cells, indicating a broad range of specificities. Moreover, the abundance of NPC-enriched CD8+ T cells in blood correlates with the infiltration of non-exhausted T cell counterparts in tumors and predicts prolonged survival, suggesting that these NPC-enriched T cells have significant potential for disease monitoring and therapeutic applications.

Development and characterization of human T-cell receptor (TCR) alpha and beta clones' library as biological standards and resources for TCR sequencing and engineering

Characterization of T-cell receptors (TCRs) repertoire was revolutionized by next-generation sequencing technologies; however, standardization using biological controls to facilitate precision of current alignment and assembly tools remains a challenge. Additionally, availability of TCR libraries for off-the-shelf cloning and engineering TCR-specific T cells is a valuable resource for TCR-based immunotherapies. We established nine human TCR α and β clones that were evaluated using the 5'-rapid amplification of cDNA ends-like RNA-based TCR sequencing on the Illumina platform. TCR sequences were extracted and aligned using MiXCR, TRUST4, and CATT to validate their sensitivity and specificity and to validate library preparation methods. The correlation between actual and expected TCR ratios within libraries confirmed accuracy of the approach. Our findings established the development of biological standards and library of TCR clones to be leveraged in TCR sequencing and engineering. The remaining human TCR clones' libraries for a more diverse biological control will be generated.

GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data

BACKGROUND

In the global effort to discover biomarkers for cancer prognosis, prediction tools have become essential resources. TCR (T cell receptor) repertoires contain important features that differentiate healthy controls from cancer patients or differentiate outcomes for patients being treated with different drugs. Considering, tools that can easily and quickly generate and identify important features out of TCR repertoire data and build accurate classifiers to predict future outcomes are essential.

RESULTS

This paper introduces GENTLE (GENerator of T cell receptor repertoire features for machine LEarning): an open-source, user-friendly web-application tool that allows TCR repertoire researchers to discover important features; to create classifier models and evaluate them with metrics; and to quickly generate visualizations for data interpretations. We performed a case study with repertoires of TRegs (regulatory T cells) and TConvs (conventional T cells) from healthy controls versus patients with breast cancer. We showed that diversity features were able to distinguish between the groups. Moreover, the classifiers built with these features could correctly classify samples ('Healthy' or 'Breast Cancer')from the TRegs repertoire when trained with the TConvs repertoire, and from the TConvs repertoire when trained with the TRegs repertoire.

CONCLUSION

The paper walks through installing and using GENTLE and presents a case study and results to demonstrate the application's utility. GENTLE is geared towards any researcher working with TCR repertoire data and aims to discover predictive features from these data and build accurate classifiers. GENTLE is available on https://github.com/dhiego22/gentle and https://share.streamlit.io/dhiego22/gentle/main/gentle.py .

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Simple Summary

The TCR is the T cell antigen receptor, and it is responsible of the T cell activation, through the HLA-antigen complex recognition. Studying the TCR repertoire in patients with cancer can help to better understand the anti-tumoural responses and it has been suggested to have predictive and or/prognostic values, both for the disease and in response to treatments. The aim of this review is to summarize TCR repertoire studies performed in patients with cancer found in the literature, thoroughly analyse the different factors that can be involved in shaping the TCR repertoire, and draw the current conclusions in this field, especially focusing on whether the TCR diversity—or its opposite, the clonality—can be used as predictors or prognostic biomarkers of the disease.

Abstract

T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.

Data Sharing and Reuse: A Method by the AIRR Community

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR ) has revolutionized the ability to study the adaptive immune response via large-scale experiments. Since 2009, AIRR sequencing (AIRR-seq) has been widely applied to survey the immune state of individuals (see "The AIRR Community Guide to Repertoire Analysis" chapter for details). One of the goals of the AIRR Community is to make the resulting AIRR-seq data FAIR (Findable, Accessible, Interoperable, and Reusable) (Wilkinson et al. Sci Data 3:1-9, 2016), with a primary goal of making it easy for the research community to reuse AIRR-seq data (Breden et al. Front Immunol 8:1418, 2017; Scott and Breden. Curr Opin Syst Biol 24:71-77, 2020). The basis for this is the MiAIRR data standard (Rubelt et al. Nat Immunol 18:1274-1278, 2017). For long-term preservation, it is recommended that researchers store their sequence read data in an INSDC repository. At the same time, the AIRR Community has established the AIRR Data Commons (Christley et al. Front Big Data 3:22, 2020), a distributed set of AIRR-compliant repositories that store the critically important annotated AIRR-seq data based on the MiAIRR standard, making the data findable, interoperable, and, because the data are annotated, more valuable in its reuse. Here, we build on the other AIRR Community chapters and illustrate how these principles and standards can be incorporated into AIRR-seq data analysis workflows. We discuss the importance of careful curation of metadata to ensure reproducibility and facilitate data sharing and reuse, and we illustrate how data can be shared via the AIRR Data Commons.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to Repertoire Analysis

Adaptive immune receptor repertoires (AIRRs) are rich with information that can be mined for insights into the workings of the immune system. Gene usage, CDR3 properties, clonal lineage structure, and sequence diversity are all capable of revealing the dynamic immune response to perturbation by disease, vaccination, or other interventions. Here we focus on a conceptual introduction to the many aspects of repertoire analysis and orient the reader toward the uses and advantages of each. Along the way, we note some of the many software tools that have been developed for these investigations and link the ideas discussed to chapters on methods provided elsewhere in this volume.

Adaptive Immune Receptor Repertoire (AIRR) Community Guide to TR and IG Gene Annotation

High-throughput sequencing of adaptive immune receptor repertoires (AIRR, i.e., IG and TR) has revolutionized the ability to carry out large-scale experiments to study the adaptive immune response. Since the method was first introduced in 2009, AIRR sequencing (AIRR-Seq) has been applied to survey the immune state of individuals, identify antigen-specific or immune-state-associated signatures of immune responses, study the development of the antibody immune response, and guide the development of vaccines and antibody therapies. Recent advancements in the technology include sequencing at the single-cell level and in parallel with gene expression, which allows the introduction of multi-omics approaches to understand in detail the adaptive immune response. Analyzing AIRR-seq data can prove challenging even with high-quality sequencing, in part due to the many steps involved and the need to parameterize each step. In this chapter, we outline key factors to consider when preprocessing raw AIRR-Seq data and annotating the genetic origins of the rearranged receptors. We also highlight a number of common difficulties with common AIRR-seq data processing and provide strategies to address them.

Molecular characterization of hypoxanthine guanine phosphoribosyltransferase mutant T cells in human blood: The concept of surrogate selection for immunologically relevant cells

Somatic cell gene mutations arise in vivo due to replication errors during DNA synthesis occurring spontaneously during normal DNA synthesis or as a result of replication on a DNA template damaged by endogenous or exogenous mutagens. In principle, changes in the frequencies of mutant cells in vivo in humans reflect changes in exposures to exogenous or endogenous DNA damaging insults, other factors being equal. It is becoming increasingly evident however, that somatic mutations in humans have a far greater range of interpretations. For example, mutations in lymphocytes provide invaluable probes for in vivo cellular and molecular processes, providing identification of clonal amplifications of these cells in autoimmune and infectious diseases, transplantation recipients, paroxysmal nocturnal hemoglobinuria (PNH), and cancer. The assay for mutations of the X-chromosomal hypoxanthine guanine phosphoribosyltransferase (HPRT) gene has gained popular acceptance for this purpose since viable mutant cells can be recovered for molecular and other analyses. Although the major application of the HPRT T cell assay remains human population monitoring, the enrichment of activated T cells in the mutant fraction in individuals with ongoing immunological processes has demonstrated the utility of surrogate selection, a method that uses somatic mutation as a surrogate marker for the in vivo T cell proliferation that underlies immunological processes to investigate clinical disorders with immunological features. Studies encompassing a wide range of clinical conditions are reviewed. Despite the historical importance of the HPRT mutation system in validating surrogate selection, there are now additional mutational and other methods for identifying immunologically active T cells. These methods are reviewed and provide insights for strategies to extend surrogate selection in future studies.

Novel Allele Detection Tool Benchmark and Application With Antibody Repertoire Sequencing Dataset

Detailed knowledge of the diverse immunoglobulin germline genes is critical for the study of humoral immunity. Hundreds of alleles have been discovered by analyzing antibody repertoire sequencing (Rep-seq or Ig-seq) data via multiple novel allele detection tools (NADTs). However, the performance of these NADTs through antibody sequences with intrinsic somatic hypermutations (SHMs) is unclear. Here, we developed a tool to simulate repertoires by integrating the full spectrum features of an antibody repertoire such as germline gene usage, junctional modification, position-specific SHM and clonal expansion based on 2152 high-quality datasets. We then systematically evaluated these NADTs using both simulated and genuine Ig-seq datasets. Finally, we applied these NADTs to 687 Ig-seq datasets and identified 43 novel allele candidates (NACs) using defined criteria. Twenty-five alleles were validated through findings of other sources. In addition to the NACs detected, our simulation tool, the results of our comparison, and the streamline of this process may benefit further humoral immunity studies via Ig-seq.

RAPID: A Rep-Seq Dataset Analysis Platform With an Integrated Antibody Database

The antibody repertoire is a critical component of the adaptive immune system and is believed to reflect an individual’s immune history and current immune status. Delineating the antibody repertoire has advanced our understanding of humoral immunity, facilitated antibody discovery, and showed great potential for improving the diagnosis and treatment of disease. However, no tool to date has effectively integrated big Rep-seq data and prior knowledge of functional antibodies to elucidate the remarkably diverse antibody repertoire. We developed a Rep-seq dataset Analysis Platform with an Integrated antibody Database (RAPID; https://rapid.zzhlab.org/), a free and web-based tool that allows researchers to process and analyse Rep-seq datasets. RAPID consolidates 521 WHO-recognized therapeutic antibodies, 88,059 antigen- or disease-specific antibodies, and 306 million clones extracted from 2,449 human IGH Rep-seq datasets generated from individuals with 29 different health conditions. RAPID also integrates a standardized Rep-seq dataset analysis pipeline to enable users to upload and analyse their datasets. In the process, users can also select set of existing repertoires for comparison. RAPID automatically annotates clones based on integrated therapeutic and known antibodies, and users can easily query antibodies or repertoires based on sequence or optional keywords. With its powerful analysis functions and rich set of antibody and antibody repertoire information, RAPID will benefit researchers in adaptive immune studies.

Benchmarking of T cell receptor repertoire profiling methods reveals large systematic biases

Monitoring the T cell receptor (TCR) repertoire in health and disease can provide key insights into adaptive immune responses, but the accuracy of current TCR sequencing (TCRseq) methods is unclear. In this study, we systematically compared the results of nine commercial and academic TCRseq methods, including six rapid amplification of complementary DNA ends (RACE)-polymerase chain reaction (PCR) and three multiplex-PCR approaches, when applied to the same T cell sample. We found marked differences in accuracy and intra- and inter-method reproducibility for T cell receptor α (TRA) and T cell receptor β (TRB) TCR chains. Most methods showed a lower ability to capture TRA than TRB diversity. Low RNA input generated non-representative repertoires. Results from the 5' RACE-PCR methods were consistent among themselves but differed from the RNA-based multiplex-PCR results. Using an in silico meta-repertoire generated from 108 replicates, we found that one genomic DNA-based method and two non-unique molecular identifier (UMI) RNA-based methods were more sensitive than UMI methods in detecting rare clonotypes, despite the better clonotype quantification accuracy of the latter.

TCRdb: a comprehensive database for T-cell receptor sequences with powerful search function

T cells and the T-cell receptor (TCR) repertoire play pivotal roles in immune response and immunotherapy. TCR sequencing (TCR-Seq) technology has enabled accurate profiling TCR repertoire and currently a large number of TCR-Seq data are available in public. Based on the urgent need to effectively re-use these data, we developed TCRdb, a comprehensive human TCR sequences database, by a uniform pipeline to characterize TCR sequences on TCR-Seq data. TCRdb contains more than 277 million highly reliable TCR sequences from over 8265 TCR-Seq samples across hundreds of tissues/clinical conditions/cell types. The unique features of TCRdb include: (i) comprehensive and reliable sequences for TCR repertoire in different samples generated by a strict and uniform pipeline of TCRdb; (ii) powerful search function, allowing users to identify their interested TCR sequences in different conditions; (iii) categorized sample metadata, enabling comparison of TCRs in different sample types; (iv) interactive data visualization charts, describing the TCR repertoire in TCR diversity, length distribution and V-J gene utilization. The TCRdb database is freely available at http://bioinfo.life.hust.edu.cn/TCRdb/ and will be a useful resource in the research and application community of T cell immunology.

Computational Fragment-Based Design Facilitates Discovery of Potent and Selective Monoamine Oxidase-B (MAO-B) Inhibitor

Parkinson's disease (PD) is one of the most common age-related neurodegenerative diseases. Inhibition of monoamine oxidase-B (MAO-B), which is mainly found in the glial cells of the brain, may lead to an elevated level of dopamine (DA) in patients. MAO-B inhibitors have been used extensively for patients with PD. However, the discovery of the selective MAO-B inhibitor is still a challenge. In this study, a computational strategy was designed for the rapid discovery of selective MAO-B inhibitors. A series of (S)-2-(benzylamino)propanamide derivatives were designed. In vitro biological evaluations revealed that (S)-1-(4-((3-fluorobenzyl)oxy)benzyl)azetidine-2-carboxamide (C3) was more potent and selective than safinamide, a promising drug for regulating MAO-B. Further studies revealed that the selectivity mechanism of C3 was due to the steric clash caused by the residue difference of Phe208 (MAO-A) and Ile199 (MAO-B). Animal studies showed that compound C3 could inhibit cerebral MAO-B activity and alleviate 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced dopaminergic neuronal loss.

Detecting pathogenic variants in autoimmune diseases using high-throughput sequencing

Sequencing the first human genome in 2003 took 15 years and cost $2.7 billion. Advances in sequencing technologies have since decreased costs to the point where it is now feasible to resequence a whole human genome for $1000 in a single day. These advances have allowed the generation of huge volumes of high‐quality human sequence data used to construct increasingly large catalogs of both population‐level and disease‐causing variation. The existence of such databases, coupled with a high‐quality human reference genome, means we are able to interrogate and annotate all types of genetic variation and identify pathogenic variants for many diseases. Increasingly, sequencing‐based approaches are being used to elucidate the underlying genetic cause of autoimmune diseases, a group of roughly 80 polygenic diseases characterized by abnormal immune responses where healthy tissue is attacked. Although sequence data generation has become routine and affordable, significant challenges remain with no gold‐standard methodology to identify pathogenic variants currently available. This review examines the latest methodologies used to identify pathogenic variants in autoimmune diseases and considers available sequencing options and subsequent bioinformatic methodologies and strategies. The development of reliable and robust sequencing and analytic workflows to detect pathogenic variants is critical to realize the potential of precision medicine programs where patient variant information is used to inform clinical practice.

Molecular T-Cell Repertoire Analysis as Source of Prognostic and Predictive Biomarkers for Checkpoint Blockade Immunotherapy

The T cells are key players of the response to checkpoint blockade immunotherapy (CBI) and monitoring the strength and specificity of antitumor T-cell reactivity remains a crucial but elusive component of precision immunotherapy. The entire assembly of T-cell receptor (TCR) sequences accounts for antigen specificity and strength of the T-cell immune response. The TCR repertoire hence represents a “footprint” of the conditions faced by T cells that dynamically evolves according to the challenges that arise for the immune system, such as tumor neo-antigenic load. Hence, TCR repertoire analysis is becoming increasingly important to comprehensively understand the nature of a successful antitumor T-cell response, and to improve the success and safety of current CBI.