Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2)

Tokunaga, Hideki; Iida, Keita; Hozawa, Atsushi; Ogishima, Soichi; Watanabe, Yoh; Shigeta, Shogo; Shimada, Muneaki; Yamaguchi-Kabata, Yumi; Tadaka, Shu; Katsuoka, Fumiki; S, Itó; Kumada, Kazuki; Hamanaka, Yohei; Fuse, Nobuo; Kinoshita, Kengo; Yamamoto, Masayuki; Yaegashi, Nobuo; Yasuda, Jun

doi:10.1371/journal.pone.0236907

Cited by 8 publications

(5 citation statements)

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“…In the BRCA2 gene, c.5164_5165delAG has been detected in the Chinese Han population [ 64 ], Macau population [ 65 ], and Taiwanese populations [ 66 ]. BRCA2 c.2339C > G has been detected in Taiwanese [ 67 ], and Japanese [ 68 ] individuals. BRCA2 c.2806_2809del has been detected in Mexican individuals [ 69 ].…”

Section: Discussionmentioning

confidence: 99%

Germline variants profiling of BRCA1 and BRCA2 in Chinese Hakka breast and ovarian cancer patients

Zhang

et al. 2022

BMC Cancer

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Objective To investigate the prevalence and spectrum of BRCA1 and BRCA2 mutations in Chinese Hakka patients with breast and ovarian cancer. Methods A total of 1,664 breast or ovarian cancer patients were enrolled for genetic testing at our hospital. Germline mutations of the BRCA gene were analysed by next-generation sequencing, including the coding regions and exon intron boundary regions. Results The 1,664 patients included 1,415 (85.04%) breast cancer patients and 245 (14.72%) ovarian cancer patients, while four (0.24%) patients had both the breast and ovarian cancers. A total of 151 variants, including 71 BRCA1 variants and 80 BRCA2 variants, were detected in the 234 (14.06%) patients. The 151 variants included 58 pathogenic variants, 8 likely pathogenic variants, and 85 variants of unknown significance (VUS). A total of 56.25% (18/32) and 65.38% (17/26) of pathogenic variants (likely pathogenic variants are not included) were distributed in exon 14 of BRCA1 and exon 11 of BRCA2, respectively. The most common pathogenic variants among this Hakka population are c.2635G > T (p.Glu879*) (n = 7) in the BRCA1 gene and c.5164_5165del (p.Ser1722Tyrfs*4) (n = 7) in the BRCA2 gene among the Hakka population. A hotspot mutation in the Chinese population, the BRCA1 c.5470_5477del variant was not found in this Hakka population. The prevalence and spectrum of variants in the BRCA genes in the Hakka patients are different from that in other ethnic groups. Conclusions The most common pathogenic variant in this population is c.2635G > T in the BRCA1 gene, and c.5164_5165delAG in the BRCA2 gene in this population. The prevalence and spectrum of variants in the BRCA1 and BRCA2 genes in the Hakka patients from southern China are different from those in other ethnic groups.

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Section: Discussionmentioning

confidence: 99%

Germline variants profiling of BRCA1 and BRCA2 in Chinese Hakka breast and ovarian cancer patients

Zhang

et al. 2022

BMC Cancer

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“…The minor allele frequency of this variant, p.A347T, is 0.0003 in 14KJPN, which is lower than that of a founder mutation of a variant conferring susceptibility to hereditary breast and ovarian cancer, BRCA1 p.L63X (0.000439), in the Japanese population, consisting with the pathogenicity of the DHODH p.A347T. 31 …”

Section: Discussionmentioning

confidence: 78%

“…The minor allele frequency of this variant, p.A347T, is 0.0003 in 14KJPN, which is lower than that of founder mutation of a variant conferring susceptibility to hereditary breast and ovarian cancer, BRCA1 p.L63X (0.000439), in the Japanese population, consisting with the pathogenicity of the DHODH p.A347T. 31 The DHODH gene encodes dihydroorotate dehydrogenase, which is localized in the inner membrane of mitochondria and plays a role in de novo pyrimidine synthesis. Experimentally, the inhibition of DHODH by leflunomide has been shown to block the development of neural crest cells and malignant melanomas in zebrafish embryo, 32 suggesting the relevance of this gene to carcinogenesis.…”

Section: Discussionmentioning

confidence: 79%

Clinical and genomic features of non‐small cell lung cancer occurring in families

Miyabe

Sato

et al. 2023

Thoracic Cancer

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Background: Exposure to environmental carcinogens, such as through smoking, is a major factor in the carcinogenesis of non-small cell lung cancer (NSCLC). However, genetic factors may also contribute. Methods: To identify candidate tumor suppressor genes for NSCLC, we included 23 patients (10 related pairs and 3 individuals) with NSCLC who had other NSCLC-affected first-degree relatives in a local hospital. Exome analyses for both germline and somatic (NSCLC specimens) DNA were performed for 17 cases. Germline exome data of these 17 cases revealed that most of the short variants were identical to the variants in 14KJPN (a Japanese reference genome panel of more than 14 000 individuals) and only a nonsynonymous variant in the DHODH gene, p.A347T, was shared between a pair of NSCLC patients in the same family. This variant is a known pathogenic variant of the gene for Miller syndrome. Results: Somatic genetic alterations in the exome data of our samples showed frequent mutations in the EGFR and TP53 genes. Principal component analysis of the patterns of 96 types of single nucleotide variants (SNVs) suggested the existence of unique mechanisms inducing somatic SNVs in each family. Delineation of mutational signatures of the somatic SNVs with deconstructSigs for the pair of germline pathogenic DHODH variant-positive cases showed that the mutational signatures of these cases included SBS3 (homologous recombination repair defect), SBS6, 15 (DNA mismatch repair), and SBS7 (ultraviolet exposure), suggesting that disordered pyrimidine production causes increased errors in DNA repair systems in these cases. Conclusion: Our results suggest the importance of the detailed collection of data on environmental exposure along with genetic information on NSCLC patients to identify the unique combinations that cause lung tumorigenesis in a particular family.

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“…Thereafter, other statistical methods, such as multivariate regression, correlation analysis, and Bayesian inference, were proposed [44,45]. These approaches have revealed pathological and possible pathological variants in case-control [46][47][48] and prospective cohort [49,50] studies and contributed to the development of databases, such as dbSNP [42], the NHGRI GWAS Catalog [51], ClinVar [52], and OncoKB [53]. However, highly sensitive and specific methods that can characterize rare and moderately deleterious variants, as well as variants of uncertain significance, in the presence of linkage disequilibrium, need to be developed [54], since such knowledge of variants is critical for establishing personalized health care [50].…”

Section: Statistical Methods and Machine Learning In Data Analysismentioning

confidence: 99%

“…These approaches have revealed pathological and possible pathological variants in case-control [46][47][48] and prospective cohort [49,50] studies and contributed to the development of databases, such as dbSNP [42], the NHGRI GWAS Catalog [51], ClinVar [52], and OncoKB [53]. However, highly sensitive and specific methods that can characterize rare and moderately deleterious variants, as well as variants of uncertain significance, in the presence of linkage disequilibrium, need to be developed [54], since such knowledge of variants is critical for establishing personalized health care [50]. In general, conventional statistics assume that explanatory variables are independent and identically distributed, for example, chi-squared tests used in GWAS and TWAS and nonparametric Wilcoxon rank-sum tests in scRNA-seq analysis.…”

Section: Statistical Methods and Machine Learning In Data Analysismentioning

confidence: 99%

Building patient‐specific models for receptor tyrosine kinase signaling networks

et al. 2021

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Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

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Novel candidates of pathogenic variants of the BRCA1 and BRCA2 genes from a dataset of 3,552 Japanese whole genomes (3.5KJPNv2)

Cited by 8 publications

References 54 publications

Germline variants profiling of BRCA1 and BRCA2 in Chinese Hakka breast and ovarian cancer patients

Germline variants profiling of BRCA1 and BRCA2 in Chinese Hakka breast and ovarian cancer patients

Clinical and genomic features of non‐small cell lung cancer occurring in families

Building patient‐specific models for receptor tyrosine kinase signaling networks

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