Neoantigen screening identifies broad TP53 mutant immunogenicity in patients with epithelial cancers.
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ABSTRACT:
SUBMITTER: Malekzadeh P
PROVIDER: S-EPMC6391139 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Mutated peptides (neoantigens) from a patient's cancer genome can serve as targets for T-cell immunity, but identifying which peptides can be presented by an MHC molecule and elicit T cells has been difficult. Although algorithms that predict MHC binding exist, they are not yet able to distinguish experimental differences in half-lives of the complexes (an immunologically relevant parameter, referred to here as kinetic stability). Improvement in determining actual neoantigen peptide/MHC stability could be important, as only a small fraction of peptides in most current vaccines are capable of eliciting CD8+ T-cell responses. Here, we used a rapid, high-throughput method to experimentally determine peptide/HLA thermal stability on a scale that will be necessary for analysis of neoantigens from thousands of patients. The method combined the use of UV-cleavable peptide/HLA class I complexes and differential scanning fluorimetry to determine the Tm values of neoantigen complexes. Measured Tm values were accurate and reproducible and were directly proportional to the half-lives of the complexes. Analysis of known HLA-A2-restricted immunogenic peptides showed that Tm values better correlated with immunogenicity than algorithm-predicted binding affinities. We propose that temperature stability information can be used as a guide for the selection of neoantigens in cancer vaccines in order to focus attention on those mutated peptides with the highest probability of being expressed on the cell surface.
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Project description:Tumor protein 53 mutation (TP53mut) is one of the most important driver events facilitating tumorigenesis, which could induce a series of chain reactions to promote tumor malignant transformation. However, the malignancy progression patterns under TP53 mutation remain less known. Clarifying the molecular landscapes of TP53mut tumors will help us understand the process of tumor development and aid precise treatment. Here, we distilled genetic and epigenetic features altered in TP53mut cancers for cluster-of-clusters analysis. Using integrated classification, we derived 5 different subtypes of TP53mut patients. These subtypes have distinct features in genomic alteration, clinical relevance, microenvironment dysregulation, and potential therapeutics. Among the 5 subtypes, COCA3 was identified as the subtype with worst prognosis, causing an immunosuppressive microenvironment and immunotherapeutic resistance. Further drug efficacy research highlighted olaparib as the most promising therapeutic agents for COCA3 tumors. Importantly, the therapeutic efficacy of olaparib in COCA3 and immunotherapy in non-COCA3 tumors was validated via in vivo experimentation. Our study explored the important molecular events and developed a subtype classification system with distinct targeted therapy strategies for different subtypes of TP53mut tumors. These multiomics classification systems provide a valuable resource that significantly expands the knowledge of TP53mut tumors and may eventually benefit in clinical practice.
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Project description:Altered protein phosphorylation in cancer cells often leads to surface presentation of phosphopeptide neoantigens. However, their role in cancer immunogenicity remains unclear. Here we describe a mechanism by which an HLA-B*0702-specific acute myeloid leukemia phosphoneoantigen, pMLL747-755 (EPR(pS)PSHSM), is recognized by a cognate T cell receptor named TCR27, a candidate for cancer immunotherapy. We show that the replacement of phosphoserine P4 with serine or phosphomimetics does not affect pMHC conformation or peptide-MHC affinity but abrogates TCR27-dependent T cell activation and weakens binding between TCR27 and pMHC. Here we describe the crystal structures for TCR27 and cognate pMHC, map of the interface produced by nuclear magnetic resonance, and a ternary complex generated using information-driven protein docking. Our data show that non-covalent interactions between the epitope phosphate group and TCR27 are crucial for TCR specificity. This study supports development of new treatment options for cancer patients through target expansion and TCR optimization.
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Project description:The development of immunological therapies that incorporate peptide antigens presented to T cells by MHC proteins is a long sought-after goal, particularly for cancer, where mutated neoantigens are being explored as personalized cancer vaccines. Although neoantigens can be identified through sequencing, bioinformatics and mass spectrometry, identifying those which are immunogenic and able to promote tumor rejection remains a significant challenge. Here we examined the potential of high-resolution structural modeling followed by energetic scoring of structural features for predicting neoantigen immunogenicity. After developing a strategy to rapidly and accurately model nonameric peptides bound to the common class I MHC protein HLA-A2, we trained a neural network on structural features that influence T cell receptor (TCR) and peptide binding energies. The resulting structurally-parameterized neural network outperformed methods that do not incorporate explicit structural or energetic properties in predicting CD8+ T cell responses of HLA-A2 presented nonameric peptides, while also providing insight into the underlying structural and biophysical mechanisms governing immunogenicity. Our proof-of-concept study demonstrates the potential for structure-based immunogenicity predictions in the development of personalized peptide-based vaccines.
| S-EPMC6724579 | biostudies-literature
Project description:TP53 (tumor protein p53) is the most commonly mutated cancer driver gene, but drugs that target mutant tumor suppressor genes, such as TP53, are not yet available. Here, we describe the identification of an antibody highly specific to the most common TP53 mutation (R175H, in which arginine at position 175 is replaced with histidine) in complex with a common human leukocyte antigen-A (HLA-A) allele on the cell surface. We describe the structural basis of this specificity and its conversion into an immunotherapeutic agent: a bispecific single-chain diabody. Despite the extremely low p53 peptide-HLA complex density on the cancer cell surface, the bispecific antibody effectively activated T cells to lyse cancer cells that presented the neoantigen in vitro and in mice. This approach could in theory be used to target cancers containing mutations that are difficult to target in conventional ways.
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Project description:Hepatocellular carcinoma (HCC) is an aggressive and chemoresistant cancer type. The development of novel therapeutic strategies is still urgently needed. Personalized or precision medicine is a new trend in cancer therapy, which treats cancer patients with specific genetic alterations. In this study, a gene signature was identified from the transcriptome of HCC patients, which was correlated with the patients' poorer prognoses. This gene signature is functionally related to mitotic cell cycle regulation, and its higher or lower expression is linked to the mutation in tumor protein p53 (TP53) or catenin beta 1 (CTNNB1), respectively. Gene-drug association analysis indicated that the taxanes, such as the clinically approved anticancer drug paclitaxel, are potential drugs targeting this mitotic gene signature. Accordingly, HCC cell lines harboring mutant TP53 or wild-type CTNNB1 genes are more sensitive to paclitaxel treatment. Therefore, our results imply that HCC patients with mutant TP53 or wild-type CTNNB1 genes may benefit from the paclitaxel therapy.
| S-EPMC8623741 | biostudies-literature
Project description:BackgroundExpression of CD103 and CD39 has been found to pinpoint tumor-reactive CD8+ T cells in a variety of solid cancers. We aimed to investigate whether these markers specifically identify neoantigen-specific T cells in colorectal cancers (CRCs) with low mutation burden.Experimental designWhole-exome and RNA sequencing of 11 mismatch repair-proficient (MMR-proficient) CRCs and corresponding healthy tissues were performed to determine the presence of putative neoantigens. In parallel, tumor-infiltrating lymphocytes (TILs) were cultured from the tumor fragments and, in parallel, CD8+ T cells were flow-sorted from their respective tumor digests based on single or combined expression of CD103 and CD39. Each subset was expanded and subsequently interrogated for neoantigen-directed reactivity with synthetic peptides. Neoantigen-directed reactivity was determined by flow cytometric analyses of T cell activation markers and ELISA-based detection of IFN-γ and granzyme B release. Additionally, imaging mass cytometry was applied to investigate the localization of CD103+CD39+ cytotoxic T cells in tumors.ResultsNeoantigen-directed reactivity was only encountered in bulk TIL populations and CD103+CD39+ (double positive, DP) CD8+ T cell subsets but never in double-negative or single-positive subsets. Neoantigen-reactivity detected in bulk TIL but not in DP CD8+ T cells could be attributed to CD4+ T cells. CD8+ T cells that were located in direct contact with cancer cells in tumor tissues were enriched for CD103 and CD39 expression.ConclusionCoexpression of CD103 and CD39 is characteristic of neoantigen-specific CD8+ T cells in MMR-proficient CRCs with low mutation burden. The exploitation of these subsets in the context of adoptive T cell transfer or engineered T cell receptor therapies is a promising avenue to extend the benefits of immunotherapy to an increasing number of CRC patients.
| S-EPMC9933759 | biostudies-literature
Project description:Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.
| S-EPMC9569519 | biostudies-literature
Project description:IntroductionIdentification of tumor specific neoantigen (TSN) immunogenicity is crucial to develop peptide/mRNA based anti-tumoral vaccines and/or adoptive T-cell immunotherapies; thus, accurate in-silico classification/prioritization proves critical for cost-effective clinical applications. Several methods were proposed as TSNs immunogenicity predictors; however, comprehensive performance comparison is still lacking due to the absence of well documented and adequate TSN databases.MethodsHere, by developing a new curated database having 199 TSNs with experimentally-validated MHC-I presentation and positive/negative immune response (ITSNdb), sixteen metrics were evaluated as immunogenicity predictors. In addition, by using a dataset emulating patient derived TSNs and immunotherapy cohorts containing predicted TSNs for tumor neoantigen burden (TNB) with outcome association, the metrics were evaluated as TSNs prioritizers and as immunotherapy response biomarkers.ResultsOur results show high performance variability among methods, highlighting the need for substantial improvement. Deep learning predictors were top ranked on ITSNdb but show discrepancy on validation databases. In overall, current predicted TNB did not outperform existing biomarkers.ConclusionRecommendations for their clinical application and the ITSNdb are presented to promote development and comparison of computational TSNs immunogenicity predictors.
| S-EPMC10411733 | biostudies-literature
Project description:Immune checkpoint blockade (ICB) therapy is a cornerstone of oncologic treatment for patients with advanced stage non-small cell lung cancer (NSCLC) and other malignancies. Neoantigen immunoediting drives ICB efficacy, yet the fundamental physiochemical characteristics of neoantigens and how neoantigen immunogenicity shapes treatment response remains poorly understood. To help address these questions, a prospective clinical trial of NSCLC patients treated with nivolumab was conducted. We assessed genomic alterations in tumors from 58 patients and performed large-scale neoepitope immunogenicity analyses, before and during treatment (CheckMate153, CA209-153). Tumors were analyzed by whole-exome and transcriptome sequencing. In responding patients, loss of mutation and neoantigen burden early during therapy associated with clinical benefit. We evaluated the immunogenicity of 1,453 candidate neoantigens and identified 502 neopeptides that bound to MHC I and 196 neopeptides that were immunogenically recognized by T cells in the setting of nivolumab treatment. These T cell reactive neoantigens were differentially present in clonal populations that underwent distinctive evolutionary trajectories across responders and nonresponders. Mapping these neoantigens to tumor clonal dynamics and clinical response revealed strong selection against immunogenic neoantigen harboring clones compared to non-immunogenic clones. Using this large collection of neoantigens, we identified position specific amino acid features related to immunogenicity, which we used to develop and validate an immunogenicity score. Changes in the genomic and neoantigen immunogenicity landscapes were accompanied by temporal changes in the tumor microenvironment. Nivolumab-induced microenvironmental evolution in NSCLC shared some similarities with that in melanoma, yet critical differences in immunologic programs were apparent from comparative network analysis between tumor types. This study provides unprecedented molecular portraits of the neoantigen landscapes underlying nivolumab’s mechanism of action.
| EGAS00001007508 | EGA