cell immunotherapy
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2022 ◽  
Vol 23 (2) ◽  
pp. 797
Tatiana Michel ◽  
Markus Ollert ◽  
Jacques Zimmer

Despite significant progress in recent years, the therapeutic approach of the multiple different forms of human cancer often remains a challenge. Besides the well-established cancer surgery, radiotherapy and chemotherapy, immunotherapeutic strategies gain more and more attention, and some of them have already been successfully introduced into the clinic. Among these, immunotherapy based on natural killer (NK) cells is considered as one of the most promising options. In the present review, we will expose the different possibilities NK cells offer in this context, compare data about the theoretical background and mechanism(s) of action, report some results of clinical trials and identify several very recent trends. The pharmaceutical industry is quite interested in NK cell immunotherapy, which will benefit the speed of progress in the field.

Wai Ki Wong ◽  
Bohan Yin ◽  
Ching Ying Katherine Lam ◽  
Yingying Huang ◽  
Jiaxiang Yan ◽  

Effective immunotherapy treats cancers by eradicating tumourigenic cells by activated tumour antigen-specific and bystander CD8+ T-cells. However, T-cells can gradually lose cytotoxicity in the tumour microenvironment, known as exhaustion. Recently, DNA methylation, histone modification, and chromatin architecture have provided novel insights into epigenetic regulations of T-cell differentiation/exhaustion, thereby controlling the translational potential of the T-cells. Thus, developing strategies to govern epigenetic switches of T-cells dynamically is critical to maintaining the effector function of antigen-specific T-cells. In this mini-review, we 1) describe the correlation between epigenetic states and T cell phenotypes; 2) discuss the enzymatic factors and intracellular/extracellular microRNA imprinting T-cell epigenomes that drive T-cell exhaustion; 3) highlight recent advances in epigenetic interventions to rescue CD8+ T-cell functions from exhaustion. Finally, we express our perspective that regulating the interplay between epigenetic changes and transcriptional programs provides translational implications of current immunotherapy for cancer treatments.

Alaleh Rezalotfi ◽  
Lea Fritz ◽  
Reinhold Förster ◽  
Berislav Bošnjak

Adaptive T cell immunotherapy holds great promise for the successful treatment of leukemia as well as other types of cancers. More recently, it was also shown to be an effective treatment option for chronic virus infections in immunosuppressed patients. Autologous or allogeneic T cells used for immunotherapy are usually genetically modified to express novel T cell or chimeric antigen receptors. The production of such cells was significantly simplified with the CRISPR/Cas system allowing deletion or insertion of novel genes at specific locations within the genome. In this review, we describe recent methodological breakthroughs important for the conduction of these genetic modifications, summarize crucial points to be considered when conducting such experiments, and highlight the potential pitfalls of these approaches.

Biology Open ◽  
2022 ◽  
Chenxiao Liu ◽  
Karolina Skorupinska-Tudek ◽  
Sven-Göran Eriksson ◽  
Ingela Parmryd

Vγ9Vδ2 T cells is the dominant γδ T cell subset in human blood. They are cytotoxic and activated by phosphoantigens whose concentrations are increased in cancer cells, making the cancer cells targets for Vγ9Vδ2 T cell immunotherapy. For successful immunotherapy, it is important both to characterise Vγ9Vδ2 T cell proliferation and optimise the assessment of their cytotoxic potential, which is the aim of this study. We found that supplementation with freshly-thawed human serum potentiated Vγ9Vδ2 T cell proliferation from peripheral mononuclear cells (PBMCs) stimulated with (E)-4-Hydroxy-3-methyl-but-2-enyl diphosphate (HMBPP) and consistently enabled Vγ9Vδ2 T cell proliferation from cryopreserved PBMCs. In cryopreserved PBMCs the proliferation was higher than in freshly prepared PBMCs. In a panel of short-chain prenyl alcohols, monophosphates and diphosphates, most diphosphates and also dimethylallyl monophosphate stimulated Vγ9Vδ2 T cell proliferation. We developed a method where the cytotoxicity of Vγ9Vδ2 T cells towards adherent cells is assessed at the single cell level using flow cytometry, which gives more clear-cut results than the traditional bulk release assays. Moreover, we found that HMBPP enhances the Vγ9Vδ2 T cell cytotoxicity towards colon cancer cells. In summary we have developed an easily interpretable method to assess the cytotoxicity of Vγ9Vδ2 T cells towards adherent cells, found that Vγ9Vδ2 T cell proliferation can be potentiated media-supplementation and how misclassification of non-responders may be avoided. Our findings will be useful in the further development of Vγ9Vδ2 T cell immunotherapy.

2021 ◽  
Xian Xian Liu ◽  
Gloria Li ◽  
Wei Lou ◽  
Juntao Gao ◽  
Simon Fong

[Background]: An emerging type of cancer treatment, known as cell immunotherapy, is gaining popularity over chemotherapy or other radia-tion therapy that causes mass destruction to our body. One favourable ap-proach in cell immunotherapy is the use of neoantigens as targets that help our body immune system identify the cancer cells from healthy cells. Neoan-tigens, which are non-autologous proteins with individual specificity, are generated by non-synonymous mutations in the tumor cell genome. Owing to its strong immunogenicity and lack of expression in normal tissues, it is now an important target for tumor immunotherapy. Neoantigens are some form of special protein fragments excreted as a by-product on the surface of cancer cells during the DNA mutation at the tumour. In cancer immunotherapies, certain neoantigens which exist only on cancer cells elicit our white blood cells (body's defender, anti-cancer T-cell) responses that fight the cancer cells while leaving healthy cells alone. Personalized cancer vaccines there-fore can be designed de novo for each individual patient, when the specific neoantigens are found to be relevant to his/her tumour. The vaccine which is usually coded in synthetic long peptides, RNA or DNA representing the neo-antigens trigger an immune response in the body to destroy the cancer cells (tumour). The specific neoantigens can be found by a complex process of biopsy and genome sequencing. Alternatively, modern technologies nowa-days tap on AI to predict the right neoantigen candidates using algorithms. However, determining the binding and non-binding of neoantigens on T-cell receptors (TCR) is a challenging computational task due to its very large search space. [Objective]: To enhance the efficiency and accuracy of traditional deep learning tools, for serving the same purpose of finding potential responsive-ness to immunotherapy through correctly predicted neoantigens. It is known that deep learning is possible to explore which novel neoantigens bind to T-cell receptors and which ones don't. The exploration may be technically ex-pensive and time-consuming since deep learning is an inherently computa-tional method. one can use putative neoantigen peptide sequences to guide personalized cancer vaccines design. [Methods]: These models all proceed through complex feature engineering, including feature extraction, dimension reduction and so on. In this study, we derived 4 features to facilitate prediction and classification of 4 HLA-peptide binding namely AAC and DC from the global sequence, and the LAAC and LDC from the local sequence information. Based on the patterns of sequence formation, a nested structure of bidirectional long-short term memory neural network called local information module is used to extract context-based features around every residue. Another bilstm network layer called global information module is introduced above local information module layer to integrate context-based features of all residues in the same HLA-peptide binding chain, thereby involving inter-residue relationships in the training process. introduced. [Results]: Finally, a more effective model is obtained by fusing the above two modules and 4 features matric, the method performs significantly better than previous prediction schemes, whose overall r-square increased to 0.0125 and 0.1064 on train and increased to 0.0782 and 0.2926 on test da-tasets. The RMSE for our proposed models trained decreased to approxi-mately 0.0745 and 1.1034, respectively, and decreased to 0.6712 and 1.6506 on test dataset. [Conclusion]: Our work has been actively refining a machine-learning model to improve neoantigen identification and predictions with the determinants for Neoantigen identification. The final experimental results show that our method is more effective than existing methods for predicting peptide types, which can help laboratory researchers to identify the type of novel HLA-peptide binding. Keywords: machine learning; Cancer Cell Immunology; HLA-peptide binding Neoantigen Prediction; HLA; Data Visualization; Novel Neoanti-gen and TCR Pairing Discovery; Vector representation

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