Lost in the crowd: identifying targetable MHC class I neoepitopes for cancer immunotherapy

Wilson, Eric; Anderson, Karen S.

doi:10.1080/14789450.2018.1545578

Cited by 12 publications

(9 citation statements)

References 124 publications

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“…Another strategy to improve the ability to predict neoepitope was the integration of potential immunogenicity assessments to the prediction process. MuPeXI algorithm ranks predicted neoepitopes by a priority score that is based on inferred abundance, MHC binding affinity, and an immunogenicity score based on similarity to non-mutated wild-type peptide [60] EpitopeHunter algorithm, which integrates RNA expression with immunogenicity prediction algorithm based on the hydrophobicity of the TCR contact region [61, 62]. Neopepsee algorithm using a machine learning algorithm trained on epitope features, including antigen processing and presentation, amino acid characteristics, the binding difference between wild-type and mutant epitope, and similarity to known epitopes, to predict the immunogenicity and reduce the false-positive rate [63].…”

Section: Prediction and Identification Of Tumor-specific Neoantigensmentioning

confidence: 99%

Tumor neoantigens: from basic research to clinical applications

et al. 2019

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Tumor neoantigen is the truly foreign protein and entirely absent from normal human organs/tissues. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs) in the context of major histocompatibility complexes (MHCs) molecules. Emerging evidence has suggested that neoantigens play a critical role in tumor-specific T cell-mediated antitumor immune response and successful cancer immunotherapies. From a theoretical perspective, neoantigen is an ideal immunotherapy target because they are distinguished from germline and could be recognized as non-self by the host immune system. Neoantigen-based therapeutic personalized vaccines and adoptive T cell transfer have shown promising preliminary results. Furthermore, recent studies suggested the significant role of neoantigen in immune escape, immunoediting, and sensitivity to immune checkpoint inhibitors. In this review, we systematically summarize the recent advances of understanding and identification of tumor-specific neoantigens and its role on current cancer immunotherapies. We also discuss the ongoing development of strategies based on neoantigens and its future clinical applications.

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Section: Prediction and Identification Of Tumor-specific Neoantigensmentioning

confidence: 99%

Tumor neoantigens: from basic research to clinical applications

et al. 2019

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“…It is a promising opportunity to target cancer specific epitopes (neoepitopes), and to generate adoptive T cell therapies and personalized cancer vaccines. However, poor antigen presentation and weak immunogenicity in tumor cells are primary causes of resistance to these therapies (Pu, Wu, Su, Mao, & Fang, ; Wilson & Anderson, ). RIG‐I like receptors and Toll‐like receptors are the main pattern recognition receptors (PRRs) involved in the regulation of Type I IFNs (Peng, Xu, & Zheng, ).…”

Section: Discussionmentioning

confidence: 99%

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

Tang

Yang

Cui

et al. 2019

Journal Cellular Physiology

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As the most commonly diagnosed malignant tumor in female population, the prognosis of breast cancer is affected by complex gene interaction networks. In this research weighted gene co-expression network analysis (WGCNA) would be utilized to build a gene co-expression network to identify potential biomarkers for prediction the prognosis of patients with breast cancer. We downloaded GSE25065 from Gene Expression Omnibus database as the test set. GSE25055 and GSE42568 were utilized to validate findings in the research. Seven modules were established in the GSE25065 by utilizing average link hierarchical clustering. Three hub genes, RSAD2, HERC5, and CCL8 were screened out from the significant module (R 2 = 0.44), which were considerably interrelated to worse prognosis. Within test dataset GSE25065, RSAD2, and CCL8 were correlated with tumor stage, grade, and lymph node metastases, whereas HERC5 was correlated with lymph node metastases and tumor grade. In the validation dataset GSE25055 and RSAD2 expression was correlated with tumor grade, stage, and size, whereas HERC5 was related to tumor stage and tumor grade, and CCL8 was associated with tumor size and tumor grade. Multivariable survival analysis demonstrated that RSAD2, HERC5, and CCL8 were independent risk factors.In conclusion, the WGCNA analysis conducted in this study screened out novel prognostic biomarkers of breast cancer. Meanwhile, further in vivo and in vitro studies are required to make the clear molecular mechanisms. K E Y W O R D S breast cancer prognosis, CCL8, HERC5, RSAD2, weighted gene correlation network analysis How to cite this article: Tang J, Yang Q, Cui Q, et al. Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer.

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“…Therefore, more comprehensive technologies, including NGS, TCR sequencing and HPLC-MS are required. Furthermore, current methods for predicting tumor neo-Ags remain at an early stage and are limited by class I rather than class II MHC antigens (92). Additional efforts are required in the development of MHC class I- and class II-restricted neo-Ags as these will provide additional information about the immune surveillance in tumor development.…”

Section: Discussionmentioning

confidence: 99%

Liquid biopsy for lung cancer immunotherapy (Review)

Cai

Wang

2019

Oncol Lett

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The recent successful use of the immune checkpoint inhibitors (CPIs) anti-programmed death receptor-1 (PD-1)/PD-1 ligand 1 in clinical trials indicates their crucial role in obtaining an effective cancer immune therapy. These CPIs have been identified to have an effective therapeutic response, particularly in tumors with high tumor mutation burden. Targeting private somatic mutations encoding immunogenic neoantigens (neo-Ags) has been developed as an autologous gene therapy. T-cell receptor-engineered T cells targeting neo-Ags are a novel option for adoptive cell therapy used for the treatment of lung cancer. However, not all patients experience an effective response from immunotherapy. Although the resistance mechanism of CPIs has been reported, its association with other treatment methods during systemic anticancer therapy remains unclear, particularly the treatment options following the emergence of drug resistance in lung cancer. The potential biomarkers used for liquid biopsy may assist in the identification of patients who would benefit the most from immunotherapy. Attempts to identify potential biomarkers for predicting clinical response to immunotherapy are underway. With regard to liquid biopsy, the present review summarizes and discusses the lung cancer management of immunotherapy for precision medicine by reviewing recent literature and associated clinical trials.

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Lost in the crowd: identifying targetable MHC class I neoepitopes for cancer immunotherapy

Cited by 12 publications

References 124 publications

Tumor neoantigens: from basic research to clinical applications

Tumor neoantigens: from basic research to clinical applications

Weighted gene correlation network analysis identifies RSAD2, HERC5, and CCL8 as prognostic candidates for breast cancer

Liquid biopsy for lung cancer immunotherapy (Review)

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