Identifying occult maternal malignancies from 1.93 million pregnant women undergoing noninvasive prenatal screening tests View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04-12

AUTHORS

Xing Ji, Jia Li, Yonghua Huang, Pi-Lin Sung, Yuying Yuan, Qiang Liu, Yan Chen, Jia Ju, Yafeng Zhou, Shujia Huang, Fang Chen, Yuan Han, Wen Yuan, Cheng Fan, Qiang Zhao, Haitao Wu, Suihua Feng, Weiqiang Liu, Zhihua Li, Jingsi Chen, Min Chen, Hong Yao, Li Zeng, Tao Ma, Shushu Fan, Jinman Zhang, Ka Yiu Yuen, So Hin Cheng, Irene Wing Shan Chik, Nien-Tzu Liu, Jianyu Zhu, Siyuan Lin, Jeremy Cao, Steve Tong, Zhiyuan Shan, Wenyan Li, Mohammad Reza Hekmat, Masoud Garshasbi, Javier Suela, Yaima Torres, Juan C. Cigudosa, F. J. Pérez Ruiz, Laura Rodríguez, Mónica García, Janez Bernik, Eva Traven, Uršula Reš, Nataša Tul, Ching-Fong Tseng, Depeng Zhao, Luming Sun, Qiong Pan, Li Shen, Mengyao Dai, Yuying Wang, Jian Wang, Huanming Yang, Ye Yin, Tao Duan, Baosheng Zhu, Mahesh Choolani, Xin Jin, Yingwei Chen, Mao Mao

ABSTRACT

PurposeMultiple chromosomal aneuploidies may be associated with maternal malignancies and can cause failure of noninvasive prenatal screening (NIPS) tests. However, multiple chromosomal aneuploidies show poor specificity and selectivity for diagnosing maternal malignancies.MethodsThis multicenter retrospective analysis evaluated 639 pregnant women who tested positive for multiple chromosomal aneuploidies on initial NIPS test between January 2016 and December 2017. Women were assessed using genome profiling of copy-number variations, which was translated to cancer risk using a novel bioinformatics algorithm called the cancer detection pipeline (CDP). Sensitivity, specificity, and positive predictive value (PPV) of diagnosing maternal malignancies were compared for multiple chromosomal aneuploidies, the CDP model, and the combination of CDP and plasma tumor markers.ResultsOf the 639 subjects, 41 maternal malignant cancer cases were diagnosed. Multiple chromosomal aneuploidies predicted maternal malignancies with a PPV of 7.6%. Application of the CDP algorithm to women with multiple chromosomal aneuploidies allowed 34 of the 41 (83%) cancer cases to be identified, while excluding 422 of 501 (84.2%) of the false positive cases. Combining the CDP with plasma tumor marker testing gave PPV of 75.0%.ConclusionThe CDP algorithm can diagnose occult maternal malignancies with a reasonable PPV in multiple chromosomal aneuploidies–positive pregnant women in NIPS tests. This performance can be further improved by incorporating findings for plasma tumor markers. More... »

PAGES

2293-2302

Journal

TITLE

Genetics in Medicine

ISSUE

10

VOLUME

21

Author Affiliations

  • Department of Genetics, Shanghai Institute for Pediatric Research, Shanghai, China
  • BGI Genomics, BGI-Shenzhen, Shenzhen, Guangdong, China
  • Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China
  • Department of Obstetrics and Gynecology, School of Medicine, National Yang-Ming University, Taipei, Taiwan
  • BGI-Wuhan, BGI-Shenzhen, Wuhan, Guangdong, China
  • Reproductive Medicine Center, Jiangmen Central Hospital, Affiliated Jiangmen Hospital of Sun Yat-Sen University, Jiangmen, Guangdong, China
  • Key Laboratory for Major Obstetric Diseases of Guangdong, Key Laboratory for Reproduction and Genetics of Guangdong Higher Education Institutes, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
  • Department of Prenatal Diagnosis and Fetal Medicine, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
  • Prenatal Diagnosis Center, Southwest Hospital, Chongqing, China
  • Department of Obstetrics and Gynecology, Bazhong Central Hospital, Bazhong, Sichuan, China
  • Department of Obstetrics and Gynecology, Sichuan Provincial People’s Hospital, Chengdu, Sichuan, China
  • Genetic Diagnosis Center and Reproductive Center, Yue Bei People’s Hospital, Shaoguan, Guangdong, China
  • Genetic Diagnosis Center, First People’s Hospital of Yunnan, Kunming, Yunnan, China
  • BGI HEALTH (HK), Hong Kong, China
  • BGI Europe A/S, Copenhagen, Denmark
  • Department of Medical Genetics, DeNA laboratory, Tehran, Iran
  • Department of Medical Genetics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
  • NIMGenetics, Madrid, Spain
  • Servicio de Ginecología y Obstetricia, Hospital General San Jorge, Huesca, Spain
  • Laboratorio de Genética Molecular AbaCid, Hospitales HM, Madrid, Spain
  • GenePlanet Ltd, Ljubljana, Slovenia
  • Dravlje Health Center-IVF, Ljubljana, Slovenia
  • Division of Obstetrics and Gynecology, Department of Perinatology, University Medical Centre, Ljubljana, Slovenia
  • Gene Health Co Ltd, Taipei, Taiwan
  • Department of Prenatal Diagnosis and Fetal Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
  • Laboratory of Clinical Genetics, Huai’an Maternity and Child Health Care Hospital of Jiangsu Province, Yangzhou University, Huai’an, Jiangsu, China
  • Department of Pathology, Shanghai Pu Nan Hospital, Shanghai, China
  • James D. Watson Institute of Genome Sciences, Hangzhou, Zhejiang, China
  • Department of Biology, University of Copenhagen, Copenhagen, Denmark
  • Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
  • BGI-Guangzhou Medical Laboratory, BGI-Shenzhen, Guangzhou, Guangdong, China
  • Center for Clinical Genetics, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University, School of Medicine, Shanghai, China
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41436-019-0510-5

    DOI

    http://dx.doi.org/10.1038/s41436-019-0510-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1113378376

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30976098


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    25 schema:description PurposeMultiple chromosomal aneuploidies may be associated with maternal malignancies and can cause failure of noninvasive prenatal screening (NIPS) tests. However, multiple chromosomal aneuploidies show poor specificity and selectivity for diagnosing maternal malignancies.MethodsThis multicenter retrospective analysis evaluated 639 pregnant women who tested positive for multiple chromosomal aneuploidies on initial NIPS test between January 2016 and December 2017. Women were assessed using genome profiling of copy-number variations, which was translated to cancer risk using a novel bioinformatics algorithm called the cancer detection pipeline (CDP). Sensitivity, specificity, and positive predictive value (PPV) of diagnosing maternal malignancies were compared for multiple chromosomal aneuploidies, the CDP model, and the combination of CDP and plasma tumor markers.ResultsOf the 639 subjects, 41 maternal malignant cancer cases were diagnosed. Multiple chromosomal aneuploidies predicted maternal malignancies with a PPV of 7.6%. Application of the CDP algorithm to women with multiple chromosomal aneuploidies allowed 34 of the 41 (83%) cancer cases to be identified, while excluding 422 of 501 (84.2%) of the false positive cases. Combining the CDP with plasma tumor marker testing gave PPV of 75.0%.ConclusionThe CDP algorithm can diagnose occult maternal malignancies with a reasonable PPV in multiple chromosomal aneuploidies–positive pregnant women in NIPS tests. This performance can be further improved by incorporating findings for plasma tumor markers.
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    39 applications
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    60 novel bioinformatics algorithm
    61 performance
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