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1.
Nat Commun ; 14(1): 2126, 2023 04 27.
Article in English | MEDLINE | ID: mdl-37105962

ABSTRACT

Checkpoint immunotherapy (CPI) has increased survival for some patients with advanced-stage bladder cancer (BCa). However, most patients do not respond. Here, we characterized the tumor and immune microenvironment in pre- and post-treatment tumors from the PURE01 neoadjuvant pembrolizumab immunotherapy trial, using a consolidative approach that combined transcriptional and genetic profiling with digital spatial profiling. We identify five distinctive genetic and transcriptomic programs and validate these in an independent neoadjuvant CPI trial to identify the features of response or resistance to CPI. By modeling the regulatory network, we identify the histone demethylase KDM5B as a repressor of tumor immune signaling pathways in one resistant subtype (S1, Luminal-excluded) and demonstrate that inhibition of KDM5B enhances immunogenicity in FGFR3-mutated BCa cells. Our study identifies signatures associated with response to CPI that can be used to molecularly stratify patients and suggests therapeutic alternatives for subtypes with poor response to neoadjuvant immunotherapy.


Subject(s)
Immune Checkpoint Inhibitors , Urinary Bladder Neoplasms , Humans , Immune Checkpoint Inhibitors/pharmacology , Immune Checkpoint Inhibitors/therapeutic use , Neoadjuvant Therapy , Urinary Bladder Neoplasms/drug therapy , Urinary Bladder Neoplasms/genetics , Gene Expression Profiling , Muscles/pathology , Tumor Microenvironment/genetics
2.
Brief Bioinform ; 23(3)2022 05 13.
Article in English | MEDLINE | ID: mdl-35471658

ABSTRACT

T cell recognition of a cognate peptide-major histocompatibility complex (pMHC) presented on the surface of infected or malignant cells is of the utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T cell receptors would greatly facilitate identification of vaccine targets for both pathogenic diseases and personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the center of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as severe acute respiratory syndrome coronavirus 2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in the training data of the models seems to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen versus cancer peptides. Overall, we demonstrate that accurate and reliable predictions of immunogenic CD8+ T cell targets remain unsolved; thus, we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.


Subject(s)
COVID-19 , Neoplasms , CD8-Positive T-Lymphocytes/metabolism , Computer Simulation , Epitopes, T-Lymphocyte , Humans , Peptides
3.
Immunogenetics ; 72(1-2): 77-84, 2020 02.
Article in English | MEDLINE | ID: mdl-31741011

ABSTRACT

Recent advances in molecular and bioinformatic methods have greatly improved our ability to study the formation of an adaptive immune response towards foreign pathogens, self-antigens, and cancer neoantigens. T cell receptors (TCR) are the key players in this process that recognize peptides presented by major histocompatibility complex (MHC). Owing to the huge diversity of both TCR sequence variants and peptides they recognize, accumulation and complex analysis of large amounts of TCR-antigen specificity data is required for understanding the structure and features of adaptive immune responses towards pathogens, vaccines, cancer, as well as autoimmune responses. In the present review, we summarize recent efforts on gathering and interpreting TCR-antigen specificity data and outline the critical role of tighter integration with other immunoinformatics data sources that include epitope MHC restriction, TCR repertoire structure models, and TCR/peptide/MHC structural data. We suggest that such integration can lead to the ability to accurately annotate individual TCR repertoires, efficiently estimate epitope and neoantigen immunogenicity, and ultimately, in silico identify TCRs specific to yet unstudied antigens and predict self-peptides related to autoimmunity.


Subject(s)
Computational Biology/methods , Databases, Protein , Epitopes, T-Lymphocyte/immunology , Receptors, Antigen, T-Cell/immunology , T-Cell Antigen Receptor Specificity/immunology , Animals , Humans
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