Benchmarking NGS-Based HLA Typing Algorithms.

Knowledge of the expected accuracy of HLA typing algorithms is important when choosing between algorithms and when evaluating the HLA typing predictions of an algorithm. This chapter guides the reader through an example benchmarking study that evaluates the performances of four NGS-based HLA typing algorithms as well as outlining factors to consider, when designing and running such a benchmarking study. The code related to this benchmarking workflow can be found at https://github.com/nikolasthuesen/springers-hla-benchmark/ .

DNA immunization with in silico predicted T-cell epitopes protects against lethal SARS-CoV-2 infection in K18-hACE2 mice.

The global SARS-CoV-2 pandemic caused significant social and economic disruption worldwide, despite highly effective vaccines being developed at an unprecedented speed. Because the first licensed vaccines target only single B-cell antigens, antigenic drift could lead to loss of efficacy against emerging SARS-CoV-2 variants. Improving B-cell vaccines by including multiple T-cell epitopes could solve this problem. Here, we show that in silico predicted MHC class I/II ligands induce robust T-cell responses and protect against severe disease in genetically modified K18-hACE2/BL6 mice susceptible to SARS-CoV-2 infection.

First in man study: Bcl-Xl_42-CAF®09b vaccines in patients with locally advanced prostate cancer.

Background: The B-cell lymphoma-extra-large (Bcl-XL) protein plays an important role in cancer cells' resistance to apoptosis. Pre-clinical studies have shown that vaccination with Bcl-XL-derived peptides can induce tumor-specific T cell responses that may lead to the elimination of cancer cells. Furthermore, pre-clinical studies of the novel adjuvant CAF®09b have shown that intraperitoneal (IP) injections of this adjuvant can improve the activation of the immune system. In this study, patients with hormone-sensitive prostate cancer (PC) received a vaccine consisting of Bcl-XL-peptide with CAF®09b as an adjuvant. The primary aim was to evaluate the tolerability and safety of IP and intramuscular (IM) administration, determine the optimal route of administration, and characterize vaccine immunogenicity. Patients and methods: Twenty patients were included. A total of six vaccinations were scheduled: in Group A (IM to IP injections), ten patients received three vaccines IM biweekly; after a three-week pause, patients then received three vaccines IP biweekly. In Group B (IP to IM injections), ten patients received IP vaccines first, followed by IM under a similar vaccination schedule. Safety was assessed by logging and evaluating adverse events (AE) according to Common Terminology Criteria for Adverse Events (CTCAE v. 4.0). Vaccines-induced immune responses were analyzed by Enzyme-Linked Immunospot and flow cytometry. Results: No serious AEs were reported. Although an increase in T cell response against the Bcl-XL-peptide was found in all patients, a larger proportion of patients in group B demonstrated earlier and stronger immune responses to the vaccine compared to patients in group A. Further, we demonstrated vaccine-induced immunity towards patient-specific CD4, and CD8 T cell epitopes embedded in Bcl-XL-peptide and an increase in CD4 and CD8 T cell activation markers CD107a and CD137 following vaccination. At a median follow-up of 21 months, no patients had experienced clinically significant disease progression. Conclusion: The Bcl-XL-peptide-CAF®09b vaccination was feasible and safe in patients with l hormone-sensitive PC. In addition, the vaccine was immunogenic and able to elicit CD4 and CD8 T cell responses with initial IP administration eliciting early and high levels of vaccine-specific responses in a higher number og patients. Clinical trial registration: https://clinicaltrials.gov, identifier NCT03412786.

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.

Identifying the specific human leukocyte antigen (HLA) allele combination of an individual is crucial in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq. This evaluation was done for the five HLA loci, HLA-A, -B, -C, -DRB1 and -DQB1 using whole-exome sequencing (WES) samples from 829 individuals. The robustness of the tools to lower depth of coverage (DOC) was evaluated by subsampling and HLA typing 230 WES samples at DOC ranging from 1X to 100X. The HLA typing accuracy was measured across four typing resolutions. Among these, we present two clinically-relevant typing resolutions (P group and pseudo-sequence), which specifically focus on the peptide binding region. On average, across the five HLA loci examined, HLA*LA was found to have the highest typing accuracy. For the individual loci, HLA-A, -B and -C, Optitype's typing accuracy was the highest and HLA*LA had the highest typing accuracy for HLA-DRB1 and -DQB1. The tools' robustness to lower DOC data varied widely and further depended on the specific HLA locus. For all Class I loci, Optitype had a typing accuracy above 95% (according to the modification of the amino acids in the functionally relevant portion of the HLA molecule) at 50X, but increasing the DOC beyond even 100X could still improve the typing accuracy of HISAT-genotype, Kourami, and STC-seq across all five HLA loci as well as HLA*LA's typing accuracy for HLA-DQB1. HLA typing is also used in studies of ancient DNA (aDNA), which is often based on sequencing data with lower quality and DOC. Interestingly, we found that Optitype's typing accuracy is not notably impaired by short read length or by DNA damage, which is typical of aDNA, as long as the DOC is sufficiently high.

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

LYRA, a webserver for lymphocyte receptor structural modeling.

The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.dtu.dk/services/LYRA/) implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. The webserver is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information. LYRA is based on the canonical structure method, that in the last 30 years has been successfully used to generate antibody models of high accuracy, and in our benchmarks this approach proves to achieve similarly good results on TCR modeling, with a benchmarked average RMSD accuracy of 1.29 and 1.48 Å for B- and T-cell receptors, respectively. To the best of our knowledge, LYRA is the first automated server for the prediction of TCR structure.