An effective and effecient peptide binding prediction approach for a broad set of HLA-DR molecules based on ordered weighted averaging of binding pocket profiles

Association of Bovine Leukemia Virus-Induced Lymphoma with BoLA-DRB3 Polymorphisms at DNA, Amino Acid, and Binding Pocket Property Levels

Bovine leukemia virus (BLV) causes enzootic bovine leucosis, a malignant B-cell lymphoma in cattle. The DNA sequence polymorphisms of bovine leukocyte antigen (BoLA)-DRB3 have exhibited a correlation with BLV-induced lymphoma in Holstein cows. However, the association may vary between different cattle breeds. Furthermore, little is known about the relationship between BLV-induced lymphoma and DRB3 at the amino acid and structural diversity levels. Here, we comprehensively analyzed the correlation between BLV-induced lymphoma and DRB3 at DNA, amino acid, and binding pocket property levels, using 106 BLV-infected asymptomatic and 227 BLV-induced lymphoma Japanese black cattle samples. DRB3*011:01 was identified as a resistance allele, whereas DRB3*005:02 and DRB3*016:01 were susceptibility alleles. Amino acid association studies showed that positions 9, 11, 13, 26, 30, 47, 57, 70, 71, 74, 78, and 86 were associated with lymphoma susceptibility. Structure and electrostatic charge modeling further indicated that binding pocket 9 of resistance DRB3 was positively charged. In contrast, alleles susceptible to lymphoma were neutrally charged. Altogether, this is the first association study of BoLA-DRB3 polymorphisms with BLV-induced lymphoma in Japanese black cattle. In addition, our results further contribute to understanding the mechanisms regarding how BoLA-DRB3 polymorphisms mediate susceptibility to BLV-induced lymphoma.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These “omics” data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno‐oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non‐small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

Best practices for bioinformatic characterization of neoantigens for clinical utility

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

A Novel Peptide Binding Prediction Approach for HLA-DR Molecule Based on Sequence and Structural Information

MHC molecule plays a key role in immunology, and the molecule binding reaction with peptide is an important prerequisite for T cell immunity induced. MHC II molecules do not have conserved residues, so they appear as open grooves. As a consequence, this will increase the difficulty in predicting MHC II molecules binding peptides. In this paper, we aim to propose a novel prediction method for MHC II molecules binding peptides. First, we calculate sequence similarity and structural similarity between different MHC II molecules. Then, we reorder pseudosequences according to descending similarity values and use a weight calculation formula to calculate new pocket profiles. Finally, we use three scoring functions to predict binding cores and evaluate the accuracy of prediction to judge performance of each scoring function. In the experiment, we set a parameter α in the weight formula. By changing α value, we can observe different performances of each scoring function. We compare our method with the best function to some popular prediction methods and ultimately find that our method outperforms them in identifying binding cores of HLA-DR molecules.

Major histocompatibility complex linked databases and prediction tools for designing vaccines

Presently, the major histocompatibility complex (MHC) is receiving considerable interest owing to its remarkable role in antigen presentation and vaccine design. The specific databases and prediction approaches related to MHC sequences, structures and binding/nonbinding peptides have been aggressively developed in the past two decades with their own benchmarks and standards. Before using these databases and prediction tools, it is important to analyze why and how the tools are constructed along with their strengths and limitations. The current review presents insights into web-based immunological bioinformatics resources that include searchable databases of MHC sequences, epitopes and prediction tools that are linked to MHC based vaccine design, including population coverage analysis. In T cell epitope forecasts, MHC class I binding predictions are very accurate for most of the identified MHC alleles. However, these predictions could be further improved by integrating proteasome cleavage (in conjugation with transporter associated with antigen processing (TAP) binding) prediction, as well as T cell receptor binding prediction. On the other hand, MHC class II restricted epitope predictions display relatively low accuracy compared to MHC class I. To date, pan-specific tools have been developed, which not only deliver significantly improved predictions in terms of accuracy, but also in terms of the coverage of MHC alleles and supertypes. In addition, structural modeling and simulation systems for peptide-MHC complexes enable the molecular-level investigation of immune processes. Finally, epitope prediction tools, and their assessments and guidelines, have been presented to immunologist for the design of novel vaccine and diagnostics.

Immunogenic peptide discovery in cancer genomes

As immunotherapies to treat malignancy continue to diversify along with the tumor types amenable to treatment, it will become very important to predict which treatment is most likely to benefit a given patient. Tumor neoantigens, novel peptides resulting from somatic tumor mutations and recognized by the immune system as foreign, are likely to contribute significantly to the efficacy of immunotherapy. Multiple in silico methods have been developed to predict whether peptides, including tumor neoantigens, will be presented by the major histocompatibility complex (MHC) Class I or Class II, and interact with the T cell receptor (TCR). The methods for neoantigen prediction will be reviewed here, along with the most important examples of their use in the field of oncology.

Strategies for the identification of T cell-recognized tumor antigens in hematological malignancies for improved graft-versus-tumor responses after allogeneic blood and marrow transplantation

Allogeneic blood and marrow transplantation (allo-BMT) is an effective immunotherapeutic treatment that can provide partial or complete remission for patients with hematological malignancies. Mature donor T cells in the donor inoculum play a central role in mediating graft-versus-tumor (GVT) responses by destroying residual tumor cells that persist after conditioning regimens. Alloreactivity towards minor histocompatibility antigens (miHA), which are varied tissue-related self-peptides presented in the context of major histocompatibility complex (MHC) molecules on recipient cells, some of which may be shared on tumor cells, is a dominant factor for the development of GVT. Potentially, GVT can also be directed to tumor-associated antigens or tumor-specific antigens that are more specific to the tumor cells themselves. The full exploitation of allo-BMT, however, is greatly limited by the development of graft-versus-host disease (GVHD), which is mediated by the donor T cell response against the miHA expressed in the recipient's cells of the intestine, skin, and liver. Because of the significance of GVT and GVHD responses in determining the clinical outcome of patients, miHA and tumor antigens have been intensively studied, and one active immunotherapeutic approach to separate these two responses has been cancer vaccination after allo-BMT. The combination of these two strategies has an advantage over vaccination of the patient without allo-BMT because his or her immune system has already been exposed and rendered unresponsive to the tumor antigens. The conditioning for allo-BMT eliminates the patient's existing immune system, including regulatory elements, and provides a more permissive environment for the newly developing donor immune compartment to selectively target the malignant cells. Utilizing recent technological advances, the identities of many human miHA and tumor antigenic peptides have been defined and are currently being evaluated in clinical and basic immunological studies for their ability to produce effective T cell responses. The first step towards this goal is the identification of targetable tumor antigens. In this review, we will highlight some of the technologies currently used to identify tumor antigens and anti-tumor T cell clones in hematological malignancies.