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Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding motifs. Based on the discovery, we make the preprocessing and coding closer to the natural biological process. Besides, due to the input being based on multiple types of features and the attention module focused on the BiGRU hidden layer, TripHLApan has learned more sequence level binding information. The application of transfer learning strategies ensures the accuracy of prediction results under special lengths (peptides in length 8) and model scalability with the data explosion. Compared with the current optimal models, TripHLApan exhibits strong predictive performance in various prediction environments with different positive and negative sample ratios. In addition, we validate the superiority and scalability of TripHLApan's predictive performance using additional latest data sets, ablation experiments and binding reconstitution ability in the samples of a melanoma patient. The results show that TripHLApan is a powerful tool for predicting the binding of HLA-I and HLA-II molecular peptides for the synthesis of tumor vaccines. TripHLApan is publicly available at https://github.com/CSUBioGroup/TripHLApan.git. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Motivation It has been proven that only a small fraction of the neoantigens presented by major histocompatibility complex (MHC) class I molecules on the cell surface can elicit T cells. This restriction can be attributed to the binding specificity of T cell receptor (TCR) and peptide-MHC complex (pMHC). Computational prediction of T cells binding to neoantigens is a challenging and unresolved task. Results In this paper, we proposed an attention-aware contrastive learning model, ATMTCR, to infer the TCR–pMHC binding specificity. For each TCR sequence, we used a transformer encoder to transform it to latent representation, and then masked a percentage of amino acids guided by attention weights to generate its contrastive view. Compared to fully-supervised baseline model, we verified that contrastive learning-based pretraining on large-scale TCR sequences significantly improved the prediction performance of downstream tasks. Interestingly, masking a percentage of amino acids with low attention weights yielded best performance compared to other masking strategies. Comparison experiments on two independent datasets demonstrated our method achieved better performance than other existing algorithms. Moreover, we identified important amino acids and their positional preference through attention weights, which indicated the potential interpretability of our proposed model. [ABSTRACT FROM AUTHOR]

Summary: The immune system plays a critical role in protecting the host against infection but is subject to numerous levels of control that are necessary to prevent pathological, tissue-damaging responses. Inappropriate inflammatory immune responses to self-antigens, innocuous commensal microorganisms, or environmental antigens can lead to chronic, debilitating, and degenerative diseases. Regulatory T cells have an essential, non-redundant, and dominant function in preventing pathological immune responses, as shown by the development of systemic fatal autoimmunity in humans and animals with a genetic deficiency in regulatory T cells. In addition to controlling immune responses, there is a growing understanding that regulatory T cells also contribute directly to tissue homeostasis by promoting tissue regeneration and repair. For these reasons, the prospect of enhancing regulatory T-cell numbers and/or function in patients represents an appealing therapeutic opportunity with potential applications in many diseases, including some where the pathological role of the immune system has only recently been recognized. Approaches to enhance regulatory T cells are now starting to be explored in clinical studies in humans. This review series brings together papers highlighting the Treg-enhancing approaches that are most advanced clinically and examples of therapeutic opportunities based on our growing understanding of regulatory T-cell functions. [ABSTRACT FROM AUTHOR]

Understanding the T-cell response to SARS-CoV-2 is key in patients who lack antibody production. We demonstrate the applicability of a functional assay to measure the T-cell response in a cohort of patients with immunodeficiency. T-cell responses to COVID are now linked to improved outcomes. This paper demonstrates a method to robustly test responses for routine diagnostic use in healthy controls and those unable to make a detectable antibody response due to underlying immune deficiency (primary or acquired). [ABSTRACT FROM AUTHOR]

Motivation Identifying rare subpopulations of cells is a critical step in order to extract knowledge from single-cell expression data, especially when the available data is limited and rare subpopulations only contain a few cells. In this paper, we present a data mining method to identify small subpopulations of cells that present highly specific expression profiles. This objective is formalized as a constrained optimization problem that jointly identifies a small group of cells and a corresponding subset of specific genes. The proposed method extends the max-sum submatrix problem to yield genes that are, for instance, highly expressed inside a small number of cells, but have a low expression in the remaining ones. Results We show through controlled experiments on scRNA-seq data that the MicroCellClust method achieves a high F 1 score to identify rare subpopulations of artificially planted human T cells. The effectiveness of MicroCellClust is confirmed as it reveals a subpopulation of CD4 T cells with a specific phenotype from breast cancer samples, and a subpopulation linked to a specific stage in the cell cycle from breast cancer samples as well. Finally, three rare subpopulations in mouse embryonic stem cells are also identified with MicroCellClust. These results illustrate the proposed method outperforms typical alternatives at identifying small subsets of cells with highly specific expression profiles. Availabilityand implementation The R and Scala implementation of MicroCellClust is freely available on GitHub, at https://github.com/agerniers/MicroCellClust/ The data underlying this article are available on Zenodo, at https://dx.doi.org/10.5281/zenodo.4580332. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Summary: Ageing dramatically affects number and function of both innate and adaptive arms of immune system, particularly T cell subsets, contributing to reduced vaccination efficacy, decreased resistance to infections and increased prevalence of cancer in older people. In the present paper, we analysed the age‐related changes in the absolute number of lymphocytes in 214 Sicilian subjects, and in the percentages of T and natural killer (NK) cells in a subcohort of donors. We compared these results with the immunophenotype of the oldest living Italian supercentenarian (aged 111 years). The results were also sorted by gender. The correlation between number/percentage of cells and age in all individuals. and separately in males and females, was examined using a simple linear regression analysis. We did not record the increase in the rate of inversion of the CD4/CD8 ratio, frequently reported as being associated with ageing in literature. Our observation was the direct consequence of a flat average trend of CD4+ and CD8+ T cell percentages in ageing donors, even when gender differences were included. Our results also suggest that CD4+ and CD8+ subsets are not affected equally by age comparing females with males, and we speculated that gender may affect the response to cytomegalovirus (CMV) infection. The supercentenarian showed a unique immunophenotypic signature regarding the relative percentages of her T cell subsets, with CD4+ and CD8+ T cell percentages and CD4+ naive T cell values in line with those recorded for the octogenarian subjects. This suggests that the supercentenarian has a naive 'younger' T cell profile comparable to that of a >80‐year‐old female. [ABSTRACT FROM AUTHOR]

Objective: Chimeric antigen receptor (CAR) T-cell therapy is a promising treatment for relapsed/refractory acute lymphoblastic leukemia (ALL) in pediatric oncology. However, little is known about the cognitive functioning of these patients. This study examines the neuropsychological functioning of pediatric patients referred for CAR T-cell therapy. Method: Data was obtained from a longitudinal observational study of CAR T-cell therapy patients (ages 1–30 years) at the Children's Hospital of Philadelphia. Neuropsychological data and neurotoxicity ratings were collected before and after CAR T-cell infusion, with brief cognitive testing at 28 days and 180 days post-infusion to assess acute changes. Cognitive testing included pencil-and-paper measures of intellectual functioning, attention, processing speed, and psychological functioning, as well as NIH Toolbox subtests. Results: Of 13 patients completing pre-infusion cognitive testing, 44% of patients scored below average (1 SD below the mean) on a timed visual-motor processing speed test. 20–25% of the sample scored below average on measures of executive functioning, attention/working memory, and expre0111111ssive vocabulary, exceeding what would be expected in a normal distribution. Fewer patients completed post-infusion assessments (n = 8, 28 days; n = 5, 6 months; n = 3, 1 year), with up to 50% scoring below average on the timed visual-motor processing speed test and 33% scoring below average on a measure of inhibitory control and selective attention after 1 year. Conclusions: Pediatric CAR T-cell therapy patients have high rates of cognitive dysfunction prior to receiving infusions, with evidence for increasing problems with timed visual-motor and executive functioning. Findings highlight the need to monitor pediatric CAR T-cell therapy patients throughout treatment. [ABSTRACT FROM AUTHOR]