Personalized Immunotherapy of Patients: Defining by Single-cell RNA-seq with Artificial Intelligence
Main Article Content
Abstract
Immunotherapy, including immune cell therapy and targeted therapy, is gradually developed through the ongoing discovery of molecular compounds or immune cells. Choosing the best one or the best combination of target compounds and immune-cell therapy is a challenge for clinical scientists and clinicians. We have found variable efficacy individually after tumor-infiltrating lymphocyte (TIL) therapy, and now TILs have been discovered in a group of heterogeneous immune cells. To select the best immunotherapy for each patient, we started to study TIL genomics, including single-cell mRNA differential display from TIL published in 2007 and single-cell RNA-seq from TIL published in 2013, set up TIL quantitative network in 2015, researched machine-learning model for immune therapy in 2022. These manual reports single-cell RNA-seq data combined with machine learning to evaluate the optimal compounds and immune cells for individual patients. The machine-learning model, one of artificial intelligence, can estimate targeting genomic variance from single-cell RNA-seq so that they can cover thirteen kinds of immune cell therapies and ongoing FDA-approved targeted therapies such as PD1 inhibitors, PDL1 inhibitors, and CTLA4 inhibitors, as well as other different treatments such as HDACI or DNMT1 inhibitors, FDA-approved drugs. Moreover, also cover Phase-1, Phase-2, Phase-3, and Phase-4 of clinical trials, such as TIL, CAR T-cells, TCR T-cells. Single-cell RNA-seq with an Artificial intelligence estimation system is much better than our published models from microarrays or just cell therapy. The medical goal is to address three issues in clinical immunotherapy: the increase of efficacy; the decrease of adverse effects and the decrease of the cost in clinical applications.
Keywords:
Artificial intelligence (AI), tumor-infiltrating lymphocytes (TIL), cytokine release syndrome (CRS), chimeric antigen receptor T-cells (CAR T-cell), T-cell receptor T-cell (TCR), natural killer cell (NK), natural killer T-cell (NKT), specific T-cells, PD-1 inhibitor, CTLA4 inhibitor, single-cell genomic analysis, single-cell RNA-seq, personalized immunotherapy
Article Details
How to Cite
LI, Biaoru.
Personalized Immunotherapy of Patients: Defining by Single-cell RNA-seq with Artificial Intelligence.
Medical Research Archives, [S.l.], v. 11, n. 8, aug. 2023.
ISSN 2375-1924.
Available at: <https://esmed.org/MRA/mra/article/view/4293>. Date accessed: 13 may 2024.
doi: https://doi.org/10.18103/mra.v11i8.4293.
Section
Research Articles
The Medical Research Archives grants authors the right to publish and reproduce the unrevised contribution in whole or in part at any time and in any form for any scholarly non-commercial purpose with the condition that all publications of the contribution include a full citation to the journal as published by the Medical Research Archives.
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[12] Pehlivan KC, Duncan BB, Lee DW. CAR-T Cell Therapy for Acute Lymphoblastic Leukemia: Transforming the Treatment of Relapsed and Refractory Disease. Curr Hematol Malig Rep. (2018) 13(5):396-406. doi: 10.1007/s11899-018-0470-x.
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[14] Li H, Fu Q, Muluh TA, Shinge SAU, Fu S, Wu J. The Application of Nanotechnology in Immunotherapy based Combinations for Cancer Treatment. Recent Pat Anticancer Drug Discov. (2023) 18(1):53-65. doi: 10.2174/1574892817666220308090954.
[15] Fathi M, Razavi SM, Sojoodi M, Ahmadi A, Ebrahimi F, Namdar A, Hojjat-Farsangi M, Gholamin S, Jadidi-Niaragh F. Targeting the CTLA-4/B7 axes in glioblastoma: preclinical evidence and clinical interventions. Expert Opin Ther Targets. (2022) 26(11):949-961. doi:10.1080/14728222.2022.2160703. Epub 2022 Dec 28.
[16] Najafi S, Mortezaee K. Advances in dendritic cell vaccination therapy of cancer. Biomed Pharmacother. (2023) 164:114954. doi: 10.1016/j.biopha.2023.114954. Epub 2023 May 29.
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