ImmuniT Platform for Improved Neoantigen Prediction in Lung Cancer
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PROVIDER: PRJNA1311830 | ENA |
REPOSITORIES: ENA
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Project description:Background: Lung cancer remains the leading cause of cancer-related mortality, with many patients responding poorly to immunotherapy due to limited tumor recognition. Neoantigen-based strategies offer a promising solution, but current discovery methods often miss key targets, particularly those with low or heterogeneous expression. To address this, we developed ImmuniT, a three-phase platform for enhanced neoantigen discovery and validation. Methods: Under an IRB-approved protocol, patients with lung cancer consented to tumor collection for ex vivo processing to modulate antigen expression. Autologous T cells from matched blood were co-cultured with treated cancer cells to expand tumor-reactive populations. The nextneopi pipeline integrated mutational, transcriptomic, and HLA data to predict candidate neoantigens, which were validated using MHC epitope tetramer staining. Results: In five patient samples, ImmuniT identified a broader spectrum of neoantigens and induced stronger T cell activation in vitro compared to conventional approaches. Notably, in one case, two neoantigens missed by standard methods were confirmed to elicit tumor-specific T cell responses in both the tumor-infiltrating and peripheral compartments. Conclusions: These findings highlight ImmuniT’s potential to expand the repertoire of actionable tumor antigens and improve personalized immunotherapy strategies, particularly for patients with limited response to existing treatments. Note: This submission provides de-identified, summary-level results from NSCLC patient samples, including normalized RNA-seq counts, differential expression (DE) results, and copy number variation (CNV) calls. Raw sequencing files (FASTQ/BAM/VCF) are not deposited due to informed consent limitations; see README.
2025-08-28 | GSE306693 | GEO
Project description:Neoantigens, which are expressed on tumor cells, are one of the main targets of an effective anti-tumor T-cell response. Cancer immunotherapies to target neoantigens are of growing interest, and are currently in early human trials, but methods to identify neoantigens either require invasive or difficult-to-obtain clinical specimens, the screening of hundreds to thousands of synthetic peptides or tandem minigenes or are only relevant to specific human leukocyte antigen (HLA) alleles. We apply deep learning to a large (N=74 patients) HLA peptide and genomic dataset from various human tumors to create a computational model of antigen presentation for neoantigen prediction. We show that our model, named EDGE, increases the positive predictive value of HLA antigen prediction by up to 9 fold. We apply EDGE to enable identification of neoantigens and neoantigen-reactive T cells using routine clinical specimens and small numbers of synthetic peptides for most common HLA alleles. EDGE could enable an improved ability to develop neoantigen-targeted immunotherapies for cancer patients.
2018-07-21 | MSV000082648 | MassIVE
Project description:Purpose: Utility of immunological treatment in cancer has increased; however, many patients do not respond to treatment. Identification of robust predictive biomarkers is required to correctly stratify patients. Although clinical trials based on adoptive T cell therapy (ACT) have yielded high response rates and many durable responses in melanoma, 50-60% of the patients have no clinical benefit. Herein, we searched for predictive biomarkers to ACT in melanoma. Methods: Whole exome- and transcriptome sequencing, neoantigen prediction and immune cell signature analysis were applied to pre-treatment melanoma samples from 27 patients recruited to a clinical phase I/II trial of ACT in stage IV melanoma. All patients had previously been treated with other immunotherapies. Results: We found that clinical benefit was associated with significantly higher neoantigen load (P=0.025). High mutation and neoantigen load were significantly associated with improved progression-free and overall survival (P=8x10^-4 and P=0.001, respectively). Further, gene-expression analysis of pre-treatment biopsies showed that clinical benefit was associated with strong immune activation signatures including a high MHC-I antigen processing and presentation score. Conclusions: These results improve our understanding of clinical benefit of ACT in melanoma, which can lead to clinically useful predictive biomarkers to be used for selecting patients that benefit from these highly intensive treatment regimens.
2017-12-04 | GSE100797 | GEO
Project description:Cell therapy with tumor-infiltrating lymphocytes (TIL) has yielded durable responses for multiple cancer types, but the causes of therapeutic resistance remain largely unknown. Here multi-dimensional analysis was performed on time serial tumor and blood in a lung cancer TIL therapy trial. Using T-cell receptor sequencing on both functionally expanded T cells and neoantigen-loaded tetramer-sorted T cells, we identified neoantigen specific TCRs. We then mapped clones into individual transcriptomes and found that neoantigen-reactive clonotypes expressed a dysfunctional program and lacked stem-like features among patients who lacked clinical benefit. Tracking neoantigen-reactive clonotypes over time, decay of antigen-reactive peripheral T-cell clonotypes was associated with the emergence of progressive disease. Further, subclonal neoantigens previously targeted by infused T cells were subsequently absent within tumors at progression, suggesting potential adaptive resistance. Our findings suggest that targeting clonal antigens and circumventing dysfunctional states may be important for conferring clinical responses to TIL therapy.
2024-12-30 | PXD049438 | Pride
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Project description:Neoantigens are promising immunogens for cancer vaccines and are traditionally delivered as adjuvanted peptide vaccines. Our goal was to understand how an adenoviral vectored neoantigen vaccine would induce tumor immunity compared to a peptide neoantigen vaccine. We generated adenovirus serotype 26 (Ad26) vaccines encoding MC38-specific neoantigens and compared them to an adjuvanted peptide MC38 neoantigen vaccine. The single-shot Ad26 vaccines induced greater neoantigen specific IFN- CD8+ T cell immune responses than the two-shot adjuvanted peptide vaccine in mice, and Ad26.VP22.7Epi also provided superior protective efficacy compared to the peptide vaccine following tumor challenge. Ad26.VP22.7Epi induced a robust immunodominant CD8+ T cell response against the Adpgk neoantigen, while the peptide vaccine induced lower responses against both Adpgk and Reps1 neoantigens. Tumor infiltrating lymphocytes (TILs) from both vaccine groups were analyzed using scRNA-seq and TCR-seq. Vaccinated mice showed increased CD8+ T cell infiltration, with the peptide vaccine inducing more infiltrating CD8+ T cells than the Ad26.VP22.7Epi vaccine. However, Ad26.VP22.7Epi induced CD8+ T cells showed more upregulation of T cell maturation, activation, and Th1 pathways compared to peptide vaccine induced CD8+ T cells, suggesting improved functional T cell quality. TCR-seq of these TILs also demonstrated that Ad26.VP22.7Epi generated larger T cell hyperexpanded clones compared to the peptide vaccine. These results suggest that the Ad26.VP22.7Epi vaccine led to improved tumor control compared with the peptide vaccine due to increased T cell hyperexpansion and functional activation. Our data suggest that future cancer vaccine development strategies should focus on inducing functional hyperexpanded CD8+ T cell responses and not only maximizing tumor infiltrating CD8+ T cell numbers.
2025-02-14 | GSE264579 | GEO
Project description:Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models
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Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients 56 lung biopsies, 4 different Phenotypes: NSCLC-squa., NSCLC-NOS, NSCLC-Adeno, Ctr.-Infl.
2008-04-10 | E-GEOD-11117 | biostudies-arrayexpress
Project description:An Improved Platform for Functional Assessment of Large Protein Libraries in Mammalian Cells
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