RESUMO
CD4+ T cells orchestrate the adaptive immune response against pathogens and cancer by recognizing epitopes presented on class II major histocompatibility complex (MHC-II) molecules. The high polymorphism of MHC-II genes represents an important hurdle toward accurate prediction and identification of CD4+ T cell epitopes. Here we collected and curated a dataset of 627,013 unique MHC-II ligands identified by mass spectrometry. This enabled us to precisely determine the binding motifs of 88 MHC-II alleles across humans, mice, cattle, and chickens. Analysis of these binding specificities combined with X-ray crystallography refined our understanding of the molecular determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP ligands. We then developed a machine-learning framework to accurately predict binding specificities and ligands of any MHC-II allele. This tool improves and expands predictions of CD4+ T cell epitopes and enables us to discover viral and bacterial epitopes following the aforementioned reverse-binding mode.
Assuntos
Epitopos de Linfócito T , Peptídeos , Humanos , Animais , Camundongos , Bovinos , Ligantes , Ligação Proteica , Galinhas/metabolismo , Aprendizado de Máquina , Antígenos de Histocompatibilidade Classe II , AlelosRESUMO
The identification of T-cell epitopes is key for a complete molecular understanding of immune recognition mechanisms in infectious diseases, autoimmunity and cancer. T-cell epitopes further provide targets for personalized vaccines and T-cell therapy, with several therapeutic applications in cancer immunotherapy and elsewhere. T-cell epitopes consist of short peptides displayed on Major Histocompatibility Complex (MHC) molecules. The recent advances in mass spectrometry (MS) based technologies to profile the ensemble of peptides displayed on MHC molecules - the so-called immunopeptidome - had a major impact on our understanding of antigen presentation and MHC ligands. On the one hand, these techniques enabled researchers to directly identify hundreds of thousands of peptides presented on MHC molecules, including some that elicited T-cell recognition. On the other hand, the data collected in these experiments revealed fundamental properties of antigen presentation pathways and significantly improved our ability to predict naturally presented MHC ligands and T-cell epitopes across the wide spectrum of MHC alleles found in human and other organisms. Here we review recent computational developments to analyze experimentally determined immunopeptidomes and harness these data to improve our understanding of antigen presentation and MHC binding specificities, as well as our ability to predict MHC ligands. We further discuss the strengths and limitations of the latest approaches to move beyond predictions of antigen presentation and tackle the challenges of predicting TCR recognition and immunogenicity.
Assuntos
Epitopos de Linfócito T , Neoplasias , Humanos , Epitopos de Linfócito T/metabolismo , Ligantes , Apresentação de Antígeno , PeptídeosRESUMO
The highly polymorphic Major Histocompatibility Complex (MHC) genes are responsible for the binding and cell surface presentation of pathogen or cancer specific T-cell epitopes. This process is fundamental for eliciting T-cell recognition of infected or malignant cells. Epitopes displayed on MHC molecules further provide therapeutic targets for personalized cancer vaccines or adoptive T-cell therapy. To help visualizing, analyzing and comparing the different binding specificities of MHC molecules, we developed the MHC Motif Atlas (http://mhcmotifatlas.org/). This database contains information about thousands of class I and class II MHC molecules, including binding motifs, peptide length distributions, motifs of phosphorylated ligands, multiple specificities or links to X-ray crystallography structures. The database further enables users to download curated datasets of MHC ligands. By combining intuitive visualization of the main binding properties of MHC molecules together with access to more than a million ligands, the MHC Motif Atlas provides a central resource to analyze and interpret the binding specificities of MHC molecules.
Assuntos
Complexo Principal de Histocompatibilidade , Epitopos de Linfócito T , Antígenos de Histocompatibilidade Classe II , Ligantes , Peptídeos/química , Ligação Proteica , Atlas como AssuntoRESUMO
The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. These data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.
Assuntos
Antígenos de Histocompatibilidade Classe I/metabolismo , Peptídeos/metabolismo , Antígenos de Histocompatibilidade Classe I/genética , Humanos , Ligantes , Espectrometria de Massas , Fosforilação , ProteômicaRESUMO
HLA-I molecules bind short peptides and present them for recognition by CD8+ T cells. The length of HLA-I ligands typically ranges from 8 to 12 aa, but variability is observed across different HLA-I alleles. In this study we collected recent in-depth HLA peptidomics data, including 12 newly generated HLA peptidomes (31,896 unique peptides) from human meningioma samples, to analyze the peptide length distribution and multiple specificity across 84 different HLA-I alleles. We observed a clear clustering of HLA-I alleles with distinct peptide length distributions, which enabled us to study the structural basis of peptide length distributions and predict peptide length distributions from HLA-I sequences. We further identified multiple specificity in several HLA-I molecules and validated these observations with binding assays. Explicitly modeling peptide length distribution and multiple specificity improved predictions of naturally presented HLA-I ligands, as demonstrated in an independent benchmarking based on the new human meningioma samples.
Assuntos
Antígenos/metabolismo , Linfócitos T CD8-Positivos/imunologia , Epitopos de Linfócito T/metabolismo , Antígenos de Histocompatibilidade Classe I/metabolismo , Epitopos Imunodominantes/metabolismo , Meningioma/imunologia , Peptídeos/metabolismo , Alelos , Apresentação de Antígeno , Antígenos/genética , Biologia Computacional , Epitopos de Linfócito T/genética , Antígenos HLA/metabolismo , Humanos , Imunidade Celular , Epitopos Imunodominantes/genética , Ligantes , Modelos Químicos , Peptídeos/genética , Polimorfismo Genético , Ligação Proteica , Especificidade do Receptor de Antígeno de Linfócitos TRESUMO
Comprehensive knowledge of the human leukocyte antigen (HLA) class-I and class-II peptides presented to T-cells is crucial for designing innovative therapeutics against cancer and other diseases. However methodologies for their purification for mass-spectrometry analysis have been a major limitation. We designed a novel high-throughput, reproducible and sensitive method for sequential immuno-affinity purification of HLA-I and -II peptides from up to 96 samples in a plate format, suitable for both cell lines and tissues. Our methodology drastically reduces sample-handling and can be completed within five hours. We challenged our methodology by extracting HLA peptides from multiple replicates of tissues (n = 7) and cell lines (n = 21, 108 cells per replicate), which resulted in unprecedented depth, sensitivity and high reproducibility (Pearson correlations up to 0.98 and 0.97 for HLA-I and HLA-II). Because of the method's achieved sensitivity, even single measurements of peptides purified from 107 B-cells resulted in the identification of more than 1700 HLA-I and 2200 HLA-II peptides. We demonstrate the feasibility of performing drug-screening by using ovarian cancer cells treated with interferon gamma (IFNγ). Our analysis revealed an augmented presentation of chymotryptic-like and longer ligands associated with IFNγ induced changes of the antigen processing and presentation machinery. This straightforward method is applicable for basic and clinical applications.
Assuntos
Antígenos de Histocompatibilidade Classe II/metabolismo , Antígenos de Histocompatibilidade Classe I/metabolismo , Interferon gama/farmacologia , Peptídeos/metabolismo , Linfócitos B/metabolismo , Linhagem Celular , Humanos , Ligantes , Neoplasias/metabolismo , Proteômica/métodos , Linfócitos T/metabolismoRESUMO
Understanding the mechanisms behind translation and its rate-limiting steps is crucial for both the development of drug targets and improvement of heterologous protein production with many biotechnological applications, such as in pharmaceutical and biofuel industries. Despite many advances in the knowledge of the ribosome structure and function, there is still much discussion around the determinants of translation elongation with experiments and computational studies pointing in different directions. Here, we use a stochastic framework to simulate the process of translation in the context of an Escherichia coli cell by gathering the available biochemical data into a ribosome kinetics description. Our results from the study of translation in E. coli at different growth rates contradict the increase of mean elongation rate with growth rate established in the literature. We show that both the level of tRNA competition and the type of cognate binding interaction contribute to the modulation of elongation rate, and that optimization of a heterologous transcript for faster elongation rate is achieved by combining the two. We derive an equation that can accurately predict codon elongation rates based on the abundances of free tRNA in the cell, and can be used to assist transcript design. Finally, we show that non-cognate tRNA-ribosome binding has an important weight in translation, and plays an active role in the modulation of mean elongation rate as shown by our amino-acid starvation/surplus studies.
Assuntos
Escherichia coli/genética , Elongação Traducional da Cadeia Peptídica , Códon/genética , Escherichia coli/citologia , Modelos Genéticos , RNA Mensageiro/genética , RNA Mensageiro/metabolismoRESUMO
Noise in genetic networks has been the subject of extensive experimental and computational studies. However, very few of these studies have considered noise properties using mechanistic models that account for the discrete movement of ribosomes and RNA polymerases along their corresponding templates (messenger RNA (mRNA) and DNA). The large size of these systems, which scales with the number of genes, mRNA copies, codons per mRNA, and ribosomes, is responsible for some of the challenges. Additionally, one should be able to describe the dynamics of ribosome exchange between the free ribosome pool and those bound to mRNAs, as well as how mRNA species compete for ribosomes. We developed an efficient algorithm for stochastic simulations that addresses these issues and used it to study the contribution and trade-offs of noise to translation properties (rates, time delays, and rate-limiting steps). The algorithm scales linearly with the number of mRNA copies, which allowed us to study the importance of genome-scale competition between mRNAs for the same ribosomes. We determined that noise is minimized under conditions maximizing the specific synthesis rate. Moreover, sensitivity analysis of the stochastic system revealed the importance of the elongation rate in the resultant noise, whereas the translation initiation rate constant was more closely related to the average protein synthesis rate. We observed significant differences between our results and the noise properties of the most commonly used translation models. Overall, our studies demonstrate that the use of full mechanistic models is essential for the study of noise in translation and transcription.
Assuntos
Genoma , Modelos Teóricos , Biossíntese de Proteínas/genética , Algoritmos , RNA Mensageiro/genética , Ribossomos/químicaRESUMO
Protein synthesis is a template polymerization process composed by three main steps: initiation, elongation, and termination. During translation, ribosomes are engaged into polysomes whose size is used for the quantitative characterization of translatome. However, simultaneous transcription and translation in the bacterial cytosol complicates the analysis of translatome data. We established a procedure for robust estimation of the ribosomal density in hundreds of genes from Lactococcus lactis polysome size measurements. We used a mechanistic model of translation to integrate the information about the ribosomal density and for the first time we estimated the protein synthesis rate for each gene and identified the rate limiting steps. Contrary to conventional considerations, we find significant number of genes to be elongation limited. This number increases during stress conditions compared to optimal growth and proteins synthesized at maximum rate are predominantly elongation limited. Consistent with bacterial physiology, we found proteins with similar rate and control characteristics belonging to the same functional categories. Under stress conditions, we found that synthesis rate of regulatory proteins is becoming comparable to proteins favored under optimal growth. These findings suggest that the coupling of metabolic states and protein synthesis is more important than previously thought.
Assuntos
Proteínas de Bactérias/genética , Biologia Computacional/métodos , Genoma Bacteriano/genética , Lactococcus lactis/genética , Lactococcus lactis/metabolismo , Polirribossomos/genética , Proteínas de Bactérias/metabolismo , Bases de Dados de Proteínas , Polirribossomos/metabolismo , Biossíntese de ProteínasRESUMO
MHC-II molecules are key mediators of antigen presentation in vertebrate species and bind to their ligands with high specificity. The very high polymorphism of MHC-II genes within species and the fast-evolving nature of these genes across species has resulted in tens of thousands of different alleles, with hundreds of new alleles being discovered yearly through large sequencing projects in different species. Here we describe how to use MixMHC2pred to predict the binding specificity of any MHC-II allele directly from its amino acid sequence. We then show how both MHC-II ligands and CD4+ T cell epitopes can be predicted in different species with our approach. MixMHC2pred is available at http://mixmhc2pred.gfellerlab.org/ .
Assuntos
Alelos , Epitopos de Linfócito T , Antígenos de Histocompatibilidade Classe II , Ligantes , Antígenos de Histocompatibilidade Classe II/genética , Antígenos de Histocompatibilidade Classe II/metabolismo , Animais , Humanos , Epitopos de Linfócito T/imunologia , Epitopos de Linfócito T/genética , Epitopos de Linfócito T/metabolismo , Ligação Proteica , Software , Biologia Computacional/métodos , Apresentação de Antígeno/genética , Sequência de AminoácidosRESUMO
The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.
Assuntos
Linfócitos T CD8-Positivos , COVID-19 , Humanos , Epitopos de Linfócito T , Apresentação de Antígeno , SARS-CoV-2 , Ligantes , Receptores de Antígenos de Linfócitos T , Antígenos HLARESUMO
Despite the establishment of design principles to optimize codon choice for heterologous expression vector design, the relationship between codon sequence and final protein yield remains poorly understood. In this work, we present a computational framework for the identification of a set of mutant codon sequences for optimized heterologous protein production, which uses a codon-sequence mechanistic model of protein synthesis. Through a sensitivity analysis on the optimal steady state configuration of protein synthesis we are able to identify the set of codons, that are the most rate limiting with respect to steady state protein synthesis rate, and we replace them with synonymous codons recognized by charged tRNAs more efficient for translation, so that the resulting codon-elongation rate is higher. Repeating this procedure, we iteratively optimize the codon sequence for higher protein synthesis rate taking into account multiple constraints of various types. We determine a small set of optimized synonymous codon sequences that are very close to each other in sequence space, but they have an impact on properties such as ribosomal utilization or secondary structure. This limited number of sequences can then be offered for further experimental study. Overall, the proposed method is very valuable in understanding the effects of the different properties of mRNA sequences on the final protein yield in heterologous protein production and it can find applications in synthetic biology and biotechnology.
Assuntos
Biotecnologia/métodos , Códon , Biossíntese de Proteínas , Proteínas/genética , Proteínas/metabolismo , Computadores , Modelos Teóricos , Engenharia de Proteínas/métodosRESUMO
CD4+ T cell activation in infectious diseases and cancer is governed by the recognition of peptides presented on class II human leukocyte antigen (HLA-II) molecules. Therefore, HLA-II ligands represent promising targets for vaccine design and personalized cancer immunotherapy. Much work has been done to identify and predict unmodified peptides presented on HLA-II molecules. However, little is known about the presentation of phosphorylated HLA-II ligands. Here, we analyzed Mass Spectrometry HLA-II peptidomics data and identified 1,943 unique phosphorylated HLA-II ligands. This enabled us to precisely define phosphorylated binding motifs for more than 30 common HLA-II alleles and to explore various molecular properties of phosphorylated peptides. Our data were further used to develop the first predictor of phosphorylated peptide presentation on HLA-II molecules.
RESUMO
The understanding of the role of B cells in patients with solid tumors remains insufficient. We found that circulating B cells produced TNFα and/or IL-6, associated with unresponsiveness and poor overall survival of melanoma patients treated with anti-CTLA4 antibody. Transcriptome analysis of B cells from melanoma metastases showed enriched expression of inflammatory response genes. Publicly available single B cell data from the tumor microenvironment revealed a negative correlation between TNFα expression and response to immune checkpoint blockade. These findings suggest that B cells contribute to tumor growth via the production of inflammatory cytokines. Possibly, these B cells are different from tertiary lymphoid structure-associated B cells, which have been described to correlate with favorable clinical outcome of cancer patients. Further studies are required to identify and characterize B cell subsets and their functions promoting or counteracting tumor growth, with the aim to identify biomarkers and novel treatment targets.
Assuntos
Melanoma , Estruturas Linfoides Terciárias , Linfócitos B , Perfilação da Expressão Gênica , Humanos , Melanoma/tratamento farmacológico , Microambiente TumoralRESUMO
CD8+ T cell recognition of peptide epitopes plays a central role in immune responses against pathogens and tumors. However, the rules that govern which peptides are truly recognized by existing T cell receptors (TCRs) remain poorly understood, precluding accurate predictions of neo-epitopes for cancer immunotherapy. Here, we capitalize on recent (neo-)epitope data to train a predictor of immunogenic epitopes (PRIME), which captures molecular properties of both antigen presentation and TCR recognition. PRIME not only improves prioritization of neo-epitopes but also correlates with T cell potency and unravels biophysical determinants of TCR recognition that we experimentally validate. Analysis of cancer genomics data reveals that recurrent mutations tend to be less frequent in patients where they are predicted to be immunogenic, providing further evidence for immunoediting in human cancer. PRIME will facilitate identification of pathogen epitopes in infectious diseases and neo-epitopes in cancer immunotherapy.
Assuntos
Apresentação de Antígeno/imunologia , Epitopos de Linfócito T/imunologia , Neoplasias/imunologia , Receptores de Antígenos de Linfócitos T/imunologia , Linfócitos T CD8-Positivos/imunologia , Epitopos de Linfócito T/genética , Humanos , Imunoterapia/métodos , Peptídeos/imunologiaRESUMO
Gene expression profiling is nowadays routinely performed on clinically relevant samples (e.g., from tumor specimens). Such measurements are often obtained from bulk samples containing a mixture of cell types. Knowledge of the proportions of these cell types is crucial as they are key determinants of the disease evolution and response to treatment. Moreover, heterogeneity in cell type proportions across samples is an important confounding factor in downstream analyses.Many tools have been developed to estimate the proportion of the different cell types from bulk gene expression data. Here, we provide guidelines and examples on how to use these tools, with a special focus on our recent computational method EPIC (Estimating the Proportions of Immune and Cancer cells). EPIC includes RNA-seq-based gene expression reference profiles from immune cells and other nonmalignant cell types found in tumors. EPIC can additionally manage user-defined gene expression reference profiles. Some unique features of EPIC include the ability to account for an uncharacterized cell type, the introduction of a renormalization step to account for different mRNA content in each cell type, and the use of single-cell RNA-seq data to derive biologically relevant reference gene expression profiles. EPIC is available as a web application ( http://epic.gfellerlab.org ) and as an R-package ( https://github.com/GfellerLab/EPIC ).
Assuntos
Perfilação da Expressão Gênica/métodos , Software , Transcriptoma , Genômica/métodos , Humanos , Neoplasias/genética , RNA Mensageiro/genética , Análise de Sequência de RNA/métodos , Análise de Célula Única/métodosRESUMO
Genomic alterations in cancer cells can influence the immune system to favor tumor growth. In non-Hodgkin lymphoma, physiological interactions between B cells and the germinal center microenvironment are coopted to sustain cancer cell proliferation. We found that follicular lymphoma patients harbor a recurrent hotspot mutation targeting tyrosine 132 (Y132D) in cathepsin S (CTSS) that enhances protein activity. CTSS regulates antigen processing and CD4+ and CD8+ T cell-mediated immune responses. Loss of CTSS activity reduces lymphoma growth by limiting communication with CD4+ T follicular helper cells while inducing antigen diversification and activation of CD8+ T cells. Overall, our results suggest that CTSS inhibition has non-redundant therapeutic potential to enhance anti-tumor immune responses in indolent and aggressive lymphomas.
Assuntos
Apresentação de Antígeno/imunologia , Linfócitos T CD4-Positivos/imunologia , Linfócitos T CD8-Positivos/imunologia , Catepsinas/genética , Linfoma não Hodgkin/imunologia , Mutação , Microambiente Tumoral/imunologia , Animais , Apoptose , Linfócitos B/imunologia , Proliferação de Células , Feminino , Centro Germinativo/imunologia , Humanos , Ativação Linfocitária/imunologia , Linfoma não Hodgkin/genética , Linfoma não Hodgkin/patologia , Masculino , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Linfócitos T Auxiliares-Indutores/imunologia , Células Tumorais CultivadasRESUMO
Predictions of epitopes presented by class II human leukocyte antigen molecules (HLA-II) have limited accuracy, restricting vaccine and therapy design. Here we combined unbiased mass spectrometry with a motif deconvolution algorithm to profile and analyze a total of 99,265 unique peptides eluted from HLA-II molecules. We then trained an epitope prediction algorithm with these data and improved prediction of pathogen and tumor-associated class II neoepitopes.
Assuntos
Epitopos/metabolismo , Antígenos de Histocompatibilidade Classe II/metabolismo , Peptídeos/análise , Algoritmos , Linhagem Celular , Biologia Computacional/métodos , Antígenos de Histocompatibilidade Classe II/química , Humanos , Espectrometria de Massas , Peptídeos/imunologiaRESUMO
Despite the promising therapeutic effects of immune checkpoint blockade (ICB), most patients with solid tumors treated with anti-PD-1/PD-L1 monotherapy do not achieve objective responses, with most tumor regressions being partial rather than complete. It is hypothesized that the absence of pre-existing antitumor immunity and/or the presence of additional tumor immune suppressive factors at the tumor microenvironment are responsible for such therapeutic failures. It is therefore clear that in order to fully exploit the potential of PD-1 blockade therapy, antitumor immune response should be amplified, while tumor immune suppression should be further attenuated. Cancer vaccines may prime patients for treatments with ICB by inducing effective anti-tumor immunity, especially in patients lacking tumor-infiltrating T-cells. These "non-inflamed" non-permissive tumors that are resistant to ICB could be rendered sensitive and transformed into "inflamed" tumor by vaccination. In this article we describe a clinical study where we use pancreatic cancer as a model, and we hypothesize that effective vaccination in pancreatic cancer patients, along with interventions that can reprogram important immunosuppressive factors in the tumor microenvironment, can enhance tumor immune recognition, thus enhancing response to PD-1/PD-L1 blockade. We incorporate into the schedule of standard of care (SOC) chemotherapy adjuvant setting a vaccine platform comprised of autologous dendritic cells loaded with personalized neoantigen peptides (PEP-DC) identified through our own proteo-genomics antigen discovery pipeline. Furthermore, we add nivolumab, an antibody against PD-1, to boost and maintain the vaccine's effect. We also demonstrate the feasibility of identifying personalized neoantigens in three pancreatic ductal adenocarcinoma (PDAC) patients, and we describe their optimal incorporation into long peptides for manufacturing into vaccine products. We finally discuss the advantages as well as the scientific and logistic challenges of such an exploratory vaccine clinical trial, and we highlight its novelty.
Assuntos
Antígenos de Neoplasias/administração & dosagem , Antineoplásicos Imunológicos/uso terapêutico , Aspirina/uso terapêutico , Carcinoma Ductal Pancreático/terapia , Quimioterapia Adjuvante , Células Dendríticas/imunologia , Imunoterapia Ativa/métodos , Terapia de Alvo Molecular , Nivolumabe/uso terapêutico , Neoplasias Pancreáticas/terapia , Sequência de Aminoácidos , Carcinoma Ductal Pancreático/tratamento farmacológico , Terapia Combinada , Exoma , Estudos de Viabilidade , Humanos , Proteínas de Neoplasias/antagonistas & inibidores , Neoplasias Pancreáticas/tratamento farmacológico , Peptídeos/imunologia , Medicina de Precisão , Receptor de Morte Celular Programada 1/antagonistas & inibidores , Estudo de Prova de Conceito , Alinhamento de Sequência , Homologia de Sequência do Ácido NucleicoRESUMO
BACKGROUND: Patient derived organoids (PDOs) can be established from colorectal cancers (CRCs) as in vitro models to interrogate cancer biology and its clinical relevance. We applied mass spectrometry (MS) immunopeptidomics to investigate neoantigen presentation and whether this can be augmented through interferon gamma (IFNγ) or MEK-inhibitor treatment. METHODS: Four microsatellite stable PDOs from chemotherapy refractory and one from a treatment naïve CRC were expanded to replicates with 100 million cells each, and HLA class I and class II peptide ligands were analyzed by MS. RESULTS: We identified an average of 9936 unique peptides per PDO which compares favorably against published immunopeptidomics studies, suggesting high sensitivity. Loss of heterozygosity of the HLA locus was associated with low peptide diversity in one PDO. Peptides from genes without detectable expression by RNA-sequencing were rarely identified by MS. Only 3 out of 612 non-silent mutations encoded for neoantigens that were detected by MS. In contrast, computational HLA binding prediction estimated that 304 mutations could generate neoantigens. One hundred ninety-six of these were located in expressed genes, still exceeding the number of MS-detected neoantigens 65-fold. Treatment of four PDOs with IFNγ upregulated HLA class I expression and qualitatively changed the immunopeptidome, with increased presentation of IFNγ-inducible genes. HLA class II presented peptides increased dramatically with IFNγ treatment. MEK-inhibitor treatment showed no consistent effect on HLA class I or II expression or the peptidome. Importantly, no additional HLA class I or II presented neoantigens became detectable with any treatment. CONCLUSIONS: Only 3 out of 612 non-silent mutations encoded for neoantigens that were detectable by MS. Although MS has sensitivity limits and biases, and likely underestimated the true neoantigen burden, this established a lower bound of the percentage of non-silent mutations that encode for presented neoantigens, which may be as low as 0.5%. This could be a reason for the poor responses of non-hypermutated CRCs to immune checkpoint inhibitors. MEK-inhibitors recently failed to improve checkpoint-inhibitor efficacy in CRC and the observed lack of HLA upregulation or improved peptide presentation may explain this.