Accurate and scalable multi-disease classification from adaptive immune repertoires

Introduction: About one-third of prostate cancer (PCa) patients will experience rising prostate specific antigen levels, a condition known as biochemical recurrence. INO-5150 is a DNA-based immunotherapy targeting PSA and PSMA previously tested with or without plasmid-encoded cytokine adjuvant IL-12 (INO-9012) in a phase 1/2, open-label, multi-center study. Treatment was well-tolerated and safe, with no treatment-related serious adverse events. Here we sought to characterize the T cell receptor (TCR) repertoire of treated patients before and after INO-5150. Experimental Procedures: 62 post-prostatectomy/radiation therapy patients with rising PSA were enrolled in the trial. INO-5150±INO-9012 was administered IM followed by electroporation with the CELLECTRA® device on day 0, weeks 3, 12 and 24. Peripheral blood mononuclear cells (PBMCs) were collected before and after treatment from a subset of patients (n=19) and stimulated with PSA or PSMA peptides, negative or positive controls. After 5 days the Complementary Determining Region 3 (CDR3) of the TCR beta chain was sequenced. Results: TCR sequences that expanded after PSA or PSMA stimulation only were considered antigen specific sequences. On average, >70% of antigen specific TCR sequences were expanded following treatment with INO-5150 +/- INO-9012, including fold increases of up to 327 fold for PSA and 471 fold for PSMA over pretreatment frequencies. All treated patients had a Shannon's entropy (H) index >3.5 for TCR diversity. Previous examination of these patients using flow cytometry suggested that half of them demonstrated expansion of a PSA/PSMA specific CD8+ T cell subset harboring lytic potential (CD38+PD1+Prf+). TCR analysis performed by grouping patients based on the presence or absence of these cells suggested PSA/PSMA specific TCR expansion occurred for both groups. Deeper analysis suggested differences in the degree of expansion, as 56% of patients with the CD38+PD1+Prf+ phenotype were found to have at least 150 TCRs with a 10 fold expansion over baseline, whereas only 20% of patients without this cell population met this criteria. Conclusions: Treatment with INO-5150 +/- INO-9012 in PCa patients drove the expansion of a diverse pool of antigen specific T cells with a high Shannon's Entropy value suggesting high TCR diversity. Patients with immune reactivity by flow cytometry were more likely to have a higher frequency of PSA/PSMA specific TCRs that expanded >10 fold over their pre-treatment value; however patients without immune reactivity also demonstrated expansion in TCRs. These data suggest that INO-5150 induced PSA/PSMA specific T cells and that TCR sequencing is a valuable tool for assessment of immune responses induced by immunotherapy that does not bias towards a specific T cell function. Citation Format: Kimberly A. Kraynyak, Matthew P. Morrow, Albert J. Sylvester, Snehal Wani, Jean Boyer, Kamal Bhatt, Trevor McMullan, Jessica Lee, Brian Sacchetta, Samantha Rosencranz, Jeffrey Skolnik, Neal Shore. Immunotherapy targeting PSA and PSMA in patients with biochemically recurrent prostate cancer expands antigen-specific T cell receptor repertoire in a PhI/II trial [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr LB-144.

ObjectiveThe immune response to invasive carcinoma has been the focus of published work, but little is known about the adaptive immune response to bronchial premalignant lesions (PMLs), precursors of lung...

Mounting experimental and clinical evidence has revealed that adaptive immune mechanisms targeting myocardial antigens are triggered by different forms of cardiac injury and impact disease progression. B and T lymphocytes recognize specific antigens via unique adaptive immune receptors generated through a somatic rearrangement process that generates a potential repertoire of 10 19 unique receptors. While the adaptive immune receptor repertoire diversity provides the basis for immunologic specificity, making sense of it can be a challenging task. In the present review, we discuss key aspects underlying the generation of TCRs (T cell receptors) and emerging tools for their study in the context of myocardial diseases. Moreover, we outline how exploring TCR repertoires could lead to a deeper understanding of myocardial pathophysiological principles and potentially serve as diagnostic tools.

Clonal Analysis of Peripheral Blood T Cells in patients with Hashimoto's Thyroiditis at Different Stages using TCR Sequencing.

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

Machine learning (ML) has shown great potential in the adaptive immune receptor repertoire (AIRR) field. However, there is a lack of large-scale ground-truth experimental AIRR data suitable for AIRR-ML-based disease diagnostics and therapeutics discovery. Simulated ground-truth AIRR data are required to complement the development and benchmarking of robust and interpretable AIRR-ML methods where experimental data is currently inaccessible or insufficient. The challenge for simulated data to be useful is incorporating key features observed in experimental repertoires. These features, such as antigen or disease-associated immune information, cause AIRR-ML problems to be challenging. Here, we introduce LIgO, a software suite, which simulates AIRR data for the development and benchmarking of AIRR-ML methods. LIgO incorporates different types of immune information both on the receptor and the repertoire level and preserves native-like generation probability distribution. Additionally, LIgO assists users in determining the computational feasibility of their simulations. We show two examples where LIgO supports the development and validation of AIRR-ML methods: (i) how individuals carrying out-of-distribution immune information impacts receptor-level prediction performance and (ii) how immune information co-occurring in the same AIRs impacts the performance of conventional receptor-level encoding and repertoire-level classification approaches. LIgO guides the advancement and assessment of interpretable AIRR-ML methods.

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

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k-mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool (http://tools.iedb.org/tcrmatch/) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

The T cell receptor (TCR) repertoire is pivotal to the human immune system, and understanding its nuances can significantly enhance our ability to forecast cancer-related immune responses. However, existing methods often overlook the intra- and inter-sequence interactions of T cell receptors (TCRs), limiting the development of sequence-based cancer-related immune status predictions. To address this challenge, we propose BertTCR, an innovative deep learning framework designed to predict cancer-related immune status using TCRs. BertTCR combines a pre-trained protein large language model with deep learning architectures, enabling it to extract deeper contextual information from TCRs. Compared to three state-of-the-art sequence-based methods, BertTCR improves the AUC on an external validation set for thyroid cancer detection by 21 percentage points. Additionally, this model was trained on over 2000 publicly available TCR libraries covering 17 types of cancer and healthy samples, and it has been validated on multiple public external datasets for its ability to distinguish cancer patients from healthy individuals. Furthermore, BertTCR can accurately classify various cancer types and healthy individuals. Overall, BertTCR is the advancing method for cancer-related immune status forecasting based on TCRs, offering promising potential for a wide range of immune status prediction tasks.

The role of T cell receptor (TCR) signaling for adaptive immune responses is essential. The ability to respond to a broad spectrum of tumor antigens requires an adaptive selection of various TCR. So far, little is known about the role of TCR richness and clonality in the cellular immune response to head and neck cancer (HNC), though the Endothelial Growth Factor Receptor (EGFR)-specific CD8+ T cell response can be enhanced by cetuximab therapy. Therefore, we investigated differences in TCR sequences between human papillomavirus (HPV)+ and HPV− HNC patients, as well as differences in TCR sequence characteristics between T cells of peripheral blood mononuclear cells (PBMC) and tumor infiltrating lymphocytes (TIL). Additionally, we were able to investigate the TCR richness and clonality in samples pre- and post- treatment in a prospective clinical trial of neoadjuvant cetuximab. Interestingly, HPV+ and HPV− HNSCC did not significantly differ in the extent of TCR clonality and richness in PBMC or TIL. However, neoadjuvant cetuximab treatment increased the number of unique TCR sequences in PBMC (p = 0.0003), which was more prominent in the clinical responder patients compared to non-responders (p = 0.04). A trend toward TCR gene focusing was observed in TIL (p = 0.1) post-treatment. Thus, an increase in richness of TCR sequences in the periphery with a focusing at the tumor site is associated with an improved treatment response, suggesting an influence of peripheral quantity and intratumoral quality on adaptive immunity in cetuximab treated patients.

MP01-10 A NOVEL AND FEASIBLE METHOD FOR QUANTIFYING TESTICULAR SPERAMATOGENESIS BY USING CREATINE CHEMICAL EXCHANGE SATURATION TRANSFER IMAGING

There is a high rate of comorbidity between autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and eating disorders (EDs). Furthermore, neuropsychological studies and genetic studies support the presence of shared background among these disorders.1-4 All of these findings suggest shared pathophysiology of these disorders. NBEA encodes neurobeachin and has been strongly implicated in ASD risk by the identification of de novo loss-of-function variants of this gene in multiple unrelated cases.5-7 Neurobeachin regulates recycling of GluN2B-NMDA receptors, and is involved in neurotransmission, synaptic formation and plasticity by a haploinsufficient genetic mechanism.8 Although we also identified an ASD patient with rare deletion disrupting this gene in a study of copy number variation (CNV),2 only limited knowledge was available about the clinical implications of NBEA deletion. It is important to accumulate high-quality case reports to improve the clinical management of patients with such CNVs. Therefore, we report a 10-year clinical course of an ASD patient with NBEA deletion, who had comorbidities of OCD, ED, and intellectual disability (ID). The patient was a 41-year-old Japanese female. Her father had a broad autism phenotype. During infancy, language developmental delay, insistence on the same clothes and foods, tactile hypersensitivity, and impairments in communication and social interactions were noticed. She had very little social contact with her peers throughout her school career. After she graduated from junior college, she had several part-time jobs but soon quit because of interpersonal conflicts in the workplace. Her developmental history is summarized in Table S1. At the age of 31, she had recurrent episodes of overeating and rapidly gained weight. In response to this sudden weight gain, she began to restrict her food intake. Thereafter, her body mass index (BMI) decreased to 12.3 from 21.1 kg/m2 in 6 months. She was hospitalized with a diagnosis of unspecified feeding or eating disorder according to the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition (DSM-5) diagnostic criteria. Additionally, during the hospitalization, she was diagnosed with ASD. Psychoeducation about ED and ASD and supportive psychotherapy were provided. Although she did not meet the diagnostic criteria for major depressive disorders, pharmacotherapy was initiated to target her depressive symptoms. Gradually, her food intake increased, her BMI recovered to 15.4 kg/m2, and she was discharged in 3 months. After regaining weight, overeating was observed, but improved after discontinuing olanzapine medication. The patient continued to receive outpatient treatment, but obsessive-compulsive symptoms with obsessive thoughts appeared. She developed compulsive behaviors such as hand or body washing, and she spent a lot of time performing such behaviors. She was diagnosed with OCD and received medication (selective serotonin reuptake inhibitors and atypical antipsychotics) and cognitive behavioral therapy, but the symptoms were intractable. Since her last hospitalization, her symptoms have been under control. The clinical time course is summarized in Fig. 1a. Her intelligence quotient score was 62 (Table S2). Array comparative genomic hybridization using NimbleGen 720k Whole-Genome Tiling Array detected a 447-kb deletion spanning exons 11–41 of NBEA2 (Fig. 1b,c). Parental CNV analysis revealed that NBEA deletion was paternally inherited (Fig. 1d). Gene expression analysis revealed that the NBEA mRNA expression level was reduced below the 15th percentile of the control group (N = 29) (Fig. 1e, Appendix S1). We reported an ASD patient with OCD, ED, ID, and NBEA deletion. Neuropsychological studies support the presence of shared traits among ASD, OCD, and EDs.1 As shown in Table S2, ASD patients with NBEA deletion have been reported.6, 7 This is the first case report of an adult with NBEA deletion. Though previously reported cases did not mention OCD and EDs, these patients had not reached the peak age of onset of these disorders. Heterozygous Nbea knockout mice exhibit not only autistic behaviors but also impaired extinction of contextual fear memory, which is associated with OCD, and abnormal feeding behavior, episodically overconsuming tastants.9, 10 Thus, the clinical phenotypes of the patient were similar to those of heterozygous Nbea knockout mice, raising the possibility that exonic NBEA deletion was responsible for the symptoms in this patient. As Nbea knockout mice show hyperphagia, patients with NBEA deletion may be more prone to episodic overeating. The patient we reported showed rapid weight gain during olanzapine medication. Choosing medicines carefully and monitoring weight are important. In conclusion, NBEA deletion may be a shared genetic underpinning of ASD, OCD, and EDs. The accumulation of clinical data of patients with NBEA deletion including the present case may contribute to establishment of appropriate clinical management of these patients in the future. We thank the patient and her family members for participating in this study. We also thank Mami Yoshida, Kiyori Monta, and Yukari Mitsui for their technical assistance. This research was supported by research grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) and the Ministry of Health, Labour and Welfare of Japan; the Japan Agency for Medical Research and Development (AMED) under Grant Nos. JP20dm0107087, JP21wm0425007, JP21dm0207075, JP21ak0101113, JP21dk0307075, JP20dk0307081, JP21dk0307103, JP21ek0109488, JP21km0405216, and JP21ek0109411; the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Nos. 17H05090, 15K19720, 18H04040, 21K07543, 21H00194, 21H04815, and 20K20602; the Uehara Memorial Foundation; and the SENSHIN Medical Research Foundation. H.K., I.K., A.Y., K.I., K.I., and B.A. declare no conflict of interest. N.T. has received research support or speakers' honoraria from, or has served as a consultant to, Otsuka, Shionogi, Takeda, Janssen, and Pfizer, outside the submitted work. T.O. has received lecture fees from Shionogi and Mochida, outside the submitted work. N.O. has received research support or speakers' honoraria from, or has served as a consultant to, Sumitomo Dainippon, Eisai, Otsuka, KAITEKI, Mitsubishi Tanabe, Shionogi, Eli Lilly, Mochida, DAIICHI SANKYO, Nihon Medi-Physics, Takeda, Meiji Seika Pharma, EA Pharma, Pfizer, MSD, Lundbeck Japan, Taisho Pharma, outside the submitted work. This study was approved by the ethics committee of the Nagoya University Graduate School of Medicine and this study complied with all the provisions of the Declaration of Helsinki. The patient and her parents gave written informed consent for genetic testing and publishing of this report. Appendix S1. Supplementary Appendix. Methods of gene expression analysis Appendix S2. Author Contributions Table S1. Time course of the patient's developmental history. Table S2. Clinical summary of cases with NBEA deletion Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

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.

Background: T-cell receptor (TCR) based immunotherapies are becoming an important cornerstone of immuno-oncology. Complete TCR sequencing requires single-cell resolution to capture both α and β chains. There is great interest in obtaining single-cell TCR sequences from archived tumor tissue. This task requires technology that can not only retrieve single cells but also sequence degraded RNA from archived tissues samples at the single cell level. The RareCyte CyteFinder® platform provides integrated multi-parameter imaging and retrieval capabilities for identification and isolation of rare cells and microscopic regions of interest (ROI) for molecular analyses. Archer has developed the Immunoverse™ platform of targeted Next-Generation Sequencing (NGS) assays to characterize the human immune repertoire from partially degraded RNA inputs. Combining these two technologies affords the unique potential to accelerate engineered cell-based therapeutic by sequencing TCR from individual cells. Methods: 40 um ROI containing 1 to 10 T-cells were isolated from fresh/frozen (OCT) melanoma or OCT and FFPE tonsil using the CyteFinder® system. In addition, single flu antigen-specific T-cells were retrieved from live cell preparations. OCT tonsil sections were stained with a 3-color panel to discriminate T cell and B cell zones, and OCT tumor sections were stained with a 6-color panel to identify immune infiltrate, while FFPE sections were stained with a nuclear marker. The Archer® Immunoverse™ TCR All Chains library preparation kit was used to generate libraries which were then sequenced on the Illumina NextSeq platform. The resultant library sequences were deduplicated, error corrected, aligned to reference V, (D), J and C regions of TCRs, and assembled to identify clonotypes from α and β chains using the Archer® Analysis tool. Results: TCR transcripts were observed from RNA isolated from as few as one cell. CDR3 sequences with V and J segments from single flu-targeting T-cells were obtained and found to match previously published sequences. TCR transcripts were observed in all ROI retrieved from OCT and FFPE tonsil when processed with the Immunoverse™ TCR assay. The numbers of transcripts observed correlated with the number of isolated cells analyzed. The sample source also affected the number of observed clones with more clones observed from cells isolated from fresh/frozen tissue. Conclusions: These results support the utility of the RareCyte platform and Archer® Immunoverse™ TCR assay for profiling RNA derived from low numbers of cells in either fresh/frozen tissue, FFPE tissue, or live cells. A workflow that combines RareCyte and Archer technologies shows promise as a method for pairing α and β chain TCR sequences from RNA isolated from a single cell. Citation Format: Laura Johnson, Luke Hartje, Lance U'Ren, Nolan Ericson, Rebecca Podyminogin, Tad George, Steve Daniel. TCR sequencing from tissue micro-regions and single cells utilizing RareCyte CytePicker and Archer Immunoverse technologies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 141.

BackgroundDespite advances with single-clone CD8+ T cells in cancer therapy, results often fall short due to immune evasion by cancer cells and the limited lifespan of administered CD8+ T cells.1–3...