Cell-Based Cancer Immunotherapy.

Cell-based Cancer Immunotherapy, Volume 183 provides the latest progress concerning research on anticancer cellular immunotherapies and their immunological, translation, or clinical aspects.

Bibliographic Details
Main Author:	Garg, Abhishek
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Galluzzi, Lorenzo
Format:	eBook
Language:	English
Published:	San Diego : Elsevier Science & Technology, 2024.
Edition:	1st ed.
Series:	Methods in cell biology ; v. 183.
Subjects:	Antigen. Immunotherapy. Cellular therapy. Tumor antigens. Immunotherapy Neoplasms > therapy Cell- and Tissue-Based Therapy Antigens, Neoplasm Immunothérapie. Thérapie cellulaire. Antigènes tumoraux. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Intro
Cell-Based Cancer Immunotherapy
Copyright
Contents
Contributors
Chapter 1: Generation and quality control of mature monocyte-derived dendritic cells for immunotherapy
Abstract
1. Introduction
2. Materials
2.1. Common disposables
2.2. Reagents
2.3. Equipment
2.4. Software
3. Methods
3.1. Isolation of CD14+ monocytes
3.1.1. Clinical large-scale immunomagnetic enrichment
3.1.2. Small-scale immunomagnetic enrichment
3.2. Generation of mature monocyte-derived dendritic cells
3.2.1. Clinical large-scale generation of mature monocyte-derived dendritic cells
3.2.2. Small-scale generation of mature monocyte-derived dendritic cells
3.3. Assessment of key characteristics of monocyte-derived dendritic cells
3.3.1. Immunophenotype
3.3.2. Antigen-uptake activity of immature dendritic cells
3.3.3. T-cell stimulatory activity of mature dendritic cells
3.3.4. Migratory response of mature dendritic cells
3.3.5. Indoleamine-2,3-dioxygenase expression and activity in mature dendritic cells
3.3.5.1. IDO activity
3.3.5.2. IDO expression
4. Notes
5. Concluding remarks
Acknowledgments
Competing interests
References
Chapter 2: Fully closed and automated enrichment of primary blood dendritic cells for cancer immunotherapy
Abstract
1. Introduction
2. Materials
2.1. Disposables
2.1.1. Disposables for DC enrichment
2.1.2. Disposables for flow cytometry
2.2. Reagents
2.2.1. Reagents for DC enrichment
2.2.2. Reagents for flow cytometry
2.3. Donors/patients
2.4. Equipment
2.5. Software
3. Methods
3.1. CliniMACS Prodigy DC isolation
3.1.1. Preparation
3.1.2. Predepletion
3.1.3. Enrichment
3.2. Purity assessment by flow cytometry
3.2.1. Cell staining
3.2.2. Flow cytometry acquisition
3.2.3. Gating and data analysis.
4. Notes
5. Concluding remarks
Acknowledgment
Conflict of interest
References
Chapter 3: Methods behind oncolytic virus-based DC vaccines in cancer: Toward a multiphase combined treatment strategy for G
Abstract
1. Introduction
2. Methods
3. Glioblastoma
4. Anticancer immunotherapy
5. The immune-editing in GBM
6. Dendritic cell vaccines as active specific immunotherapy for GBM
7. The changing landscape of immunotherapy of GBM
8. Integration of DC vaccination within the first-line combined treatment for GBM
9. Challenges to design randomized clinical trials with dendritic cell vaccines as part of first-line treatment of GBM
10. Immunogenic cell death immunotherapy for GBM
10.1. Newcastle disease virus
10.2. Electromagnetic fields
10.3. ICD immunotherapy at the IOZK
11. Extracellular microvesicles and apoptotic bodies: A new source of tumor antigens for DC vaccines?
12. Individualized multimodal immunotherapy as part of first-line multiphase combined treatment for GBM
13. IMI integrated during and after standard of care improves OS in adults with IDH1 wild-type GBM
14. Individualized multimodal immunotherapy in the current health care systems
15. The evidence
16. Quality of life
17. Perspectives
17.1. Tumor-associated virus-specific T cells
17.2. Bone marrow-derived T cells
18. The model of multiphase combined treatment for patients with GBM
Acknowledgments
References
Chapter 4: Identification of TCR repertoire patterns linked with anti-cancer immunotherapy
Abstract
1. Introduction
2. Materials
2.1. TCR data files
2.2. Code repository and tutorials
2.2.1. ClusTCR (v1.0.2)
2.2.2. Scikit-Bio (v0.5.6)
3. Methods
3.1. Preprocessing of online available TCR-seq data
3.2. Exploration of TCR repertoire diversity.
3.2.1. TCR repertoire richness
3.2.2. Shannon diversity
3.2.3. Pielou's evenness
3.2.4. Simpson index and Gini-Simpson index
3.2.5. TCRs per percentile
3.2.6. Gini coefficient (inequality)
3.2.7. Visual representation of the diversity metrics
3.2.8. Results and interpretation of the diversity analysis
3.3. Exploration of overlap between repertoires
3.3.1. Jaccard and Morisita distance
3.3.2. Public TCR sequences
3.3.3. Results of repertoire overlap analysis
3.4. Differential TCR frequency analysis
3.5. Clustering repertoires with ClusTCR
Acknowledgments
References
Chapter 5: Training of epitope-TCR prediction models with healthy donor-derived cancer-specific T&amp
spi
Abstract
1. Introduction
2. Materials
2.1. Common disposables
2.2. Cells and reagents
2.3. Equipment
2.4. Software
3. Methods
3.1. Expansion of WT137-45-reactive T-cell clones from healthy donor buffy coats
3.2. In-house production of WT137-45/HLA-A2 tetramers
3.3. Sorting of WT137-45-specific T cells
3.4. RNA library preparation for next-generation sequencing
3.5. Sequencing of epitope-specific TCRs
3.6. Model training
4. Notes
5. Concluding remarks
Acknowledgments
Conflicts of interest
References
Chapter 6: Methods behind neoantigen prediction for personalized anticancer vaccines
Abstract
1. Introduction
2. Materials
2.1. Input data
2.1.1. Samples
2.1.2. Genome reference files
2.2. Hardware
2.3. Software
3. Methods
3.1. Pre-processing
3.1.1. Quality control using FASTQC
3.1.2. Trimming using Trim Galore!
3.2. DNA analysis
3.2.1. Read alignment using BWA
3.2.2. Sort the SAM file by coordinate using GATK SortSam
3.2.3. BQSR with elPrep
3.2.4. Call Somatic SNV and INDEL variants using GATK MuTect2
3.3. RNA analysis.
3.3.1. Read alignment with STAR
3.3.2. Identify duplicates with elPrep
3.3.3. Split reads into exon segments with SplitNCigarReads
3.3.4. BQSR with elPrep
3.3.5. Somatic SNV and INDEL calling using Strelka2
3.4. Obtaining a final variant list
3.4.1. Identify overlaps between DNA and RNA variants using bcftools isec
3.5. HLA-typing
3.6. Neoantigen prioritization
3.6.1. Expression analysis with Kallisto
3.6.2. p-HLA binding affinity prediction and peptide extraction with MuPeXI
4. Concluding remarks
5. Addendum
5.1. Workflow management systems: Snakemake
5.2. Example Snakefile
References
Chapter 7: Methods for generating the CD137L-DC-EBV-VAX anti-cancer vaccine
Abstract
1. Introduction
2. Materials
2.1. Common disposables
2.2. Cells and reagents
2.3. Common equipment
3. Methods
3.1. Immobilization of anti-CD137L antibody onto cell culture dish
3.2. Cell processing and seeding onto anti-CD137L antibody coated dish
3.2.1. Processing of leukapheresis product
3.2.2. Peripheral blood mononuclear cell (PBMC) isolation
3.2.3. Red blood cell lysis and platelet removal
3.2.4. Seeding of cells for culture
3.2.5. Cryopreservation of excess PBMC
3.3. Removal of non-adhered cells
3.4. Maturation and pulsing of CD137L-DC
3.5. Harvesting and cryopreservation of CD137L-DC-EBV-VAX
4. Validation of CD137L-DC
4.1. Characterization of matured CD137L-DC
4.2. Phenotypic and functional characterization of cryopreserved CD137L-DC
4.3. Comparison of mo-DC and CD137L-DC-induced anti-EBV responses
5. Notes
6. Concluding remarks
Competing interests
References
Chapter 8: Gold standard assessment of immunogenic cell death induced by photodynamic therapy: From in vitro to tumor mou ...
Abstract
1. Introduction.
2. Materials and step-by-step procedures
2.1. Analysis of light absorption and fluorescence of PS
2.2. Assessment of cellular uptake of PS and its photodynamic activity against tumor cells
2.2.1. Semi-quantitative analysis of cellular uptake of PS
2.2.2. Analysis of subcellular localization of PS in tumor cells
2.2.3. Estimation of dark toxicity and photodynamic efficiency of PS against tumor cells
2.3. Assessment of dark toxicity effects of PS on non-cancerous cells
2.3.1. Preparation of primary cortical cell cultures
2.3.2. Determination of long-term dark toxicity effects of PS on normal brain cells
2.3.3. Determination of the safety concentration of PS for PDT in relation to normal brain cells
2.4. Determination of the type of PDT-induced cancer cell death by inhibitor analysis
2.5. Assessment of regulated cell death in PDT-induced tumor cells
2.6. Determination of the profile of DAMPs released from PDT-induced tumor cells
2.6.1. Assessment of surface exposure of CRT
2.6.2. ATP release analysis
2.6.3. HMGB1 release analysis
2.7. Analysis of efferocytosis (i.e., phagocytosis) of PDT-induced tumor cells by dendritic cells
2.7.1. Isolation of murine BMDCs
2.7.2. Analysis of efferocytosis
2.8. Analysis of phenotypic status of dendritic cells in the presence of PDT-induced tumor cells
2.9. Syngeneic heterotopic prophylactic vaccination mice tumor model
2.9.1. Preparation of anti-cancer vaccine based on the PDT-induced tumor cells
2.9.2. Procedure of mouse vaccination with dead/dying tumor cells
2.9.3. Preparation of viable tumor cells for challenge
2.9.4. Mouse challenge with viable tumor cells
2.9.5. Tumor growth measurements
2.10. DCs-based mice prophylactic vaccination orthotopic tumor model.