Bioinformatics for Cancer Immunotherapy: Methods and Protocols (Methods in Molecular Biology, 2120)

✍ Scribed by Sebastian Boegel (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 309
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

This volume focuses on a variety of in silico protocols of the latest bioinformatics tools and computational pipelines developed for neo-antigen identification and immune cell analysis from high-throughput sequencing data for cancer immunotherapy. The chapters in this book cover topics that discuss the two emerging concepts in recognition of tumor cells using endogenous T cells: cancer vaccines against neo-antigens presented on HLA class I and II alleles, and checkpoint inhibitors. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.

Cutting-edge and authoritative, Bioinformatics for Cancer Immunotherapy: Methods and Protocols is a valuable research tool for any scientist and researcher interested in learning more about this exciting and developing field.

✦ Table of Contents

Preface
Contents
Contributors
Chapter 1: Bioinformatics for Cancer Immunotherapy
1 Introduction
2 Mutation Detection
3 Epitope Prediction
4 TCR Sequencing
5 Immune Cell Quantification
6 Outlook
References
Chapter 2: An Individualized Approach for Somatic Variant Discovery
1 Introduction
2 Materials
2.1 The Java Development Kit
2.2 Downloading PRESM
2.3 Anaconda
2.4 The Genome Analysis Toolkit
2.5 Pindel, Burrows-Wheeler Aligner, and Samtools
2.6 The Genome Aggregation Database
2.7 Human Build 37
2.8 ICGC-TCGA DREAM Genomic Mutation Calling Challenge
3 Methods
3.1 Data Preprocessing
3.2 Germline Variant Calling in Patient-Matched Normal Samples
3.3 Personalized Reference Construction
3.4 Somatic Variant Calling in Tumor Samples
3.5 Interpreting Variant Call Format Files
4 Notes
References
Chapter 3: Ensemble-Based Somatic Mutation Calling in Cancer Genomes
1 Introduction
2 Materials
2.1 Environment
2.2 Input Requirements
2.3 Test Dataset
3 Methods
3.1 Running SMuRF
3.2 Retrieving Gene Annotation Information
3.3 Interpreting SMuRF Output
3.4 Tweaking the Precision and Recall of SMuRF
3.5 Evaluation of SMuRF Predictions
3.6 Saving Output
4 Notes
References
Chapter 4: SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations
1 Introduction
2 Materials
2.1 Software
2.2 Download and Install SomaticSeq
3 Methods
3.1 Running SomaticSeq in Tumor-Normal Paired Mode
3.1.1 Inputs and Parameters
3.2 Running SomaticSeq in Tumor-Only Single Mode
3.2.1 Inputs and Parameters for Tumor-Only Mode
3.2.2 Interpreting the Output Files
3.3 Running Compatible Somatic Mutation Callers
3.3.1 Inputs and Parameters
3.4 Create Training Data Sets
3.4.1 Have Sequencing Replicates for the Normal
3.4.2 Split a Normal Data Set Into Designated Tumor and Normal
3.4.3 Merge Tumor-Normal and Then Random Split Them
3.4.4 Inputs and Parameters
4 Notes
References
Chapter 5: HLA Typing from RNA Sequencing and Applications to Cancer
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.2.1 arcasHLA Installation
2.2.2 Dependencies
2.3 Databases and Datasets
2.3.1 1000 Genomes Project
2.3.2 AlleleFrequencies Net Database
2.3.3 Sequence Read Archive
3 Methods
3.1 Selecting Reference
3.2 Selecting a Population
3.2.1 (Optional) Using Variants to Infer an Individual´s Population
3.3 Read Extraction
3.4 Genotyping
3.4.1 Running arcasHLA Genotype
3.4.2 Running arcasHLA Partial
3.4.3 Merging Genotyping Results
3.4.4 Converting HLA Genotypes
3.4.5 Interpreting Genotyping Results
4 Notes
References
Chapter 6: Rapid High-Resolution Typing of Class I HLA Genes by Nanopore Sequencing
1 Introduction
2 Materials
2.1 Software
2.2 Hardware
2.3 Sample Data Set
3 Methods
3.1 Loading the VirtualBox Image
3.2 Run Athlon to Analyze the Sample Data Set
3.3 Interpreting the Output
3.4 Additional Analysis Files
3.5 Accessing User Data Set
4 Notes
References
Chapter 7: HLApers: HLA Typing and Quantification of Expression with Personalized Index
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Download HLApers
2.4 Download IMGT and Gencode Datasets
3 Methods
3.1 Strategy
3.2 Running HLApers
3.2.1 Preparing the Reference
3.2.2 In Silico HLA Genotyping
3.2.3 Quantification of HLA Expression
3.3 Interpreting Output
4 Example Applications
4.1 Estimation of HLA Expression at the Population Level and Downstream Analyses
4.2 Identification of Biomarkers for Cancer Susceptibility and Treatment Response
5 Notes
References
Chapter 8: High-Throughput MHC I Ligand Prediction Using MHCflurry
1 Introduction
2 Materials
3 Methods
3.1 Generating Predictions
3.2 Training Your Own Predictors
3.2.1 Fitting Models
3.2.2 Model Selection
3.3 Using MHCflurry from Python
4 Notes
References
Chapter 9: In Silico Prediction of Tumor Neoantigens with TIminer
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Dataset Availability
3 Methods
3.1 TIminer Download and Installation
3.2 Running TIminer
3.3 Pipeline for Neoantigen Prediction with TIminerAPI
3.3.1 Protein Mutation Prediction with TIminerAPI.executeVepDir
3.3.2 HLA-Type Determination with TIminerAPI.executeOptitypeDir
3.3.3 MHC I Binding Affinity Prediction with TIminerAPI.executeNetmhcpanDir
3.3.4 Gene Expression Quantification with TIminerAPI.executeKallistoDir
3.3.5 Filtering of Candidate Neoantigens
3.4 Additional Analyses with TIminer
3.5 Full and Custom Pipelines
3.6 Results Interpretation
4 Notes
References
Chapter 10: OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.3 Data
2.4 Installation
3 Methods
3.1 Setup Instructions
3.2 Configuration
3.2.1 Sample Configuration
3.2.2 Pipeline Parameter Configuration
3.3 Running the Pipeline
3.3.1 Reference Genome Processing
3.3.2 Other Pipeline Options
3.4 Interpreting the Output
4 Notes
References
Chapter 11: Improving MHC-I Ligand Identification by Incorporating Targeted Searches of Mass Spectrometry Data
1 Introduction
2 Materials
2.1 Hardware-System Requirements
2.2 LC-MS/MS Data from an MHC-I Immunoprecipitation Experiment (See Note 1)
2.3 Input Reference Protein Database
2.4 Dependency Software for SpectMHC
2.5 Locally Installed SpectMHC Database Compiler Tool
2.6 Proteomics Database Search Software
3 Methods
3.1 Identifying MHC or HLA Haplotype
3.2 Building a Targeted MHC/HLA Database with SpectMHC
3.3 Performing a Database Search with a SpectMHC-Generated Fasta File
3.4 Applications for SpectMHC
3.4.1 TAA Analysis
3.4.2 TSA Analysis
3.4.3 Viral Epitope Discovery
3.4.4 Noncanonically Translated Ligand Analysis
4 Notes
References
Chapter 12: The SysteMHC Atlas: a Computational Pipeline, a Website, and a Data Repository for Immunopeptidomic Analyses
1 Introduction
2 Materials
2.1 Software
2.2 Hardware
2.3 Datasets
3 Methods
3.1 Overall
3.2 Data Conversion
3.3 Database Search
3.4 Statistical Validation
3.5 Allele Annotation
3.6 Construction of Spectral Libraries and DIA Analysis
3.7 Web Interface
4 Notes
References
Chapter 13: Identification of Epitope-Specific T Cells in T-Cell Receptor Repertoires
1 Introduction
2 Materials
2.1 TCRex Tool
2.2 TCR Data Files
3 Methods
3.1 TCR-Epitope Binding Predictions Using Validated TCRex Prediction Models
3.2 TCR-Epitope Binding Predictions Using Custom Prediction Models
3.3 Interpreting the Results
3.3.1 Epitope-TCR Binding Predictions
3.3.2 Epitope Specificity Enrichment Analysis
3.3.3 Classifier Statistics
4 Notes
References
Chapter 14: Modeling and Viewing T Cell Receptors Using TCRmodel and TCR3d
1 Introduction
2 Materials
2.1 Hardware/Software
2.2 Input Template Data
3 Methods
3.1 TCR3d Database
3.1.1 TCR Chains
3.1.2 Germline Genes
3.1.3 TCR Complexes
3.1.4 Interface Analysis
3.1.5 Structural Viewing Options
3.1.6 CDR Clusters
3.1.7 TCR Sequences
3.1.8 TCR Docking Benchmark Set
3.1.9 CDR3 Sequence Search
3.1.10 Subsequence or Motif Search
3.1.11 Peptide Sequence Search
3.1.12 TCR Variable Domain Sequence Search
3.2 Modeling Using the TCRmodel Web Server
3.2.1 Input Variable Domain Sequence
3.2.2 Generate from Germline Genes
3.2.3 TCRmodel Server Results
3.3 Modeling Using the RosettaTCR Protocol
3.3.1 Input Sequences
3.3.2 Parsing TCR Segments
3.3.3 Assign TCR Segments Manually
3.3.4 Template Selection
3.3.5 User-Provided Templates
3.3.6 Blacklist Templates
3.3.7 Loop Refinement
3.3.8 Interpreting the Output
3.4 Example Application and Results
4 Notes
References
Chapter 15: In Silico Cell-Type Deconvolution Methods in Cancer Immunotherapy
1 Introduction
2 Mathematical Background
3 Methods for Cell-Type Deconvolution
4 The Impact of Gene Signatures
5 Guide
6 Conclusions
References
Chapter 16: Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from B...
1 Introduction
2 Materials
2.1 Available Methods
2.2 Installation
2.2.1 Installation Through Conda Package Manager
2.2.2 Installation as Standard R Package (install.packages)
2.2.3 Installing CIBERSORT
2.3 Issues and Support
3 Methods
3.1 Estimation of Immune Cell Contents
3.1.1 Input
3.1.2 Output
3.2 Case Study
3.3 Conclusion
4 Notes
References
Chapter 17: EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data
1 Introduction
2 Materials
2.1 Hardware
2.2 Software
2.2.1 Web Application
2.2.2 R-package
2.3 Getting EPIC
2.3.1 Web Application
2.3.2 R-package
2.4 Get RNA-Seq Data
3 Methods
3.1 Running EPIC
3.1.1 Web Application
3.1.2 R-package
3.2 Interpreting the Output
3.3 Example of Further Processing EPIC Outputs
4 Notes
References
Chapter 18: Computational Deconvolution of Tumor-Infiltrating Immune Components with Bulk Tumor Gene Expression Data
1 Introduction
2 Materials
2.1 Tumor Purity Estimation
2.2 Selection of Informative Genes and Model Simplification
2.3 Selection of Immune Cell Types
2.4 Constrained Least Square Fitting
3 Methods
3.1 Overview of TIMER Website
3.2 Gene Module
3.3 Survival Module
3.4 Mutation and SCNA Modules
3.5 DiffExp Module
3.6 Correlation Module
4 Notes
References
Chapter 19: Cell-Type Enrichment Analysis of Bulk Transcriptomes Using xCell
1 Introduction
2 Materials
3 Methods
3.1 Loading xCell
3.2 Data Input
3.3 xCell Pipeline
3.4 Significance Analysis
3.5 xCell Usage
3.6 Example of Using xCell
3.6.1 Data Gathering
3.6.2 Generating xCell Scores
3.6.3 Correlating xCell Scores and CyTOF Immunoprofilings
3.6.4 Downstream Analysis with xCell Scores
4 Notes
References
Chapter 20: Cap Analysis of Gene Expression (CAGE): A Quantitative and Genome-Wide Assay of Transcription Start Sites
1 Introduction
1.1 Background
1.2 Overview of Cap Analysis of Gene Expression
1.3 History of CAGE Protocols
2 Materials
2.1 Equipment
2.2 Reagents
3 Methods
3.1 Outline of the Experimental Process
3.2 RNA Quality and Quantity
3.3 RT
3.4 Diol Oxidation with Sodium Periodate
3.5 Biotinylation
3.6 RNase ONE Digestion (See Note 5)
3.7 Cap Trapping
3.8 Quality Check of cDNA
3.9 Adapter Ligation
3.10 Quality Check of Sequencing Library
3.11 Sequencing on Illumina HiSeq2500 by One-Shot Loading
3.12 Outline of Computational Processing
3.13 Raw Read Preprocessing
3.14 Alignment of CAGE Reads
3.15 Quantification of Transcription Initiation Activities
3.16 Visualization and Quality Assessment
3.17 Reference Sets of TSS Regions in Human and Mouse
3.18 Approaches to Identifying TSS Regions
3.19 Promoter-Level Expression Analysis
3.20 Applications of CAGE to Study Cancers and Cancer Immunotherapy
4 Notes
References
Index