Genome-wide Analysis of Common Copy Number Variation and Epithelial Ovarian Cancer Risk

Reid, Brett M.; Permuth, Jennifer B.; Chen, Y. Ann; Fridley, Brooke L.; Iversen, Edwin S.; Chen, Zhihua; Jim, Heather; Vierkant, Robert A.; Cunningham, Julie M.; Barnholtz‐Sloan, Jill S.; Narod, Steven A.; Risch, Harvey A.; Schildkraut, Joellen M.; Goode, Ellen L.; Monteiro, Alvaro N.A.; Sellers, Thomas A.

doi:10.1158/1055-9965.epi-18-0833

Cited by 24 publications

(26 citation statements)

References 63 publications

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“…These mutations were shown to disrupt the RAD51C mRNA splicing and are considered to be likely pathogenic variants [20,98]. Large mutations were not included in risk estimates, however, it is suggested that they may contribute to familial breast/ovarian cancer risk [99,100]. As mentioned in the Results section, large mutations constitute a substantial fraction of all reported mutations, especially in the RAD51C and RAD51D genes; therefore, they may also influence the risk of OC.…”

Section: Discussionmentioning

confidence: 99%

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

2020

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Background: It is estimated that more than 20% of ovarian cancer cases are associated with a genetic predisposition that is only partially explained by germline mutations in the BRCA1 and BRCA2 genes. Recently, several pieces of evidence showed that mutations in three genes involved in the homologous recombination DNA repair pathway, i.e., BRIP1, RAD51C, and RAD51D, are associated with a high risk of ovarian cancer. To more precisely estimate the ovarian cancer risk attributed to BRIP1, RAD51C, and RAD51D mutations, we performed a meta-analysis based on a comparison of a total of~29,400 ovarian cancer patients from 63 studies and a total of~116,000 controls from the gnomAD database. Results: The analysis allowed precise estimation of ovarian cancer risks attributed to mutations in BRIP1, RAD51C, and RAD51D, confirming that all three genes are ovarian cancer high-risk genes (odds ratio (OR) = 4.94, 95%CIs:4.07-6.00, p < 0.0001; OR = 5.59, 95%CIs:4.42-7.07, p < 0.0001; and OR = 6.94, 95%CIs:5.10-9.44, p < 0.0001, respectively). In the present report, we show, for the first time, a mutation-specific risk analysis associated with distinct, recurrent, mutations in the genes. Conclusions: The meta-analysis provides evidence supporting the pathogenicity of BRIP1, RAD51C, and RAD51D mutations in relation to ovarian cancer. The level of ovarian cancer risk conferred by these mutations is relatively high, indicating that after BRCA1 and BRCA2, the BRIP1, RAD51C, and RAD51D genes are the most important ovarian cancer risk genes, cumulatively contributing to~2% of ovarian cancer cases. The inclusion of the genes into routine diagnostic tests may influence both the prevention and the potential treatment of ovarian cancer.

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Section: Discussionmentioning

confidence: 99%

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

2020

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“…We also found that the severity of response to SARS-CoV-2 infection had a small, but significant, portion that was predictable from UK Biobank chromosomal-scale length variation [23]. Another group used a GWAS-type analysis employing logistic regression with copy number variation data collected from about 1800 ovarian cancer cases and 1800 controls to demonstrate that some germ line DNA copy number variations occur more frequently in women who develop epithelial ovarian cancer than in those who do not develop that form of cancer [24].…”

Section: Resultsmentioning

confidence: 99%

A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations

Brody

2021

Preprint

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Glioblastoma multiforme is the most common form of brain cancer. Several lines of evidence suggest that glioblastoma multiforme has a genetic basis. A genetic test that could identify people who are at high risk of developing glioblastoma multiforme could improve our understanding of this form of brain cancer.Using the Cancer Genome Atlas (TCGA) dataset, we found common germ line DNA copy number variations in the TCGA population. We tested whether different sets of these germ line DNA copy number variations could effectively distinguish patients with glioblastoma multiforme from others in the TCGA dataset. We used a gradient boosting machine, a machine learning classification algorithm, to classify TCGA patients solely based on a set of germline DNA copy number variations.We found that this machine learning algorithm could classify TCGA glioblastoma multiforme patients from the other TCGA patients with an area under the curve (AUC) of the receiver operating characteristic curve (AUC=0.875). Grouped into quintiles, the highest ranked quintile by the machine learning algorithm had an odds ratio of 3.78 (95% CI 3.25-4.40) higher than the average odds ratio and about 40 (95% CI 20-70) times higher than the lowest quintile.The identification of an effective germ line genetic test to stratify risk of developing glioblastoma multiforme should lead to a better understanding of how this cancer forms. This result might ultimately lead to better treatments of glioblastoma multiforme.

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“…CNVs for NSCLC distinguished lung cancer from normal lung tissues, can be a prognostic indicator [46]. A number of low-to-common frequency CNVs might in uence the risk of epithelial ovarian cancer and tumor-gene expression [47]. DNA copy number losses in basal-like breast cancers were associated with signi cantly increased genomic instability and poor patient survival [48].…”

Section: Discussionmentioning

confidence: 99%

Clinicopathological and Molecular Characteristics of Atypical Pulmonary Sclerosing Pneumocytoma With Multiple Metastases

Wang¹,

Yang²,

Jiang³

et al. 2020

Preprint

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Background: Few patients with pulmonary sclerosing pneumocytoma (PSP) may suffer from recurrence and oligometastasis as it is a benign tumor or has a low malignancy potential. Herein, an in-depth study, based on an extremely rare PSP case with atypical features, was carried out to elaborate the potential mechanism underlying the rapid malignant progression. Methods: The clinicopathological data of this atypical PSP (AP) case were obtained. Formalin‑fixed and paraffin‑embedded tissues from all the lesions of AP and five classic PSP cases (as control) were used for whole-exome sequencing (WES) and immunohistochemistry. Results: A 23-year-old male showed a 6.5-cm pulmonary nodule in the right middle lobe (RML) and enlarged mediastinal lymph nodes (LNs). He underwent thoracoscopic RML lobectomy, systematic LNs dissection, and mediastinal lymphadenectomy. The metastases to the cervical LNs and liver were detected in a short period and then resected. Postoperative pathological examination confirmed the diagnosis of PSP in all the lesions, based on the typical histological characteristics and immunophenotypes. Furthermore, WES identified both AKT1 E17K somatic mutation and TP53 C176Y germline mutation in this AP case. The genomic evolution analysis showed different evolutionary branches in the metastatic tumors distinct from the primary lesion. Moreover, compared to the control group, the AP case showed high copy number variations (CNVs) and significantly high copy number instability (CNI). Conclusions: We speculated that the atypical histopathological features and malignant behaviors may be due to the co-mutations of somatic AKT1 E17K and germline TP53 C176Y, combined with the high CNVs and CNI.

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Genome-wide Analysis of Common Copy Number Variation and Epithelial Ovarian Cancer Risk

Cited by 24 publications

References 63 publications

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

BRIP1, RAD51C, and RAD51D mutations are associated with high susceptibility to ovarian cancer: mutation prevalence and precise risk estimates based on a pooled analysis of ~30,000 cases

A Genetic Risk Score for Glioblastoma Multiforme Based on Copy Number Variations

Clinicopathological and Molecular Characteristics of Atypical Pulmonary Sclerosing Pneumocytoma With Multiple Metastases

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