Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies

A pan-cancer gamma delta T cell repertoire

This report presents the largest collection of gamma-delta T cell receptor (γδ TCR) reads in human cancer to date, analyzing about 11,000 patient tumor samples across 33 cancer types using the TRUST4 algorithm. Despite γδ T cells being a small fraction of the T cell population, they play a key role in both innate and adaptive immunity. Our comprehensive analysis reveals their significant presence across all cancer types, specifically highlighting the diverse spectrum and clonality patterns of their γδ receptors. This research highlights the complex roles of γδ T cells in tumor tissues and their potential as prognostic biomarkers. We also demonstrate the utility of T cell receptor gamma (TRG) and delta (TRD) gene expression values from standard RNA-seq data. Ultimately, our work establishes a fundamental resource for future tumor-infiltrating γδ T cell research and may facilitate the development of novel γδ-T-cell-based therapeutic strategies. Together, we demonstrate the strong diversity and prognostic potential of γδ T cells in multiple cancer types.

Loss of TET2 increases B-1 cell number and IgM production while limiting CDR3 diversity

Recent studies have demonstrated a role for Ten-Eleven Translocation-2 (TET2), an epigenetic modulator, in regulating germinal center formation and plasma cell differentiation in B-2 cells, yet the role of TET2 in regulating B-1 cells is largely unknown. Here, B-1 cell subset numbers, IgM production, and gene expression were analyzed in mice with global knockout of TET2 compared to wildtype (WT) controls. Results revealed that TET2-KO mice had elevated numbers of B-1a and B-1b cells in their primary niche, the peritoneal cavity, as well as in the bone marrow (B-1a) and spleen (B-1b). Consistent with this finding, circulating IgM, but not IgG, was elevated in TET2-KO mice compared to WT. Analysis of bulk RNASeq of sort purified peritoneal B-1a and B-1b cells revealed reduced expression of heavy and light chain immunoglobulin genes, predominantly in B-1a cells from TET2-KO mice compared to WT controls. As expected, the expression of IgM transcripts was the most abundant isotype in B-1 cells. Yet, only in B-1a cells there was a significant increase in the proportion of IgM transcripts in TET2-KO mice compared to WT. Analysis of the CDR3 of the BCR revealed an increased abundance of replicated CDR3 sequences in B-1 cells from TET2-KO mice, which was more clearly pronounced in B-1a compared to B-1b cells. V-D-J usage and circos plot analysis of V-J combinations showed enhanced usage of VH11 and VH12 pairings. Taken together, our study is the first to demonstrate that global loss of TET2 increases B-1 cell number and IgM production and reduces CDR3 diversity, which could impact many biological processes and disease states that are regulated by IgM.

Multiomics Data Reveal the Important Role of ANXA2R in T Cell-mediated Rejection After Renal Transplantation

BACKGROUND

T cell-mediated rejection (TCMR) is a severe issue after renal transplantation, but research on its T cell-receptor (TCR) repertoire is lacking. This study intended to elucidate the TCR repertoire landscape in TCMR and hence identify novel potential targets.

METHODS

A total of 12 multiomics data sets were collected. The TRUST4 algorithm was used to construct and analyze the TCR repertoire in renal allografts with TCMR and stable renal function. Then, novel TCR-related key genes were identified through various criteria and literature research. In bulk transcriptome, cell line, single-cell transcriptome data sets, multiple immune cell infiltration algorithms, and gene set enrichment analysis were used to analyze potential mechanisms of the identified key gene. Twenty-three pathological sections were collected for immunofluorescence staining in the clinical cohort. Finally, the diagnostic and prognostic values of ANXA2R were evaluated in multiple renal transplant data sets.

RESULTS

Allografts with TCMR showed significantly increased clonotype and specific clonal expansion. ANXA2R was found to be a novel key gene for TCMR and showed strong positive connections with the TCR complex and lymphocyte cells, especially CD8 + T cells. Immunofluorescence staining confirmed the existence of ANXA2R + CD8 + T cells, with their percentage significantly elevated in TCMR compared with stable renal function. Finally, both mRNA and protein levels of ANXA2R showed promising diagnostic and prognostic value for renal transplant recipients.

CONCLUSIONS

ANXA2R , identified as a novel TCR-related gene, had critical roles in clinicopathology, diagnosis, and prognosis in renal transplantation, which offered promising potential therapeutic targets.

Ovarian cancer is detectable from peripheral blood using machine learning over T-cell receptor repertoires

The extraordinary diversity of T cells and B cells is critical for body maintenance. This diversity has an important role in protecting against tumor formation. In humans, the T-cell receptor (TCR) repertoire is generated through a striking stochastic process called V(D)J recombination, in which different gene segments are assembled and modified, leading to extensive variety. In ovarian cancer (OC), an unfortunate 80% of cases are detected late, leading to poor survival outcomes. However, when detected early, approximately 94% of patients live longer than 5 years after diagnosis. Thus, early detection is critical for patient survival. To determine whether the TCR repertoire obtained from peripheral blood is associated with tumor status, we collected blood samples from 85 women with or without OC and obtained TCR information. We then used machine learning to learn the characteristics of samples and to finally predict, over a set of unseen samples, whether the person is with or without OC. We successfully stratified the two groups, thereby associating the peripheral blood TCR repertoire with the formation of OC tumors. A careful study of the origin of the set of T cells most informative for the signature indicated the involvement of a specific invariant natural killer T (iNKT) clone and a specific mucosal-associated invariant T (MAIT) clone. Our findings here support the proposition that tumor-relevant signal is maintained by the immune system and is coded in the T-cell repertoire available in peripheral blood. It is also possible that the immune system detects tumors early enough for repertoire technologies to inform us near the beginning of tumor formation. Although such detection is made by the immune system, we might be able to identify it, using repertoire data from peripheral blood, to offer a pragmatic way to search for early signs of cancer with minimal patient burden, possibly with enhanced sensitivity.

Comprehensive characterization and database construction of immune repertoire in the largest Chinese glioma cohort

Immune receptor repertoire is valuable for developing immunotherapeutic interventions, but remains poorly understood across glioma subtypes including IDH wild type, IDH mutation without 1p/19q codeletion (IDHmut-noncodel) and IDH mutation with 1p/19q codeletion (IDHmut-codel). We assembled over 320,000 TCR/BCR clonotypes from the largest glioma cohort of 913 RNA sequencing samples in the Chinese population, finding that immune repertoire diversity was more prominent in the IDH wild type (the most aggressive glioma). Fewer clonotypes were shared within each glioma subtype, indicating high heterogeneity of the immune repertoire. The TRA-CDR3 was longer in private than in public clonotypes in IDH wild type. CDR3 variable motifs had higher proportions of hydrophobic residues in private than in public clonotypes, suggesting private CDR3 sequences have greater potential for tumor antigen recognition. Finally, we developed GTABdb, a web-based database designed for hosting, exploring, visualizing, and analyzing glioma immune repertoire. Our study will facilitate developing glioma immunotherapy.

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.

IGH repertoire analysis at scale: deciphering the complexity of B cell infiltration and migration in esophageal squamous cell carcinoma

Tumor-infiltrating B-lineage cells have become predictors of prognosis and immunotherapy responses in various cancers. However, limited knowledge about their infiltration and migration patterns has hindered the understanding of their anti-tumor functions. Here, we examined the immunoglobulin heavy chain (IGH) repertoires in 496 multi-regional tumor, 107 normal tissue, and 48 metastatic lymph node samples obtained from 107 patients with esophageal squamous cell carcinoma (ESCC). Our study revealed higher IgG-type B-lineage cells infiltration in tumors than in healthy tissue, which was associated with improved patient outcomes. Genes such as ACTN1, COL6A5, and pathways like focal adhesion, which shapes the physical structure of tumors, could affect B-lineage cell infiltration. Notably, the IGH sequence was used as an identity-tag to monitor B cell migration, and their infiltration schema within the tumor were depicted based on our multi-regional tumor specimens. This analysis revealed an escalation in B cell clones overlapped between metastatic lymph nodes and tumors. Therefore, the Lymph Node Activation Index was defined, which could predict the outcomes of patients with lymph node metastasis. This research introduces a novel framework for probing B cell infiltration and migration within the tumor microenvironment using large-scale transcriptome data, while simultaneously providing fresh perspectives on B cell immunology within ESCC.

Features of the TCR repertoire associate with patients' clinical and molecular characteristics in acute myeloid leukemia

Background

Allogeneic hematopoietic stem cell transplant remains the most effective strategy for patients with high-risk acute myeloid leukemia (AML). Leukemia-specific neoantigens presented by the major histocompatibility complexes (MHCs) are recognized by the T cell receptors (TCR) triggering the graft-versus-leukemia effect. A unique TCR signature is generated by a complex V(D)J rearrangement process to form TCR capable of binding to the peptide-MHC. The generated TCR repertoire undergoes dynamic changes with disease progression and treatment.

Method

Here we applied two different computational tools (TRUST4 and MIXCR) to extract the TCR sequences from RNA-seq data from The Cancer Genome Atlas (TCGA) and examine the association between features of the TCR repertoire in adult patients with AML and their clinical and molecular characteristics.

Results

We found that only ~30% of identified TCR CDR3s were shared by the two computational tools. Yet, patterns of TCR associations with patients' clinical and molecular characteristics based on data obtained from either tool were similar. The numbers of unique TCR clones were highly correlated with patients' white blood cell counts, bone marrow blast percentage, and peripheral blood blast percentage. Multivariable regressions of TCRA and TCRB median normalized number of unique clones with mutational status of AML patients using TRUST4 showed significant association of TCRA or TCRB with WT1 mutations, WBC count, %BM blast, and sex (adjusted in TCRB model). We observed a correlation between TCRA/B number of unique clones and the expression of T cells inhibitory signal genes (TIGIT, LAG3, CTLA-4) and foxp3, but not IL2RA, CD69 and TNFRSF9 suggestive of exhausted T cell phenotypes in AML.

Conclusion

Benchmarking of computational tools is needed to increase the accuracy of the identified clones. The utilization of RNA-seq data enables identification of highly abundant TCRs and correlating these clones with patients' clinical and molecular characteristics. This study further supports the value of high-resolution TCR-Seq analyses to characterize the TCR repertoire in patients.

RNA-Seq and Immune Repertoire Analysis of Normal and Hepatocellular Carcinoma Relapse After Liver Transplantation

Background

Hepatocellular carcinoma (HCC) relapse is the main reason for the poor prognosis of HCC after Liver transplantation (LT). This study aimed to explore the molecular mechanisms and immune repertoire profiles of HCC relapse.

Material and Methods

RNA-seq of blood samples from patients with normal (n=12) and HCC relapse (n=6) after LT was performed to identify differentially expressed genes (DEGs) and key signalling pathways. The DEGs and immune genes were further analyzed by bioinformatics. TRUST4 was used to analyze the differences in the immune repertoire between the two groups. Another 11 blood samples from patients with HCC who had received LT were collected for RT-qPCR verification of key genes.

Results

A total of 131 upregulated and 157 downregulated genes were identified using RNA-seq, and GO enrichment analysis revealed that the top 15 pathways were immune-related. The PPI network identified 10 key genes. Immune infiltration analysis revealed a significant difference in the five immune cell types between the two groups. A total of 83 intersecting genes were obtained by intersecting DEGs and immune genes. 6 key genes, including MX1, ISG15, OAS1, PRF1, SPP1, and THBS1 were obtained according to the intersection of DEGs, PPI network top 10 genes and immune intersecting genes. Immune repertoire analysis showed that the usage frequency of variable (V) and joining (J) genes in the normal group was higher than that in the relapse group. RT-qPCR validation showed that the expression levels of key genes were consistent with the RNA-seq results.

Conclusion

Our study identified key pathways and genes that could help determine whether transplant recipients are more prone to HCC relapse. Immune repertoire analysis revealed a difference in the usage frequency of VJ genes between the normal and relapse groups, providing a research direction for immunotherapy in patients with HCC relapse after liver transplantation.

Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires

The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.

Single-cell transcriptome analysis profiles the expression features of TMEM173 in BM cells of high-risk B-cell acute lymphoblastic leukemia

BACKGROUND

As an essential regulator of type I interferon (IFN) response, TMEM173 participates in immune regulation and cell death induction. In recent studies, activation of TMEM173 has been regarded as a promising strategy for cancer immunotherapy. However, transcriptomic features of TMEM173 in B-cell acute lymphoblastic leukemia (B-ALL) remain elusive.

METHODS

Quantitative real-time PCR (qRT-PCR) and western blotting (WB) were applied to determine the mRNA and protein levels of TMEM173 in peripheral blood mononuclear cells (PBMCs). TMEM173 mutation status was assessed by Sanger sequencing. Single-cell RNA sequencing (scRNA-seq) analysis was performed to explore the expression of TMEM173 in different types of bone marrow (BM) cells.

RESULTS

The mRNA and protein levels of TMEM173 were increased in PBMCs from B-ALL patients. Besides, frameshift mutation was presented in TMEM173 sequences of 2 B-ALL patients. ScRNA-seq analysis identified the specific transcriptome profiles of TMEM173 in the BM of high-risk B-ALL patients. Specifically, expression levels of TMEM173 in granulocytes, progenitor cells, mast cells, and plasmacytoid dendritic cells (pDCs) were higher than that in B cells, T cells, natural killer (NK) cells, and dendritic cells (DCs). Subset analysis further revealed that TMEM173 and pyroptosis effector gasdermin D (GSDMD) restrained in precursor-B (pre-B) cells with proliferative features, which expressed nuclear factor kappa-B (NF-κB), CD19, and Bruton's tyrosine kinase (BTK) during the progression of B-ALL. In addition, TMEM173 was associated with the functional activation of NK cells and DCs in B-ALL.

CONCLUSIONS

Our findings provide insights into the transcriptomic features of TMEM173 in the BM of high-risk B-ALL patients. Targeted activation of TMEM173 in specific cells might provide new therapeutic strategies for B-ALL patients.