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Bulashevska A, Nacsa Z, Lang F, Braun M, Machyna M, Diken M, Childs L, König R. Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy. Front Immunol 2024; 15:1394003. [PMID: 38868767 PMCID: PMC11167095 DOI: 10.3389/fimmu.2024.1394003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 05/13/2024] [Indexed: 06/14/2024] Open
Abstract
Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.
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Affiliation(s)
- Alla Bulashevska
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Zsófia Nacsa
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Franziska Lang
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Markus Braun
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Martin Machyna
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Mustafa Diken
- TRON - Translational Oncology at the University Medical Center of the Johannes Gutenberg University gGmbH, Mainz, Germany
| | - Liam Childs
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
| | - Renate König
- Host-Pathogen-Interactions, Paul-Ehrlich-Institut, Langen, Germany
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Sugiyama N, Terry FE, Gutierrez AH, Hirano T, Hoshi M, Mizuno Y, Martin W, Yasunaga S, Niiro H, Fujio K, De Groot AS. Individual and population-level variability in HLA-DR associated immunogenicity risk of biologics used for the treatment of rheumatoid arthritis. Front Immunol 2024; 15:1377911. [PMID: 38812524 PMCID: PMC11134572 DOI: 10.3389/fimmu.2024.1377911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Accepted: 04/24/2024] [Indexed: 05/31/2024] Open
Abstract
Hypothesis While conventional in silico immunogenicity risk assessments focus on measuring immunogenicity based on the potential of therapeutic proteins to be processed and presented by a global population-wide set of human leukocyte antigen (HLA) alleles to T cells, future refinements might adjust for HLA allele frequencies in different geographic regions or populations, as well for as individuals in those populations. Adjustment by HLA allele distribution may reveal risk patterns that are specific to population groups or individuals, which current methods that rely on global-population HLA prevalence may obscure. Key findings This analysis uses HLA frequency-weighted binding predictions to define immunogenicity risk for global and sub-global populations. A comparison of assessments tuned for North American/European versus Japanese/Asian populations suggests that the potential for anti-therapeutic responses (anti-therapeutic antibodies or ATA) for several commonly prescribed Rheumatoid Arthritis (RA) therapeutic biologics may differ, significantly, between the Caucasian and Japanese populations. This appears to align with reports of differing product-related immunogenicity that is observed in different populations. Relevance to clinical practice Further definition of population-level (regional) and individual patient-specific immunogenic risk profiles may enable prescription of the RA therapeutic with the highest probability of success to each patient, depending on their population of origin and/or their individual HLA background. Furthermore, HLA-specific immunogenicity outcomes data are limited, thus there is a need to expand HLA-association studies that examine the relationship between HLA haplotype and ATA in the clinic.
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Affiliation(s)
- Naonobu Sugiyama
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | | | - Toshitaka Hirano
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Masato Hoshi
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | - Yasushi Mizuno
- Rheumatology, Inflammation and Immunology Medical Affairs, Pfizer Japan Inc., Tokyo, Japan
| | | | - Shin’ichiro Yasunaga
- Department of Biochemistry, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Hiroaki Niiro
- Department of Medical Education, Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
| | - Keishi Fujio
- Department of Allergy and Rheumatology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
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Kim D, Song J, Mancuso N, Mangul S, Jung J, Jang W. Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis. Arthritis Res Ther 2024; 26:47. [PMID: 38336809 PMCID: PMC10858498 DOI: 10.1186/s13075-024-03280-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear. METHODS To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk. RESULTS We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks. CONCLUSION Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.
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Affiliation(s)
- Daeun Kim
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Jaeseung Song
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea
| | - Nicholas Mancuso
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
| | - Serghei Mangul
- Department of Quantitative and Computational Biology, USC Dornsife College of Letters, Arts and Sciences, University of Southern California, Los Angeles, CA, USA
- Titus Family Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
| | - Junghyun Jung
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
- Center for Genetic Epidemiology, Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Hollywood, CA, USA.
| | - Wonhee Jang
- Department of Life Sciences, Dongguk University-Seoul, Seoul, 04620, Republic of Korea.
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Dhanushkumar T, M E S, Selvam PK, Rambabu M, Dasegowda KR, Vasudevan K, George Priya Doss C. Advancements and hurdles in the development of a vaccine for triple-negative breast cancer: A comprehensive review of multi-omics and immunomics strategies. Life Sci 2024; 337:122360. [PMID: 38135117 DOI: 10.1016/j.lfs.2023.122360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 12/15/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023]
Abstract
Triple-Negative Breast Cancer (TNBC) presents a significant challenge in oncology due to its aggressive behavior and limited therapeutic options. This review explores the potential of immunotherapy, particularly vaccine-based approaches, in addressing TNBC. It delves into the role of immunoinformatics in creating effective vaccines against TNBC. The review first underscores the distinct attributes of TNBC and the importance of tumor antigens in vaccine development. It then elaborates on antigen detection techniques such as exome sequencing, HLA typing, and RNA sequencing, which are instrumental in identifying TNBC-specific antigens and selecting vaccine candidates. The discussion then shifts to the in-silico vaccine development process, encompassing antigen selection, epitope prediction, and rational vaccine design. This process merges computational simulations with immunological insights. The role of Artificial Intelligence (AI) in expediting the prediction of antigens and epitopes is also emphasized. The review concludes by encapsulating how Immunoinformatics can augment the design of TNBC vaccines, integrating tumor antigens, advanced detection methods, in-silico strategies, and AI-driven insights to advance TNBC immunotherapy. This could potentially pave the way for more targeted and efficacious treatments.
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Affiliation(s)
- T Dhanushkumar
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Santhosh M E
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Prasanna Kumar Selvam
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Majji Rambabu
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - K R Dasegowda
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India
| | - Karthick Vasudevan
- Department of Biotechnology, School of Applied Sciences, REVA University, Bengaluru 560064, India.
| | - C George Priya Doss
- Laboratory of Integrative Genomics, Department of Integrative Biology, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, India.
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Shigemizu D, Fukunaga K, Yamakawa A, Suganuma M, Fujita K, Kimura T, Watanabe K, Mushiroda T, Sakurai T, Niida S, Ozaki K. The HLA-DRB1*09:01-DQB1*03:03 haplotype is associated with the risk for late-onset Alzheimer's disease in APOE [Formula: see text]4-negative Japanese adults. NPJ AGING 2024; 10:3. [PMID: 38167405 PMCID: PMC10761915 DOI: 10.1038/s41514-023-00131-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/22/2023] [Indexed: 01/05/2024]
Abstract
Late-onset Alzheimer's disease (LOAD) is the most common cause of dementia among those older than 65 years. The onset of LOAD is influenced by neuroinflammation. The human leukocyte antigen (HLA) system is involved in regulating inflammatory responses. Numerous HLA alleles and their haplotypes have shown varying associations with LOAD in diverse populations, yet their impact on the Japanese population remains to be elucidated. Here, we conducted a comprehensive investigation into the associations between LOAD and HLA alleles within the Japanese population. Using whole-genome sequencing (WGS) data from 303 LOAD patients and 1717 cognitively normal (CN) controls, we identified four-digit HLA class I alleles (A, B, and C) and class II alleles (DRB1, DQB1, and DPB1). We found a significant association between the HLA-DRB1*09:01-DQB1*03:03 haplotype and LOAD risk in APOE [Formula: see text]4-negative samples (odds ratio = 1.81, 95% confidence interval = 1.38-2.38, P = 2.03[Formula: see text]). These alleles not only showed distinctive frequencies specific to East Asians but demonstrated a high degree of linkage disequilibrium in APOE [Formula: see text]4-negative samples (r2 = 0.88). Because HLA class II molecules interact with T-cell receptors (TCRs), we explored potential disparities in the diversities of TCR α chain (TRA) and β chain (TRB) repertoires between APOE [Formula: see text]4-negative LOAD and CN samples. Lower diversity of TRA repertoires was associated with LOAD in APOE [Formula: see text]4-negative samples, irrespective of the HLA DRB1*09:01-DQB1*03:03 haplotype. Our study enhances the understanding of the etiology of LOAD in the Japanese population and provides new insights into the underlying mechanisms of its pathogenesis.
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Affiliation(s)
- Daichi Shigemizu
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan.
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, 734-8551, Japan.
| | - Koya Fukunaga
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Akiko Yamakawa
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Mutsumi Suganuma
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Kosuke Fujita
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
- Japan Society for the Promotion of Science, Tokyo, 102-0083, Japan
| | - Tetsuaki Kimura
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Ken Watanabe
- NCGG Biobank, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Taisei Mushiroda
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan
| | - Takashi Sakurai
- Department of Prevention and Care Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan
| | - Shumpei Niida
- Core Facility Administration, Research Institute, National Center for Geriatrics and Gerontology, Aichi, 474-8511, Japan
| | - Kouichi Ozaki
- Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, Obu, Aichi, 474-8511, Japan.
- RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, 230-0045, Japan.
- Department of Cardiovascular Medicine, Hiroshima University Graduate School of Biomedical and Health Sciences, Hiroshima, 734-8551, Japan.
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Wang S, Wang M, Chen L, Pan G, Wang Y, Li SC. SpecHLA enables full-resolution HLA typing from sequencing data. CELL REPORTS METHODS 2023; 3:100589. [PMID: 37714157 PMCID: PMC10545945 DOI: 10.1016/j.crmeth.2023.100589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 06/20/2023] [Accepted: 08/21/2023] [Indexed: 09/17/2023]
Abstract
Reconstructing diploid sequences of human leukocyte antigen (HLA) genes, i.e., full-resolution HLA typing, from sequencing data is challenging. The high homogeneity across HLA genes and the high heterogeneity within HLA alleles complicate the identification of genomic source loci for sequencing reads. Here, we present SpecHLA, which utilizes fine-tuned reads binning and local assembly to achieve accurate full-resolution HLA typing. SpecHLA accepts sequencing data from paired-end, 10×-linked-reads, high-throughput chromosome conformation capture (Hi-C), Pacific Biosciences (PacBio), and Oxford Nanopore Technology (ONT). It can also incorporate pedigree data and genotype frequency to refine typing. In 32 Human Genome Structural Variation Consortium, Phase 2 (HGSVC2) samples, SpecHLA achieved 98.6% accuracy for G-group-resolution HLA typing, inferring entire HLA alleles with an average of three mismatches fewer, ten gaps fewer, and 590 bp less edit distance than HISAT-genotype per allele. Additionally, SpecHLA exhibited a 2-field typing accuracy of 98.6% in 875 real samples. Finally, SpecHLA detected HLA loss of heterozygosity with 99.7% specificity and 96.8% sensitivity in simulated samples of cancer cell lines.
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Affiliation(s)
- Shuai Wang
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong
| | - Mengyao Wang
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong
| | - Lingxi Chen
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong
| | - Guangze Pan
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong
| | - Yanfei Wang
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong
| | - Shuai Cheng Li
- City University of Hong Kong, Department of Computer Science, Kowloon, Hong Kong.
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Wu X, Wang H, Xue F, Jiang T, Chen N, Wang T, Zhang Y, Zhang G, Fu J, Wu Q. Frequency changes in HLA-I alleles: A marker to guide immunotherapy in lung adenocarcinoma patients and its relationship with tumor mutational burden and PD-L1 expression. Thorac Cancer 2023; 14:1932-1940. [PMID: 37246469 PMCID: PMC10344735 DOI: 10.1111/1759-7714.14939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/06/2023] [Accepted: 05/08/2023] [Indexed: 05/30/2023] Open
Abstract
BACKGROUND The aim of the study was to investigate differences in HLA-I alleles between lung adenocarcinoma patients and healthy controls and determine their association with PD-L1 expression and tumor mutational burden (TMB) to understand the mechanism underlying lung adenocarcinoma susceptibility. METHODS Differences in HLA allele frequencies between the two groups were analyzed in a case-control study. PD-L1 expression and TMB in lung adenocarcinoma patients were determined and their relationships with HLA-I were analyzed. RESULTS The lung adenocarcinoma group showed significantly higher HLA-A*30:01 (p = 0.0067, odds ratio [OR], 1.834; 95% confidence interval [CI]: 1.176-2.860), B*13:02 (p = 0.0050, OR, 1.855; 95% CI: 1.217-2.829), and C*06:02 (p = 0.0260, OR, 1.478; 95% CI: 1.060-2.060) and significantly lower B*51:01 (p = 0.0290, OR, 0.6019; 95% CI: 0.3827-0.9467), and C*14:02 (p = 0.0255, OR, 0.5089; 95% CI: 0.2781-0.9312) than the control group. Haplotype analysis results showed that HLA-A*30:01-B*13:02 (p = 0.0100, OR, 1.909; 95% CI: 1.182-3.085), A*11:01-C*01:02 (p = 0.0056, OR, 1.909; 95% CI: 1.182-3.085), A*30:01-C*06:02 (p = 0.0111, OR, 1.846; 95% CI: 1.147-2.969), and B*13:02-C*06:02 (p = 0.0067, OR, 1.846; 95% CI: 1.147-2.969) frequencies significantly increased and B*51:01-C*14:02 (p = 0.0219, OR, 0.490; 95% CI: 0.263-0.914) frequency significantly decreased in lung adenocarcinoma patients. Three-locus haplotype analysis showed that HLA-A*30:01-B*13:02-C*06:02 frequency (p = 0.0100, OR, 1.909; 95% CI: 1.182-3.085) significantly increased in patients. CONCLUSION HLA-A*30:01, B*13:02, and C*06:02 may be the susceptibility genes and HLA-B*51:01 and C*14:01 act as the resistance genes of lung adenocarcinoma. The changes in HLA-I allele frequencies had no association with PD-L1 expression and TMB among these patients.
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Affiliation(s)
- Xuanpeng Wu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Hao Wang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Fei Xue
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Tao Jiang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Nanzheng Chen
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Tianju Wang
- HLA Typing Laboratory, Blood Center of the Shaanxi ProvinceInstitute of Xi'an Blood BankXi'anChina
| | - Yong Zhang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Guangjian Zhang
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Junke Fu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Qifei Wu
- Department of Thoracic SurgeryThe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
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Claeys A, Merseburger P, Staut J, Marchal K, Van den Eynden J. Benchmark of tools for in silico prediction of MHC class I and class II genotypes from NGS data. BMC Genomics 2023; 24:247. [PMID: 37161318 PMCID: PMC10170851 DOI: 10.1186/s12864-023-09351-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 04/30/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND The Human Leukocyte Antigen (HLA) genes are a group of highly polymorphic genes that are located in the Major Histocompatibility Complex (MHC) region on chromosome 6. The HLA genotype affects the presentability of tumour antigens to the immune system. While knowledge of these genotypes is of utmost importance to study differences in immune responses between cancer patients, gold standard, PCR-derived genotypes are rarely available in large Next Generation Sequencing (NGS) datasets. Therefore, a variety of methods for in silico NGS-based HLA genotyping have been developed, bypassing the need to determine these genotypes with separate experiments. However, there is currently no consensus on the best performing tool. RESULTS We evaluated 13 MHC class I and/or class II HLA callers that are currently available for free academic use and run on either Whole Exome Sequencing (WES) or RNA sequencing data. Computational resource requirements were highly variable between these tools. Three orthogonal approaches were used to evaluate the accuracy on several large publicly available datasets: a direct benchmark using PCR-derived gold standard HLA calls, a correlation analysis with population-based allele frequencies and an analysis of the concordance between the different tools. The highest MHC-I calling accuracies were found for Optitype (98.0%) and arcasHLA (99.4%) on WES and RNA sequencing data respectively, while for MHC-II HLA-HD was the most accurate tool for both data types (96.2% and 99.4% on WES and RNA data respectively). CONCLUSION The optimal strategy for HLA genotyping from NGS data depends on the availability of either WES or RNA data, the size of the dataset and the available computational resources. If sufficient resources are available, we recommend Optitype and HLA-HD for MHC-I and MHC-II genotype calling respectively.
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Affiliation(s)
- Arne Claeys
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Peter Merseburger
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
| | - Jasper Staut
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Kathleen Marchal
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Information Technology, Ghent University, IDLab, Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium
| | - Jimmy Van den Eynden
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium.
- Cancer Research Institute Ghent, Ghent, Belgium.
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Nishida N, Ohashi J, Suda G, Chiyoda T, Tamaki N, Tomiyama T, Ogasawara S, Sugiyama M, Kawai Y, Khor SS, Nagasaki M, Fujimoto A, Tsuchiura T, Ishikawa M, Matsuda K, Yano H, Yoshizumi T, Izumi N, Hasegawa K, Sakamoto N, Mizokami M, Tokunaga K. Prediction Model with HLA-A*33:03 Reveals Number of Days to Develop Liver Cancer from Blood Test. Int J Mol Sci 2023; 24:ijms24054761. [PMID: 36902191 PMCID: PMC10003621 DOI: 10.3390/ijms24054761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
The development of liver cancer in patients with hepatitis B is a major problem, and several models have been reported to predict the development of liver cancer. However, no predictive model involving human genetic factors has been reported to date. For the items incorporated in the prediction model reported so far, we selected items that were significant in predicting liver carcinogenesis in Japanese patients with hepatitis B and constructed a prediction model of liver carcinogenesis by the Cox proportional hazard model with the addition of Human Leukocyte Antigen (HLA) genotypes. The model, which included four items-sex, age at the time of examination, alpha-fetoprotein level (log10AFP) and presence or absence of HLA-A*33:03-revealed an area under the receiver operating characteristic curve (AUROC) of 0.862 for HCC prediction within 1 year and an AUROC of 0.863 within 3 years. A 1000 repeated validation test resulted in a C-index of 0.75 or higher, or sensitivity of 0.70 or higher, indicating that this predictive model can distinguish those at high risk of developing liver cancer within a few years with high accuracy. The prediction model constructed in this study, which can distinguish between chronic hepatitis B patients who develop hepatocellular carcinoma (HCC) early and those who develop HCC late or not, is clinically meaningful.
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Affiliation(s)
- Nao Nishida
- Genome Medical Science Project, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan
- Department of Genomic Function and Diversity, Medical Research Institute, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
- Correspondence: ; Tel.: +81-473723501
| | - Jun Ohashi
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan
| | - Goki Suda
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo 060-8638, Japan
| | - Takehiro Chiyoda
- Hepato-Biliary-Pancreatic Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Nobuharu Tamaki
- Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Musashino 180-8610, Japan
| | - Takahiro Tomiyama
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Sachiko Ogasawara
- Department of Pathology, Kurume University School of Medicine, Kurume 830-0011, Japan
| | - Masaya Sugiyama
- Department of Viral Pathogenesis and Controls, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan
| | - Yosuke Kawai
- Genome Medical Science Project-Toyama, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Seik-Soon Khor
- Genome Medical Science Project-Toyama, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Masao Nagasaki
- Human Biosciences Unit for the Top Global Course Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Kyoto 606-8507, Japan
| | - Akihiro Fujimoto
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0003, Japan
| | - Takayo Tsuchiura
- Genome Medical Science Project, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan
| | - Miyuki Ishikawa
- Genome Medical Science Project, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo 108-8639, Japan
| | - Hirohisa Yano
- Department of Pathology, Kurume University School of Medicine, Kurume 830-0011, Japan
| | - Tomoharu Yoshizumi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Fukuoka 812-8582, Japan
| | - Namiki Izumi
- Department of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Musashino 180-8610, Japan
| | - Kiyoshi Hasegawa
- Hepato-Biliary-Pancreatic Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
| | - Naoya Sakamoto
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo 060-8638, Japan
| | - Masashi Mizokami
- Genome Medical Science Project, National Center for Global Health and Medicine, Ichikawa 272-8516, Japan
| | - Katsushi Tokunaga
- Genome Medical Science Project-Toyama, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
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10
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Yuan Y, Gao F, Chang Y, Zhao Q, He X. Advances of mRNA vaccine in tumor: a maze of opportunities and challenges. Biomark Res 2023; 11:6. [PMID: 36650562 PMCID: PMC9845107 DOI: 10.1186/s40364-023-00449-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
High-frequency mutations in tumor genomes could be exploited as an asset for developing tumor vaccines. In recent years, with the tremendous breakthrough in genomics, intelligence algorithm, and in-depth insight of tumor immunology, it has become possible to rapidly target genomic alterations in tumor cell and rationally select vaccine targets. Among a variety of candidate vaccine platforms, the early application of mRNA was limited by instability low efficiency and excessive immunogenicity until the successful development of mRNA vaccines against SARS-COV-2 broken of technical bottleneck in vaccine preparation, allowing tumor mRNA vaccines to be prepared rapidly in an economical way with good performance of stability and efficiency. In this review, we systematically summarized the classification and characteristics of tumor antigens, the general process and methods for screening neoantigens, the strategies of vaccine preparations and advances in clinical trials, as well as presented the main challenges in the current mRNA tumor vaccine development.
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Affiliation(s)
- Yuan Yuan
- grid.413247.70000 0004 1808 0969Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China ,grid.412793.a0000 0004 1799 5032Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fan Gao
- grid.413247.70000 0004 1808 0969Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China ,grid.412793.a0000 0004 1799 5032Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Chang
- grid.413247.70000 0004 1808 0969Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China ,grid.413247.70000 0004 1808 0969Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, China
| | - Qiu Zhao
- grid.413247.70000 0004 1808 0969Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China ,grid.413247.70000 0004 1808 0969Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, China
| | - Xingxing He
- grid.413247.70000 0004 1808 0969Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan, China ,grid.412793.a0000 0004 1799 5032Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China ,grid.413247.70000 0004 1808 0969Hubei Clinical Center and Key Laboratory of Intestinal and Colorectal Diseases, Wuhan, China
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11
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Garsed DW, Pandey A, Fereday S, Kennedy CJ, Takahashi K, Alsop K, Hamilton PT, Hendley J, Chiew YE, Traficante N, Provan P, Ariyaratne D, Au-Yeung G, Bateman NW, Bowes L, Brand A, Christie EL, Cunningham JM, Friedlander M, Grout B, Harnett P, Hung J, McCauley B, McNally O, Piskorz AM, Saner FAM, Vierkant RA, Wang C, Winham SJ, Pharoah PDP, Brenton JD, Conrads TP, Maxwell GL, Ramus SJ, Pearce CL, Pike MC, Nelson BH, Goode EL, DeFazio A, Bowtell DDL. The genomic and immune landscape of long-term survivors of high-grade serous ovarian cancer. Nat Genet 2022; 54:1853-1864. [PMID: 36456881 PMCID: PMC10478425 DOI: 10.1038/s41588-022-01230-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 10/17/2022] [Indexed: 12/03/2022]
Abstract
Fewer than half of all patients with advanced-stage high-grade serous ovarian cancers (HGSCs) survive more than five years after diagnosis, but those who have an exceptionally long survival could provide insights into tumor biology and therapeutic approaches. We analyzed 60 patients with advanced-stage HGSC who survived more than 10 years after diagnosis using whole-genome sequencing, transcriptome and methylome profiling of their primary tumor samples, comparing this data to 66 short- or moderate-term survivors. Tumors of long-term survivors were more likely to have multiple alterations in genes associated with DNA repair and more frequent somatic variants resulting in an increased predicted neoantigen load. Patients clustered into survival groups based on genomic and immune cell signatures, including three subsets of patients with BRCA1 alterations with distinctly different outcomes. Specific combinations of germline and somatic gene alterations, tumor cell phenotypes and differential immune responses appear to contribute to long-term survival in HGSC.
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Affiliation(s)
- Dale W Garsed
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
| | - Ahwan Pandey
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Sian Fereday
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Catherine J Kennedy
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Kazuaki Takahashi
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynecology, The Jikei University School of Medicine, Tokyo, Japan
| | - Kathryn Alsop
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Phineas T Hamilton
- The Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
| | - Joy Hendley
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Yoke-Eng Chiew
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Nadia Traficante
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Pamela Provan
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | | | - George Au-Yeung
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Nicholas W Bateman
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD, USA
| | - Leanne Bowes
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Royal Women's Hospital, Parkville, Victoria, Australia
| | - Alison Brand
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
| | - Elizabeth L Christie
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia
| | - Julie M Cunningham
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | - Michael Friedlander
- Prince of Wales Clinical School, University of New South Wales, Sydney, New South Wales, Australia
| | | | - Paul Harnett
- The University of Sydney, Sydney, New South Wales, Australia
- Crown Princess Mary Cancer Centre, Westmead Hospital, Sydney, New South Wales, Australia
| | - Jillian Hung
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
| | - Bryan McCauley
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Orla McNally
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- The Royal Women's Hospital, Parkville, Victoria, Australia
- Department of Obstetrics and Gynaecology, The University of Melbourne, Parkville, Victoria, Australia
| | - Anna M Piskorz
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Flurina A M Saner
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
- Department of Obstetrics and Gynecology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Robert A Vierkant
- Division of Clinical Trials and Biostatistics, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Chen Wang
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Stacey J Winham
- Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - James D Brenton
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Department of Oncology, University of Cambridge, Cambridge, UK
| | - Thomas P Conrads
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA
| | - George L Maxwell
- Women's Health Integrated Research Center, Gynecologic Cancer Center of Excellence, Uniformed Services University and Walter Reed National Military Medical Center, Bethesda, MD, USA
- Women's Health Integrated Research Center, Women's Service Line, Inova Health System, Falls Church, VA, USA
| | - Susan J Ramus
- School of Clinical Medicine, Faculty of Medicine and Health, University of NSW, Sydney, New South Wales, Australia
- Adult Cancer Program, Lowy Cancer Research Centre, University of NSW, Sydney, New South Wales, Australia
| | - Celeste Leigh Pearce
- Department of Epidemiology and Rogel Cancer Center, University of Michigan, Ann Arbor, MI, USA
| | - Malcolm C Pike
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Brad H Nelson
- The Deeley Research Centre, BC Cancer, Victoria, British Columbia, Canada
- Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada
| | - Ellen L Goode
- Division of Epidemology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Anna DeFazio
- The Westmead Institute for Medical Research, Sydney, New South Wales, Australia
- Department of Gynaecological Oncology, Westmead Hospital, Sydney, New South Wales, Australia
- The University of Sydney, Sydney, New South Wales, Australia
- The Daffodil Centre, The University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
| | - David D L Bowtell
- Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia.
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12
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Wang W, Yuan T, Ma L, Zhu Y, Bao J, Zhao X, Zhao Y, Zong Y, Zhang Y, Yang S, Qiu X, Shen S, Wu R, Wu T, Wang H, Gao D, Wang P, Chen L. Hepatobiliary Tumor Organoids Reveal HLA Class I Neoantigen Landscape and Antitumoral Activity of Neoantigen Peptide Enhanced with Immune Checkpoint Inhibitors. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2105810. [PMID: 35665491 PMCID: PMC9353440 DOI: 10.1002/advs.202105810] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/08/2022] [Indexed: 05/28/2023]
Abstract
Neoantigen-directed therapy lacks preclinical models recapitulating neoantigen characteristics of original tumors. It is urgent to develop a platform to assess T cell response for neoantigen screening. Here, immunogenic potential of neoantigen-peptides of tumor tissues and matched organoids (n = 27 pairs) are analyzed by Score tools with whole genome sequencing (WGS)-based human leukocyte antigen (HLA)-class-I algorithms. The comparisons between 9203 predicted neoantigen-peptides from 2449 mutations of tumor tissues and 9991 ones from 2637 mutations of matched organoids demonstrate that organoids preserved majority of genetic features, HLA alleles, and similar neoantigen landscape of original tumors. Higher neoantigen load is observed in tumors with early stage. Multiomics analysis combining WGS, RNA-seq, single-cell RNA-seq, mass spectrometry filters out 93 candidate neoantigen-peptides with strong immunogenic potential for functional validation in five organoids. Immunogenic peptides are defined by inducing increased CD107aCD137IFN-γ expressions and IFN-γ secretion of CD8 cells in flow cytometry and enzyme-linked immunosorbent assay assays. Nine immunogenic peptides shared by at least two individuals are validated, including peptide from TP53R90S . Organoid killing assay confirms the antitumor activity of validated immunogenic peptide-reactive CD8 cells, which is further enhanced in the presence of immune checkpoint inhibitors. The study characterizes HLA-class-I neoantigen landscape in hepatobiliary tumor, providing practical strategy with tumor organoid model for neoantigen-peptide identification in personalized immunotherapy.
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Affiliation(s)
- Wenwen Wang
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
| | - Tinggan Yuan
- School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Lili Ma
- School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yanjing Zhu
- The International Cooperation Laboratory on Signal TransductionEastern Hepatobiliary Surgery HospitalSecond Military Medical UniversityShanghai200438China
- National Center for Liver CancerShanghai200441China
| | - Jinxia Bao
- School of MedicineNanjing UniversityNanjing210093China
| | - Xiaofang Zhao
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
| | - Yan Zhao
- Institute of Metabolism and Integrative BiologyFudan UniversityShanghai200433China
| | - Yali Zong
- Institute of Metabolism and Integrative BiologyFudan UniversityShanghai200433China
| | - Yani Zhang
- Institute of Metabolism and Integrative BiologyFudan UniversityShanghai200433China
| | - Shuai Yang
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
| | - Xinyao Qiu
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
| | - Siyun Shen
- The International Cooperation Laboratory on Signal TransductionEastern Hepatobiliary Surgery HospitalSecond Military Medical UniversityShanghai200438China
- National Center for Liver CancerShanghai200441China
| | - Rui Wu
- Department of Biliary Surgery IEastern Hepatobiliary Surgery HospitalSecond Military Medical UniversityShanghai200438China
| | - Tong Wu
- The International Cooperation Laboratory on Signal TransductionEastern Hepatobiliary Surgery HospitalSecond Military Medical UniversityShanghai200438China
- National Center for Liver CancerShanghai200441China
| | - Hongyang Wang
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
- The International Cooperation Laboratory on Signal TransductionEastern Hepatobiliary Surgery HospitalSecond Military Medical UniversityShanghai200438China
- National Center for Liver CancerShanghai200441China
| | - Dong Gao
- University of Chinese Academy of SciencesBeijing100049China
- State Key Laboratory of Cell BiologyShanghai Key Laboratory of Molecular AndrologyShanghai Institute of Biochemistry and Cell BiologyCAS Center for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghai200031China
- Institute for Stem Cell and RegenerationChinese Academy of SciencesBeijing100101China
| | - Peng Wang
- School of Life Science and TechnologyShanghaiTech UniversityShanghai201210China
- CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai200031China
- University of Chinese Academy of SciencesBeijing100049China
| | - Lei Chen
- Fudan University Shanghai Cancer CenterDepartment of OncologyShanghai Medical CollegeFudan UniversityShanghai200032China
- National Center for Liver CancerShanghai200441China
- Key Laboratory of Signaling Regulation and Targeting Therapy of Liver Cancer (SMMU)Ministry of EducationShanghai200438China
- Shanghai Key Laboratory of Hepatobiliary Tumor Biology (EHBH)Shanghai200438China
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13
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Zhang X, Fang Q, Zhu S, Wu X, HuangboYuan, Liu Z, Xu Y, Chen T, Zeng Y, Zhang T. Environmental risk factors and genetic markers of Kaposi's sarcoma associated herpesvirus infection among Uygur population in Xinjiang, China. J Med Virol 2022; 94:2755-2765. [PMID: 35043408 DOI: 10.1002/jmv.27600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 01/07/2022] [Accepted: 01/16/2022] [Indexed: 11/10/2022]
Abstract
OBJECTIVES Kaposi sarcoma-associated herpesvirus (KSHV) is endemic in Xinjiang, China. Determinants of KSHV seropositivity among high-risk groups are not well understood. We seek to identify genetic and environmental predisposing factors for KSHV infection among Uygurs in this endemic region. METHODS A cross-sectional study was performed among Uygur population in Xinjiang, China. KSHV-antibodies were detected using immunofluorescence assay (IFA) and human leukocyte antigen (HLA) alleles were genotyped. Univariate and multivariate logistic regression analysis were applied to explore the environmental and genetic risk factors of KSHV seropositivity. RESULTS A total of 721 participants were included. The seroprevalence of KSHV was 24.1% among this population. Sweet-food preference (OR 1.85, 95%CI 1.03-3.34), and coronary heart disease (OR 1.91, 95 %CI 1.24-2.94) were statistically correlated with KSHV infection. HLA-DQB1*06:09 were found to significantly increase the risk of KSHV infection under all 3 models (ORAllelic =4.06; ORDominant =3.27; ORRecessive =8.06). Six SNPs (SNP0260, SNP0361, SNP0797, SNP0852, SNP1159, SNP1375) in the DQB1 and DRB1 region and haploid type GTCTAACTAATC in block 17 were statistically associated with KSHV infection. CONCLUSION We demonstrated that genetic variations in HLA-DQB1/DRB1 and environmental risk factors were strongly associated with KSHV infection among this population. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Xin Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China.,Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Qiwen Fang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China.,Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China
| | - Sibo Zhu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Xuefu Wu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China
| | - HuangboYuan
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China
| | - Zhenqiu Liu
- State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, 200438, China
| | - Yiyun Xu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China
| | - Tao Chen
- Xinjiang Ili center for diseases control and prevention, Xinjiang, China
| | - Yan Zeng
- Key Laboratory of Xinjiang Endemic and Ethnic Disease and Department of Biochemistry, School of Medicine, Shihezi University, 832000, Shihezi, Xinjiang, China
| | - Tiejun Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China. Key Laboratory of Public Health Safety (Fudan University), Ministry of Education, China.,Shanghai Institute of Infectious Disease and Biosecurity, School of Public Health, Fudan University, Shanghai, China.,Yiwu Research Institue, Fudan University, Yiwu, China
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14
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Das A, Sudhaman S, Morgenstern D, Coblentz A, Chung J, Stone SC, Alsafwani N, Liu ZA, Karsaneh OAA, Soleimani S, Ladany H, Chen D, Zatzman M, Cabric V, Nobre L, Bianchi V, Edwards M, Sambira Nahum LC, Ercan AB, Nabbi A, Constantini S, Dvir R, Yalon-Oren M, Campino GA, Caspi S, Larouche V, Reddy A, Osborn M, Mason G, Lindhorst S, Bronsema A, Magimairajan V, Opocher E, De Mola RL, Sabel M, Frojd C, Sumerauer D, Samuel D, Cole K, Chiaravalli S, Massimino M, Tomboc P, Ziegler DS, George B, Van Damme A, Hijiya N, Gass D, McGee RB, Mordechai O, Bowers DC, Laetsch TW, Lossos A, Blumenthal DT, Sarosiek T, Yen LY, Knipstein J, Bendel A, Hoffman LM, Luna-Fineman S, Zimmermann S, Scheers I, Nichols KE, Zapotocky M, Hansford JR, Maris JM, Dirks P, Taylor MD, Kulkarni AV, Shroff M, Tsang DS, Villani A, Xu W, Aronson M, Durno C, Shlien A, Malkin D, Getz G, Maruvka YE, Ohashi PS, Hawkins C, Pugh TJ, Bouffet E, Tabori U. Genomic predictors of response to PD-1 inhibition in children with germline DNA replication repair deficiency. Nat Med 2022; 28:125-135. [PMID: 34992263 PMCID: PMC8799468 DOI: 10.1038/s41591-021-01581-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 10/15/2021] [Indexed: 02/08/2023]
Abstract
Cancers arising from germline DNA mismatch repair deficiency or polymerase proofreading deficiency (MMRD and PPD) in children harbour the highest mutational and microsatellite insertion–deletion (MS-indel) burden in humans. MMRD and PPD cancers are commonly lethal due to the inherent resistance to chemo-irradiation. Although immune checkpoint inhibitors (ICIs) have failed to benefit children in previous studies, we hypothesized that hypermutation caused by MMRD and PPD will improve outcomes following ICI treatment in these patients. Using an international consortium registry study, we report on the ICI treatment of 45 progressive or recurrent tumors from 38 patients. Durable objective responses were observed in most patients, culminating in a 3 year survival of 41.4%. High mutation burden predicted response for ultra-hypermutant cancers (>100 mutations per Mb) enriched for combined MMRD + PPD, while MS-indels predicted response in MMRD tumors with lower mutation burden (10–100 mutations per Mb). Furthermore, both mechanisms were associated with increased immune infiltration even in ‘immunologically cold’ tumors such as gliomas, contributing to the favorable response. Pseudo-progression (flare) was common and was associated with immune activation in the tumor microenvironment and systemically. Furthermore, patients with flare who continued ICI treatment achieved durable responses. This study demonstrates improved survival for patients with tumors not previously known to respond to ICI treatment, including central nervous system and synchronous cancers, and identifies the dual roles of mutation burden and MS-indels in predicting sustained response to immunotherapy. Hypermutation and microsatellite burden determine responses and long-term survival following PD-1 blockade in children and young adults with refractory cancers resulting from germline DNA replication repair deficiency.
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Affiliation(s)
- Anirban Das
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatric Haematology/ Oncology, Tata Medical Centre, Kolkata, India
| | - Sumedha Sudhaman
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Daniel Morgenstern
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Ailish Coblentz
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jiil Chung
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Simone C Stone
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Noor Alsafwani
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Pathology, College of Medicine, Imam Abdulrahman Bin Faisal University (IAU), Dammam, Saudi Arabia
| | - Zhihui Amy Liu
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Ola Abu Al Karsaneh
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Basic Medical Sciences, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Shirin Soleimani
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Hagay Ladany
- Biotechnology and Food Engineering, Technion - Israel Institute of Technology, Tel-Aviv, Israel
| | - David Chen
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Matthew Zatzman
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Vanja Cabric
- Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Liana Nobre
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Vanessa Bianchi
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Melissa Edwards
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Lauren C Sambira Nahum
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Ayse B Ercan
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.,Institute of Medical Science, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Arash Nabbi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Shlomi Constantini
- Department of Pediatric Neurosurgery, Dana Children's Hospital, Tel-Aviv, Israel
| | - Rina Dvir
- Department of Pediatric Hematology-Oncology, Tel-Aviv Sourasky Medical Centre, Tel-Aviv, Israel
| | - Michal Yalon-Oren
- Department of Pediatric Hematology-Oncology, Sheba Medical Centre, Ramat Gan, Israel
| | - Gadi Abebe Campino
- Department of Pediatric Hematology-Oncology, Sheba Medical Centre, Ramat Gan, Israel
| | - Shani Caspi
- Department of Pediatric Hematology-Oncology, Sheba Medical Centre, Ramat Gan, Israel
| | - Valerie Larouche
- Department of Paediatric Haematology/Oncology, Centre Hospitalier de Quebec-Universite Laval, Quebec City, Quebec, Canada
| | - Alyssa Reddy
- Departments of Neurology and Pediatrics, University of California, San Francisco, CA, USA
| | - Michael Osborn
- Women's and Children's Hospital, North Adelaide, South Australia, Australia
| | - Gary Mason
- Department of Pediatrics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Scott Lindhorst
- Neuro-Oncology, Department of Neurosurgery, and Department of Medicine, Division of Hematology/Medical Oncology, Medical University of South Carolina, Charleston, SC, USA
| | - Annika Bronsema
- Department of Paediatric Haematology and Oncology, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Vanan Magimairajan
- Department of Paediatric Haematology-Oncology, Cancer Care Manitoba, Research Institute in Oncology and Haematology, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Enrico Opocher
- Paediatric Haematology, Oncology and Stem Cell Transplant Division, Padua University Hospital, Padua, Italy
| | - Rebecca Loret De Mola
- Pediatric Hematology-Oncology, Helen DeVos Children's Hospital, Grand Rapids, MI, USA
| | - Magnus Sabel
- Department of Paediatrics, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, Gothenburg, Sweden.,Queen Silvia Children's Hospital, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Charlotta Frojd
- Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - David Sumerauer
- Department of Paediatric Haematology and Oncology, Second Faculty of Medicine, Motol University Hospital, Charles University, Prague, Czech Republic
| | - David Samuel
- Department of Pediatric Oncology, Valley Children's Hospital, Madera, CA, USA
| | - Kristina Cole
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelpha, PA, USA
| | - Stefano Chiaravalli
- Paediatric Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Maura Massimino
- Paediatric Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Patrick Tomboc
- Department of Pediatrics, J.W. Ruby Memorial Hospital - West Virginia University, Morgantown, WV, USA
| | - David S Ziegler
- Kids Cancer Centre, Sydney Children's Hospital, Randwick, New South Wales, Australia.,School of Women's and Children's Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Ben George
- Division of Hematology and Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - An Van Damme
- Department of Paediatric Haematology and Oncology, Saint Luc University Hospital, Université Catholique de Louvain, Brussels, Belgium
| | - Nobuko Hijiya
- Division of Pediatric Hematology/Oncology/Stem Cell Transplantation, Columbia University Irving Medical Centre, New York, NY, USA
| | - David Gass
- Atrium Health Levine Children's Hospital, Charlotte, NC, USA
| | - Rose B McGee
- Cancer Predisposition Division, Oncology Department, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Oz Mordechai
- Department of Pediatric Hematology Oncology, Rambam Health Care Campus, Haifa, Israel
| | - Daniel C Bowers
- Department of Pediatrics, The University of Texas Southwestern Medical School, Dallas, TX, USA
| | - Theodore W Laetsch
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelpha, PA, USA
| | - Alexander Lossos
- Department of Oncology, Leslie and Michael Gaffin Center for Neuro-Oncology, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Deborah T Blumenthal
- Neuro-Oncology Service, Tel-Aviv Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel-Aviv, Israel
| | | | - Lee Yi Yen
- Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Jeffrey Knipstein
- Division of Pediatric Hematology/ Oncology/ BMT, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anne Bendel
- Department of Pediatric Hematology-Oncology, Children's Hospitals and Clinics of Minnesota, St Paul, MN, USA
| | | | - Sandra Luna-Fineman
- Department of Pediatrics, Anschutz Medical Campus, Children's Hospital of Colorado, Aurora, CO, USA
| | - Stefanie Zimmermann
- Paediatric Haematology and Oncology, University Hospital Frankfurt, Frankfurt, Germany
| | - Isabelle Scheers
- Paediatric Gastroenterology, Hepatology and Nutrition Unit, Cliniques Universitaires St Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Kim E Nichols
- Cancer Predisposition Division, Oncology Department, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Michal Zapotocky
- Department of Paediatric Haematology and Oncology, Second Faculty of Medicine, Motol University Hospital, Charles University, Prague, Czech Republic
| | - Jordan R Hansford
- Children's Cancer Centre, Royal Children's Hospital, Murdoch Children's Research Institute, University of Melbourne, Parkville, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia
| | - John M Maris
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelpha, PA, USA
| | - Peter Dirks
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.,Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada.,Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Michael D Taylor
- The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.,Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada.,Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Abhaya V Kulkarni
- Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada.,Child Health Evaluative Sciences, Research Institute, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Manohar Shroff
- Department of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Derek S Tsang
- Radiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Anita Villani
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Wei Xu
- Department of Biostatistics, Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.,Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Melyssa Aronson
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Carol Durno
- Zane Cohen Centre for Digestive Diseases, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Adam Shlien
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - David Malkin
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Gad Getz
- Massachusetts General Hospital Cancer Center and Department of Pathology, Charlestown, MA, USA.,Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yosef E Maruvka
- Biotechnology and Food Engineering, Technion - Israel Institute of Technology, Tel-Aviv, Israel
| | - Pamela S Ohashi
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Immunology, University of Toronto, Toronto, Ontario, Canada
| | - Cynthia Hawkins
- Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada.,Program in Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Trevor J Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Eric Bouffet
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Uri Tabori
- Division of Haematology Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada. .,Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Ontario, Canada. .,The Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada. .,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada. .,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.
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15
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Dhall A, Jain S, Sharma N, Naorem LD, Kaur D, Patiyal S, Raghava GPS. In silico tools and databases for designing cancer immunotherapy. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 129:1-50. [PMID: 35305716 DOI: 10.1016/bs.apcsb.2021.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Shipra Jain
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Neelam Sharma
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Leimarembi Devi Naorem
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Dilraj Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India
| | - Gajendra P S Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, India.
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16
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Lee M, Seo JH, Song S, Song IH, Kim SY, Kim YA, Gong G, Kim JE, Lee HJ. A New Human Leukocyte Antigen Typing Algorithm Combined With Currently Available Genotyping Tools Based on Next-Generation Sequencing Data and Guidelines to Select the Most Likely Human Leukocyte Antigen Genotype. Front Immunol 2021; 12:688183. [PMID: 34659196 PMCID: PMC8517438 DOI: 10.3389/fimmu.2021.688183] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 09/15/2021] [Indexed: 12/25/2022] Open
Abstract
Background High-precision human leukocyte antigen (HLA) genotyping is crucial for anti-cancer immunotherapy, but existing tools predicting HLA genotypes using next-generation sequencing (NGS) data are insufficiently accurate. Materials and Methods We compared availability, accuracy, correction score, and complementary ratio of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,005 cases from the 1000 Genomes Project data. We created a new HLA-genotyping algorithm combining tools based on the precision and the accuracy of tools’ combinations. Then, we assessed the new algorithm’s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction–sequencing-based typing (PCR-SBT) results. Results Regardless of the type of tool, the calls presented by more than six tools concordantly showed high accuracy and precision. The accuracy of the group with at least six concordant calls was 100% (97/97) in HLA-A, 98.2% (112/114) in HLA-B, 97.3% (142/146) in HLA-C. The precision of the group with at least six concordant calls was over 98% in HLA-ABC. We additionally calculated the accuracy of the combination tools considering the complementary ratio of each tool and the accuracy of each tool, and the accuracy was over 98% in all groups with six or more concordant calls. We created a new algorithm that matches the above results. It was to select the HLA type if more than six out of eight tools presented a matched type. Otherwise, determine the HLA type experimentally through PCR-SBT. When we applied the new algorithm to 39 in-house cases, there were more than six matching calls in all HLA-A, B, and C, and the accuracy of these concordant calls was 100%. Conclusions HLA genotyping accuracy using NGS data could be increased by combining the current HLA genotyping tools. This new algorithm could also be useful for preliminary screening to decide whether to perform an additional PCR-based experimental method instead of using tools with NGS data.
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Affiliation(s)
- Miseon Lee
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Jeong-Han Seo
- Department of Biomedical Sciences, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.,NeogenTC Corp, Seoul, South Korea
| | | | - In Hye Song
- Department of Hospital Pathology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Su Yeon Kim
- University of Ulsan College of Medicine, Seoul, South Korea
| | | | - Gyungyub Gong
- Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Jeong Eun Kim
- Department of Oncology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Hee Jin Lee
- Department of Biomedical Sciences, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea.,NeogenTC Corp, Seoul, South Korea.,Department of Pathology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
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17
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Sato T, Adachi N, Kimura R, Hosomichi K, Yoneda M, Oota H, Tajima A, Toyoda A, Kanzawa-Kiriyama H, Matsumae H, Koganebuchi K, Shimizu KK, Shinoda KI, Hanihara T, Weber A, Kato H, Ishida H. Whole-Genome Sequencing of a 900-Year-Old Human Skeleton Supports Two Past Migration Events from the Russian Far East to Northern Japan. Genome Biol Evol 2021; 13:6355032. [PMID: 34410389 PMCID: PMC8449830 DOI: 10.1093/gbe/evab192] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2021] [Indexed: 12/18/2022] Open
Abstract
Recent studies on paleogenomics have reported some Paleolithic and Neolithic genomes that have provided new insights into the human population history in East and Northeast Asia. However, there remain some cases where more recent migration events need to be examined to elucidate the detailed formation process of local populations. Although the area around northern Japan is one of the regions archaeologically suggested to have been affected by migration waves after the Neolithic period, the genetic source of these migrations are still unclear. Thus, genomic data from such past migrant populations would be highly informative to clarify the detailed formation process of local populations in this region. Here, we report the genome sequence of a 900-year-old adult female (NAT002) belonging to the prehistoric Okhotsk people, who have been considered to be the past migrants to northern Japan after the Neolithic period. We found a close relationship between NAT002 and modern Lower Amur populations and past admixture events between the Amur, Jomon, and Kamchatka ancestries. The admixture dating suggested migration of Amur-related ancestry at approximately 1,600 BP, which is compatible with the archaeological evidence regarding the settlement of the Okhotsk people. Our results also imply migration of Kamchatka-related ancestry at approximately 2,000 BP. In addition, human leukocyte antigen (HLA) typing detected the HLA-B*40 allele, which is reported to increase the risk of arthritis, suggesting the genetic vulnerability of NAT002 to hyperostosis, which was observed around her chest clavicle.
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Affiliation(s)
- Takehiro Sato
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan.,Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Noboru Adachi
- Department of Legal Medicine, Graduate School of Medicine, University of Yamanashi, Chuo, Japan
| | - Ryosuke Kimura
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
| | - Kazuyoshi Hosomichi
- Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Minoru Yoneda
- The University Museum, The University of Tokyo, Tokyo, Japan
| | - Hiroki Oota
- Department of Anatomy, Kitasato University School of Medicine, Sagamihara, Japan.,Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan
| | - Atsushi Tajima
- Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Atsushi Toyoda
- Comparative Genomics Laboratory, National Institute of Genetics, Mishima, Japan
| | | | - Hiromi Matsumae
- Kihara Institute for Biological Research (KIBR), Yokohama City University, Yokohama, Japan.,Department of Molecular Life Science, School of Medicine, Tokai University, Isehara, Japan
| | - Kae Koganebuchi
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Tokyo, Japan.,Department of Biological Structure, Kitasato University Graduate School of Medical Sciences, Sagamihara, Japan.,Advanced Medical Research Center, Faculty of Medicine, University of the Ryukyus, Nishihara, Japan
| | - Kentaro K Shimizu
- Kihara Institute for Biological Research (KIBR), Yokohama City University, Yokohama, Japan.,Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich, Switzerland
| | - Ken-Ichi Shinoda
- Department of Anthropology, National Museum of Nature and Science, Tsukuba, Japan
| | - Tsunehiko Hanihara
- Department of Anatomy, Kitasato University School of Medicine, Sagamihara, Japan
| | - Andrzej Weber
- Department of Anthropology, University of Alberta, Edmonton, Alberta, Canada.,Research Centre "Baikal Region", Irkutsk State University, Irkutsk, Russia.,Laboratoire Méditerranéen de Préhistoire Europe Afrique (LAMPEA) - UMR 7269, Aix-Marseille Université, Aix-en-Provence, France
| | - Hirofumi Kato
- Centre for Ainu and Indigenous Studies, Hokkaido University, Sapporo, Japan
| | - Hajime Ishida
- Department of Human Biology and Anatomy, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
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18
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Aguiar VRC, Augusto DG, Castelli EC, Hollenbach JA, Meyer D, Nunes K, Petzl-Erler ML. An immunogenetic view of COVID-19. Genet Mol Biol 2021; 44:e20210036. [PMID: 34436508 PMCID: PMC8388242 DOI: 10.1590/1678-4685-gmb-2021-0036] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/12/2021] [Indexed: 02/06/2023] Open
Abstract
Meeting the challenges brought by the COVID-19 pandemic requires an interdisciplinary approach. In this context, integrating knowledge of immune function with an understanding of how genetic variation influences the nature of immunity is a key challenge. Immunogenetics can help explain the heterogeneity of susceptibility and protection to the viral infection and disease progression. Here, we review the knowledge developed so far, discussing fundamental genes for triggering the innate and adaptive immune responses associated with a viral infection, especially with the SARS-CoV-2 mechanisms. We emphasize the role of the HLA and KIR genes, discussing what has been uncovered about their role in COVID-19 and addressing methodological challenges of studying these genes. Finally, we comment on questions that arise when studying admixed populations, highlighting the case of Brazil. We argue that the interplay between immunology and an understanding of genetic associations can provide an important contribution to our knowledge of COVID-19.
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Affiliation(s)
- Vitor R. C. Aguiar
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Danillo G. Augusto
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
- Universidade Federal do Paraná, Departamento de Genética, Curitiba,
PR, Brazil
| | - Erick C. Castelli
- Universidade Estadual Paulista, Faculdade de Medicina de Botucatu,
Departamento de Patologia, Botucatu, SP, Brazil
| | - Jill A. Hollenbach
- University of California, UCSF Weill Institute for Neurosciences,
Department of Neurology, San Francisco, CA, USA
| | - Diogo Meyer
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
| | - Kelly Nunes
- Universidade de São Paulo, Departamento de Genética e Biologia
Evolutiva, São Paulo, SP, Brazil
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19
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Xu Y, Su GH, Ma D, Xiao Y, Shao ZM, Jiang YZ. Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence. Signal Transduct Target Ther 2021; 6:312. [PMID: 34417437 PMCID: PMC8377461 DOI: 10.1038/s41392-021-00729-7] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/06/2021] [Accepted: 07/18/2021] [Indexed: 02/07/2023] Open
Abstract
Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.
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Affiliation(s)
- Ying Xu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Guan-Hua Su
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ding Ma
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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20
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Nordin J, Pettersson M, Rosenberg LH, Mathioudaki A, Karlsson Å, Murén E, Tandre K, Rönnblom L, Kastbom A, Cedergren J, Eriksson P, Söderkvist P, Lindblad-Toh K, Meadows JRS. Association of Protective HLA-A With HLA-B∗27 Positive Ankylosing Spondylitis. Front Genet 2021; 12:659042. [PMID: 34335681 PMCID: PMC8320510 DOI: 10.3389/fgene.2021.659042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 06/09/2021] [Indexed: 11/21/2022] Open
Abstract
Objectives To further elucidate the role of the MHC in ankylosing spondylitis by typing 17 genes, searching for HLA-B∗27 independent associations and assessing the impact of sex on this male biased disease. Methods High-confidence two-field resolution genotyping was performed on 310 cases and 2196 controls using an n-1 concordance method. Protein-coding variants were called from next-generation sequencing reads using up to four software programs and the consensus result recorded. Logistic regression tests were applied to the dataset as a whole, and also in stratified sets based on sex or HLA-B∗27 status. The amino acids driving association were also examined. Results Twenty-five HLA protein-coding variants were significantly associated to disease in the population. Three novel protective associations were found in a HLA-B∗27 positive population, HLA-A∗24:02 (OR = 0.4, CI = 0.2–0.7), and HLA-A amino acids Leu95 and Gln156. We identified a key set of seven loci that were common to both sexes, and robust to change in sample size. Stratifying by sex uncovered three novel risk variants restricted to the female population (HLA-DQA1∗04.01, -DQB1∗04:02, -DRB1∗08:01; OR = 2.4–3.1). We also uncovered a set of neutral variants in the female population, which in turn conferred strong effects in the male set, highlighting how population composition can lead to the masking of true associations. Conclusion Population stratification allowed for a nuanced investigation into the tightly linked MHC region, revealing novel HLA-B∗27 signals as well as replicating previous HLA-B∗27 dependent results. This dissection of signals may help to elucidate sex biased disease predisposition and clinical progression.
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Affiliation(s)
- Jessika Nordin
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.,Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Mats Pettersson
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Lina Hultin Rosenberg
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Argyri Mathioudaki
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Åsa Karlsson
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Eva Murén
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Karolina Tandre
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Lars Rönnblom
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Alf Kastbom
- Department of Rheumatology, University Hospital Linköping, Linköping, Sweden.,Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Jan Cedergren
- Department of Rheumatology, University Hospital Linköping, Linköping, Sweden.,Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Per Eriksson
- Department of Rheumatology, University Hospital Linköping, Linköping, Sweden.,Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Peter Söderkvist
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.,Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Jennifer R S Meadows
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
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21
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Multi-step screening of neoantigens' HLA- and TCR-interfaces improves prediction of survival. Sci Rep 2021; 11:9983. [PMID: 33976291 PMCID: PMC8113358 DOI: 10.1038/s41598-021-89016-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 04/19/2021] [Indexed: 12/21/2022] Open
Abstract
Improvement of risk stratification through prognostic biomarkers may enhance the personalization of cancer patient monitoring and treatment. We used Ancer, an immunoinformatic CD8, CD4, and regulatory T cell neoepitope screening system, to perform an advanced neoantigen analysis of genomic data derived from the urothelial cancer cohort of The Cancer Genome Atlas. Ancer demonstrated improved prognostic stratification and five-year survival prediction compared to standard analyses using tumor mutational burden or neoepitope identification using NetMHCpan and NetMHCIIpan. The superiority of Ancer, shown in both univariate and multivariate survival analyses, is attributed to the removal of neoepitopes that do not contribute to tumor immunogenicity based on their homology with self-epitopes. This analysis suggests that the presence of a higher number of unique, non-self CD8- and CD4-neoepitopes contributes to cancer survival, and that prospectively defining these neoepitopes using Ancer is a novel prognostic or predictive biomarker.
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22
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Yu Y, Wang K, Fahira A, Yang Q, Sun R, Li Z, Wang Z, Shi Y. Systematic comparative study of computational methods for HLA typing from next-generation sequencing. HLA 2021; 97:481-492. [PMID: 33655664 DOI: 10.1111/tan.14244] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/21/2021] [Accepted: 02/25/2021] [Indexed: 12/25/2022]
Abstract
The human leukocyte antigen (HLA) system plays an important role in hematopoietic stem cell transplantation (HSCT) and organ transplantations, immune disorders as well as oncological immunotherapy. However, HLA typing remains a challenging task due to the high level of polymorphism and homology among HLA genes. Based on the high-throughput next-generation sequencing data, new HLA typing algorithms and software tools were developed. But there is still a deficit of systematic comparative studies to assist in the selection of the optimal analytical approaches under different conditions. Here, we present a detailed comparison of eight software tools for HLA typing on different real datasets (whole-genome sequencing, whole-exome sequencing and transcriptomic sequencing data) and in-silico samples with different sequencing lengths, depths, and error rates. We figure out the algorithms with the best efficiency in different scenarios, and demonstrate the effect of different raw reads on analytical performances. Our results provide a comprehensive picture of specifications and performances of the eight existing HLA genotyping algorithms, which could assist researchers in selecting the most appropriate tool for specific raw datasets.
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Affiliation(s)
- Yuechun Yu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Ke Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Aamir Fahira
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Qiangzhen Yang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Renliang Sun
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhiqiang Li
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Zhuo Wang
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
| | - Yongyong Shi
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, China
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23
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Liu P, Yao M, Gong Y, Song Y, Chen Y, Ye Y, Liu X, Li F, Dong H, Meng R, Chen H, Zheng A. Benchmarking the Human Leukocyte Antigen Typing Performance of Three Assays and Seven Next-Generation Sequencing-Based Algorithms. Front Immunol 2021; 12:652258. [PMID: 33868290 PMCID: PMC8045758 DOI: 10.3389/fimmu.2021.652258] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 03/03/2021] [Indexed: 11/13/2022] Open
Abstract
With the great progress made recently in next generation sequencing (NGS) technology, sequencing accuracy and throughput have increased, while the cost for data has decreased. Various human leukocyte antigen (HLA) typing algorithms and assays have been developed and have begun to be used in clinical practice. In this study, we compared the HLA typing performance of three HLA assays and seven NGS-based HLA algorithms and assessed the impact of sequencing depth and length on HLA typing accuracy based on 24 benchmarked samples. The algorithms HISAT-genotype and HLA-HD showed the highest accuracy at both the first field and the second field resolution, followed by HLAscan. Our internal capture-based HLA assay showed comparable performance with whole exome sequencing (WES). We found that the minimal depth was 100X for HISAT-genotype and HLA-HD to obtain more than 90% accuracy at the third field level. The top three algorithms were quite robust to the change of read length. Thus, we recommend using HISAT-genotype and HLA-HD for NGS-based HLA genotyping because of their higher accuracy and robustness to read length. We propose that a minimal sequence depth for obtaining more than 90% HLA typing accuracy at the third field level is 100X. Besides, targeting capture-based NGS HLA typing may be more suitable than WES in clinical practice due to its lower sequencing cost and higher HLA sequencing depth.
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Affiliation(s)
- Ping Liu
- Department of Oncology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Minya Yao
- The First Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou, China
| | - Yu Gong
- Department of Urology, Second Affiliated Hospital, Zhejiang University College of Medicine, Hangzhou, China
| | - Yunjie Song
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Yanan Chen
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Yizhou Ye
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Xiao Liu
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Fugen Li
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Hua Dong
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Rui Meng
- Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Chen
- The Bioinformatics Department, 3DMed Inc., Shanghai, China
| | - Aiwen Zheng
- Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institute of Cancer and Basic Medicine (IBMC), Chinese Academy of Sciences, Hangzhou, China
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24
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New challenges, new opportunities: Next generation sequencing and its place in the advancement of HLA typing. Hum Immunol 2021; 82:478-487. [PMID: 33551127 DOI: 10.1016/j.humimm.2021.01.010] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 12/29/2020] [Accepted: 01/18/2021] [Indexed: 02/07/2023]
Abstract
The Human Leukocyte Antigen (HLA) system has a critical role in immunorecognition, transplantation, and disease association. Early typing techniques provided the foundation for genotyping methods that revealed HLA as one of the most complex, polymorphic regions of the human genome. Next Generation Sequencing (NGS), the latest molecular technology introduced in clinical tissue typing laboratories, has demonstrated advantages over other established methods. NGS offers high-resolution sequencing of entire genes in time frames and price points considered unthinkable just a few years ago, contributing a wealth of data informing histocompatibility assessment and standards of clinical care. Although the NGS platforms share a high-throughput massively parallel processing model, differing chemistries provide specific strengths and weaknesses. Research-oriented Third Generation Sequencing and related advances in bioengineering continue to broaden the future of NGS in clinical settings. These diverse applications have demanded equally innovative strategies for data management and computational bioinformatics to support and analyze the unprecedented volume and complexity of data generated by NGS. We discuss some of the challenges and opportunities associated with NGS technologies, providing a comprehensive picture of the historical developments that paved the way for the NGS revolution, its current state and future possibilities for HLA typing.
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25
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Dilthey AT. State-of-the-art genome inference in the human MHC. Int J Biochem Cell Biol 2021; 131:105882. [PMID: 33189874 DOI: 10.1016/j.biocel.2020.105882] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 10/29/2020] [Accepted: 11/04/2020] [Indexed: 12/20/2022]
Abstract
The Major Histocompatibility Complex (MHC) on the short arm of chromosome 6 is associated with more diseases than any other region of the genome; it encodes the antigen-presenting Human Leukocyte Antigen (HLA) proteins and is one of the key immunogenetic regions of the genome. Accurate genome inference and interpretation of MHC association signals have traditionally been hampered by the region's uniquely complex features, such as high levels of polymorphism; inter-gene sequence homologies; structural variation; and long-range haplotype structures. Recent algorithmic and technological advances have, however, significantly increased the accessibility of genetic variation in the MHC; these developments include (i) accurate SNP-based HLA type imputation; (ii) genome graph approaches for variation-aware genome inference from next-generation sequencing data; (iii) long-read-based diploid de novo assembly of the MHC; (iv) cost-effective targeted MHC sequencing methods. Applied to hundreds of thousands of samples over the last years, these technologies have already enabled significant biological discoveries, for example in the field of autoimmune disease genetics. Remaining challenges concern the development of integrated methods that leverage haplotype-resolved de novo assembly of the MHC for the development of improved MHC genotyping methods for short reads and the construction of improved reference panels for SNP-based imputation. Improved genome inference in the MHC can crucially contribute to an improved genetic and functional understanding of many immune-related phenotypes and diseases.
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Affiliation(s)
- Alexander T Dilthey
- Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany; Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany; Institute of Medical Microbiology and Hospital Hygiene, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
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26
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Li X, Zhou C, Chen K, Huang B, Liu Q, Ye H. Benchmarking HLA genotyping and clarifying HLA impact on survival in tumor immunotherapy. Mol Oncol 2021; 15:1764-1782. [PMID: 33411982 PMCID: PMC8253103 DOI: 10.1002/1878-0261.12895] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 12/03/2020] [Accepted: 01/05/2021] [Indexed: 11/08/2022] Open
Abstract
Human leukocyte antigen (HLA) genotyping gains intensive attention due to its critical role in cancer immunotherapy. It is still a challenging issue to generate reliable HLA genotyping results through in silico tools. In addition, the survival impact of HLA alleles in tumor prognosis and immunotherapy remains controversial. In this study, the benchmarking of HLA genotyping on TCGA is performed and a ‘Gun‐Bullet’ model which helps to clarify the survival impact of HLA allele is presented. The performance of HLA class I genotyping is generally better than class II. POLYSOLVER, OptiType, and xHLA perform generally better at HLA class I calling with an accuracy of 0.954, 0.949, and 0.937, respectively. HLA‐HD obtained the highest accuracy of 0.904 on HLA class II alleles calling. Each HLA genotyping tool displayed specific error patterns. The ensemble HLA calling from the top‐3 tools is superior to any individual one. HLA alleles show distinct survival impact among cancers. Cytolytic activity (CYT) was proposed as the underlying mechanism to interpret the survival impact of HLA alleles in the ‘Gun‐Bullet’ model for fighting cancer. A strong HLA allele plus a high tumor mutation burden (TMB) could stimulate intensive immune CYT, leading to extended survival. We established an up to now most reliable TCGA HLA benchmark dataset, composing of concordance alleles generated from eight prevalently used HLA genotyping tools. Our findings indicate that reliable HLA genotyping should be performed based on concordance alleles integrating multiple tools and incorporating TMB background with HLA genotype, which helps to improve the survival prediction compared to HLA genotyping alone.
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Affiliation(s)
- Xiangyong Li
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.,Sinotech Genomics, Shenzhen, China
| | - Chi Zhou
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Ke Chen
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Bingding Huang
- College of Big Data and Internet, Shenzhen Technology University, Shenzhen, China
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Hao Ye
- Sinotech Genomics, Shenzhen, China.,CStone Pharmaceuticals Co., Ltd., Suzhou, China
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27
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Chen R, Lee WC, Fujimoto J, Li J, Hu X, Mehran R, Rice D, Swisher SG, Sepesi B, Tran HT, Chow CW, Little LD, Gumbs C, Haymaker C, Heymach JV, Wistuba II, Lee JJ, Futreal PA, Zhang J, Reuben A, Tsao AS, Zhang J. Evolution of Genomic and T-cell Repertoire Heterogeneity of Malignant Pleural Mesothelioma Under Dasatinib Treatment. Clin Cancer Res 2020; 26:5477-5486. [PMID: 32816946 PMCID: PMC7709879 DOI: 10.1158/1078-0432.ccr-20-1767] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 06/30/2020] [Accepted: 08/05/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE Malignant pleural mesothelioma (MPM) is considered an orphan disease with few treatment options. Despite multimodality therapy, the majority of MPMs recur and eventually become refractory to any systemic treatment. One potential mechanism underlying therapeutic resistance may be intratumor heterogeneity (ITH), making MPM challenging to eradicate. However, the ITH architecture of MPM and its clinical impact have not been well studied. EXPERIMENTAL DESIGN We delineated the immunogenomic ITH by multiregion whole-exome sequencing and T-cell receptor (TCR) sequencing of 69 longitudinal MPM specimens from nine patients with resectable MPM, who were treated with dasatinib. RESULTS The median total mutation burden before dasatinib treatment was 0.65/Mb, similar with that of post-dasatinib treatment (0.62/Mb). The median proportion of mutations shared by any given pair of two tumor regions within the same tumors was 80% prior to and 83% post-dasatinib treatment indicating a relatively homogenous genomic landscape. T-cell clonality, a parameter indicating T-cell expansion and reactivity, was significantly increased in tumors after dasatinib treatment. Furthermore, on average, 82% of T-cell clones were restricted to individual tumor regions, with merely 6% of T-cell clones shared by all regions from the same tumors indicating profound TCR heterogeneity. Interestingly, patients with higher T-cell clonality and higher portion of T cells present across all tumor regions in post-dasatinib-treated tumors had significantly longer survival. CONCLUSIONS Despite the homogeneous genomic landscape, the TCR repertoire is extremely heterogeneous in MPM. Dasatinib may potentially induce T-cell response leading to improved survival.
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MESH Headings
- Adult
- Aged
- Clonal Evolution/genetics
- Dasatinib/administration & dosage
- Dasatinib/adverse effects
- Evolution, Molecular
- Female
- Genetic Heterogeneity
- Genome, Human/drug effects
- Genomics
- Humans
- Male
- Mesothelioma, Malignant/drug therapy
- Mesothelioma, Malignant/genetics
- Mesothelioma, Malignant/pathology
- Middle Aged
- Mutation/genetics
- Neoplasm Proteins/genetics
- Neoplasm Recurrence, Local/drug therapy
- Neoplasm Recurrence, Local/genetics
- Neoplasm Recurrence, Local/pathology
- Progression-Free Survival
- Receptors, Antigen, T-Cell/genetics
- T-Lymphocytes/drug effects
- T-Lymphocytes/pathology
- Exome Sequencing
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Affiliation(s)
- Runzhe Chen
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Won-Chul Lee
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Junya Fujimoto
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jun Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Xin Hu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Reza Mehran
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - David Rice
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Stephen G Swisher
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Boris Sepesi
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Hai T Tran
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Chi-Wan Chow
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Latasha D Little
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Curtis Gumbs
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Cara Haymaker
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Ignacio I Wistuba
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - P Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Jianhua Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Anne S Tsao
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
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28
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Zhuo X, Quann K, Parr K, Stewart R, Shlomchik W, Ito S, Berndt A. HLA‐poll: An ensemble suite of human leukocyte antigen‐prediction tools for whole‐exome and whole‐genome sequencing data. Int J Lab Hematol 2020. [DOI: 10.1111/ijlh.13280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Xinming Zhuo
- UPMC Genome CenterUniversity of Pittsburgh Medical Center Pittsburgh Pennsylvania USA
| | - Kevin Quann
- Division of Hematology‐Oncology Department of Medicine Hematopoietic Stem Cell Transplant and Cell Therapy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Kayla Parr
- Division of Hematology‐Oncology Department of Medicine Hematopoietic Stem Cell Transplant and Cell Therapy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Rachel Stewart
- UPMC Genome CenterUniversity of Pittsburgh Medical Center Pittsburgh Pennsylvania USA
| | - Warren Shlomchik
- Division of Hematology‐Oncology Department of Medicine Hematopoietic Stem Cell Transplant and Cell Therapy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Sawa Ito
- Division of Hematology‐Oncology Department of Medicine Hematopoietic Stem Cell Transplant and Cell Therapy University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Annerose Berndt
- UPMC Genome CenterUniversity of Pittsburgh Medical Center Pittsburgh Pennsylvania USA
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29
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Cismaru AL, Grimm L, Rudin D, Ibañez L, Liakoni E, Bonadies N, Kreutz R, Hallberg P, Wadelius M, Haschke M, Largiadèr CR, Amstutz U. High-Throughput Sequencing to Investigate Associations Between HLA Genes and Metamizole-Induced Agranulocytosis. Front Genet 2020; 11:951. [PMID: 32973882 PMCID: PMC7473498 DOI: 10.3389/fgene.2020.00951] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 07/29/2020] [Indexed: 12/18/2022] Open
Abstract
Background and Objective: Agranulocytosis is a rare and potentially life-threatening complication of metamizole (dipyrone) intake that is characterized by a loss of circulating neutrophil granulocytes. While the mechanism underlying this adverse drug reaction is not well understood, involvement of the immune system has been suggested. In addition, associations between genetic variants in the Human Leukocyte Antigen (HLA) region and agranulocytosis induced by other drugs have been reported. The aim of the present study was to assess whether genetic variants in classical HLA genes are associated with the susceptibility to metamizole-induced agranulocytosis (MIA) in a European population by targeted resequencing of eight HLA genes. Design: A case-control cohort of Swiss patients with a history of neutropenia or agranulocytosis associated with metamizole exposure (n = 53), metamizole-tolerant (n = 39) and unexposed controls (n = 161) was recruited for this study. A high-throughput resequencing (HTS) and high-resolution typing method was used to sequence and analyze eight HLA loci in a discovery subset of this cohort (n = 31 cases, n = 38 controls). Identified candidate alleles were investigated in the full Swiss cohort as well as in two independent cohorts from Germany and Spain using HLA imputation from genome-wide SNP array data. In addition, variant calling based on HTS data was performed in the discovery subset for the class I genes HLA-A, -B, and -C using the HLA-specific mapper hla-mapper. Results: Eight candidate alleles (p < 0.05) were identified in the discovery subset, of which HLA-C∗04:01 was associated with MIA in the full Swiss cohort (p < 0.01) restricted to agranulocytosis (ANC < 0.5 × 109/L) cases. However, no candidate allele showed a consistent association in the Swiss, German and Spanish cohorts. Analysis of individual sequence variants in class I genes produced consistent results with HLA typing but did not reveal additional small nucleotide variants associated with MIA. Conclusion: Our results do not support an HLA-restricted T cell-mediated immune mechanism for MIA. However, we established an efficient high-resolution (three-field) eight-locus HTS HLA resequencing method to interrogate the HLA region and demonstrated the feasibility of its application to pharmacogenetic studies.
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Affiliation(s)
- Anca Liliana Cismaru
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Graduate School for Cellular and Biomedical Sciences, University of Bern, Bern, Switzerland
| | - Livia Grimm
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Deborah Rudin
- Division of Clinical Pharmacology & Toxicology, University Hospital Basel, Basel, Switzerland.,Department of Biomedicine, University of Basel, Basel, Switzerland
| | - Luisa Ibañez
- Clinical Pharmacology Service, Hospital Universitari Vall d'Hebron, Department of Pharmacology, Therapeutics and Toxicology, Fundació Institut Català de Farmacologia, Autonomous University of Barcelona, Barcelona, Spain
| | - Evangelia Liakoni
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Institute of Pharmacology, University of Bern, Bern, Switzerland
| | - Nicolas Bonadies
- Department of Hematology and Central Hematology Laboratory, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Reinhold Kreutz
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institut für Klinische Pharmakologie und Toxikologie, Berlin, Germany
| | - Pär Hallberg
- Department of Medical Sciences, Clinical Pharmacology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Mia Wadelius
- Department of Medical Sciences, Clinical Pharmacology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Manuel Haschke
- Clinical Pharmacology and Toxicology, Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Institute of Pharmacology, University of Bern, Bern, Switzerland
| | - Carlo R Largiadèr
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Ursula Amstutz
- Department of Clinical Chemistry, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
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30
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Liu C, Duffy BF, Weimer ET, Montgomery MC, Jennemann JE, Hill R, Phelan D, Lay L, Parikh BA. Performance of a multiplexed amplicon-based next-generation sequencing assay for HLA typing. PLoS One 2020; 15:e0232050. [PMID: 32324777 PMCID: PMC7179861 DOI: 10.1371/journal.pone.0232050] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 04/06/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Next-generation sequencing (NGS) has enabled efficient high-resolution typing of human leukocyte antigen (HLA) genes with minimal ambiguity. Most commercially available assays amplify individual or subgroup of HLA genes by long-range PCR followed by library preparation and sequencing. The AllType assay simplifies the workflow by amplifying 11 transplant-relevant HLA genes in one PCR reaction. Here, we report the performance of this unique workflow evaluated using 218 genetically diverse samples. METHODS Five whole genes (HLA-A/B/C/DQA1/DPA1) and six near-whole genes (HLA-DRB1/DRB345/DQB1/DPB1; excluding exon 1 and part of intron 1) were amplified in a multiplexed, long-range PCR. Manual library preparation was performed per manufacturer's protocol, followed by template preparation and chip loading on the Ion Chef, and sequencing on the Ion S5 sequencer. Pre-specified rules for quality control and repeat testing were followed; technologists were blinded to the reference results. The concordance between AllType and reference results was determined at 2-field resolution. We also describe the ranges of input DNA and library concentrations, read number per sample and per locus, and key health metrics in relation to typing results. RESULTS The concordance rates were 98.6%, 99.8% and 99.9% at the sample (n = 218), genotype (n = 1688), and allele (n = 3376) levels, respectively. Three genotypes were discordant, all of which shared the same G group typing results with the reference. Most ambiguous genotypes (116 out of 144, 80.6%) were due to the lack of exon 1 and intron 1 coverage for HLA-DRB1/DRB345/DQB1/DPB1 genes. A broad range of input DNA concentrations and library concentrations were tolerated. Per sample read numbers were adequate for accurate genotyping. Per locus read numbers showed some inter-lot variations, and a trend toward improved inter-locus balance was observed with later lots of reagents. CONCLUSION The AllType assay on the Ion Chef/Ion S5 platform offers a robust and efficient workflow for clinical HLA typing at the 2-field resolution. The multiplex PCR strategy simplifies the laboratory procedure without compromising the typing accuracy.
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Affiliation(s)
- Chang Liu
- Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Brian F. Duffy
- HLA Laboratory, Barnes-Jewish Hospital, St. Louis, Missouri, United States of America
| | - Eric T. Weimer
- Department of Pathology & Laboratory Medicine, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina, United States of America
- Molecular Immunology Laboratory, McLendon Clinical Laboratories, UNC Hospitals, Chapel Hill, North Carolina, United States of America
| | - Maureen C. Montgomery
- Molecular Immunology Laboratory, McLendon Clinical Laboratories, UNC Hospitals, Chapel Hill, North Carolina, United States of America
| | - Jo-Ellen Jennemann
- HLA Laboratory, Barnes-Jewish Hospital, St. Louis, Missouri, United States of America
| | - Rachel Hill
- HLA Laboratory, Barnes-Jewish Hospital, St. Louis, Missouri, United States of America
| | - Donna Phelan
- HLA Laboratory, Barnes-Jewish Hospital, St. Louis, Missouri, United States of America
| | - Lindsay Lay
- HLA Laboratory, Barnes-Jewish Hospital, St. Louis, Missouri, United States of America
| | - Bijal A. Parikh
- Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology, School of Medicine, Washington University in St. Louis, St. Louis, Missouri, United States of America
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31
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Manjili MH. The premise of personalized immunotherapy for cancer dormancy. Oncogene 2020; 39:4323-4330. [PMID: 32322001 PMCID: PMC7260096 DOI: 10.1038/s41388-020-1295-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Revised: 03/27/2020] [Accepted: 04/06/2020] [Indexed: 12/20/2022]
Abstract
Progress in cancer therapies has resulted in improved survival of patients with early stage breast cancer. However, mortality remains high in patients with distant recurrence of the disease after initially successful treatment of early stage breast cancer. To this end, tumor recurrences have been attributed to the presence of dormant tumor cells in breast cancer patients and cancer survivors. Current clinical practice guidelines recommend a “wait and watch” approach for tumor recurrence. This is because of our limited understanding of tumor dormancy. Dormant tumor cells are quiescent, and thus, do not respond to chemotherapies or radiation therapies, and they are not operable. Therefore, immunotherapy is the only option for the treatment of tumor dormancy. However, gaps in our knowledge as to dormancy-specific antigens prevent a relapse preventing vaccine design. Here, we provide a critical review of cancer immunotherapy, and discuss empirical evidence related to naturally-occurring tumor dormancy and treatment-induced tumor dormancy at the site of primary tumor and in distant organs before and after cancer therapies. Finally, we suggest that personalized vaccines targeting dormancy-associated neoantigens, which can be given to patients with early stage disease after the completion of neoadjuvant therapies and tumor resection as well as to cancer survivors could eliminate relapse causing dormant cells and offer a cure for cancer.
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Affiliation(s)
- Masoud H Manjili
- Department of Microbiology & Immunology, VCU School of Medicine, VCU Institute of Molecular Medicine, Massey Cancer Center, Richmond, VA, USA.
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32
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Smith CC, Bixby LM, Miller KL, Selitsky SR, Bortone DS, Hoadley KA, Vincent BG, Serody JS. Using RNA Sequencing to Characterize the Tumor Microenvironment. Methods Mol Biol 2020; 2055:245-272. [PMID: 31502156 DOI: 10.1007/978-1-4939-9773-2_12] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
RNA sequencing (RNA-seq) is an integral tool in immunogenomics, allowing for interrogation of the transcriptome of a tumor and its microenvironment. Analytical methods to deconstruct the genomics data can then be applied to infer gene expression patterns associated with the presence of various immunocyte populations. High quality RNA-seq is possible from formalin-fixed, paraffin-embedded (FFPE), fresh-frozen, and fresh tissue, with a wide variety of sequencing library preparation methods, sequencing platforms, and downstream bioinformatics analyses currently available. Selection of an appropriate library preparation method is largely determined by tissue type, quality of RNA, and quantity of RNA. Downstream of sequencing, many analyses can be applied to the data, including differential gene expression analysis, immune gene signature analysis, gene pathway analysis, T/B-cell receptor inference, HLA inference, and viral transcript quantification. In this chapter, we will describe our workflow for RNA-seq from bulk tissue to evaluable data, including extraction of RNA, library preparation methods, sequencing of libraries, alignment and quality assurance of data, and initial downstream analyses of RNA-seq data to extract relevant immunogenomics features. Systems biology methods that draw additional insights by integrating these features are covered further in Chapters 28 - 30 .
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Affiliation(s)
- C C Smith
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - L M Bixby
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K L Miller
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - S R Selitsky
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - D S Bortone
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - K A Hoadley
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - B G Vincent
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - J S Serody
- Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. .,Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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33
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Nordin J, Ameur A, Lindblad-Toh K, Gyllensten U, Meadows JRS. SweHLA: the high confidence HLA typing bio-resource drawn from 1000 Swedish genomes. Eur J Hum Genet 2019; 28:627-635. [PMID: 31844174 PMCID: PMC7170882 DOI: 10.1038/s41431-019-0559-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 11/12/2019] [Accepted: 11/26/2019] [Indexed: 11/10/2022] Open
Abstract
There is a need to accurately call human leukocyte antigen (HLA) genes from existing short-read sequencing data, however there is no single solution that matches the gold standard of Sanger sequenced lab typing. Here we aimed to combine results from available software programs, minimizing the biases of applied algorithm and HLA reference. The result is a robust HLA population resource for the published 1000 Swedish genomes, and a framework for future HLA interrogation. HLA 2nd-field alleles were called using four imputation and inference methods for the classical eight genes (class I: HLA-A, HLA-B, HLA-C; class II: HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DRB1). A high confidence population set (SweHLA) was determined using an n−1 concordance rule for class I (four software) and class II (three software) alleles. Results were compared across populations and individual programs benchmarked to SweHLA. Per gene, 875 to 988 of the 1000 samples were genotyped in SweHLA; 920 samples had at least seven loci called. While a small fraction of reference alleles were common to all software (class I = 1.9% and class II = 4.1%), this did not affect the overall call rate. Gene-level concordance was high compared to European populations (>0.83%), with COX and PGF the dominant SweHLA haplotypes. We noted that 15/18 discordant alleles (delta allele frequency >2) were previously reported as disease-associated. These differences could in part explain across-study genetic replication failures, reinforcing the need to use multiple software solutions. SweHLA demonstrates a way to use existing NGS data to generate a population resource agnostic to individual HLA software biases.
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Affiliation(s)
- Jessika Nordin
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.
| | - Adam Ameur
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ulf Gyllensten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jennifer R S Meadows
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
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34
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D'Antonio M, Reyna J, Jakubosky D, Donovan MKR, Bonder MJ, Matsui H, Stegle O, Nariai N, D'Antonio-Chronowska A, Frazer KA. Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease. eLife 2019; 8:e48476. [PMID: 31746734 PMCID: PMC6904215 DOI: 10.7554/elife.48476] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 11/19/2019] [Indexed: 02/06/2023] Open
Abstract
The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease. We create a comprehensive map of regulatory variation in the MHC region using WGS from 419 individuals to call eight-digit HLA types and RNA-seq data from matched iPSCs. Building on this regulatory map, we explored GWAS signals for 4083 traits, detecting colocalization for 180 disease loci with eQTLs. We show that eQTL analyses taking HLA type haplotypes into account have substantially greater power compared with only using single variants. We examined the association between the 8.1 ancestral haplotype and delayed colonization in Cystic Fibrosis, postulating that downregulation of RNF5 expression is the likely causal mechanism. Our study provides insights into the genetic architecture of the MHC region and pinpoints disease associations that are due to differential expression of HLA genes and non-HLA genes.
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Affiliation(s)
- Matteo D'Antonio
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Joaquin Reyna
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
- Biomedical Sciences Graduate ProgramUniversity of California, San DiegoLa JollaUnited States
| | - David Jakubosky
- Biomedical Sciences Graduate ProgramUniversity of California, San DiegoLa JollaUnited States
- Bioinformatics and Systems Biology Graduate ProgramUniversity of California, San DiegoSan DiegoUnited States
| | - Margaret KR Donovan
- Bioinformatics and Systems Biology Graduate ProgramUniversity of California, San DiegoSan DiegoUnited States
- Department of Biomedical InformaticsUniversity of California, San DiegoSan DiegoUnited States
| | - Marc-Jan Bonder
- European Molecular Biology Laboratory, European Bioinformatics InstituteCambridgeUnited Kingdom
| | - Hiroko Matsui
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
| | - Oliver Stegle
- European Molecular Biology Laboratory, European Bioinformatics InstituteCambridgeUnited Kingdom
| | - Naoki Nariai
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Agnieszka D'Antonio-Chronowska
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
| | - Kelly A Frazer
- Institute for Genomic MedicineUniversity of California, San DiegoSan DiegoUnited States
- Department of PediatricsRady Children’s Hospital, University of California, San DiegoSan DiegoUnited States
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35
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Abstract
Tumor cells acquire distinct genetic characteristics as a means to survive and proliferate indefinitely. Changes in the genetic code can also translate in changes at the protein level, therefore creating a distinguishable signature unique for tumor cells, and absent in normal tissues. The presence of discernable moieties in tumors is particularly attractive because it represents a therapeutic opportunity to target tumor cells with specificity, while sparing non-transformed cells. In this sense neoantigens, short peptides containing a mutated sequence, are seen attractive therapeutic targets because of their confinement within tumor cells. Neoantigens can be recognized with high affinity and specificity by tumor-targeting T cells, which consequently can initiate a potent anti-tumor immune response. While this is feasible and it has been tested in numerous cancer types including melanoma, colon and lung cancer, to mention a few, there are technical challenges in identifying immunogenic neoantigens. In this manuscript we address the topic of neoantigen identification from tumor samples, offering a technical overview of the bioinformatic methods utilized to profile the neoantigenic load of tumor samples obtained from clinical specimens. This is meant to guide readers through the steps of neoantigen identification using genomic data, by suggesting tools and methods that can provide, with a high degree of confidence, reliable results for downstream in vitro and in vivo applications.
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Affiliation(s)
- Sebastiano Battaglia
- Center For Immunotherapy, Department of Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, NY, United States.
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36
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Chen B, Khodadoust MS, Olsson N, Wagar LE, Fast E, Liu CL, Muftuoglu Y, Sworder BJ, Diehn M, Levy R, Davis MM, Elias JE, Altman RB, Alizadeh AA. Predicting HLA class II antigen presentation through integrated deep learning. Nat Biotechnol 2019; 37:1332-1343. [PMID: 31611695 PMCID: PMC7075463 DOI: 10.1038/s41587-019-0280-2] [Citation(s) in RCA: 187] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 09/09/2019] [Indexed: 12/21/2022]
Abstract
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture; https://maria.stanford.edu/ ), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and protease cleavage signatures. Because it leverages these diverse training data and our improved machine learning framework, MARIA (area under the curve = 0.89-0.92) outperformed existing methods in validation datasets. Across independent cancer neoantigen studies, peptides with high MARIA scores are more likely to elicit strong CD4+ T cell responses. MARIA allows identification of immunogenic epitopes in diverse cancers and autoimmune disease.
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Affiliation(s)
- Binbin Chen
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Michael S Khodadoust
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Niclas Olsson
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
| | - Lisa E Wagar
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
| | - Ethan Fast
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Nash, Vaduz, Liechtenstein
| | - Chih Long Liu
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Yagmur Muftuoglu
- Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Brian J Sworder
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Maximilian Diehn
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA
| | - Ronald Levy
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA
| | - Mark M Davis
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Joshua E Elias
- Department of Chemical and Systems Biology, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, Stanford University, Stanford, CA, USA
| | - Russ B Altman
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ash A Alizadeh
- Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Stem Cell Biology & Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Center for Cancer Systems Biology, Stanford University, Stanford, CA, USA.
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37
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Klasberg S, Surendranath V, Lange V, Schöfl G. Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping. Transfus Med Hemother 2019; 46:312-325. [PMID: 31832057 PMCID: PMC6876610 DOI: 10.1159/000502487] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 07/30/2019] [Indexed: 12/16/2022] Open
Abstract
The advent of next generation sequencing (NGS) has altered the face of genotyping the human leukocyte antigen (HLA) system in clinical, stem cell donor registry, and research contexts. NGS has led to a dramatically increased sequencing throughput at high accuracy, while being more time and cost efficient than precursor technologies. This has led to a broader and deeper profiling of the key genes in the human immunogenetic make-up. The rapid evolution of sequencing technologies is evidenced by the development of varied short-read sequencing platforms with differing read lengths and sequencing capacities to long-read sequencing platforms capable of profiling full genes without fragmentation. Concomitantly, there has been development of a diverse set of computational analyses and software tools developed to deal with the various strengths and limitations of the sequencing data generated by the different sequencing platforms. This review surveys the different modalities involved in generating NGS HLA profiling sequence data. It systematically describes various computational approaches that have been developed to achieve HLA genotyping to different degrees of resolution. At each stage, this review enumerates the drawbacks and advantages of each of the platforms and analysis approaches, thus providing a comprehensive picture of the current state of HLA genotyping technologies.
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38
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Richters MM, Xia H, Campbell KM, Gillanders WE, Griffith OL, Griffith M. Best practices for bioinformatic characterization of neoantigens for clinical utility. Genome Med 2019; 11:56. [PMID: 31462330 PMCID: PMC6714459 DOI: 10.1186/s13073-019-0666-2] [Citation(s) in RCA: 125] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Accepted: 08/16/2019] [Indexed: 12/13/2022] Open
Abstract
Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.
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Affiliation(s)
- Megan M Richters
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Huiming Xia
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA
| | - Katie M Campbell
- Division of Hematology and Oncology, Medical Plaza Driveway, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA
| | - William E Gillanders
- Department of Surgery, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Obi L Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
| | - Malachi Griffith
- Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
- Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
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39
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HLA-VBSeq v2: improved HLA calling accuracy with full-length Japanese class-I panel. Hum Genome Var 2019; 6:29. [PMID: 31240105 PMCID: PMC6584547 DOI: 10.1038/s41439-019-0061-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/26/2019] [Accepted: 05/27/2019] [Indexed: 12/22/2022] Open
Abstract
HLA-VBSeq is an HLA calling tool developed to infer the most likely HLA types from high-throughput sequencing data. However, there is still room for improvement in specific genetic groups because of the diversity of HLA alleles in human populations. Here, we present HLA-VBSeq v2, a software application that makes use of a new Japanese HLA reference panel to enhance calling accuracy for Japanese HLA class-I genes. Our analysis showed significant improvements in calling accuracy in all HLA regions, with prediction accuracies achieving over 99.0, 97.8, and 99.8% in HLA-A, B and C, respectively.
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40
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3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. Hum Genome Var 2019; 6:28. [PMID: 31240104 PMCID: PMC6581902 DOI: 10.1038/s41439-019-0059-5] [Citation(s) in RCA: 104] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 04/27/2019] [Indexed: 12/13/2022] Open
Abstract
The first step towards realizing personalized healthcare is to catalog the genetic variations in a population. Since the dissemination of individual-level genomic information is strictly controlled, it will be useful to construct population-level allele frequency panels with easy-to-use interfaces. In the Tohoku Medical Megabank Project, we sequenced nearly 4000 individuals from a Japanese population and constructed an allele frequency panel of 3552 individuals after removing related samples. The panel is called the 3.5KJPNv2. It was constructed by using a standard pipeline including the 1KGP and gnomAD algorithms to reduce technical biases and to allow comparisons to other populations. Our database is the first large-scale panel providing the frequencies of variants present on the X chromosome and on the mitochondria in the Japanese population. All the data are available on our original database at https://jmorp.megabank.tohoku.ac.jp. A new database provides information on the frequency of genetic variations within 3552 Japanese individuals, and facilitates comparisons with other populations. The reference panel, constructed by Kengo Kinoshita of Tohoku University, Sendai, and colleagues in Japan is also the first large-scale database to provide genetic variation frequency information on the X chromosome and mitochondrial DNA in the Japanese population. The methods used to sequence the genetic data are similar to those used in other large databases, allowing comparisons with other populations. The population size and methods used to compile the database overcome limitations in previous Japanese reference panels. This and similar databases that catalog genetic variations within populations can improve efforts towards personalizing healthcare and contribute to the study of human population genetics. The database is publicly available online.
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41
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Hayashi S, Moriyama T, Yamaguchi R, Mizuno S, Komura M, Miyano S, Nakagawa H, Imoto S. ALPHLARD-NT: Bayesian Method for Human Leukocyte Antigen Genotyping and Mutation Calling through Simultaneous Analysis of Normal and Tumor Whole-Genome Sequence Data. J Comput Biol 2019; 26:923-937. [PMID: 30942618 PMCID: PMC6748403 DOI: 10.1089/cmb.2018.0224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Human leukocyte antigen (HLA) genes provide useful information on the relationship between cancer and the immune system. Despite the ease of obtaining these data through next-generation sequencing methods, interpretation of these relationships remains challenging owing to the complexity of HLA genes. To resolve this issue, we developed a Bayesian method, ALPHLARD-NT, to identify HLA germline and somatic mutations as well as HLA genotypes from whole-exome sequencing (WES) and whole-genome sequencing (WGS) data. ALPHLARD-NT showed 99.2% accuracy for WGS-based HLA genotyping and detected five HLA somatic mutations in 25 colon cancer cases. In addition, ALPHLARD-NT identified 88 HLA somatic mutations, including recurrent mutations and a novel HLA-B type, from WES data of 343 colon adenocarcinoma cases. These results demonstrate the potential of ALPHLARD-NT for conducting an accurate analysis of HLA genes even from low-coverage data sets. This method can become an essential tool for comprehensive analyses of HLA genes from WES and WGS data, helping to advance understanding of immune regulation in cancer as well as providing guidance for novel immunotherapy strategies.
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Affiliation(s)
- Shuto Hayashi
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Takuya Moriyama
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Rui Yamaguchi
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Shinichi Mizuno
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Mitsuhiro Komura
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | - Satoru Miyano
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
| | | | - Seiya Imoto
- Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan
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42
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Mimori T, Yasuda J, Kuroki Y, Shibata TF, Katsuoka F, Saito S, Nariai N, Ono A, Nakai-Inagaki N, Misawa K, Tateno K, Kawai Y, Fuse N, Hozawa A, Kuriyama S, Sugawara J, Minegishi N, Suzuki K, Kinoshita K, Nagasaki M, Yamamoto M. Construction of full-length Japanese reference panel of class I HLA genes with single-molecule, real-time sequencing. THE PHARMACOGENOMICS JOURNAL 2019; 19:136-146. [PMID: 29352165 PMCID: PMC6462828 DOI: 10.1038/s41397-017-0010-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Revised: 10/11/2017] [Accepted: 11/06/2017] [Indexed: 12/30/2022]
Abstract
Human leukocyte antigen (HLA) is a gene complex known for its exceptional diversity across populations, importance in organ and blood stem cell transplantation, and associations of specific alleles with various diseases. We constructed a Japanese reference panel of class I HLA genes (ToMMo HLA panel), comprising a distinct set of HLA-A, HLA-B, HLA-C, and HLA-H alleles, by single-molecule, real-time (SMRT) sequencing of 208 individuals included in the 1070 whole-genome Japanese reference panel (1KJPN). For high-quality allele reconstruction, we developed a novel pipeline, Primer-Separation Assembly and Refinement Pipeline (PSARP), in which the SMRT sequencing and additional short-read data were used. The panel consisted of 139 alleles, which were all extended from known IPD-IMGT/HLA sequences, contained 40 with novel variants, and captured more than 96.5% of allelic diversity in 1KJPN. These newly available sequences would be important resources for research and clinical applications including high-resolution HLA typing, genetic association studies, and analyzes of cis-regulatory elements.
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Affiliation(s)
- Takahiro Mimori
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
- Graduate School of Medicine, Tohoku University, Sendai, Japan.
| | - Yoko Kuroki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- National Research Institute for Child Health and Development, Tokyo, Japan
| | - Tomoko F Shibata
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Sakae Saito
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Naoki Nariai
- Department of Pediatrics and Rady Children's Hospital, University of California, San Diego, USA
| | - Akira Ono
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | | | - Kazuharu Misawa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Keiko Tateno
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
| | - Yosuke Kawai
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- International Research Institute of Disaster Science, Tohoku University, Sendai, Japan
| | - Junichi Sugawara
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
| | - Kichiya Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
- Tohoku University Hospital, Tohoku University, Sendai, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan
| | - Masao Nagasaki
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan.
- Graduate School of Medicine, Tohoku University, Sendai, Japan.
- Graduate School of Information Sciences, Tohoku University, Sendai, Japan.
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan
- Graduate School of Medicine, Tohoku University, Sendai, Japan
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43
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Bauer DC, Zadoorian A, Wilson LOW, Thorne NP. Evaluation of computational programs to predict HLA genotypes from genomic sequencing data. Brief Bioinform 2019; 19:179-187. [PMID: 27802932 PMCID: PMC6019030 DOI: 10.1093/bib/bbw097] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2016] [Indexed: 12/15/2022] Open
Abstract
Motivation Despite being essential for numerous clinical and research applications, high-resolution human leukocyte antigen (HLA) typing remains challenging and laboratory tests are also time-consuming and labour intensive. With next-generation sequencing data becoming widely accessible, on-demand in silico HLA typing offers an economical and efficient alternative. Results In this study we evaluate the HLA typing accuracy and efficiency of five computational HLA typing methods by comparing their predictions against a curated set of > 1000 published polymerase chain reaction-derived HLA genotypes on three different data sets (whole genome sequencing, whole exome sequencing and transcriptomic sequencing data). The highest accuracy at clinically relevant resolution (four digits) we observe is 81% on RNAseq data by PHLAT and 99% accuracy by OptiType when limited to Class I genes only. We also observed variability between the tools for resource consumption, with runtime ranging from an average of 5 h (HLAminer) to 7 min (seq2HLA) and memory from 12.8 GB (HLA-VBSeq) to 0.46 GB (HLAminer) per sample. While a minimal coverage is required, other factors also determine prediction accuracy and the results between tools do not correlate well. Therefore, by combining tools, there is the potential to develop a highly accurate ensemble method that is able to deliver fast, economical HLA typing from existing sequencing data.
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Affiliation(s)
| | - Armella Zadoorian
- CSIRO, Sydney, Australia.,School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia
| | | | | | - Natalie P Thorne
- Murdoch Childrens Research Institute, Royal Children's Hospital, Parkville, Australia.,Department of Medical Biology, The University of Melbourne, Parkville, Australia.,Melbourne Genomics Health Alliance, Parkville, Australia.,Walter and Eliza Hall Institute, Parkville, Australia
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44
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Yasuda J, Kinoshita K, Katsuoka F, Danjoh I, Sakurai-Yageta M, Motoike IN, Kuroki Y, Saito S, Kojima K, Shirota M, Saigusa D, Otsuki A, Kawashima J, Yamaguchi-Kabata Y, Tadaka S, Aoki Y, Mimori T, Kumada K, Inoue J, Makino S, Kuriki M, Fuse N, Koshiba S, Tanabe O, Nagasaki M, Tamiya G, Shimizu R, Takai-Igarashi T, Ogishima S, Hozawa A, Kuriyama S, Sugawara J, Tsuboi A, Kiyomoto H, Ishii T, Tomita H, Minegishi N, Suzuki Y, Suzuki K, Kawame H, Tanaka H, Taki Y, Yaegashi N, Kure S, Nagami F, Kosaki K, Sutoh Y, Hachiya T, Shimizu A, Sasaki M, Yamamoto M. Genome analyses for the Tohoku Medical Megabank Project towards establishment of personalized healthcare. J Biochem 2019; 165:139-158. [PMID: 30452759 DOI: 10.1093/jb/mvy096] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Accepted: 11/10/2018] [Indexed: 12/29/2022] Open
Abstract
Personalized healthcare (PHC) based on an individual's genetic make-up is one of the most advanced, yet feasible, forms of medical care. The Tohoku Medical Megabank (TMM) Project aims to combine population genomics, medical genetics and prospective cohort studies to develop a critical infrastructure for the establishment of PHC. To date, a TMM CommCohort (adult general population) and a TMM BirThree Cohort (birth+three-generation families) have conducted recruitments and baseline surveys. Genome analyses as part of the TMM Project will aid in the development of a high-fidelity whole-genome Japanese reference panel, in designing custom single-nucleotide polymorphism (SNP) arrays specific to Japanese, and in estimation of the biological significance of genetic variations through linked investigations of the cohorts. Whole-genome sequencing from >3,500 unrelated Japanese and establishment of a Japanese reference genome sequence from long-read data have been done. We next aim to obtain genotype data for all TMM cohort participants (>150,000) using our custom SNP arrays. These data will help identify disease-associated genomic signatures in the Japanese population, while genomic data from TMM BirThree Cohort participants will be used to improve the reference genome panel. Follow-up of the cohort participants will allow us to test the genetic markers and, consequently, contribute to the realization of PHC.
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Affiliation(s)
- Jun Yasuda
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Kengo Kinoshita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Applied Information Sciences, Graduate School of Information Sciences, Tohoku University, 6-6-05 Aramaki Aza Aoba, Aoba-ku, Sendai, Japan.,Institute of Development, Aging, and Cancer, Tohoku University, 4-1, Seiryo-machi, Aoba-ku, Sendai, Japan
| | - Fumiki Katsuoka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Inaho Danjoh
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Mika Sakurai-Yageta
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Ikuko N Motoike
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Yoko Kuroki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Sakae Saito
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Kaname Kojima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Matsuyuki Shirota
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,United Centers for Advanced Research and Translational Medicine
| | - Daisuke Saigusa
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Akihito Otsuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Junko Kawashima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Yumi Yamaguchi-Kabata
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Shu Tadaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Yuichi Aoki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Takahiro Mimori
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Kazuki Kumada
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Jin Inoue
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Satoshi Makino
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Miho Kuriki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Nobuo Fuse
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Seizo Koshiba
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Osamu Tanabe
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Masao Nagasaki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Gen Tamiya
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Ritsuko Shimizu
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Molecular Hematology, Tohoku University Graduate School of Medicine, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Takako Takai-Igarashi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Soichi Ogishima
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Atsushi Hozawa
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Shinichi Kuriyama
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,International Research Institute of Disaster Science, Tohoku University, 468-1, Aramaki Aza Aoba, Aoba-ku, Sendai, Japan
| | - Junichi Sugawara
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Akito Tsuboi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Hideyasu Kiyomoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Tadashi Ishii
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Education and Support for Community Medicine, Tohoku University Graduate School of Medicine, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Hiroaki Tomita
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,International Research Institute of Disaster Science, Tohoku University, 468-1, Aramaki Aza Aoba, Aoba-ku, Sendai, Japan
| | - Naoko Minegishi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Yoichi Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Kichiya Suzuki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Hiroshi Kawame
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Hiroshi Tanaka
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Medical Data Science Promotion Office, Tokyo Medical and Dental University, 2-3-10 Kandasurugadai, Chiyoda-ku, Tokyo, Japan
| | - Yasuyuki Taki
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Institute of Development, Aging, and Cancer, Tohoku University, 4-1, Seiryo-machi, Aoba-ku, Sendai, Japan
| | - Nobuo Yaegashi
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Obstetrics and Gynecology
| | - Shigeo Kure
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Pediatrics, Tohoku University Graduate School of Medicine, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | - Fuji Nagami
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
| | | | - Kenjiro Kosaki
- Center for Medical Genetics, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, Japan
| | - Yoichi Sutoh
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center
| | - Tsuyoshi Hachiya
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center
| | - Atsushi Shimizu
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center
| | - Makoto Sasaki
- Iwate Tohoku Medical Megabank Organization, Disaster Reconstruction Center.,Division of Ultrahigh Field MRI, Institute for Biomedical Sciences Iwate Medical University, 2-1-1 Nishitokuta, Yahaba, Shiwa, Iwate, Japan
| | - Masayuki Yamamoto
- Tohoku Medical Megabank Organization, Tohoku University, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan.,Department of Medical Biochemistry, Tohoku University Graduate School of Medicine, 2-1, Seiryo-Machi, Aoba-ku, Sendai, Japan
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45
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Empirical evaluation of variant calling accuracy using ultra-deep whole-genome sequencing data. Sci Rep 2019; 9:1784. [PMID: 30741997 PMCID: PMC6370902 DOI: 10.1038/s41598-018-38346-0] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 12/19/2018] [Indexed: 11/09/2022] Open
Abstract
In the design of whole-genome sequencing (WGS) studies, sequencing depth is a crucial parameter to define variant calling accuracy and study cost, with no standard recommendations having been established. We empirically evaluated the variant calling accuracy of the WGS pipeline using ultra-deep WGS data (approximately 410×). We randomly sampled sequence reads and constructed a series of simulation WGS datasets with a variety of gradual depths (n = 54; from 0.05× to 410×). Next, we evaluated the genotype concordances of the WGS data with those in the SNP microarray data or the WGS data using all the sequence reads. In addition, we assessed the accuracy of HLA allele genotyping using the WGS data with multiple software tools (PHLAT, HLA-VBseq, HLA-HD, and SNP2HLA). The WGS data with higher depths showed higher concordance rates, and >13.7× depth achieved as high as >99% of concordance. Comparisons with the WGS data using all the sequence reads showed that SNVs achieved >95% of concordance at 17.6× depth, whereas indels showed only 60% concordance. For the accuracy of HLA allele genotyping using the WGS data, 13.7× depth showed sufficient accuracy while performance heterogeneity among the software tools was observed (the highest concordance of 96.9% was observed with HLA-HD). Improvement in HLA genotyping accuracy by further increasing the depths was limited. These results suggest a medium degree of the WGS depth setting (approximately 15×) to achieve both accurate SNV calling and cost-effectiveness, whereas relatively higher depths are required for accurate indel calling.
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46
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Wang X, Fang H, Cheng Y, Li L, Sun X, Fu T, Huang P, Zhang A, Feng Z, Li C, Huang X, Li G, Du P, Yang H, Fang X, Li F, Gao Q, Liu B. The molecular landscape of synchronous colorectal cancer reveals genetic heterogeneity. Carcinogenesis 2019; 39:708-718. [PMID: 29546405 PMCID: PMC5932564 DOI: 10.1093/carcin/bgy040] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Accepted: 03/10/2018] [Indexed: 12/22/2022] Open
Abstract
Synchronous colorectal cancers (syCRCs), which present two or more lesions at diagnosis, are rare and pose a great challenge for clinical management. Although some predisposing factors associated with syCRCs have been studied with limited accession, the full repertoire of genomic events among the lesions within an individual and the causes of syCRCs remain unclear. We performed whole-exome sequencing of 40 surgical tumour samples of paired lesions from 20 patients to characterize the genetic alterations. Lesions from same patient showed distinct landscapes of somatic aberrations and shared few mutations, which suggests that they originate and develop independently, although they shared the similar genetic background. Canonical genes, such as APC, KRAS, TP53 and PIK3CA, were frequently mutated in the syCRCs, and most of them show different mutation profile compared with solitary colorectal cancer. We identified a recurrent somatic alteration (K15fs) in RPL22 in 25% of the syCRCs. Functional analysis indicated that mutated RPL22 may suppress cell apoptosis and promote the epithelial–mesenchymal transition (EMT). Potential drug targets were identified in several signalling pathways, and they present great discrepancy between lesions from the same patient. Our data show that the syCRCs within the same patient present great genetic heterogeneity, and they may be driven by distinct molecular events and develop independently. The discrepancy of potential drug targets and mutation burden in lesions from one patient provides valuable information in clinical management for patients with syCRCs.
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Affiliation(s)
- Xiangfeng Wang
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Hu Fang
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Yong Cheng
- Department of Gastrointestinal Surgery, the 1st affiliated hospital of CQMU, Chongqing, China
| | - Lin Li
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | | | - Tao Fu
- Department of Gastrointestinal Surgery II, Renmin Hospital of Wuhan University, Wuhan, China
| | | | - Anping Zhang
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
| | | | - Chunxue Li
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
| | | | - Guangyan Li
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Peina Du
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | | | | | - Fan Li
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
| | - Qiang Gao
- BGI Genomics, BGI-Shenzhen, Shenzhen, China
| | - Baohua Liu
- Department of Gastrointestinal Surgery, Research Institute of Surgery, Daping Hospital, Third Military Medical University, Chongqing, China
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KANZAWA-KIRIYAMA HIDEAKI, JINAM TIMOTHYA, KAWAI YOSUKE, SATO TAKEHIRO, HOSOMICHI KAZUYOSHI, TAJIMA ATSUSHI, ADACHI NOBORU, MATSUMURA HIROFUMI, KRYUKOV KIRILL, SAITOU NARUYA, SHINODA KENICHI. Late Jomon male and female genome sequences from the Funadomari site in Hokkaido, Japan. ANTHROPOL SCI 2019. [DOI: 10.1537/ase.190415] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Affiliation(s)
| | - TIMOTHY A. JINAM
- Division of Population Genetics, National Institute of Genetics, Mishima
| | - YOSUKE KAWAI
- Department of Human Genetics, Graduate School of Medicine, The University of Tokyo, Tokyo
| | - TAKEHIRO SATO
- Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa
| | - KAZUYOSHI HOSOMICHI
- Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa
| | - ATSUSHI TAJIMA
- Department of Bioinformatics and Genomics, Graduate School of Medical Sciences, Kanazawa University, Kanazawa
| | - NOBORU ADACHI
- Department of Legal Medicine, Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Chuo
| | - HIROFUMI MATSUMURA
- Second Division of Physical Therapy, School of Health Sciences, Sapporo Medical University, Sapporo
| | - KIRILL KRYUKOV
- Department of Molecular Life Science, School of Medicine, Tokai University, Isehara
| | - NARUYA SAITOU
- Division of Population Genetics, National Institute of Genetics, Mishima
| | - KEN-ICHI SHINODA
- Department of Anthropology, National Museum of Nature and Science, Tsukuba
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48
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Jia Q, Wu W, Wang Y, Alexander PB, Sun C, Gong Z, Cheng JN, Sun H, Guan Y, Xia X, Yang L, Yi X, Wan YY, Wang H, He J, Futreal PA, Li QJ, Zhu B. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun 2018; 9:5361. [PMID: 30560866 PMCID: PMC6299138 DOI: 10.1038/s41467-018-07767-w] [Citation(s) in RCA: 255] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Accepted: 11/23/2018] [Indexed: 12/26/2022] Open
Abstract
Combining whole exome sequencing, transcriptome profiling, and T cell repertoire analysis, we investigate the spatial features of surgically-removed biopsies from multiple loci in tumor masses of 15 patients with non-small cell lung cancer (NSCLC). This revealed that the immune microenvironment has high spatial heterogeneity such that intratumoral regional variation is as large as inter-personal variation. While the local total mutational burden (TMB) is associated with local T-cell clonal expansion, local anti-tumor cytotoxicity does not directly correlate with neoantigen abundance. Together, these findings caution against that immunological signatures can be predicted solely from TMB or microenvironmental analysis from a single locus biopsy.
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Affiliation(s)
- Qingzhu Jia
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, 400037, China
- Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
| | - Wei Wu
- Department of Cardiothorathic Surgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China
| | - Yuqi Wang
- Geneplus-Beijing Institute, Beijing, 102206, China
| | - Peter B Alexander
- Department of Immunology, Duke University Medical Center, Durham, 27710, NC, USA
| | - Chengdu Sun
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, 400037, China
- Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
| | - Zhihua Gong
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, 400037, China
- Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
| | - Jia-Nan Cheng
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, 400037, China
- Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China
- Biomedical Analysis Center, Third Military Medical University, Chongqing, 400038, China
| | - Huaibo Sun
- Geneplus-Beijing Institute, Beijing, 102206, China
| | - Yanfang Guan
- Geneplus-Beijing Institute, Beijing, 102206, China
| | - Xuefeng Xia
- Geneplus-Beijing Institute, Beijing, 102206, China
- Houston Methodist Research Institute, Houston, 77030, TX, USA
| | - Ling Yang
- Geneplus-Beijing Institute, Beijing, 102206, China
| | - Xin Yi
- Geneplus-Beijing Institute, Beijing, 102206, China
| | - Yisong Y Wan
- Department of Microbiology and Immunology, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, 27514, NC, USA
| | - Haidong Wang
- Department of Cardiothorathic Surgery, Southwest Hospital, Third Military Medical University, Chongqing, 400038, China
| | - Ji He
- GeneCast Biotechnology Co., Ltd, Beijing, 102206, China
| | - P Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Qi-Jing Li
- Department of Immunology, Duke University Medical Center, Durham, 27710, NC, USA.
- Biomedical Analysis Center, Third Military Medical University, Chongqing, 400038, China.
| | - Bo Zhu
- Institute of Cancer, Xinqiao Hospital, Third Military Medical University, Chongqing, 400037, China.
- Chongqing Key Laboratory of Tumor Immunotherapy, Chongqing, 400037, China.
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Yi M, Qin S, Zhao W, Yu S, Chu Q, Wu K. The role of neoantigen in immune checkpoint blockade therapy. Exp Hematol Oncol 2018; 7:28. [PMID: 30473928 PMCID: PMC6240277 DOI: 10.1186/s40164-018-0120-y] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Accepted: 11/09/2018] [Indexed: 02/07/2023] Open
Abstract
Immune checkpoint inhibitor induces tumor rejection by activated host immune system. The anti-tumor immune response consists of capture, presentation, recognition of neoantigen, as well as subsequent killing of tumor cell. Due to the interdependence among this series of stepwise events, neoantigen profoundly influences the efficacy of anti-immune checkpoint therapy. Moreover, the neoantigen-specific T cell reactivity is the cornerstone of multiple immunotherapies. In fact, several strategies targeting neoantigen have been attempted for synergetic effect with immune checkpoint inhibitor. Increasing neoantigen presentation to immune system by oncolytic virus, radiotherapy, or cancer vaccine is feasible to enhance neoantigen-specific T cell reactivity in theory. However, some obstacles have not been overcome in practice such as dynamic variation of neoantigen landscape, identification of potential neoantigen, maintenance of high T cell titer post vaccination. In addition, adoptive T cell transfer is another approach to enhance neoantigen-specific T cell reactivity, especially for patients with severe immunosuppression. In this review, we highlighted the advancements of neoantigen and innovative explorations of utilization of neoantigen repertoire in immune checkpoint blockade therapy.
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Affiliation(s)
- Ming Yi
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Shuang Qin
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Weiheng Zhao
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Shengnan Yu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Qian Chu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
| | - Kongming Wu
- Department of Oncology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China
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50
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Hayashi S, Yamaguchi R, Mizuno S, Komura M, Miyano S, Nakagawa H, Imoto S. ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data. BMC Genomics 2018; 19:790. [PMID: 30384854 PMCID: PMC6211482 DOI: 10.1186/s12864-018-5169-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 10/15/2018] [Indexed: 12/22/2022] Open
Abstract
Background Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases. Results We developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 3rd field resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 2nd field resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data. Conclusions ALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes. Electronic supplementary material The online version of this article (10.1186/s12864-018-5169-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Shuto Hayashi
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Rui Yamaguchi
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Shinichi Mizuno
- Center for Advanced Medical Innovation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Mitsuhiro Komura
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Satoru Miyano
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Hidewaki Nakagawa
- RIKEN Center for Integrative Medical Sciences, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan
| | - Seiya Imoto
- Health Intelligence Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan.
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