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

Further knowledge and developments in resistance mechanisms to immune checkpoint inhibitors

The past decade has witnessed a revolution in cancer treatment, shifting from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular immune-checkpoint inhibitors (ICIs). These immunotherapies release the host's immune system against the tumor and have shown unprecedented durable remission for patients with cancers that were thought incurable, such as metastatic melanoma, metastatic renal cell carcinoma (RCC), microsatellite instability (MSI) high colorectal cancer and late stages of non-small cell lung cancer (NSCLC). However, about 80% of the patients fail to respond to these immunotherapies and are therefore left with other less effective and potentially toxic treatments. Identifying and understanding the mechanisms that enable cancerous cells to adapt to and eventually overcome therapy can help circumvent resistance and improve treatment. In this review, we describe the recent discoveries on the onco-immunological processes which govern the tumor microenvironment and their impact on the resistance to PD-1/PD-L1 checkpoint blockade.

Maintenance Pembrolizumab Therapy in Patients with Metastatic HER2-negative Breast Cancer with Prior Response to Chemotherapy

PURPOSE

Accumulating toxicities hinder indefinite chemotherapy for many patients with metastatic/recurrent HER2-negative breast cancer. We conducted a phase II trial of pembrolizumab monotherapy following induction chemotherapy to determine the efficacy of maintenance immunotherapy in patients with metastatic HER2-negative inflammatory breast cancer (IBC) and non-IBC triple-negative breast cancer (TNBC) and a biomarker study.

PATIENTS AND METHODS

Patients with a complete response, partial response, or stable disease (SD) after at least three cycles of chemotherapy for HER2-negative breast cancer received pembrolizumab, regardless of programmed death-ligand 1 expression. Pembrolizumab (200 mg) was administered every 3 weeks until disease progression, intolerable toxicity, or 2 years of pembrolizumab exposure. The endpoints included the 4-month disease control rate (DCR), progression-free survival (PFS), overall survival, and response biomarkers in the blood.

RESULTS

Of 43 treated patients, 11 had metastatic IBC and 32 non-IBC TNBC. The 4-month DCR was 58.1% [95% confidence interval (CI), 43.4-72.9]. For all patients, the median PFS was 4.8 months (95% CI, 3.0-7.1 months). The toxicity profile was similar to the previous pembrolizumab monotherapy study. Patients with high T-cell clonality at baseline had a longer PFS with pembrolizumab treatment than did those with low T-cell clonality (10.4 vs. 3.6 months, P = 0.04). Patients who achieved SD also demonstrated a significant increase in T-cell clonality during therapy compared with those who did not achieve SD (20% vs. 5.9% mean increase, respectively; P = 0.04).

CONCLUSIONS

Pembrolizumab monotherapy achieved durable treatment responses. Patients with a high baseline T-cell clonality had prolonged disease control with pembrolizumab.

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.

Genomic and T cell repertoire biomarkers associated with malignant mesothelioma survival

BACKGROUND

Malignant mesothelioma (MM) is an exceedingly rare tumor with poor prognosis due to the limited availability of effective treatment. Immunotherapy has emerged as a novel treatment approach for MM, but less than 40% of the patients benefit from it. Thus, it is necessary to identify accurate and effective biomarkers that can predict the overall survival (OS) and immunotherapy efficacy for MM.

METHODS

DNA sequencing was used to identify the genomic landscape based on the data from 86 Chinese patients. T cell receptor (TCR) sequencing was used to characterize MM TCR repertoires of 28 patients between October 2016 and April 2023.

RESULTS

Patients with TP53, NF2, or CDKN2A variants at the genomic level, as well as those exhibiting lower Shannon index (<6.637), lower evenness (<0.028), or higher clonality (≥0.194) according to baseline tumor tissue TCR indexes, demonstrated poorer OS. Furthermore, patients with TP53, CDKN2A, or CDKN2B variants and those with a lower evenness (<0.030) in baseline tumor tissue showed worse immunotherapy efficacy. The present study is the first to identify five special TCR Vβ-Jβ rearrangements associated with MM immunotherapy efficacy.

CONCLUSIONS

The present study reported the largest-scale genomic landscape and TCR repertoire of MM in Chinese patients and identified genomic and TCR biomarkers for the prognosis and immunotherapy efficacy in MM. The study results might provide new insights for prospective MM trials using specific genes, TCR indexes, and TCR clones as biomarkers and offer a reference for future antitumor drugs based on TCR-specific clones.

Advances in Therapeutic Cancer Vaccines, Their Obstacles, and Prospects Toward Tumor Immunotherapy

Over the past few decades, cancer immunotherapy has experienced a significant revolution due to the advancements in immune checkpoint inhibitors (ICIs) and adoptive cell therapies (ACTs), along with their regulatory approvals. In recent times, there has been hope in the effectiveness of cancer vaccines for therapy as they have been able to stimulate de novo T-cell reactions against tumor antigens. These tumor antigens include both tumor-associated antigen (TAA) and tumor-specific antigen (TSA). Nevertheless, the constant quest to fully achieve these abilities persists. Therefore, this review offers a broad perspective on the existing status of cancer immunizations. Cancer vaccine design has been revolutionized due to the advancements made in antigen selection, the development of antigen delivery systems, and a deeper understanding of the strategic intricacies involved in effective antigen presentation. In addition, this review addresses the present condition of clinical tests and deliberates on their approaches, with a particular emphasis on the immunogenicity specific to tumors and the evaluation of effectiveness against tumors. Nevertheless, the ongoing clinical endeavors to create cancer vaccines have failed to produce remarkable clinical results as a result of substantial obstacles, such as the suppression of the tumor immune microenvironment, the identification of suitable candidates, the assessment of immune responses, and the acceleration of vaccine production. Hence, there are possibilities for the industry to overcome challenges and enhance patient results in the coming years. This can be achieved by recognizing the intricate nature of clinical issues and continuously working toward surpassing existing limitations.

Harnessing the Transcriptional Signatures of CAR-T-Cells and Leukemia/Lymphoma Using Single-Cell Sequencing Technologies

Chimeric antigen receptor (CAR)-T-cell therapy has greatly improved outcomes for patients with relapsed or refractory hematological malignancies. However, challenges such as treatment resistance, relapse, and severe toxicity still hinder its widespread clinical application. Traditional transcriptome analysis has provided limited insights into the complex transcriptional landscape of both leukemia cells and engineered CAR-T-cells, as well as their interactions within the tumor microenvironment. However, with the advent of single-cell sequencing techniques, a paradigm shift has occurred, providing robust tools to unravel the complexities of these factors. These techniques enable an unbiased analysis of cellular heterogeneity and molecular patterns. These insights are invaluable for precise receptor design, guiding gene-based T-cell modification, and optimizing manufacturing conditions. Consequently, this review utilizes modern single-cell sequencing techniques to clarify the transcriptional intricacies of leukemia cells and CAR-Ts. The aim of this manuscript is to discuss the potential mechanisms that contribute to the clinical failures of CAR-T immunotherapy. We examine the biological characteristics of CAR-Ts, the mechanisms that govern clinical responses, and the intricacies of adverse events. By exploring these aspects, we hope to gain a deeper understanding of CAR-T therapy, which will ultimately lead to improved clinical outcomes and broader therapeutic applications.

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow).

Biomarkers of immunotherapy for non-small cell lung cancer

Immunotherapy is revolutionizing the treatment of non-small cell lung cancer by targeting immune checkpoint proteins, including programmed death-1, programmed death ligand 1 and cytotoxic T-lymphocyte-associated antigen 4. Several immune checkpoint inhibitors, including programmed death ligand 1 inhibitors, programmed death-1 inhibitors and cytotoxic T-lymphocyte-associated antigen 4 inhibitors, were approved for the treatment of patients with advanced non-small cell lung cancer. Programmed death ligand 1 expression is currently the only predictive biomarker for immune checkpoint inhibitors to guide the treatment strategy in these patients. However, programmed death ligand 1 expression is not a perfect biomarker for predicting the efficacy of immunotherapy. Therefore, various biomarkers such as tumour mutation burden, tumour microenvironment, gut microbiome and T-cell receptor repertoire have been proposed to predict the efficacy of immunotherapy more accurately. Additionally, combining different biomarkers may provide a more accurate prediction of response to immunotherapy. This article reports the review of the latest evidence of the predictive marker of immunotherapy in patients with advanced non-small cell lung cancer.

Characterization of Immune Cell Populations of Cutaneous Neurofibromas in Neurofibromatosis 1

Cutaneous neurofibromas (cNFs) are characteristic of neurofibromatosis 1 (NF1), yet their immune microenvironment is incompletely known. A total of 61 cNFs from 10 patients with NF1 were immunolabeled for different types of T cells and macrophages, and the cell densities were correlated with clinical characteristics. Eight cNFs and their overlying skin were analyzed for T cell receptor CDR domain sequences, and mass spectrometry of 15 cNFs and the overlying skin was performed to study immune-related processes. Intratumoral T cells were detected in all cNFs. Tumors from individuals younger than the median age of the study participants (33 years), growing tumors, and tumors smaller than the data set median showed increased T cell density. Most samples displayed intratumoral or peritumoral aggregations of CD3-positive cells. T cell receptor sequencing demonstrated that the skin and cNFs host distinct T cell populations, whereas no dominant cNF-specific T cell clones were detected. Unique T cell clones were fewer in cNFs than in skin, and mass spectrometry suggested lower expression of proteins related to T cell-mediated immunity in cNFs than in skin. CD163-positive cells, suggestive of M2 macrophages, were abundant in cNFs. Human cNFs have substantial T cell and macrophage populations that may be tumor-specific.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

MIX-TPI: a flexible prediction framework for TCR-pMHC interactions based on multimodal representations

MOTIVATION

The interactions between T-cell receptors (TCR) and peptide-major histocompatibility complex (pMHC) are essential for the adaptive immune system. However, identifying these interactions can be challenging due to the limited availability of experimental data, sequence data heterogeneity, and high experimental validation costs.

RESULTS

To address this issue, we develop a novel computational framework, named MIX-TPI, to predict TCR-pMHC interactions using amino acid sequences and physicochemical properties. Based on convolutional neural networks, MIX-TPI incorporates sequence-based and physicochemical-based extractors to refine the representations of TCR-pMHC interactions. Each modality is projected into modality-invariant and modality-specific representations to capture the uniformity and diversities between different features. A self-attention fusion layer is then adopted to form the classification module. Experimental results demonstrate the effectiveness of MIX-TPI in comparison with other state-of-the-art methods. MIX-TPI also shows good generalization capability on mutual exclusive evaluation datasets and a paired TCR dataset.

AVAILABILITY AND IMPLEMENTATION

The source code of MIX-TPI and the test data are available at: https://github.com/Wolverinerine/MIX-TPI.

Optimization of a high-throughput shotgun immunoproteomics pipeline for antigen identification

Identification of proteins which initiate and/or perpetuate adaptive immune responses has potential to greatly impact pre-clinical and clinical work across numerous fields. To date, however, the methodologies available to identify antigens responsible for driving adaptive immune responses have been plagued by numerous issues which have drastically limited their widespread adoption. Therefore, in this study, we sought to optimize a shotgun immunoproteomics approach to alleviate these persistent issues and create a high-throughput, quantitative methodology for antigen identification. Three individual components of a previously published approach, namely the protein extraction, antigen elution, and LC-MS/MS analysis steps, were optimized in a systematic manner. These studies determined that preparation of protein extracts using a one-step tissue disruption method in immunoprecipitation (IP) buffer, eluting antigens from affinity chromatography columns with 1% trifluoroacetic acid (TFA), and TMT-labeling & multiplexing equal volumes of eluted samples for LC-MS/MS analysis, resulted in quantitative longitudinal antigen identification, with reduced variability between replicates and increased total number of antigens identified. This optimized pipeline provides a multiplexed, highly reproducible, and fully quantitative approach to antigen identification which is broadly applicable to determine the role of antigenic proteins in inciting (i.e., primary antigens) and perpetuating (i.e., secondary antigens) a wide range of diseases. SIGNIFICANCE: Using a systematic, hypothesis-driven approach, we identified potential improvements for three individual steps of a previously published approach for antigen-identification. Optimization of each step created a methodology which resolved many of the persistent issues associated with previous antigen identification approaches. The optimized high-throughput shotgun immunoproteomics approach described herein identifies more than five times as many unique antigens as the previously published method, greatly reduces protocol cost and mass spectrometry time per experiment, minimizes both inter- and intra-experimental variability, and ensures each experiment is fully quantitative. Ultimately, this optimized antigen identification approach has the potential to facilitate novel antigen identification studies, allowing evaluation of the adaptive immune response in a longitudinal manner and encourage innovations in a wide array of fields.

Deciphering and advancing CAR T-cell therapy with single-cell sequencing technologies

Chimeric antigen receptor (CAR) T-cell therapy has made remarkable progress in cancer immunotherapy, but several challenges with unclear mechanisms hinder its wide clinical application. Single-cell sequencing technologies, with the powerful unbiased analysis of cellular heterogeneity and molecular patterns at unprecedented resolution, have greatly advanced our understanding of immunology and oncology. In this review, we summarize the recent applications of single-cell sequencing technologies in CAR T-cell therapy, including the biological characteristics, the latest mechanisms of clinical response and adverse events, promising strategies that contribute to the development of CAR T-cell therapy and CAR target selection. Generally, we propose a multi-omics research mode to guide potential future research on CAR T-cell therapy.