Lower frequencies of circulating suppressive regulatory T cells and higher frequencies of CD4+ naïve T cells at baseline are associated with severe immune-related adverse events in immune checkpoint inhibitor-treated melanoma.

BACKGROUND: Immune-related adverse events (irAEs) are major barriers of clinical management and further development of immune checkpoint inhibitors (ICIs) for cancer therapy. Therefore, biomarkers associated with the onset of severe irAEs are needed. In this study, we aimed to identify immune features detectable in peripheral blood and associated with the development of severe irAEs that required clinical intervention. METHODS: We used a 43-marker mass cytometry panel to characterize peripheral blood mononuclear cells from 28 unique patients with melanoma across 29 lines of ICI therapy before treatment (baseline), before the onset of irAEs (pre-irAE) and at the peak of irAEs (irAE-max). In the 29 lines of ICI therapy, 18 resulted in severe irAEs and 11 did not. RESULTS: Unsupervised and gated population analysis showed that patients with severe irAEs had a higher frequency of CD4+ naïve T cells and lower frequency of CD16+ natural killer (NK) cells at all time points. Gated population analysis additionally showed that patients with severe irAEs had fewer T cell immunoreceptor with Ig and ITIM domain (TIGIT+) regulatory T cells at baseline and more activated CD38+ CD4+ central memory T cells (TCM) and CD39+ and Human Leukocyte Antigen-DR Isotype (HLA-DR)+ CD8+ TCM at peak of irAEs. The differentiating immune features at baseline were predominantly seen in patients with gastrointestinal and cutaneous irAEs and type 1 diabetes. Higher frequencies of CD4+ naïve T cells and lower frequencies of CD16+ NK cells were also associated with clinical benefit to ICI therapy. CONCLUSIONS: This study demonstrates that high-dimensional immune profiling can reveal novel blood-based immune signatures associated with risk and mechanism of severe irAEs. Development of severe irAEs in melanoma could be the result of reduced immune inhibitory capacity pre-ICI treatment, resulting in more activated TCM cells after treatment.

PEPMatch: a tool to identify short peptide sequence matches in large sets of proteins.

BACKGROUND: Numerous tools exist for biological sequence comparisons and search. One case of particular interest for immunologists is finding matches for linear peptide T cell epitopes, typically between 8 and 15 residues in length, in a large set of protein sequences. Both to find exact matches or matches that account for residue substitutions. The utility of such tools is critical in applications ranging from identifying conservation across viral epitopes, identifying putative epitope targets for allergens, and finding matches for cancer-associated neoepitopes to examine the role of tolerance in tumor recognition. RESULTS: We defined a set of benchmarks that reflect the different practical applications of short peptide sequence matching. We evaluated a suite of existing methods for speed and recall and developed a new tool, PEPMatch. The tool uses a deterministic k-mer mapping algorithm that preprocesses proteomes before searching, achieving a 50-fold increase in speed over methods such as the Basic Local Alignment Search Tool (BLAST) without compromising recall. PEPMatch's code and benchmark datasets are publicly available. CONCLUSIONS: PEPMatch offers significant speed and recall advantages for peptide sequence matching. While it is of immediate utility for immunologists, the developed benchmarking framework also provides a standard against which future tools can be evaluated for improvements. The tool is available at https://nextgen-tools.iedb.org , and the source code can be found at https://github.com/IEDB/PEPMatch .

Epitope-Specific T Cell Receptor Data and Tools in the Immune Epitope Database.

T cells play a critical role in the response of the immune system to non-self antigens. The Immune Epitope Database (IEDB) continuously curates published T cell data, among other immune-related data, and allows users to create custom queries based on their research interests and aims. The IEDB's companion site, the IEDB Analysis Resource, features a number of tools for analysis and prediction of immune epitopes; these include TCRMatch, a new tool used to predict candidate epitopes for T cell receptors with unknown specificity. Together these resources enable users to browse and filter large amounts of published data as well as generate hypotheses to guide future experiments.

CD4+CCR6+ T cells dominate the BCG-induced transcriptional signature.

BACKGROUND: The century-old Mycobacterium bovis Bacillus Calmette-Guerin (BCG) remains the only licensed vaccine against tuberculosis (TB). Despite this, there is still a lot to learn about the immune response induced by BCG, both in terms of phenotype and specificity. METHODS: We investigated immune responses in adult individuals pre and 8 months post BCG vaccination. We specifically determined changes in gene expression, cell subset composition, DNA methylome, and the TCR repertoire induced in PBMCs and CD4 memory T cells associated with antigen stimulation by either BCG or a Mycobacterium tuberculosis (Mtb)-derived peptide pool. FINDINGS: Following BCG vaccination, we observed increased frequencies of CCR6+ CD4 T cells, which includes both Th1* (CXCR3+CCR6+) and Th17 subsets, and mucosal associated invariant T cells (MAITs). A large number of immune response genes and pathways were upregulated post BCG vaccination with similar patterns observed in both PBMCs and memory CD4 T cells, thus suggesting a substantial role for CD4 T cells in the cellular response to BCG. These upregulated genes and associated pathways were also reflected in the DNA methylome. We described both qualitative and quantitative changes in the BCG-specific TCR repertoire post vaccination, and importantly found evidence for similar TCR repertoires across different subjects. INTERPRETATION: The immune signatures defined herein can be used to track and further characterize immune responses induced by BCG, and can serve as reference for benchmarking novel vaccination strategies.

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and ß sequence data.

Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that "shallow" convolutional neural network (CNN) architectures are adequate to deal with the problem complexity imposed by the length variations of TCRs. We demonstrate that current public bulk CDR3ß-pMHC binding data overall is of low quality and that the development of accurate prediction models is contingent on paired α/ß TCR sequence data corresponding to at least 150 distinct pairs for each investigated pMHC. In comparison, models trained on CDR3α or CDR3ß data alone demonstrated a variable and pMHC specific relative performance drop. Together these findings support that T-cell specificity is predictable given the availability of accurate and sufficient paired TCR sequence data. NetTCR-2.0 is publicly available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.0 .

TCRMatch: Predicting T-Cell Receptor Specificity Based on Sequence Similarity to Previously Characterized Receptors.

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 ß-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.

Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA-DQ2.5.

Binding prediction tools are commonly used to identify peptides presented on MHC class II molecules. Recently, a wealth of data in the form of naturally eluted ligands has become available and discrepancies between ligand elution data and binding predictions have been reported. Quantitative metrics for such comparisons are currently lacking. In this study, we assessed how efficiently MHC class II binding predictions can identify naturally eluted peptides, and investigated instances with discrepancies between the two methods in detail. We found that, in general, MHC class II eluted ligands are predicted to bind to their reported restriction element with high affinity. But, for several studies reporting an increased number of ligands that were not predicted to bind, we found that the reported MHC restriction was ambiguous. Additional analyses determined that most of the ligands predicted to not bind, are predicted to bind other co-expressed MHC class II molecules. For selected alleles, we addressed discrepancies between elution data and binding predictions by experimental measurements and found that predicted and measured affinities correlate well. For DQA1*05:01/DQB1*02:01 (DQ2.5) however, binding predictions did miss several peptides that were determined experimentally to be binders. For these peptides and several known DQ2.5 binders, we determined key residues for conferring DQ2.5 binding capacity, which revealed that DQ2.5 utilizes two different binding motifs, of which only one is predicted effectively. These findings have important implications for the interpretation of ligand elution data and for the improvement of MHC class II binding predictions.

Deletion of Topoisomerase 1 in excitatory neurons causes genomic instability and early onset neurodegeneration.

Topoisomerase 1 (TOP1) relieves torsional stress in DNA during transcription and facilitates the expression of long (>100 kb) genes, many of which are important for neuronal functions. To evaluate how loss of Top1 affected neurons in vivo, we conditionally deleted (cKO) Top1 in postmitotic excitatory neurons in the mouse cerebral cortex and hippocampus. Top1 cKO neurons develop properly, but then show biased transcriptional downregulation of long genes, signs of DNA damage, neuroinflammation, increased poly(ADP-ribose) polymerase-1 (PARP1) activity, single-cell somatic mutations, and ultimately degeneration. Supplementation of nicotinamide adenine dinucleotide (NAD+) with nicotinamide riboside partially blocked neurodegeneration, and increased the lifespan of Top1 cKO mice by 30%. A reduction of p53 also partially rescued cortical neuron loss. While neurodegeneration was partially rescued, behavioral decline was not prevented. These data indicate that reducing neuronal loss is not sufficient to limit behavioral decline when TOP1 function is disrupted.

Neurons with Complex Karyotypes Are Rare in Aged Human Neocortex.

A subset of human neocortical neurons harbors complex karyotypes wherein megabase-scale copy-number variants (CNVs) alter allelic diversity. Divergent levels of neurons with complex karyotypes (CNV neurons) are reported in different individuals, yet genome-wide and familial studies implicitly assume a single brain genome when assessing the genetic risk architecture of neurological disease. We assembled a brain CNV atlas using a robust computational approach applied to a new dataset (>800 neurons from 5 neurotypical individuals) and to published data from 10 additional neurotypical individuals. The atlas reveals that the frequency of neocortical neurons with complex karyotypes varies widely among individuals, but this variability is not readily accounted for by tissue quality or CNV detection approach. Rather, the age of the individual is anti-correlated with CNV neuron frequency. Fewer CNV neurons are observed in aged individuals than in young individuals.

Single neuron transcriptome analysis can reveal more than cell type classification: Does it matter if every neuron is unique?

A recent single cell mRNA sequencing study by Dueck et al. compares neuronal transcriptomes to the transcriptomes of adipocytes and cardiomyocytes. Single cell omic approaches such as those used by the authors are at the leading edge of molecular and biophysical measurement. Many groups are currently employing single cell sequencing approaches to understand cellular heterogeneity in cancer and during normal development. These single cell approaches also are beginning to address long-standing questions regarding nervous system diversity. Beyond an innate interest in cataloging cell type diversity in the brain, single cell neuronal diversity has important implications for neurotypic neural circuit function and for neurological disease. Herein, we review the authors' methods and findings, which most notably include evidence of unique expression profiles in some single neurons.