Evidence map›Paper›PMID 36324133›Full record

ArticleBMC cancer2022

Clinical application of artificial neural network (ANN) modeling to predict BRCA1/2 germline deleterious variants in Chinese bilateral primary breast cancer patients.

Yan Li, Lili Chen, Jinxing Lv, Xiaobin Chen, Bangwei Zeng, Minyan Chen, Wenhui Guo, Yuxiang Lin, Liuwen Yu, Jialin Hou and 10 more

Abstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors.

Yan Li *Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Lili Chen *Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Jinxing Lv *Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Xiaobin Chen *Department of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Bangwei ZengNosocomial Infection Control Branch, Fujian Medical University Union Hospital, Fuzhou, Fujian Province, China.
Minyan ChenDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Wenhui GuoDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Yuxiang LinDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Liuwen YuDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Jialin HouDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Jing LiDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Peng ZhouDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Wenzhe ZhangDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Shengmei LiDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Xuan JinDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Weifeng CaiDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Kun ZhangDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China.
Yeyuan HuangFujian Medical University, 350001, Fuzhou, Fujian Province, China.
Chuan WangDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China. chuanwang1968@outlook.com.
Fangmeng FuDepartment of Breast Surgery, Fujian Medical University Union Hospital, No.29, Xin Quan Road, Gulou District, 350001, Fuzhou, Fujian Province, China. ffm@fjmu.edu.cn.

Funding

Joint Funds for the Innovation of Science and Technology, Fujian Province 2018Y9205Joint Funds for the Innovation of Science and Technology, Fujian Province 2019Y9103Joint Key Funds for the Health and Education of Fujian Province 2019-WJ-23
6 · The paper itself

Abstract

backgroundBilateral breast cancer (BBC), as well as ovarian cancer, are significantly associated with germline deleterious variants in BRCA1/2, while BRCA1/2 germline deleterious variants carriers can exquisitely benefit from poly (ADP-ribose) polymerase (PARP) inhibitors. However, formal genetic testing could not be carried out for all patients due to extensive use of healthcare resources, which in turn results in high medical costs. To date, existing BRCA1/2 deleterious variants prediction models have been developed in women of European or other descent who are quite genetically different from Asian population. Therefore, there is an urgent clinical need for tools to predict the frequency of BRCA1/2 deleterious variants in Asian BBC patients balancing the increased demand for and cost of cancer genetics services.

methodsThe entire coding region of BRCA1/2 was screened for the presence of germline deleterious variants by the next generation sequencing in 123 Chinese BBC patients. Chi-square test, univariate and multivariate logistic regression were used to assess the relationship between BRCA1/2 germline deleterious variants and clinicopathological characteristics. The R software was utilized to develop artificial neural network (ANN) and nomogram modeling for BRCA1/2 germline deleterious variants prediction.

resultsAmong 123 BBC patients, we identified a total of 20 deleterious variants in BRCA1 (8; 6.5%) and BRCA2 (12; 9.8%). c.5485del in BRCA1 is novel frameshift deleterious variant. Deleterious variants carriers were younger at first diagnosis (P = 0.0003), with longer interval between two tumors (P = 0.015), at least one medullary carcinoma (P = 0.001), and more likely to be hormone receptor negative (P = 0.006) and HER2 negative (P = 0.001). Area under the receiver operating characteristic curve was 0.903 in ANN and 0.828 in nomogram modeling individually (P = 0.02).

conclusionThis study shows the spectrum of the BRCA1/2 germline deleterious variants in Chinese BBC patients and indicates that the ANN can accurately predict BRCA deleterious variants than conventional statistical linear approach, which confirms the BRCA1/2 deleterious variants carriers at the lowest costs without adding any additional examinations.

Indexed as

Breast NeoplasmsOvarian NeoplasmsBRCA1 ProteinBRCA2 ProteinChinaFemaleGenetic Predisposition to DiseaseGerm CellsGerm-Line MutationHumansNeural Networks, ComputerBRCA1 ProteinBRCA1 protein, humanBRCA2 ProteinArtificial neural networkBilateral breast cancerBRCA1BRCA2Germline deleterious variant

Identifiers

PMID36324133
PMCPMC9628090

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.