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.
| S-EPMC6324732 | biostudies-literature
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.
| S-EPMC9746906 | biostudies-literature
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.
| S-EPMC8208645 | 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.
| EGAS00001007509 | EGA
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
Project description:BACKGROUND: In breast cancers, only a minority of patients fully benefit from the different chemotherapy regimens currently in use. Identification of markers that could predict the response to a particular regimen would thus be critically important for patient care. In cell lines or animal models, tumor protein p53 (TP53) plays a critical role in modulating the response to genotoxic drugs. TP53 is activated in response to DNA damage and triggers either apoptosis or cell-cycle arrest, which have opposite effects on cell fate. Yet, studies linking TP53 status and chemotherapy response have so far failed to unambiguously establish this paradigm in patients. Breast cancers with a TP53 mutation were repeatedly shown to have a poor outcome, but whether this reflects poor response to treatment or greater intrinsic aggressiveness of the tumor is unknown. METHODS AND FINDINGS: In this study we analyzed 80 noninflammatory breast cancers treated by frontline (neoadjuvant) chemotherapy. Tumor diagnoses were performed on pretreatment biopsies, and the patients then received six cycles of a dose-dense regimen of 75 mg/m(2) epirubicin and 1,200 mg/m(2) cyclophosphamide, given every 14 days. After completion of chemotherapy, all patients underwent mastectomies, thus allowing for a reliable assessment of chemotherapy response. The pretreatment biopsy samples were used to determine the TP53 status through a highly efficient yeast functional assay and to perform RNA profiling. All 15 complete responses occurred among the 28 TP53-mutant tumors. Furthermore, among the TP53-mutant tumors, nine out of ten of the highly aggressive basal subtypes (defined by basal cytokeratin [KRT] immunohistochemical staining) experienced complete pathological responses, and only TP53 status and basal subtype were independent predictors of a complete response. Expression analysis identified many mutant TP53-associated genes, including CDC20, TTK, CDKN2A, and the stem cell gene PROM1, but failed to identify a transcriptional profile associated with complete responses among TP53 mutant tumors. In patients with unresponsive tumors, mutant TP53 status predicted significantly shorter overall survival. The 15 patients with responsive TP53-mutant tumors, however, had a favorable outcome, suggesting that this chemotherapy regimen can overcome the poor prognosis generally associated with mutant TP53 status. CONCLUSIONS: This study demonstrates that, in noninflammatory breast cancers, TP53 status is a key predictive factor for response to this dose-dense epirubicin-cyclophosphamide regimen and further suggests that the basal subtype is exquisitely sensitive to this association. Given the well-established predictive value of complete responses for long-term survival and the poor prognosis of basal and TP53-mutant tumors treated with other regimens, this chemotherapy could be particularly suited for breast cancer patients with a mutant TP53, particularly those with basal features.
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