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

Genetics in Medicine, Springer Nature, ISSN 1530-0366

Volume 21, 10, 2019

DOI:10.1038/s41436-019-0510-5, Dimensions: pub.1113378376, PMID: 30976098,



  1. (1) Shanghai Jiao Tong University, grid.16821.3c
  2. (2) XinHua Hospital, grid.412987.1
  3. (3) Beijing Genomics Institute, grid.21155.32
  4. (4) Jiangmen Central Hospital, grid.459671.8
  5. (5) National Yang Ming University, grid.260770.4
  6. (6) Taipei Veterans General Hospital, grid.278247.c
  7. (7) BGI-Wuhan, BGI-Shenzhen, Wuhan, Guangdong, China
  8. (8) Third Affiliated Hospital of Guangzhou Medical University, grid.417009.b
  9. (9) Southwest Hospital, grid.416208.9
  10. (10) Department of Obstetrics and Gynecology, Bazhong Central Hospital, Bazhong, Sichuan, China
  11. (11) Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, grid.410646.1
  12. (12) Yue Bei People's Hospital, grid.478147.9
  13. (13) First People's Hospital of Yunnan Province, grid.414918.1
  14. (14) Kunming University of Science and Technology, grid.218292.2
  15. (15) BGI HEALTH (HK), Hong Kong, China
  16. (16) BGI Europe (Denmark), grid.493273.8
  17. (17) Department of Medical Genetics, DeNA laboratory, Tehran, Iran
  18. (18) Tarbiat Modares University, grid.412266.5
  19. (19) NIMGenetics, Madrid, Spain
  20. (20) Hospital General San Jorge, grid.415076.1
  21. (21) Laboratorio de Genética Molecular AbaCid, Hospitales HM, Madrid, Spain
  22. (22) GenePlanet Ltd, Ljubljana, Slovenia
  23. (23) Dravlje Health Center-IVF, Ljubljana, Slovenia
  24. (24) Ljubljana University Medical Centre, grid.29524.38
  25. (25) Gene Health Co Ltd, Taipei, Taiwan
  26. (26) Tongji University, grid.24516.34
  27. (27) Yangzhou University, grid.268415.c
  28. (28) Department of Pathology, Shanghai Pu Nan Hospital, Shanghai, China
  29. (29) James D. Watson Institute of Genome Sciences, Hangzhou, Zhejiang, China
  30. (30) University of Copenhagen, grid.5254.6, KU
  31. (31) National University of Singapore, grid.4280.e
  32. (32) BGI-Guangzhou Medical Laboratory, BGI-Shenzhen, Guangzhou, Guangdong, China
  33. (33) South China University of Technology, grid.79703.3a


PURPOSE: Multiple 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. METHODS: This 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. RESULTS: Of 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%. CONCLUSION: The 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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