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T-cell receptor gene rearrangement. (a) Variable (V ), joining (J ) and constant regions (C) constitute the TCR a-chain. (b) Variable (V ), joining (J ) and constant regions (C) constitute the TCR b-chain, with an additional diversity (D) region. Segments from each region are recombined, with additional nucleotide additions, to generate each rearranged TCR. These processes generate substantial T cell diversity. (c,d) Hypervariable complementarity-determining regions (CDR1CDR3) of the a-chain (c) and b-chain (d ). CDR1 and CDR2 regions are encoded on the V region, while the most variable CDR3 region straddles the V(D)J junction.
Source publication
A highly diverse T-cell receptor (TCR) repertoire is a fundamental property of an effective immune system, and is associated with efficient control of viral infections and other pathogens. However, direct measurement of total TCR diversity is impossible. The diversity is high and the frequency distribution of individual TCRs is heavily skewed; the...
Citations
... This is an important advantage for practical applications in which sample sizes vary between ecological communities. Such variation has become an increasingly important concern in ecology, as the field has moved to apply techniques developed for field studies with well-controlled sampling effort to the assessment of microbiome [8,10] or immune repertoire [16,36,40] diversity from high-throughput sequencing experiments. By using the unbiased estimator introduced here ecologists can avoid the loss of information inherent in the common practice of subsampling larger samples down to the smallest sample size, known as rarefaction. ...
Quantification of measurement uncertainty is crucial for robust scientific inference, yet accurate estimates of this uncertainty remain elusive for ecological measures of diversity. Here, we address this longstanding challenge by deriving a closed-form unbiased estimator for the sampling variance of Simpson's diversity index. In numerical tests the estimator consistently outperforms existing approaches, particularly for applications in which species richness exceeds sample size. We apply the estimator to quantify biodiversity loss in marine ecosystems and to demonstrate ligand-dependent contributions of T-cell-receptor chains to specificity, illustrating its versatility across fields. The novel estimator provides researchers with a reliable method for comparing diversity between samples, essential for quantifying biodiversity trends and making informed conservation decisions.
Published by the American Physical Society 2024
... However, biomedical control problems present unique hurdles to this approach. Human biology exhibits significantly greater variability across individuals [11][12][13][14] compared to engineered systems. Additionally, many systems crucial for human health, such as the immune system or the human microbiome, encompass highly diverse cell populations [15]. ...
The vision of personalized medicine is to identify interventions that maintain or restore a person's health based on their individual biology. Medical digital twins, computational models that integrate a wide range of health-related data about a person and can be dynamically updated, are a key technology that can help guide medical decisions. Such medical digital twin models can be high-dimensional, multi-scale, and stochastic. To be practical for healthcare applications, they often need to be simplified into low-dimensional surrogate models that can be used for the optimal design of interventions. This paper introduces surrogate modeling algorithms for the purpose of optimal control applications. As a use case, we focus on agent-based models (ABMs), a common model type in biomedicine for which there are no readily available optimal control algorithms. By deriving surrogate models that are based on systems of ordinary differential equations, we show how optimal control methods can be employed to compute effective interventions, which can then be lifted back to a given ABM. The relevance of the methods introduced here extends beyond medical digital twins to other complex dynamical systems.
... 23 Repertoires were down sampled using rarefaction to the smallest sample to ensure comparability using the R package vegan as previously described. 25 All diversity and dissimilarity indexes were computed on down sampled data using the R package vegan. Normalized Shannon's diversity was used to measure distribution of clones, where higher values indicate a more diverse repertoire and lower values indicate a more clonally expanded repertoire. ...
Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRβ) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRβ repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRβ repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRβ clonotypes was observed in responding tumours. Machine learning identified TCRβ CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRβ signatures associated with ICT response.
... An individual has up to a billion of unique T-cell clonotypes 1,2 , each identified by its TCR. A TCR is a heterodimer, consisting of α and β chains in ~95% T cells, and δ and γ chains in ~5% T cells. ...
The dog serves as a key translational model in cancer immunotherapy. Understanding the T cell receptor (TCR) repertoire is needed for various cancer immunotherapies. Compared to humans where >300 million TCRs have been identified, <100 canine TCRs are reported. To address this deficiency, we assembled >200,000 complete TCR complementarity-determining region 3 (CDR3) sequences from RNA-seq data published for ~2,000 canine samples of blood, lymph node, and other tissues, of which 613 are tumors. We collected 1,324 human RNA-seq samples to compare the similarities and differences in T-cell repertoires between humans and dogs. Notably, our analysis revealed distinct variable gene usage patterns between blood samples and solid tissues in both canine and human samples for TRA and TRB loci. Moreover, our investigation led to the discovery of novel V gene and allele candidates in the canine genome. Our findings also revealed that the canine CDR3 resembled human CDR3 in terms of length and motifs. Additionally, our study unveiled shared traits in cancer TCRs between dogs and humans, including longer lengths and higher hydrophobicity of private CDR3s. Our results indicated the diversity of canine to be more comparable to that of humans than mice. Our study provides an initial landscape of the canine TCR repertoire, highlighting both its similarities and differences with the human counterpart, thus laying the groundwork for future research in comparative immunology and vaccine development.
... The V(D)J-recombination creates diversity both from a combinatorial effect by choosing which genes to include and a junctional effect stemming from random nucleotide insertions and deletions in the ligation process of the chosen gene segments. Together the two chains can form a vast TCR diversity with estimates ranging from 10 15 to 10 20 , being orders of magnitudes larger than the estimated amount of cells in the human body 3:7 Á 10 13 (Laydon et al. 2015). Similarly as the TCRs, the pMHCs are very diverse. ...
Motivation
T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging.
Results
We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models.
Code availability
https://github.com/DaniTheOrange/EPIC-TRACE
Supplementary information
Supplementary data are available at Bioinformatics online.
... 13 TCR repertoire is commonly measured using multiple metrics, such as number of unique clones, evenness, Shannon diversity and convergence. [14][15][16] The pre-treatment TCR repertoire has been found to be associated with clinical outcomes in NSCLC and other cancers. High intratumoural TCR diversity before therapy was reported to be associated with worse survival among 15 NSCLC patients. ...
Background
The circulating T-cell receptor (TCR) repertoire is a dynamic representation of overall immune responses in an individual.
Materials and methods
We prospectively collected baseline blood from patients treated with first-line pembrolizumab monotherapy or in combination with chemotherapy. TCR repertoire metrics were correlated with clinical benefit rate (CBR), progression-free survival (PFS), overall survival (OS) and immune-related adverse events (irAEs). We built a logistic regression classifier by fitting all four TCR-β repertoire metrics to the immune checkpoint inhibitor (ICI) CBR data. In the subsequent receiver operating characteristic (ROC) analysis of the resulting logistic regression model probabilities, the best cut-off value was selected to maximise sensitivity to predict CBR to ICI.
Results
We observed an association between reduced number of unique clones and CBR among patients treated with pembrolizumab monotherapy (cohort 1) [risk ratio = 2.86, 95% confidence interval (CI) 1.04-8.73, P = 0.039]. For patients treated with pembrolizumab plus chemotherapy (cohort 2), increased number of unique clones [hazard ratio (HR) = 2.96, 95% CI 1.28-6.88, P = 0.012] and Shannon diversity (HR = 2.73, 95% CI 1.08-6.87, P = 0.033), and reduced evenness (HR = 0.43, 95% CI 0.21-0.90, P = 0.025) and convergence (HR = 0.41, 95% CI 0.19-0.90, P = 0.027) were associated with improved PFS, while only an increased number of unique clones (HR = 4.62, 95% CI 1.52-14.02, P = 0.007) were associated with improved OS. Logistic regression models combining the TCR repertoire metrics improved the prediction of CBR (cohorts 1 and 2) and were strongly associated with PFS (cohort 1, HR = 0.38, 95% CI 0.19-0.78, P = 0.009) and OS (cohort 2, HR = 0.20, 95% CI 0.05-0.76, P < 0.0001). Reduced TCR conversion was associated with increased frequency of irAEs needing systemic steroid treatment.
Conclusion
Combined pre-treatment circulating TCR metrics might serve as a predictive biomarker for clinical outcomes among patients with advanced non-small-cell lung cancer treated with pembrolizumab alone or in combination with chemotherapy.
... However, TCR polyclonality or diversity (using Chao1 richness estimator for TCR Richness) 73,74 was relatively higher in MSI CRC IFNG + CD8 + T cells, albeit non-significant ( Fig. 3b). High TCR expansion despite low TCR diversity in MSS CRC IFNG + CD8 + T cells could be indicative of low tumoral antigenic diversity 75 . ...
CD8 ⁺ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8 ⁺ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8 ⁺ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8 ⁺ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific ‘self-like’ features. MSI CRC CD8 ⁺ T cells showed tumor-specific activation reminiscent of canonical ‘T cell hot’ tumors, whereas the MSS CRC CD8 ⁺ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of ‘pseudo-T cell hot’ tumors. Consequently, MSI and MSS CRC CD8 ⁺ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8 ⁺ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8 ⁺ T cells to build a bulk tumor transcriptome classification for CRC patients. This “Immune Subtype Classification” (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8 ⁺ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.
... During T-cell maturation, each thymocyte develops its own TCR variant by recombination of distinct V, D, and J gene segments, as well as random deletion and/or insertion of nucleotides at junctions. This results in a very broad TCR repertoire, which is essential for enhancing the protective immunity's potential coverage of pathogens and antigens [1]. Recognition of foreign antigenic peptides, presented by MHC, results in T-cell activation and clonal expansion. ...
... The Chao diversity index, a measure of TCR repertoire diversity [1,[17][18][19], demonstrates that the activated CD8 + T-cell subset contains a less diverse repertoire than the nonactivated CD8 + T-cell subset. This was statistically significant within the COVID-19 patient group (n = 26), but not in the previously exposed healthy control group (n = 10) (Figure 2A). ...
T cell-based diagnostic tools identify pathogen exposure, but lack differentiation between recent and historic exposures in acute infectious diseases. Here, T cell receptor (TCR) RNA sequencing was performed on HLADR+/CD38+ CD8+ T cell subsets of hospitalized COVID-19 patients (n = 30) and healthy controls (n = 30; ten of whom had previously been exposed to SARS-CoV-2). CDR3α and CDR3β TCR regions were clustered separately before epitope specificity annotation using a database of SARS-CoV-2 associated CDR3α and CDR3β sequences corresponding to >1000 SARS-CoV-2 epitopes. The depth of the SARS-CoV-2 associated CDR3α/β sequences differentiated COVID-19 patients from the healthy controls with a receiver operating characteristic curve (ROC) area under the curve (AUC) of 0.84 ± 0.10. Hence, annotating TCR sequences of activated CD8+ T cells can be used to diagnose an acute viral infection and discriminate it from historic exposure. In essence, this work presents a new paradigm of applying the T cell repertoire to accomplish TCR-based diagnostics.
... The V(D)J-recombination creates diversity both from a combinatorial effect by choosing which genes to include and a junctional effect stemming from random nucleotide insertions and deletions in the ligation process of the chosen gene segments. Together the two chains can form a vast TCR diversity with estimates ranging from 10 15 to 10 20 , being orders of magnitudes larger than the estimated amount of cells in the human body 3.7 · 10 13 [16]. Similarly as the TCRs, the pMHCs are very diverse. ...
T cells play an essential role in adaptive immune system to fight pathogens and cancer but may also give rise to autoimmune diseases. The recognition of a peptide-MHC (pMHC) complex by a T cell receptor (TCR) is required to elicit an immune response. Many machine learning models have been developed to predict the binding, but generalizing predictions to pMHCs outside the training data remains challenging.
We have developed a new machine learning model that utilizes information about the TCR from both α and β chains, epitope sequence, and MHC. Our method uses ProtBERT embeddings for the amino acid sequences of both chains and the epitope, as well as convolution and multi-head attention architectures. We show the importance of each input feature as well as the benefit of including epitopes with only a few TCRs to the training data. We evaluate our model on existing databases and show that it compares favorably against other state-of-the-art models.
... This suggests that RNA-Seq can potentially complement TCR-Seq technology in T-cell-rich tissues to successfully estimate the overall diversity of the sample and effectively detect clones with greater frequencies. The diversity estimated based on high-throughput measures usually underestimates true diversity [2,5,27,28], but in cases where there is a major clonotype existing with high relative frequency than even capturing the small portion of the repertoire, one can estimate the diversity. However, when comparing different samples, the commonly used diversity measurement, SDI, is limited when comparing samples with different TCR repertoire sizes. ...
The ability to identify and track T-cell receptor (TCR) sequences from patient samples is becoming central to the field of cancer research and immunotherapy. Tracking genetically engineered T cells expressing TCRs that target specific tumor antigens is important to determine the persistence of these cells and quantify tumor responses. The available high-throughput method to profile TCR repertoires is generally referred to as TCR sequencing (TCR-Seq). However, the available TCR-Seq data are limited compared with RNA sequencing (RNA-Seq). In this paper, we have benchmarked the ability of RNA-Seq-based methods to profile TCR repertoires by examining 19 bulk RNA-Seq samples across 4 cancer cohorts including both T-cell-rich and T-cell-poor tissue types. We have performed a comprehensive evaluation of the existing RNA-Seq-based repertoire profiling methods using targeted TCR-Seq as the gold standard. We also highlighted scenarios under which the RNA-Seq approach is suitable and can provide comparable accuracy to the TCR-Seq approach. Our results show that RNA-Seq-based methods are able to effectively capture the clonotypes and estimate the diversity of TCR repertoires, as well as provide relative frequencies of clonotypes in T-cell-rich tissues and low-diversity repertoires. However, RNA-Seq-based TCR profiling methods have limited power in T-cell-poor tissues, especially in highly diverse repertoires of T-cell-poor tissues. The results of our benchmarking provide an additional appealing argument to incorporate RNA-Seq into the immune repertoire screening of cancer patients as it offers broader knowledge into the transcriptomic changes that exceed the limited information provided by TCR-Seq.
