Similar T-cell Receptor Research Articles

Demonstration of T-Cell Monotypia Using Anti-TCRbeta1/2 (TRBC1/2) Immunostaining as a Rapid and Cost-Effective Alternative to PCR-Based Clonality Studies for the Diagnosis of T-Cell Lymphoma

Background/Objectives: T-cell lymphomas are often histologically indistinguishable from benign T-cell infiltrates, and diagnosis typically relies on slow, complex, and expensive multiplexed PCR reactions, requiring significant training and experience to interpret them. We aimed to raise highly specific antibodies against the two alternatively used and very similar T-cell receptor beta constant regions, TCRbeta1 and TCRbeta2, encoded by the TRBC1 and TRBC2 gene segments, respectively. We sought to demonstrate the feasibility of detecting TCRbeta1 and TCRbeta2 immunohistochemically in routine clinical (formalin-fixed, paraffin-embedded (FFPE)) tissue sections as a novel diagnostic strategy for T-cell lymphomas. Methods: Recombinant rabbit antibodies were validated using Western blotting and FFPE immunostaining of T-cell leukemia lines. The immunostaining of FFPE tissue containing benign and lymphomatous T-cell populations was undertaken, with corroboration by BaseScopeTM high-sensitivity in situ hybridization and quantitative real-time PCR (Q-PCR). An additional Q-PCR literature review and analysis of publicly available RNAseq data was used to determine the TCRbeta2/TCRbeta1 ratio cut-off to separate benign and malignant T-cell populations. Results: Our TCRbeta1/TCRbeta2 antibody pair gave highly specific FFPE tissue staining. All benign samples analyzed (immunohistochemically, by BaseScopeTM, by Q-PCR, and by RNAseq data analysis) had TCRbeta1/TCRbeta2 or TRBC1/TRBC2 ranges well within the previously published flow cytometric benign range (TCRbeta2/TCRbeta1 = 0.18:1–5.7:1), while samples of T-cell lymphoma did not. One out of thirteen (7.7%) lymphoma samples showed some detectable TCRbeta1/TCRbeta2 protein co-expression, and 4 out of 13 (30.8%) T-cell lymphomas showed a TRBC1/TRBC2 transcript co-expression using BaseScopeTM. Conclusions: Analyzing T-cell monotypia immunohistochemically, analogous to B-cell monotypia (kappa: lambda ratio for B-cell and plasma cell neoplasms), could make the diagnosis of T-cell lymphomas cheaper, quicker, and more accurate. Larger studies are needed to validate our antibodies for clinical use.

Paired single-cell RNA sequencing and T cell receptor sequencing reveals both pro- and anti-inflammatory roles of T cells in patients with calcific aortic valve disease

Abstract Background Calcified aortic valve disease (CAVD) is a chronic unresolvable inflammatory disorder that affects 25% of adults over 65 years old, leading to aortic stenosis, heart failure, and death [1]. Since the molecular mechanisms of its pathogenesis are not well understood, therapies to treat root causes of the disease remain to be developed, with valve replacement as the major intervention strategy currently. Increasing evidence indicates that substantial T cells infiltrate in the aortic valve during calcification, and clonal expanded CD8+ T cells have been identified in the calcificed aortic valve by bulk T cell receptor (TCR) repertoire sequencing [2]. Purpose To decipher T cell heterogenity and understand the precise mechanisms of T cell subsets involving CAVD progression as a basis for novel therapeutic targets. Methods In Cohort 1, we collected aortic valves from CAVD (n=3) and healthy (n=2) patients for single-cell RNA sequencing (scRNA-seq). In Cohort 2, we performed paired scRNA-seq and TCR sequencing (scTCR-seq) on aortic valves obtained from CAVD (n=3) and healthy (n=3) patients to evaluate both gene expression and clonality. Pathway enrichment analysis and cell-cell interaction analysis elucidated the function of T cells in the calcificied valves. Immunohistochemistry and fluorescence-activated cell sorting were recruited to verify the key findings. Results scRNA-seq demonstrated a conversion from anti-inflammatory regulatory T (Treg) cells towards pro-inflammatory T helper 17 (Th17) cells in the calcificied aortic valve. Paired scRNA-seq and scTCR-seq revealed CAVD-related signaling pathways (osteoclast differentiation, NOD-like receptor signaling pathways, IL-17 signaling pathways) significantly upregulated in highly expanded CD8+ T cells in the calcified valve, with upregulation of pro-inflammatory mediators such as IFNG, CCL4 and CCL5, suggesting pro-inflammation functions in clonally expanded T cells. Moreover, we characterized tissue-resident memory T cells, which resembled similar transcriptome and TCR clonality as exhausted T cells in calcified valves. These cells up-regulated anti-inflammation-related transcriptomes but their cellular fraction decreased in calcified valves, implying anti-inflammatory functions of these tissue-resident memory and exhausted T cells. Conclusion This study elucidates transcriptomic and immune profiling of T cells in the calcified aortic valve, therefore shedding light on the pathophysiological mechanisms underlying aortic valve calcification from the novel perspective of tissue-specific T lymphocytes, providing promising targets for immune checkpoint-based therapeutic interventions.

Single-cell analysis of bronchoalveolar cells in inflammatory and fibrotic post-COVID lung disease.

Persistent radiological lung abnormalities are evident in many survivors of acute coronavirus disease 2019 (COVID-19). Consolidation and ground glass opacities are interpreted to indicate subacute inflammation whereas reticulation is thought to reflect fibrosis. We sought to identify differences at molecular and cellular level, in the local immunopathology of post-COVID inflammation and fibrosis. We compared single-cell transcriptomic profiles and T cell receptor (TCR) repertoires of bronchoalveolar cells obtained from convalescent individuals with each radiological pattern, targeting lung segments affected by the predominant abnormality. CD4 central memory T cells and CD8 effector memory T cells were significantly more abundant in those with inflammatory radiology. Clustering of similar TCRs from multiple donors was a striking feature of both phenotypes, consistent with tissue localised antigen-specific immune responses. There was no enrichment for known SARS-CoV-2-reactive TCRs, raising the possibility of T cell-mediated immunopathology driven by failure in immune self-tolerance. Post-COVID radiological inflammation and fibrosis show evidence of shared antigen-specific T cell responses, suggesting a role for therapies targeting T cells in limiting post-COVID lung damage.

Abstract 3559: A validated bioinformatics tool-set for predicting TCR specificity

Abstract Background: The T-cell receptor (TCR) is a transmembrane receptor on T cells, which is responsible for recognizing any foreign antigen derived peptide, presented on infected or abnormal cells by the MHC-I complex. Predicting TCR specificity computationally is a long-standing problem. Many methods leverage large libraries of TCR sequences and cognate epitopes as input for machine learning and predict specificity from sequence alone. Most methods generally do not perform very well, especially in the case of unseen peptides, i.e. for cases where peptides were not part of the input library. Methods: Here we describe three independent but interacting in-silico modules for TCR discovery. 1) The first module predicts specificity from deep phenotypic T cell profiles. We produce these profiles using a high-throughput pipeline, which combines multiplexed CyTOF with subsequent VDJ CITE-Seq. The generated multiomics data is aggregated into a database called TCAPS, which is the basis for machine learning models. These models can predict T cell specificity from the deep phenotype alone, i.e. without TCR sequence information. The resolution however is limited to the target class and hence the actual epitope has to be determined through subsequent deorphanization. This deorphanization step is augmented by the other two modules also described here. 2) The second module enables ultrafast sequence similarity search, which is used to find similar TCRs with known epitope specificity 3) The third module ranks TCR candidates for prioritisation for in-vitro validation. This is achieved by running the Alphafold-based structure prediction program TCRDock. Results: 1) We demonstrate how TCR specificity can be correctly predicted from multiomics deep T-cell profiles and specifically highlight two in-vitro validated predictions, for which the corresponding epitope/antigen were not part of the input panel. 2) Using a recently published TCR clustering benchmark TCRScapes, we demonstrate the superior performance of our ultrafast sequence similarity search. 3) We furthermore showcase TCRdock-directed experiments on TCRs specific for SF3B1mut-induced splice variants. TCRdock correctly ranked TCR candidates, including artificial combinations, whose functionality we prove through in-vitro validation. Conclusions: We describe three modules for TCR discovery and demonstrate their utility by benchmarking and in-vitro validation of multiple predictions, including two examples against unseen epitopes/antigens. Citation Format: Andreas Wilm, Florian Schmidt, Melissa Wirawan, Manisha Cooray, Kan Xing Wu, Katja Fink, Dan MacLeod. A validated bioinformatics tool-set for predicting TCR specificity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3559.

T Cell Repertoire Homogeneity and Blood-Gut Overlap in Patients With Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) causes a marked increase in the number of T cells in the intestinal mucosa. Debate exists about whether these excess cells arise from local clonal proliferation or recruitment from the periphery. CD8+ T cells were sorted from colon biopsy specimens and blood for T-cell receptor (TCR) β-chain sequencing. Biopsy specimens from inflamed or uninflamed colon from ulcerative colitis or Crohn's disease cohorts were compared with colon biopsy specimens from people without IBD, as well as with autologous blood α4β7+, α4β7- effector/memory, terminal effector/memory CD45RA+ T cell, and mucosal-associated invariant T-cell CD8 subpopulations. CD8 TCR diversity in mucosa and blood did not correlate with inflammation. Repertoire overlap between any 2 distinct locations of a given person's colon was consistently high, although often lower between inflamed and uninflamed sites. CD8 TCR repertoires overlapped between the colon and each peripheral blood subpopulation studied, with the highest overlap seen for integrin α4β7+ T cells. Inflamed tissue consistently overlapped more than uninflamed tissue with each blood subpopulation. CD8 T-cell clones are spread homogenously throughout the length of the colon. Although TCR repertoire overlap is greater within than between inflamed and uninflamed colon segments, a similar TCR diversity in both argues against local clonal expansion being the main source of excess cytotoxic T cells in inflamed mucosa. Rather, the increased TCR overlap observed between blood and inflamed mucosa supports the significance of T-cell trafficking in IBD pathogenesis, particularly concerning α4β7+ T-cell populations.

Identification of intratumoral and peripheral T-cell receptor (TCR) repertoire features associated with acquired (Ar) and primary (Pr) resistance to immune checkpoint inhibitors (ICI).

59 Background: Our understanding of the mechanisms of Pr vs Ar to ICI is limited. T-cells are the main driver of ICI response. Therefore, interrogation of intratumoral and peripheral TCR repertoire could expand our comprehension of T-cell mediated factors underlying Pr and Ar to ICI. Methods: The Immune Resistance Interrogation Study (NCT04243720) is a prospective study to comprehensively characterize cancers with Pr vs Ar to ICI (Genta et. al., ASCO 2021). We conducted TCRβ capture and sequencing (CapTCR-seq) on paired tumors and blood samples collected from solid tumor patients (pts) at the time of progression on ICI. Tumor and peripheral TCR diversities were calculated for each pt. Grouping Lymphocyte Interactions by Paratope Hotspots (GLIPHII) was used to investigate the potential antigen specificities of intratumoral T-cells. Pt-derived TCRs were pooled with TCRs from a public database with known antigen specificities (TCRdb). GLIPHII grouped pt-derived and external TCRs with similar CDR3s (hypervariable TCR domains) in clusters. The clusters were then converted into a network model representing unique TCRs as nodes. The number of connections between each pt-derived TCR and similar TCRs from TCRdb was defined as node degree. Comparisons between groups were done using Mann Whitney U Test. Results: As of February 2023, 75 pts (45 Pr/30 Ar) were enrolled. CapTCR-Seq in blood and tumor samples was completed in 13 pts (7Pr/6Ar) with the following characteristics: median age 57 years (26-77), 8 male (62%), 8 melanoma (62%), 4 HNSCC (31%), and 1 GE-junction (7%). 5 pts received PD-1/PD-L1 inhibitor monotherapy (38%), 8 pts ICl-based combinations (62%). No significant differences in intratumoral (10.3 [1.3-34.2] vs 15.2 [6.6-32.4] p=0.29) and peripheral (145.5 [65.3- 521.9] vs 136.0 [35.1-474.4] p=0.53) Shannon diversities were observed between Pr and Ar. Twelve of 13 (92%) pts had at least 1 intratumoral TCR clustered with TCRdb derived TCRs. A higher median of node degree was observed in non-melanoma tumors (5.5 [2-12] vs 2.0 [1-4] p=0.03). A trend towards a higher median of node degree was reported in Ar vs Pr (5.5 [1-12] vs 2.0 [1-4] p=0 .07 ). Some of the GLIPHll-identified clusters contained TCRs derived from multiple Ar pts. Conclusions: lntratumoral TCRs from non-melanoma and Ar pts had a higher node degree, indicating similarity with a larger number of external TCRs. The difference between Pr and Ar was not significant, potentially due to the small sample size. Similar TCRs belonging to the same GLIPHll-identified clusters were shared among multiple Ar pts. If confirmed in a larger dataset, these findings might suggest the existence of a set of intratumoral exhausted T-cells shared by multiple pts contributing to Ar but not to Pr. Pt accrual, sample collection and analysis are ongoing. Additional data will be presented.

Detection of high-grade serous ovarian carcinoma using adaptive immune response and T cell repertoire (201)

<b>Objectives:</b> Identifying high-grade serous ovarian cancer (HGSOC) at its early or preinvasive stage is paramount in improving ovarian cancer (OC) mortality. In contrast to the conventional approaches relying on assessing tumor-released proteins, or imaging assessment, we hypothesized that it is feasible to identify cancer signals from the adaptive immune system. It has been reported that shared sequence similarity in the hypervariable CDR3 region of the T cell receptors (TCR) can be used as a surrogate for shared antigen-specificity. Under this observation, clustering similar TCRs has proven powerful in identifying antigen-specific T cell groups. Our objective was to demonstrate a distinct T cell immune repertoire signature for HGSOCs. <b>Methods:</b> Targeted TCR repertoire sequencing using the immunoSEQ platform was performed on blood samples from cancer patients and healthy controls in the public domain. The CDR3 regions from these patients were used as training data to develop a computational framework to systematically investigate TCR repertoires in cancer patients. ImmunoSEQ platform was performed on blood samples from 45 non-treated HGSOCs, ten non-HGSOCs, 31 benign cysts, and 34 healthy controls collected under an IRB-approved protocol. This data was used as a proof-of-principle analysis of our computational framework to differentiate ovarian cancers from benign cysts and healthy controls. <b>Results:</b> Our training data consisted of 20 million cancer-associated TCR sequences and 500,000 non-cancer TCR extracted from 50 healthy donors. Pooled TCRs from the above samples were partitioned into 5,000 clusters. The centroid of each cluster represents an antigen-specific TCR signature, referred to as ‘repertoire functional unit' (RFU). Each RFU can be perceived as a ‘gene' of the TCR repertoire, which is responsible for recognizing a class of unknown antigens. Although their antigen-specificities remain uncharacterized, the relative abundance of RFUs in each repertoire can reveal important information of the disease signatures, including ovarian cancer. With this transformation, each repertoire sample was converted into a 5,000 vector of the frequency of the same RFUs, disregarding the large diversity of the original CDR3 sequences. An unsupervised principal component analysis (PCA) of the sample-by-RFU matrix was able to differentiate HGSOCs from benign and normal controls (p=2.5, 10-6, one-way ANOVA). We next performed a proof-of-principal analysis by using the RFUs as predictors for a cohort of 120 HGSOC and control patients collected from our biore-pository. We showed that a simple machine learning model using different numbers of selected RFUs achieved high prediction accuracies ranging from 97.3% to 100%. <b>Conclusions:</b> This result suggested that the HGSOC tumor significantly altered the blood TCR repertoire composition, causing a sufficient shift in the RFU composition that dominated the variation between cancer and non-cancer samples. Proof-of principle analysis demonstrates a strong and distinctive immune repertoire signature of HGSOCs with high prediction accuracy.

Geospatial Immune Heterogeneity Reflects the Diverse Tumor–Immune Interactions in Intrahepatic Cholangiocarcinoma

This study elucidates the impact of spatial immune heterogeneity upon tumor evolution of iCCA and reveals distinct immune evasion mechanisms developed in different immune microenvironments, which can be exploited for the development of personalized immunotherapy strategies. This article is highlighted in the In This Issue feature, p. 2221.

Characteristics and Clinical Significance of T-Cell Receptor Repertoire in Hepatocellular Carcinoma.

Several studies have demonstrated that the T-cell receptor (TCR) repertoire is associated with prognosis and immune therapy response in several types of cancer. However, the comprehensive features of TCR repertoire in tumor-infiltrating and circulating T cells, as well as its clinical significance of diagnosis in hepatocellular carcinoma (HCC) patients, are still unknown. In this study, we perform paired tumor/peritumoral tissues and peripheral blood samples from 58 patients with HCC and sequenced them with high-throughput TCR to comprehensively analyze the characteristics of TCR and the clinical significance of peripheral TCR sequence. By exploring the abundance and diversity of TCR repertoires, we observe that there was a significantly higher TCR diversity in peripheral blood than in tumoral and peritumoral tissues, while tumoral and peritumoral tissues showed similar TCR diversity. A substantial difference in the usage frequencies of several Vβ, Jβ genes, and TCRβ VJ pairings was found among three types of tissues. Moreover, we reveal that HCC patients have a unique profile of TCR repertoire in peripheral blood in contrast to healthy individuals. We further establish an HCC diagnostic model based on TCRβ VJ pairing usage in peripheral blood, which yields a best-fit area under the curve (AUC) of 0.9746 ± 0.0481 (sensitivity = 0.9675 ± 0.0603, specificity = 0.9998 ± 0.0007, average of 100 repeats) in the test set. Our study describes the characteristics of tissue infiltration and circulating T-cell bank in patients with HCC and shows the potential of using circulating TCR sequence as a biomarker for the non-invasive diagnosis of patients with HCC.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

TCR meta-clonotypes for biomarker discovery with tcrdist3 enabled identification of public, HLA-restricted clusters of SARS-CoV-2 TCRs.

T-cell receptors (TCRs) encode clinically valuable information that reflects prior antigen exposure and potential future response. However, despite advances in deep repertoire sequencing, enormous TCR diversity complicates the use of TCR clonotypes as clinical biomarkers. We propose a new framework that leverages experimentally inferred antigen-associated TCRs to form meta-clonotypes - groups of biochemically similar TCRs - that can be used to robustly quantify functionally similar TCRs in bulk repertoires across individuals. We apply the framework to TCR data from COVID-19 patients, generating 1831 public TCR meta-clonotypes from the SARS-CoV-2 antigen-associated TCRs that have strong evidence of restriction to patients with a specific human leukocyte antigen (HLA) genotype. Applied to independent cohorts, meta-clonotypes targeting these specific epitopes were more frequently detected in bulk repertoires compared to exact amino acid matches, and 59.7% (1093/1831) were more abundant among COVID-19 patients that expressed the putative restricting HLA allele (false discovery rate [FDR]<0.01), demonstrating the potential utility of meta-clonotypes as antigen-specific features for biomarker development. To enable further applications, we developed an open-source software package, tcrdist3, that implements this framework and facilitates flexible workflows for distance-based TCR repertoire analysis.

Non-Genetic Determinants of Clonotypic T Cell Expansion Following Allogeneic Stem Cell Transplant

Background:The immunologic basis of acute GVHD fundamentally involves alloreactive donor T cells that recognize foreign major histocompatibility complex (MHC)-peptide structures derived from both major and minor antigen mismatches with the host. Within this paradigm, the relationship between the donor and recipient genetics represents a closed system that dictates the potential ability of any given T cell receptor (TCR) to expand, raising the question of whether there are predictable aspects of TCR reconstitution at a clonal level. Take a hypothetical example - if genetically identical twins were to receive allogeneic grafts from the same donor and both recipients develop GVHD, would one expect similar TCRs to be clonally expanded? It has been challenging to rigorously explore this phenomenon, however, because of the vast combinatorial diversity of αβ TCRs, the high prevalence of low copy number TCRs, and sampling constraints - all of which render tracking and comparing TCR expansion between the donor and host difficult.Methods:We address these challenges in order to better understand the predictability TCR clonal dynamics through an analysis platform utilizing 1) a series of matched and mismatched murine transplant experiments where genetically identical littermates receive T cells from the same polyclonal donor pool, thus creating multiple transplant replicates simulating the twin transplant system describe above (Fig 1), and 2) probabilistic modeling of individual TCR frequencies to account for partitioning stochasticity (variation in how low copy number TCRs are distributed from donor to recipient). We conduct high-throughput DNA-based TCR amplicon sequencing for both donor and post-transplant recipient samples to generate over 20 million TCRs and model the expansion rates of all identifiable TCRs in each transplant system using a Bayesian approach.Results:While overall V and J gene usage were similar amongst identical recipients (Fig 2), we find that a small fraction of TCR clonotypes appears to have widely disparate clone counts amongst identical recipients receiving the same donor T cell pool. For example, we saw 9,739, 129 and 0 copies of a particular TCR in 3 different recipients in our B6->B6D2F1 system (Fig 3). In order to distinguish whether TCR count discrepancies seen across identical recipients is simply a reflection of donor partitioning stochasticity or true differential expansion (Fig 4), we apply a Bayesian algorithm to identify differential expanders, which represent TCRs that are asymmetrically expanded between recipients of a genetically identical pair (Fig 5). These TCRs can be generated from both memory and naïve T cell compartments.The presence of these differentially expanded clones amongst identical recipients suggests that non-genetic dependent mechanisms may influence which TCRs expand post-transplant. We next show that broad gut decontamination of microbiota with peri-transplant vancomycin, gentamicin, cefoxitin and metronidazole dramatically reduced the fraction of differential expanders (p<0.0001). However, the change in inflammation from microbiome depletion did not appear to drive this difference, as 1) MyD88/TRIF double knockout recipients (deficient TLR signaling) did not show a reduction in differential expanders, and 2) altering conditioning intensity (900cGy to 1300 cGy TBI) also did not change the fraction of differential expanders. Rather, the difference is likely antigenically driven, as differential expanders are enriched in antigen specificity compared to other TCR sequences (p<0.0001) based on published algorithm that identify TCRs with similar amino acid sequence overlap.Conclusions:These results refine our current understanding of clonal T cell selection and expansion after allogeneic BMT and suggests that for a given transplant system, individual TCR selection is not solely dictated by genetic donor and recipient major and/or minor histocompatibility disparities. Rather, microbiota-derived molecules appear to behave as minor antigens to direct systemic clonal TCR selection. These data suggest a novel mechanism by which the microbiome may modulate transplant outcome, challenging current paradigms suggesting the microbiota primarily drive inflammation via their PAMP activities. [Display omitted] DisclosuresHill: Applied Molecular Transport: Research Funding; Syndax Pharmaceuticals: Research Funding; Compass Therapeutics: Research Funding; NapaJun Pharma: Consultancy; Generon corporation: Consultancy; iTeos Therapeutics: Consultancy, Research Funding; Neoleukin Therapeutics: Consultancy; Roche: Research Funding.

SY23-1 Understanding immune checkpoint inhibitors from a viewpoint of the cancer immunobiology

Immune checkpoint inhibitors (ICIs) targeting programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) can restore pre-existing anti-cancer immunity and have achieved a durable clinical response across the various type of tumors including breast cancer. The success of these ICIs has turned physicians' gaze on cancer immunobiology. Cytotoxic T cells become exhausted in a series of interaction between tumors and immune cells and the clonal replacement of tumor infiltrating lymphocytes (TILs) occurred from the peripheral blood. TCF1-positive stem-like T cells retain high proliferation potential and self-renewal in antigen presenting cell (APC)-niches of the primary tumors. Once these pre-exhausted T cells were reinvigorated by ICIS, they could be expanded and differentiated to functional CD8-positive effector T cells. Draining lymph node (DLN) is the secondary lymphoid organ where the naive T cells are primed by APCs and differentiated to effector T cells. The therapeutic effects of PD-L1 blockage were completely canceled in mice undergoing completion of axillary lymph node dissection. Moreover, tumor-positive DLNs showed a lower T cell receptor (TCR) diversity than tumor-negative ones and had similar TCR repertoires to TILs. These findings suggested that antigen-specific T cell clones could be enriched in tumor-positive DLNs and recruited from the distal sites to the tumor microenvironment. Accumulating results provide the evidence that neoadjuvant therapy combined with ICIs might generate an immune response regardless of PD-L1 status in early-stage triple negative breast cancer and a greater ICI benefit appeared in patients with tumor-positive DLNs. Deep understanding of the underlying immunobiology might lead to biomarker-driven strategies to identify patients who are more likely to experience a favorable effect from ICIs. I will summarize emerging evidences from T cell basic science to clinical practice in the field of cancer immunotherapy.

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Unique Pattern of Intrahepatic T-cell Clonality in Biliary Atresia Livers Versus Intestinal Controls: A Pilot Study

Biliary atresia (BA) is a rare infantile cholangiopathy of unclear etiology proposed by some to be due to virus-induced autoreactive T-cell-mediated inflammation. The hallmark of T cell activity is clonal expansion of T lymphocytes expressing similar T-cell receptor (TCR) variable regions of the β-chain. Objective: To test our hypothesis that BA liver tissues would show clonal expansion of 1 or several TCRs. Methods: The complementarity-determining region 3 region of the β-chain of the TCR was characterized using next-generation sequencing of 7 BA liver samples (age 51 ± 14 days) and 9 intestinal control samples (age 38 ± 16 days). Following sequencing, clonality scores, various VDJ recombinations, total and productive templates, and complementarity-determining region 3 length were measured using the immunoSEQ Analyzer. Results: Next-generation sequencing revealed 1 common TCR rearrangement in 3 BA samples not found in controls. There was a highly diverse TCR population among BA liver and the control samples. The clonality scores ranged from 0.0004 to 0.0062 using a Shannon’s entropy score, with numbers close to 0 being highly diverse and numbers close to 1 being highly clonal. The most common TCR VDJ recombinations comprised 1.47–12.9% of the total population of TCR for the BA tissues and 1.05–10.3% for the control samples. Conclusions: Our results show a highly diverse TCR repertoire among all of our samples. However, predominant TCR clonality was not found in any sample. Further studies are required for any possible antigenic triggers responsible for the unique T-cell rearrangements observed in the BA samples.

Generation of self-reactive, shared T-cell receptor α chains in the human thymus.

The T-cell receptor (TCR) repertoire is generated in a semistochastic process of gene recombination and pairing of TCRα to TCRβ chains with the estimated total TCR diversity of >108. Despite this high diversity, similar or identical TCR chains are found to recur in immune responses. Here, we analyzed the thymic generation of TCR sequences previously associated with recognition of self- and nonself-antigens, represented by sequences associated with autoimmune diabetes and HIV, respectively. Unexpectedly, in the CD4+ compartment TCRα chains associated with the recognition of self-antigens were generated in significantly higher numbers than TCRα chains associated with the recognition of nonself-antigens. The analysis of the circulating repertoire further showed that these chains are not lost in negative selection nor predominantly converted to the regulatory T-cell lineage. The high abundance of self-reactive TCRα chains in multiple individuals suggests that the human thymus has a predilection to generate self-reactive TCRα chains independently of the HLA-type and that the individual risk of autoimmunity may be modulated by the TCRβ repertoire associated with these chains.

Recipient-specific T-cell repertoire reconstitution in the gut following murine hematopoietic cell transplant

AbstractGraft-versus-host disease (GVHD) is a complication of hematopoietic cell transplantation (HCT) caused by alloreactive T cells. Murine models of HCT are used to understand GVHD and T-cell reconstitution in GVHD target organs, most notably the gastrointestinal (GI) tract where the disease contributes most to patient mortality. T-cell receptor (TCR) repertoire sequencing was used to measure T-cell reconstitution from the same donor graft (C57BL/6 H-2b) in the GI tract of different recipients across a spectrum of matching, from syngeneic (C57BL/6), to minor histocompatibility (MHC) antigen mismatch BALB.B (H-2b), to major MHC mismatched B10.BR (H-2k) and BALB/c (H-2d). Although the donor T-cell pools had highly similar TCR, the TCR repertoire after HCT was very specific to recipients in each experiment independent of geography. A single invariant natural killer T clone was identifiable in every recipient group and was enriched in syngeneic recipients according to clonal count and confirmatory flow cytometry. Using a novel cluster analysis of the TCR repertoire, we could classify recipient groups based only on their CDR3 size distribution or TCR repertoire relatedness. Using a method for evaluating the contribution of common TCR motifs to relatedness, we found that reproducible sets of clones were associated with specific recipient groups within each experiment and that relatedness did not necessarily depend on the most common clones in allogeneic recipients. This finding suggests that TCR reconstitution is highly stochastic and likely does not depend on the evaluation of the most expanded TCR clones in any individual recipient but instead depends on a complex polyclonal architecture.

Lattice Light-Sheet Microscopy Multi-dimensional Analyses (LaMDA) of T-Cell Receptor Dynamics Predict T-Cell Signaling States.

Lattice light-sheet microscopy provides large amounts of high-dimensional, high-spatiotemporal resolution imaging data of cell surface receptors across the 3D surface of live cells, but user-friendly analysis pipelines are lacking. Here, we introduce lattice light-sheet microscopy multi-dimensional analyses (LaMDA), an end-to-end pipeline comprised of publicly available software packages that combines machine learning, dimensionality reduction, and diffusion maps to analyze surface receptor dynamics and classify cellular signaling states without the need for complex biochemical measurements or other prior information. We use LaMDA to analyze images of T-cell receptor (TCR) microclusters on the surface of live primary Tcells under resting and stimulated conditions. We observe global spatial and temporal changes of TCRs across the 3D cell surface, accurately differentiate stimulated cells from unstimulated cells, precisely predict attenuated T-cell signaling after CD4 and CD28 receptor blockades, and reliably discriminate between structurally similar TCR ligands. All instructions needed to implement LaMDA are included in this paper.

Lattice light-sheet microscopy Multi-Dimensional Analyses (LaMDA) of T-cell receptor dynamics predict T-cell signaling states

Abstract Cell surface receptors dynamically change in response to cell signaling, but a direct linkage between receptor dynamics and cell state has not been established. Here we report the development of lattice light-sheet microscopy multi-dimensional analyses (LaMDA), a pipeline that combines high spatiotemporal-resolution four-dimensional lattice light-sheet microscopy, machine learning, and diffusion maps to analyze T-cell receptor (TCR) dynamics and predict T-cell signaling states without the need for complex biochemical measurements. LaMDA images thousands of TCR microclusters on the surface of live primary cells to collect high-dimensional dynamic data for machine learning, which extracts key dynamic features to build predictive diffusion maps. LaMDA spatiotemporally reveals global changes of TCRs across the 3D cell surface, accurately differentiates stimulated cells from unstimulated cells, precisely predicts attenuated T-cell signaling after CD4 and CD28 receptor blockades, and reliably discriminates between structurally similar TCR ligands. We anticipate broad usage of this approach for other receptors and cells, as well as for guiding the design and development of future immunotherapies for cancer, infection, and autoimmunity.

Investigation of Antigen-Specific T-Cell Receptor Clusters in Human Cancers.

Cancer antigen-specific T cells are key components in antitumor immune response, yet their identification in the tumor microenvironment remains challenging, as most cancer antigens are unknown. Recent advance in immunology suggests that similar T-cell receptor (TCR) sequences can be clustered to infer shared antigen specificity. This study aims to identify antigen-specific TCRs from the tumor genomics sequencing data. We used the TRUST (Tcr Repertoire Utilities for Solid Tissue) algorithm to assemble the TCR hypervariable CDR3 regions from 9,700 bulk tumor RNA-sequencing (RNA-seq) samples, and developed a computational method, iSMART, to group similar TCRs into antigen-specific clusters. Integrative analysis on the TCR clusters with multi-omics datasets was performed to profile cancer-associated T cells and to uncover novel cancer antigens. Clustered TCRs are associated with signatures of T-cell activation after antigen encounter. We further elucidated the phenotypes of clustered T cells using single-cell RNA-seq data, which revealed a novel subset of tissue-resident memory T-cell population with elevated metabolic status. An exciting application of the TCR clusters is to identify novel cancer antigens, exemplified by our identification of a candidate cancer/testis gene, HSFX1, through integrated analysis of HLA alleles and genomics data. The target was further validated using vaccination of humanized HLA-A*02:01 mice and ELISpot assay. Finally, we showed that clustered tumor-infiltrating TCRs can differentiate patients with early-stage cancer from healthy donors, using blood TCR repertoire sequencing data, suggesting potential applications in noninvasive cancer detection. Our analysis on the antigen-specific TCR clusters provides a unique resource for alternative antigen discovery and biomarker identification for cancer immunotherapies.