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Masuda T, Ito H, Hirata J, Sakaue S, Ueda Y, Kimura T, Takeuchi F, Murakami Y, Matsuda K, Matsuo K, Okada Y. Fine Mapping of the Major Histocompatibility Complex Region and Association of the HLA-B*52:01 Allele With Cervical Cancer in Japanese Women. JAMA Netw Open 2020; 3:e2023248. [PMID: 33119109 PMCID: PMC7596586 DOI: 10.1001/jamanetworkopen.2020.23248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
IMPORTANCE Understanding the genetic contribution of the major histocompatibility complex (MHC) region to the risk of cervical cancer (CC) will help understand how immune responses to infection with human papillomaviruses are associated with CC. OBJECTIVE To determine whether the HLA-B*52:01 allele is associated with CC in Japanese women. DESIGN, SETTING, AND PARTICIPANTS This was a multicenter genetic association study. Genotype and phenotype data were obtained from BioBank Japan Project. Additional patients with CC were enrolled from the Aichi Cancer Center Research Institute. An MHC fine-mapping study was conducted on CC risk in the Japanese population by applying a human leukocyte antigen (HLA) imputation method to the large-scale CC genome-wide association study data of using the Japanese population-specific HLA reference panel. Participants included 540 women in BioBank Japan Project with CC or 39 829 women without gynecologic diseases, malignant neoplasms, and MHC-related diseases as controls. An additional 168 patients with CC were recruited from Aichi Cancer Center Research Institute. Histopathological subtypes and clinical stages were not considered. Participants with low genotype call rate, closely related participants, and outliers in the principal component analysis were excluded. Data analysis was performed from August 2018 to January 2020. MAIN OUTCOMES AND MEASURES Loci within the MHC region associated with CC risk, and the direction and size of association. RESULTS A total of 704 CC cases and 39 556 controls were analyzed. All participants were Japanese women with a median (range) age of 67 (18 to 100) years. One of the class I HLA alleles of HLA-B*52:01 was significantly associated with CC risk (odds ratio, 1.60; 95% CI, 1.38-1.86; P = 7.4 × 10-10). Allele frequency spectra of HLA-B*52:01 are heterogeneous among worldwide populations with high frequency in Japanese populations (0.109 in controls), suggesting its population-specific risk associated with CC. The conditional analysis suggested that HLA-B*52:01 could explain most of the MHC risk associated with CC because no other HLA alleles remained significant after conditioning on the HLA-B*52:01. The HLA amino acid residue-based analysis suggested that HLA-B p.Tyr171His located in the peptide-binding groove was associated with the most significant CC risk (odds ratio, 1.47; 95% CI, 1.30-1.66; P = 1.2 × 10-9). CONCLUSIONS AND RELEVANCE The results of this study contribute to understanding of the genetic background of CC. The results suggest that immune responses mediated by class I HLA molecules are associated with susceptibility to CC.
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Affiliation(s)
- Tatsuo Masuda
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan
- Now with StemRIM Institute of Regeneration-Inducing Medicine, Osaka University, Osaka, Japan
| | - Hidemi Ito
- Division of Cancer Information and Control, Aichi Cancer Center Research Institute, Aichi, Japan
- Department of Descriptive Cancer Epidemiology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Jun Hirata
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Pharmaceutical Discovery Research Laboratories, Teijin Pharma Limited, Hino, Japan
| | - Saori Sakaue
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Allergy and Rheumatology, The University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Yutaka Ueda
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Tadashi Kimura
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, National Center for Global Health and Medicine Research Institute, Tokyo, Japan
| | - Yoshinori Murakami
- Institute of Medical Science, Division of Molecular Pathology, The University of Tokyo, Tokyo, Japan
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, The University of Tokyo Graduate School of Frontier Sciences, Tokyo, Japan
| | - Keitaro Matsuo
- Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Aichi, Japan
- Department of Cancer Epidemiology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
- Laboratory of Statistical Immunology, World Premier International Research Center Initiative, Osaka University Immunology Frontier Research Center, Osaka University, Osaka, Japan
- Institute for Open and Transdisciplinary Research Initiatives, Integrated Frontier Research for Medical Science Division, Osaka University, Osaka, Japan
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Dhall A, Patiyal S, Kaur H, Bhalla S, Arora C, Raghava GPS. Computing Skin Cutaneous Melanoma Outcome From the HLA-Alleles and Clinical Characteristics. Front Genet 2020; 11:221. [PMID: 32273881 PMCID: PMC7113398 DOI: 10.3389/fgene.2020.00221] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 02/25/2020] [Indexed: 12/16/2022] Open
Abstract
Human leukocyte antigen (HLA) are essential components of the immune system that stimulate immune cells to provide protection and defense against cancer. Thousands of HLA alleles have been reported in the literature, but only a specific set of HLA alleles are present in an individual. The capability of the immune system to recognize cancer-associated mutations depends on the presence of a particular set of alleles, which elicit an immune response to fight against cancer. Therefore, the occurrence of specific HLA alleles affects the survival outcome of cancer patients. In the current study, prediction models were developed, using 401 cutaneous melanoma patients, to predict the overall survival (OS) of patients using their clinical data and HLA alleles. We observed that the presence of certain favorable superalleles like HLA-B∗55 (HR = 0.15, 95% CI 0.034-0.67), HLA-A∗01 (HR = 0.5, 95% CI 0.3-0.8), is responsible for the improved OS. In contrast, the presence of certain unfavorable superalleles such as HLA-B∗50 (HR = 2.76, 95% CI 1.284-5.941), HLA-DRB1∗12 (HR = 3.44, 95% CI 1.64-7.2) is responsible for the poor survival. We developed prediction models using key 14 HLA superalleles, demographic, and clinical characteristics for predicting high-risk cutaneous melanoma patients and achieved HR = 4.52 (95% CI 3.088-6.609, p-value = 8.01E-15). Eventually, we also provide a web-based service to the community for predicting the risk status in cutaneous melanoma patients (https://webs.iiitd.edu.in/raghava/skcmhrp/).
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Affiliation(s)
- Anjali Dhall
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Sumeet Patiyal
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Harpreet Kaur
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
- Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh, India
| | - Sherry Bhalla
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Chakit Arora
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
| | - Gajendra P. S. Raghava
- Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India
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