ImmunoMap: A Bioinformatics Tool for T-cell Repertoire Analysis

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

NAIR: Network Analysis of Immune Repertoire

T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection.

Pipeline to characterize antigen-specific TCR repertoires in tumors: Examples from an HPV16 tumor model

Immunotherapies that improve T cell-based anti-tumor immunity have revolutionized cancer. However, the underlying mechanisms of cancer immune responsiveness are still not fully understood. Using immune competent mice for preclinical development of novel mono and combination therapies is a common strategy, and to monitor the T cell response inside tumors and in the periphery offers valuable insight. T cells recognize target cells by based on the binding between the T cell receptor (on T cells) and peptides presented on MHC-I (on tumor cells). As such, the T cell receptor can be used as a "barcode" for a specific T cell clone. Via TCR sequencing, the sequence of this "barcode" can be identified, and eventually, the TCR repertoire in a sample can be assessed as a whole. This information can be useful in multiple ways, including but not excluded to: (i) tracing specific clones in tissues and in blood, and (ii) determine clonal expansion of a specific clone in the tumor microenvironment which suggest anti-tumor activity of the clone in question. This protocol can be used as a guide from experimental design through TCR-sequencing to analysis of the repertoire. Instead of being specifically focused on one type of TCR-sequencing, this protocol can be used as a resource and contains links and references to useful information that has to be considered. Lastly, certain common metrics when analyzing the TCR repertoire are given and discussed.

Antigen-specificity measurements are the key to understanding T cell responses

Antigen-specific T cells play a central role in the adaptive immune response and come in a wide range of phenotypes. T cell receptors (TCRs) mediate the antigen-specificities found in T cells. Importantly, high-throughput TCR sequencing provides a fingerprint which allows tracking of specific T cells and their clonal expansion in response to particular antigens. As a result, many studies have leveraged TCR sequencing in an attempt to elucidate the role of antigen-specific T cells in various contexts. Here, we discuss the published approaches to studying antigen-specific T cells and their specific TCR repertoire. Further, we discuss how these methods have been applied to study the TCR repertoire in various diseases in order to characterize the antigen-specific T cells involved in the immune control of disease.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

Cross-reactivity of SARS-CoV-2- and influenza A-specific T cells in individuals exposed to SARS-CoV-2

Cross-reactive immunity between SARS-CoV-2 and other related coronaviruses has been well-documented, and it may play a role in preventing severe COVID-19. Epidemiological studies early in the pandemic showed a geographical association between high influenza vaccination rates and lower incidence of SARS-CoV-2 infection. We, therefore, analyzed whether exposure to influenza A virus (IAV) antigens could influence the T cell repertoire in response to SARS-CoV-2, indicating a heterologous immune response between these 2 unrelated viruses. Using artificial antigen-presenting cells (aAPCs) combined with real-time reverse-transcription PCR (RT-qPCR), we developed a sensitive assay to quickly screen for antigen-specific T cell responses and detected a significant correlation between responses to SARS-CoV-2 epitopes and IAV dominant epitope (M158-66). Further analysis showed that some COVID-19 convalescent donors exhibited both T cell receptor (TCR) specificity and functional cytokine responses to multiple SARS-CoV-2 epitopes and M158-66. Utilizing an aAPC-based stimulation/expansion assay, we detected cross-reactive T cells with specificity to SARS-CoV-2 and IAV. In addition, TCR sequencing of the cross-reactive and IAV-specific T cells revealed similarities between the TCR repertoires of the two populations. These results indicate that heterologous immunity shaped by our exposure to other unrelated endemic viruses may affect our immune response to novel viruses such as SARS-CoV-2.

Deep learning reveals predictive sequence concepts within immune repertoires to immunotherapy

T cell receptor (TCR) sequencing has been used to characterize the immune response to cancer. However, most analyses have been restricted to quantitative measures such as clonality that do not leverage the complementarity-determining region 3 (CDR3) sequence. We use DeepTCR, a framework of deep learning algorithms, to reveal sequence concepts that are predictive of response to immunotherapy. We demonstrate that DeepTCR can predict response and use the model to infer the antigenic specificities of the predictive signature and their unique dynamics during therapy. The predictive signature of nonresponse is associated with high frequencies of TCRs predicted to recognize tumor-specific antigens, and these tumor-specific TCRs undergo a higher degree of dynamic changes on therapy in nonresponders versus responders. These results are consistent with a biological model where the hallmark of nonresponders is an accumulation of tumor-specific T cells that undergo turnover on therapy, possibly because of the dysfunctional state of these T cells in nonresponders.

Utility of Bulk T-Cell Receptor Repertoire Sequencing Analysis in Understanding Immune Responses to COVID-19

Measuring immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 19 (COVID-19), can rely on antibodies, reactive T cells and other factors, with T-cell-mediated responses appearing to have greater sensitivity and longevity. Because each T cell carries an essentially unique nucleic acid sequence for its T-cell receptor (TCR), we can interrogate sequence data derived from DNA or RNA to assess aspects of the immune response. This review deals with the utility of bulk, rather than single-cell, sequencing of TCR repertoires, considering the importance of study design, in terms of cohort selection, laboratory methods and analysis. The advances in understanding SARS-CoV-2 immunity that have resulted from bulk TCR repertoire sequencing are also be discussed. The complexity of sequencing data obtained by bulk repertoire sequencing makes analysis challenging, but simple descriptive analyses, clonal analysis, searches for specific sequences associated with immune responses to SARS-CoV-2, motif-based analyses, and machine learning approaches have all been applied. TCR repertoire sequencing has demonstrated early expansion followed by contraction of SARS-CoV-2-specific clonotypes, during active infection. Maintenance of TCR repertoire diversity, including the maintenance of diversity of anti-SARS-CoV-2 response, predicts a favourable outcome. TCR repertoire narrowing in severe COVID-19 is most likely a consequence of COVID-19-associated lymphopenia. It has been possible to follow clonotypic sequences longitudinally, which has been particularly valuable for clonotypes known to be associated with SARS-CoV-2 peptide/MHC tetramer binding or with SARS-CoV-2 peptide-induced cytokine responses. Closely related clonotypes to these previously identified sequences have been shown to respond with similar kinetics during infection. A possible superantigen-like effect of the SARS-CoV-2 spike protein has been identified, by means of observing V-segment skewing in patients with severe COVID-19, together with structural modelling. Such a superantigen-like activity, which is apparently absent from other coronaviruses, may be the basis of multisystem inflammatory syndrome and cytokine storms in COVID-19. Bulk TCR repertoire sequencing has proven to be a useful and cost-effective approach to understanding interactions between SARS-CoV-2 and the human host, with the potential to inform the design of therapeutics and vaccines, as well as to provide invaluable pathogenetic and epidemiological insights.

TCR-L: an analysis tool for evaluating the association between the T-cell receptor repertoire and clinical phenotypes

Background

T cell receptors (TCRs) play critical roles in adaptive immune responses, and recent advances in genome technology have made it possible to examine the T cell receptor (TCR) repertoire at the individual sequence level. The analysis of the TCR repertoire with respect to clinical phenotypes can yield novel insights into the etiology and progression of immune-mediated diseases. However, methods for association analysis of the TCR repertoire have not been well developed.

Methods

We introduce an analysis tool, TCR-L, for evaluating the association between the TCR repertoire and disease outcomes. Our approach is developed under a mixed effect modeling, where the fixed effect represents features that can be explicitly extracted from TCR sequences while the random effect represents features that are hidden in TCR sequences and are difficult to be extracted. Statistical tests are developed to examine the two types of effects independently, and then the p values are combined.

Results

Simulation studies demonstrate that (1) the proposed approach can control the type I error well; and (2) the power of the proposed approach is greater than approaches that consider fixed effect only or random effect only. The analysis of real data from a skin cutaneous melanoma study identifies an association between the TCR repertoire and the short/long-term survival of patients.

Conclusion

The TCR-L can accommodate features that can be extracted as well as features that are hidden in TCR sequences. TCR-L provides a powerful approach for identifying association between TCR repertoire and disease outcomes.

Modulation of the Human Pancreatic Ductal Adenocarcinoma Immune Microenvironment by Stereotactic Body Radiotherapy

PURPOSE

Stereotactic body radiotherapy (SBRT) is an emerging treatment modality for pancreatic ductal adenocarcinoma (PDAC), which can effectively prime cytotoxic T cells by inducing immunogenic tumor cell death in preclinical models. SBRT effects on human PDAC have yet to be thoroughly investigated; therefore, this study aimed to characterize immunomodulation in the human PDAC tumor microenvironment following therapy.

EXPERIMENTAL DESIGN

Tumor samples were obtained from patients with resectable PDAC. Radiotherapy was delivered a median of 7 days prior to surgical resection, and sections were analyzed by multiplex IHC (mIHC), RNA sequencing, and T-cell receptor sequencing (TCR-seq).

RESULTS

Analysis of SBRT-treated tumor tissue indicated reduced tumor cell density and increased immunogenic cell death relative to untreated controls. Radiotherapy promoted collagen deposition; however, vasculature was unaffected and spatial analyses lacked evidence of T-cell sequestration. Conversely, SBRT resulted in fewer tertiary lymphoid structures and failed to lessen or reprogram abundant immune suppressor populations. Higher percentages of PD-1⁺ T cells were observed following SBRT, and a subset of tumors displayed more clonal T-cell repertoires.

CONCLUSIONS

These findings suggest that SBRT augmentation of antitumor immunogenicity may be dampened by an overabundance of refractory immunosuppressive populations, and support the continued development of SBRT/immunotherapy combination for human PDAC.

Position-Scanning Peptide Libraries as Particle Immunogens for Improving CD8+ T-Cell Responses

Short peptides reflecting major histocompatibility complex (MHC) class I (MHC-I) epitopes frequently lack sufficient immunogenicity to induce robust antigen (Ag)-specific CD8+ T cell responses. In the current work, it is demonstrated that position-scanning peptide libraries themselves can serve as improved immunogens, inducing Ag-specific CD8+ T cells with greater frequency and function than the wild-type epitope. The approach involves displaying the entire position-scanning library onto immunogenic nanoliposomes. Each library contains the MHC-I epitope with a single randomized position. When a recently identified MHC-I epitope in the glycoprotein gp70 envelope protein of murine leukemia virus (MuLV) is assessed, only one of the eight positional libraries tested, randomized at amino acid position 5 (Pos5), shows enhanced induction of Ag-specific CD8+ T cells. A second MHC-I epitope from gp70 is assessed in the same manner and shows, in contrast, multiple positional libraries (Pos1, Pos3, Pos5, and Pos8) as well as the library mixture give rise to enhanced CD8+ T cell responses. The library mixture Pos1-3-5-8 induces a more diverse epitope-specific T-cell repertoire with superior antitumor efficacy compared to an established single mutation mimotope (AH1-A5). These data show that positional peptide libraries can serve as immunogens for improving CD8+ T-cell responses against endogenously expressed MHC-I epitopes.

The methods and advances of adaptive immune receptors repertoire sequencing

The adaptive immune response is a powerful tool, capable of recognizing, binding to, and neutralizing a vast number of internal and external threats via T or B lymphatic receptors with widespread sets of antigen specificities. The emergence of high-throughput sequencing technology and bioinformatics provides opportunities for research in the fields of life sciences and medicine. The analysis and annotation for immune repertoire data can reveal biologically meaningful information, including immune prediction, target antigens, and effective evaluation. Continuous improvements of the immunological repertoire sequencing methods and analysis tools will help to minimize the experimental and calculation errors and realize the immunological information to meet the clinical requirements. That said, the clinical application of adaptive immune repertoire sequencing requires appropriate experimental methods and standard analytical tools. At the population cell level, we can acquire the overview of cell groups, but the information about a single cell is not obtained accurately. The information that is ignored may be crucial for understanding the heterogeneity of each cell, gene expression and drug response. The combination of high-throughput sequencing and single-cell technology allows us to obtain single-cell information with low-cost and high-throughput. In this review, we summarized the current methods and progress in this area.

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.

Concepts and Applications of Information Theory to Immuno-Oncology

Recent successes of immune-modulating therapies for cancer have stimulated research on information flow within the immune system and, in turn, clinical applications of concepts from information theory. Through information theory, one can describe and formalize, in a mathematically rigorous fashion, the function of interconnected components of the immune system in health and disease. Specifically, using concepts including entropy, mutual information, and channel capacity, one can quantify the storage, transmission, encoding, and flow of information within and between cellular components of the immune system on multiple temporal and spatial scales. To understand, at the quantitative level, immune signaling function and dysfunction in cancer, we present a methodology-oriented review of information-theoretic treatment of biochemical signal transduction and transmission coupled with mathematical modeling.

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.

T-cell CX3CR1 expression as a dynamic blood-based biomarker of response to immune checkpoint inhibitors

Immune checkpoint inhibitors (ICI) have revolutionized treatment for various cancers; however, durable response is limited to only a subset of patients. Discovery of blood-based biomarkers that reflect dynamic change of the tumor microenvironment, and predict response to ICI, will markedly improve current treatment regimens. Here, we investigate CX3C chemokine receptor 1 (CX3CR1), a marker of T-cell differentiation, as a predictive correlate of response to ICI therapy. Successful treatment of tumor-bearing mice with ICI increases the frequency and T-cell receptor clonality of the peripheral CX3CR1+CD8+ T-cell subset that includes an enriched repertoire of tumor-specific and tumor-infiltrating CD8+ T cells. Furthermore, an increase in the frequency of the CX3CR1+ subset in circulating CD8+ T cells early after initiation of anti-PD-1 therapy correlates with response and survival in patients with non-small cell lung cancer. Collectively, these data support T-cell CX3CR1 expression as a blood-based dynamic early on-treatment predictor of response to ICI therapy.

Overcoming primary and acquired resistance to anti-PD-L1 therapy by induction and activation of tumor-residing cDC1s

The ability of cancer cells to ensure T-cell exclusion from the tumor microenvironment is a significant mechanism of resistance to anti-PD-1/PD-L1 therapy. Evidence indicates crucial roles of Batf3-dependent conventional type-1 dendritic cells (cDC1s) for inducing antitumor T-cell immunity; however, strategies to maximize cDC1 engagement remain elusive. Here, using multiple orthotopic tumor mouse models resistant to anti-PD-L1-therapy, we are testing the hypothesis that in situ induction and activation of tumor-residing cDC1s overcomes poor T-cell infiltration. In situ immunomodulation with Flt3L, radiotherapy, and TLR3/CD40 stimulation induces an influx of stem-like Tcf1⁺ Slamf6⁺ CD8⁺ T cells, triggers regression not only of primary, but also untreated distant tumors, and renders tumors responsive to anti-PD-L1 therapy. Furthermore, serial in situ immunomodulation (ISIM) reshapes repertoires of intratumoral T cells, overcomes acquired resistance to anti-PD-L1 therapy, and establishes tumor-specific immunological memory. These findings provide new insights into cDC1 biology as a critical determinant to overcome mechanisms of intratumoral T-cell exclusion.

Characteristics of TCR Repertoire Associated With Successful Immune Checkpoint Therapy Responses

Immunotherapies have revolutionized cancer treatment. In particular, immune checkpoint therapy (ICT) leads to durable responses in some patients with some cancers. However, the majority of treated patients do not respond. Understanding immune mechanisms that underlie responsiveness to ICT will help identify predictive biomarkers of response and develop treatments to convert non-responding patients to responding ones. ICT primarily acts at the level of adaptive immunity. The specificity of adaptive immune cells, such as T and B cells, is determined by antigen-specific receptors. T cell repertoires can be comprehensively profiled by high-throughput sequencing at the bulk and single-cell level. T cell receptor (TCR) sequencing allows for sensitive tracking of dynamic changes in antigen-specific T cells at the clonal level, giving unprecedented insight into the mechanisms by which ICT alters T cell responses. Here, we review how the repertoire influences response to ICT and conversely how ICT affects repertoire diversity. We will also explore how changes to the repertoire in different anatomical locations can better correlate and perhaps predict treatment outcome. We discuss the advantages and limitations of current metrics used to characterize and represent TCR repertoire diversity. Discovery of predictive biomarkers could lie in novel analysis approaches, such as network analysis of amino acids similarities between TCR sequences. Single-cell sequencing is a breakthrough technology that can link phenotype with specificity, identifying T cell clones that are crucial for successful ICT. The field of immuno-sequencing is rapidly developing and cross-disciplinary efforts are required to maximize the analysis, application, and validation of sequencing data. Unravelling the dynamic behavior of the TCR repertoire during ICT will be highly valuable for tracking and understanding anti-tumor immunity, biomarker discovery, and ultimately for the development of novel strategies to improve patient outcomes.

Radiation induces dynamic changes to the T cell repertoire in renal cell carcinoma patients

Clinical studies combining radiation and immunotherapy have shown promising response rates, strengthening efforts to sensitize tumors to immune-mediated attack. Thus, there is an ongoing surge in trials using preconditioning regimens with immunotherapy. Yet, due to the scarcity of resected tumors treated in situ with radiotherapy, there has been little investigation of radiation's sole contributions to local and systemic antitumor immunity in patients. Without this access, translational studies have been limited to evaluating circulating immune subsets and systemic remodeling of peripheral T cell receptor repertoires. This constraint has left gaps in how radiation impacts intratumoral responses and whether tumor-resident T cell clones are amplified following treatment. Therefore, to interrogate the immune impact of radiation on the tumor microenvironment and test the hypothesis that radiation initiates local and systemic expansion of tumor-resident clones, we analyzed renal cell carcinomas from patients treated with stereotactic body radiation therapy. Transcriptomic comparisons were evaluated by bulk RNA sequencing. T cell receptor sequencing monitored repertoires during treatment. Pathway analysis showed radiation-specific enrichment of immune-related processes, and T cell receptor sequencing revealed increased clonality in radiation-treated tumors. The frequency of identified, tumor-enriched clonotypes was tracked across serial blood samples. We observed increased abundance of tumor-enriched clonotypes at 2 wk postradiation compared with pretreatment levels; however, this expansion was not sustained, and levels contracted toward baseline by 4 wk posttreatment. Taken together, these results indicate robust intratumoral immune remodeling and a window of tumor-resident T cell expansion following radiation that may be leveraged for the rational design of combinatorial strategies.

T cell receptor sequence clustering and antigen specificity

There has been increasing interest in the role of T cells and their involvement in cancer, autoimmune and infectious diseases. However, the nature of T cell receptor (TCR) epitope recognition at a repertoire level is not yet fully understood. Due to technological advances a plethora of TCR sequences from a variety of disease and treatment settings has become readily available. Current efforts in TCR specificity analysis focus on identifying characteristics in immune repertoires which can explain or predict disease outcome or progression, or can be used to monitor the efficacy of disease therapy. In this context, clustering of TCRs by sequence to reflect biological similarity, and especially to reflect antigen specificity have become of paramount importance. We review the main TCR sequence clustering methods and the different similarity measures they use, and discuss their performance and possible improvement. We aim to provide guidance for non-specialists who wish to use TCR repertoire sequencing for disease tracking, patient stratification or therapy prediction, and to provide a starting point for those aiming to develop novel techniques for TCR annotation through clustering.

RNA-Seq-Based TCR Profiling Reveals Persistently Increased Intratumoral Clonality in Responders to Anti-PD-1 Therapy

Substantial effort is being invested in the search for peripheral or intratumoral T cell receptor (TCR) repertoire features that could predict the response to immunotherapy. Here we demonstrate the utility of MiXCR software for TCR and immunoglobulin repertoire extraction from RNA-Seq data obtained from sorted tumor-infiltrating T and B cells. We use this approach to extract TCR repertoires from RNA-Seq data obtained from sorted tumor-infiltrating CD4⁺ and CD8⁺ T cells in an HKP1 (Kras^G12Dp53^-/-) syngeneic mouse model of lung cancer after anti-PD-1 treatment. For both subsets, we demonstrate decreased TCR diversity in response to therapy. At a later time point, repertoire diversity is restored in progressing disease but remains decreased in responders to therapy in both CD4⁺ and CD8⁺ subsets. These observations complement previous studies and suggest that stably increased intratumoral CD4⁺ and CD8⁺ T cell clonality after anti-PD-1/PD-L1 therapy could serve as a predictor of long-term response.

Commensal bacteria stimulate antitumor responses via T cell cross-reactivity

Recent studies show gut microbiota modulate antitumor immune responses; one proposed mechanism is cross-reactivity between antigens expressed in commensal bacteria and neoepitopes. We found that T cells targeting an epitope called SVYRYYGL (SVY), expressed in the commensal bacterium Bifidobacterium breve (B. breve), cross-react with a model neoantigen, SIYRYYGL (SIY). Mice lacking B. breve had decreased SVY-reactive T cells compared with B. breve-colonized mice, and the T cell response was transferable by SVY immunization or by cohousing mice without Bifidobacterium with ones colonized with Bifidobacterium. Tumors expressing the model SIY neoantigen also grew faster in mice lacking B. breve compared with Bifidobacterium-colonized animals. B. breve colonization also shaped the SVY-reactive TCR repertoire. Finally, SVY-specific T cells recognized SIY-expressing melanomas in vivo and led to decreased tumor growth and extended survival. Our work demonstrates that commensal bacteria can stimulate antitumor immune responses via cross-reactivity and how bacterial antigens affect the T cell landscape.

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.

Enrichment of melanoma-associated T cells in 6-thioguanine-resistant T cells from metastatic melanoma patients

This study examines whether 6-thioguanine resistant T cells (mutant) from metastatic melanoma patients are enriched for melanoma-associated T cells compared to T cells obtained analogously without thioguanine selection (wild-type). Melanoma-associated antigen pentamer staining was performed on 5 tumour and 9 peripheral blood samples from metastatic melanoma patients. T cell receptor beta chain repertoire was examined via Sanger sequencing of mutant and wild-type in blood and tumour from metastatic melanoma patients at times of tumour progression (n = 8) and via Illumina sequencing in tumour derived T cells and in uncultured T cells (uncultured), wild-type and mutant from blood before and after immune checkpoint blockade (n = 1). Mutant from tumour (3 of 5; P < 0.001), but not blood (0 of 9), were enriched compared to wild-type for binding melanoma-associated antigen pentamers. T cell receptor beta analysis in patients with tumour progression (n = 8) detected increased melanoma associated T cells in mutant compared to wild-type from blood (Monte Carlo P = 10). Comparison of blood samples before and after immune checkpoint blockade with prior tumor from one metastatic melanoma patient detected increased T cell receptor beta sharing between tumour and mutant compared to tumour and wild-type or tumour and uncultured: 11.0% (72/656), 1.5% (206/13 639) and 1.3% (381/29 807), respectively (Monte Carlo P = 10 for mutant versus wild-type and mutant versus uncultured). These data demonstrate that mutant in metastatic melanoma patients are enriched for melanoma-associated T cells and are candidate probes to study in vivo melanoma-reactive T cells.

TCR Repertoire Analysis Reveals Mobilization of Novel CD8+ T Cell Clones Into the Cancer-Immunity Cycle Following Anti-CD4 Antibody Administration

Depletion of CD4⁺ cells using an anti-CD4 monoclonal antibody (anti-CD4 mAb) induces the expansion of tumor-reactive CD8⁺ T cells and strong antitumor effects in several murine tumor models. However, it is not known whether the anti-CD4 mAb treatment activates a particular or a broad spectrum of tumor-reactive CD8⁺ T cell clones. To investigate the changes in the TCR repertoire induced by the anti-CD4 mAb treatment, we performed unbiased high-throughput TCR sequencing in a B16F10 mouse subcutaneous melanoma model. By Inter-Organ Clone Tracking analysis, we demonstrated that anti-CD4 mAb treatment increased the diversity and combined frequency of CD8⁺ T cell clones that overlapped among the tumor, draining lymph node (dLN), and peripheral blood repertoires. Interestingly, the anti-CD4 mAb treatment-induced expansion of overlapping clones occurred mainly in the dLN rather than in the tumor. Overall, the Inter-Organ Clone Tracking analysis revealed that anti-CD4 mAb treatment enhances the mobilization of a wide variety of tumor-reactive CD8⁺ T cell clones into the Cancer-Immunity Cycle and thus induces a robust antitumor immune response in mice.

Radiotherapy induces responses of lung cancer to CTLA-4 blockade

Focal radiation therapy enhances systemic responses to anti-CTLA-4 antibodies in preclinical studies and in some patients with melanoma1-3, but its efficacy in inducing systemic responses (abscopal responses) against tumors unresponsive to CTLA-4 blockade remained uncertain. Radiation therapy promotes the activation of anti-tumor T cells, an effect dependent on type I interferon induction in the irradiated tumor4-6. The latter is essential for achieving abscopal responses in murine cancers6. The mechanisms underlying abscopal responses in patients treated with radiation therapy and CTLA-4 blockade remain unclear. Here we report that radiation therapy and CTLA-4 blockade induced systemic anti-tumor T cells in chemo-refractory metastatic non-small-cell lung cancer (NSCLC), where anti-CTLA-4 antibodies had failed to demonstrate significant efficacy alone or in combination with chemotherapy7,8. Objective responses were observed in 18% of enrolled patients, and 31% had disease control. Increased serum interferon-β after radiation and early dynamic changes of blood T cell clones were the strongest response predictors, confirming preclinical mechanistic data. Functional analysis in one responding patient showed the rapid in vivo expansion of CD8 T cells recognizing a neoantigen encoded in a gene upregulated by radiation, supporting the hypothesis that one explanation for the abscopal response is radiation-induced exposure of immunogenic mutations to the immune system.

The Mutation-Associated Neoantigen Functional Expansion of Specific T Cells (MANAFEST) Assay: A Sensitive Platform for Monitoring Antitumor Immunity

Mutation-associated neoantigens (MANA) are a target of antitumor T-cell immunity. Sensitive, simple, and standardized assays are needed to assess the repertoire of functional MANA-specific T cells in oncology. Assays analyzing in vitro cytokine production such as ELISpot and intracellular cytokine staining have been useful but have limited sensitivity in assessing tumor-specific T-cell responses and do not analyze antigen-specific T-cell repertoires. The FEST (Functional Expansion of Specific T cells) assay described herein integrates T-cell receptor sequencing of short-term, peptide-stimulated cultures with a bioinformatic platform to identify antigen-specific clonotypic amplifications. This assay can be adapted for all types of antigens, including MANAs via tumor exome-guided prediction of MANAs. Following in vitro identification by the MANAFEST assay, the MANA-specific CDR3 sequence can be used as a molecular barcode to detect and monitor the dynamics of these clonotypes in blood, tumor, and normal tissue of patients receiving immunotherapy. MANAFEST is compatible with high-throughput routine clinical and lab practices. Cancer Immunol Res; 6(8); 888-99. ©2018 AACR.

Radiotherapy and CTLA-4 Blockade Shape the TCR Repertoire of Tumor-Infiltrating T Cells

Immune checkpoint inhibitors activate T cells to reject tumors. Unique tumor mutations are key T-cell targets, but a comprehensive understanding of the nature of a successful antitumor T-cell response is lacking. To investigate the T-cell receptor (TCR) repertoire associated with treatment success versus failure, we used a well-characterized mouse carcinoma that is rejected by CD8 T cells in mice treated with radiotherapy (RT) and anti-CTLA-4 in combination, but not as monotherapy, and comprehensively analyzed tumor-infiltrating lymphocytes (TILs) by high-throughput sequencing of the TCRΒ CDR3 region. The combined treatment increased TIL density and CD8/CD4 ratio. Assessment of the frequency of T-cell clones indicated that anti-CTLA-4 resulted in fewer clones and a more oligoclonal repertoire compared with untreated tumors. In contrast, RT increased the CD8/CD4 ratio and broadened the TCR repertoire, and when used in combination with anti-CTLA-4, these selected T-cell clones proliferated. Hierarchical clustering of CDR3 sequences showed a treatment-specific clustering of TCRs that were shared by different mice. Abundant clonotypes were commonly shared between animals and yet treatment-specific. Analysis of amino-acid sequence similarities revealed a significant increase in the number and richness of dominant CDR3 motifs in tumors treated with RT + anti-CTLA-4 compared with control. The repertoire of TCRs reactive with a single tumor antigen recognized by CD8⁺ T cells was heterogeneous but highly clonal, irrespective of treatment. Overall, data support a model whereby a diverse TCR repertoire is required to achieve tumor rejection and may underlie the synergy between RT and CTLA-4 blockade. Cancer Immunol Res; 6(2); 139-50. ©2017 AACR.