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DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.

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收录情况： ◇ SCIE

作者：
Liu Jiaqi[1,2] * ; Zhao Hengqiang[3,4] * ; Zheng Yu[5] * ; Dong Lin[6] * ; Zhao Sen[3,4] * ; Huang Yukuan[2,7] ; Huang Shengkai[8] ; Qian Tianyi[1] ; Zou Jiali[9] ; Liu Shu[10] ; Li Jun[11] ; Yan Zihui[3,4] ; Li Yalun[12] ; Zhang Shuo[13] ; Huang Xin[14] ; Wang Wenyan[15] ; Li Yiqun[16] ; Wang Jie[17] ; Ming Yue[18] ; Li Xiaoxin[19] ; Xing Zeyu[1] ; Qin Ling[20] ; Zhao Zhengye[3,4] ; Jia Ziqi[1] ; Li Jiaxin[1] ; Liu Gang[1] ; Zhang Menglu[1] ; Feng Kexin[1] ; Wu Jiang[1] ; Zhang Jianguo[3,21,22] ; Yang Yongxin[23] ; Wu Zhihong[4,19,21,22] ; Liu Zhihua[24] ; Ying Jianming[6] ; Wang Xin[1] ; Su Jianzhong[25,2,7] ; Wang Xiang[1] ; Wu Nan[3,4,21,22] ;
机构： [1]Department of Breast Surgical Oncology, National Cancer Center/NationalClinical Research Center for Cancer/Cancer Hospital, Chinese Academyof Medical Sciences and Peking Union Medical College, Beijing 100021,China [2]Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou325027, China [3]Department of Orthopedic Surgery, Peking Union MedicalCollege Hospital, Peking Union Medical College and Chinese Academyof Medical Sciences, Beijing 100730, China [4]Beijing Key Laboratory for GeneticResearch of Skeletal Deformity, Peking Union Medical College Hospital,Peking Union Medical College and Chinese Academy of Medical Sciences,Beijing 100730, China [5]Fintech Innovation Center, Southwestern Universityof Finance and Economics, Chengdu 611130, China [6]Department of Pathology,National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking UnionMedical College, Beijing 100021, China [7]School of Biomedical Engineering,School of Ophthalmology & Optometry and Eye Hospital, Wenzhou MedicalUniversity, Wenzhou 325027, China [8]Department of Laboratory Medicine,National Cancer Center /National Clinical Research Center for Cancer/CancerHospital, Chinese Academy of Medical Sciences and Peking Union MedicalCollege, Beijing 100021, China [9]Department of Breast Surgery, GuiyangMaternal and Child Healthcare Hospital, Guiyang 550001, China [10]Departmentof Breast Surgery, the Affiliated Hospital of Guizhou Medical University,Guiyang 550004, China [11]Department of Molecular Pathology, the AffiliatedCancer Hospital of Zhengzhou University, Zhengzhou 450000, China [12]Department of Breast Surgery, the Affiliated Yantai Yuhuangding Hospitalof Qingdao University, Yantai 264000, China [13]Department of Breast Surgery,the Fourth Hospital of Hebei Medical University, Shijiazhuang 050019, Hebei,China [14]Department of Breast Surgery, Peking Union Medical College Hospital,Peking Union Medical College and Chinese Academy of Medical Sciences,Beijing 100730, China [15]Department of Breast Surgery, Beijing Tiantan Hospital,Capital Medical University, Beijing 100070, China [16]Department of Oncology,National Cancer Center /National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking UnionMedical College, Beijing 100021, China [17]Department of Ultrasound, NationalCancer Center/National Clinical Research Center for Cancer/Cancer Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Beijing 100021, China [18]PET‑CT Center, National Cancer Center/National ClinicalResearch Center for Cancer/Cancer Hospital, Chinese Academy of MedicalSciences and Peking Union Medical College, Beijing 100021, China [19]MedicalResearch Center, Beijing Key Laboratory for Genetic Research of SkeletalDeformity & Key Laboratory of Big Data for Spinal Deformities, All at PekingUnion Medical College Hospital, Peking Union Medical College and ChineseAcademy of Medical Sciences, Beijing 100730, China [20]Department of BreastSurgical Oncology, Cancer Hospital of HuanXing, Beijing 100021, China [21]KeyLaboratory of Big Data for Spinal Deformities, Peking Union Medical Collegeand Chinese Academy of Medical Sciences, Beijing 100730, China [22]State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical CollegeHospital, Peking Union Medical College and Chinese Academy of MedicalSciences, Beijing 100730, China [23]Machine Intelligence Group, Universityof Edinburgh, Edinburgh EH8 9YL, UK [24]State Key Laboratory of MolecularOncology, National Cancer Center/National Clinical Research Centerfor Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and PekingUnion Medical College, Beijing 100021, China [25]Wenzhou Institute, Universityof Chinese Academy of Sciences, Wenzhou 325011, China
出处：
DOI： 10.1186/s13073-022-01027-9
ISSN：

关键词： Hereditary breast cancer Deep learning BRCA1/2 Genetic test Genotype-phenotype correlation

摘要：
Identifying breast cancer patients with DNA repair pathway-related germline pathogenic variants (GPVs) is important for effectively employing systemic treatment strategies and risk-reducing interventions. However, current criteria and risk prediction models for prioritizing genetic testing among breast cancer patients do not meet the demands of clinical practice due to insufficient accuracy.The study population comprised 3041 breast cancer patients enrolled from seven hospitals between October 2017 and 11 August 2019, who underwent germline genetic testing of 50 cancer predisposition genes (CPGs). Associations among GPVs in different CPGs and endophenotypes were evaluated using a case-control analysis. A phenotype-based GPV risk prediction model named DNA-repair Associated Breast Cancer (DrABC) was developed based on hierarchical neural network architecture and validated in an independent multicenter cohort. The predictive performance of DrABC was compared with currently used models including BRCAPRO, BOADICEA, Myriad, PENN II, and the NCCN criteria.In total, 332 (11.3%) patients harbored GPVs in CPGs, including 134 (4.6%) in BRCA2, 131 (4.5%) in BRCA1, 33 (1.1%) in PALB2, and 37 (1.3%) in other CPGs. GPVs in CPGs were associated with distinct endophenotypes including the age at diagnosis, cancer history, family cancer history, and pathological characteristics. We developed a DrABC model to predict the risk of GPV carrier status in BRCA1/2 and other important CPGs. In predicting GPVs in BRCA1/2, the performance of DrABC (AUC = 0.79 [95% CI, 0.74-0.85], sensitivity = 82.1%, specificity = 63.1% in the independent validation cohort) was better than that of previous models (AUC range = 0.57-0.70). In predicting GPVs in any CPG, DrABC (AUC = 0.74 [95% CI, 0.69-0.79], sensitivity = 83.8%, specificity = 51.3% in the independent validation cohort) was also superior to previous models in their current versions (AUC range = 0.55-0.65). After training these previous models with the Chinese-specific dataset, DrABC still outperformed all other methods except for BOADICEA, which was the only previous model with the inclusion of pathological features. The DrABC model also showed higher sensitivity and specificity than the NCCN criteria in the multi-center validation cohort (83.8% and 51.3% vs. 78.8% and 31.2%, respectively, in predicting GPVs in any CPG). The DrABC model implementation is available online at http://gifts.bio-data.cn/ .By considering the distinct endophenotypes associated with different CPGs in breast cancer patients, a phenotype-driven prediction model based on hierarchical neural network architecture was created for identification of hereditary breast cancer. The model achieved superior performance in identifying GPV carriers among Chinese breast cancer patients.© 2022. The Author(s).

基金：
National Natural Science Foundation of China (81802669 to J.L., 81501852 and 82072391 to N.W., 61871294 to J.S., 81472046 and 81772299 to Z.W.), the CAMS Innovation Fund for Medical Sciences (2020-I2M-C&T-B-068 to J.L., 2020-I2M-C&T-A-015 to Y.M., 2021-I2M- 1-051 to N.W., and 2021-I2M-1-052 to Z.W.), the Beijing Hope Run Special Fund (LC2020B05 to J.L.), Beijing Natural Science Foundation (JQ20032 to N.W.), Tsinghua University-Peking Union Medical College Hospital Initiative Scientific Research Program (to N.W.), the PUMC Youth Fund & the Fundamental Research Funds for the Central Universities (No.3332019052 to Y. M.), Nonprofit Central Research Institute Fund of Chinese Academy of Medical Sciences (No. 2019PT320025), Science Foundation of Zhejiang Province (LR19C060001 to J.S), and the Fundamental Research Funds for Wenzhou Institute of University of Chinese Academy of Sciences (WIBEZD2017009-05 to J.S.).
语种：
外文
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中科院分区：
出版当年[2022]版：
大类 | 1 区 生物学
小类 | 1 区 遗传学
最新[2025]版：
大类 | 1 区 生物学
小类 | 1 区 遗传学
JCR分区：
出版当年[2022]版：
Q1 GENETICS & HEREDITY
最新[2023]版：
Q1 GENETICS & HEREDITY

影响因子： 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者：
第一作者机构： [1]Department of Breast Surgical Oncology, National Cancer Center/NationalClinical Research Center for Cancer/Cancer Hospital, Chinese Academyof Medical Sciences and Peking Union Medical College, Beijing 100021,China [2]Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou325027, China
共同第一作者：
通讯作者：
通讯机构： [2]Institute of Biomedical Big Data, Wenzhou Medical University, Wenzhou325027, China [3]Department of Orthopedic Surgery, Peking Union MedicalCollege Hospital, Peking Union Medical College and Chinese Academyof Medical Sciences, Beijing 100730, China [4]Beijing Key Laboratory for GeneticResearch of Skeletal Deformity, Peking Union Medical College Hospital,Peking Union Medical College and Chinese Academy of Medical Sciences,Beijing 100730, China [7]School of Biomedical Engineering,School of Ophthalmology & Optometry and Eye Hospital, Wenzhou MedicalUniversity, Wenzhou 325027, China [21]KeyLaboratory of Big Data for Spinal Deformities, Peking Union Medical Collegeand Chinese Academy of Medical Sciences, Beijing 100730, China [22]State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical CollegeHospital, Peking Union Medical College and Chinese Academy of MedicalSciences, Beijing 100730, China [25]Wenzhou Institute, Universityof Chinese Academy of Sciences, Wenzhou 325011, China
推荐引用方式(GB/T 7714)：
Liu Jiaqi,Zhao Hengqiang,Zheng Yu,et al.DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.[J].GENOME MEDICINE.2022,14(1):doi:10.1186/s13073-022-01027-9.
APA：
Liu Jiaqi,Zhao Hengqiang,Zheng Yu,Dong Lin,Zhao Sen...&Wu Nan.(2022).DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data..GENOME MEDICINE,14,(1)
MLA：
Liu Jiaqi,et al."DrABC: deep learning accurately predicts germline pathogenic mutation status in breast cancer patients based on phenotype data.".GENOME MEDICINE 14..1(2022)

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