Effective Identification of Maternal Malignancies in Pregnancies Undergoing Noninvasive Prenatal Testing

Jia Li; Jia Ju; Qiang Zhao; Weiqiang Liu; Yuying Yuan; Qiang Liu; Lijun Zhou; Yuan Han; Wen Yuan; Yonghua Huang; Yingjun Xie; Zhihua Li; Jingsi Chen; Shuyu Huang; Rufang Chen; Wei Li; Meihua Tan; Danchen Wang; Si Zhou; Jian Zhang; Fanwei Zeng; Nan Yu; Fengxia Su; Min Chen; Yunsheng Ge; Yanming Huang; Xin Jin

doi:10.3389/fgene.2022.802865

Effective Identification of Maternal Malignancies in Pregnancies Undergoing Noninvasive Prenatal Testing

Front Genet. 2022 Feb 10:13:802865. doi: 10.3389/fgene.2022.802865. eCollection 2022.

Authors

Jia Li^{1

2}, Jia Ju^{3

4}, Qiang Zhao^{5

6}, Weiqiang Liu⁷, Yuying Yuan¹, Qiang Liu¹, Lijun Zhou¹, Yuan Han⁸, Wen Yuan⁸, Yonghua Huang⁵, Yingjun Xie⁹, Zhihua Li¹⁰, Jingsi Chen¹⁰, Shuyu Huang¹¹, Rufang Chen¹¹, Wei Li¹, Meihua Tan^{1

4}, Danchen Wang¹², Si Zhou^{1

2

13}, Jian Zhang¹⁴, Fanwei Zeng¹, Nan Yu¹, Fengxia Su³, Min Chen¹⁰, Yunsheng Ge¹⁴, Yanming Huang¹⁵, Xin Jin^{3

16}

Affiliations

¹ BGI Genomics, BGI-Shenzhen, Shenzhen, China.
² Hebei Industrial Technology Research Institute of Genomics in Maternal & Child Health, Shijiazhuang BGI Genomics Co., Ltd., Shijiazhuang, China.
³ BGI-Shenzhen, Shenzhen, China.
⁴ BGI Education Center, University of Chinese Academy of Sciences, Shenzhen, China.
⁵ Department of Obstetrics and Gynecology, Jiangmen Central Hospital, Jiangmen, China.
⁶ Reproductive Medicine Center, Jiangmen Central Hospital, Jiangmen, China.
⁷ Central Lab, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen, China.
⁸ BGI-Wuhan, BGI-Shenzhen, Wuhan, China.
⁹ Department of Obstetrics and Gynecology, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
¹⁰ Department of Prenatal Diagnosis and Fetal Medical, Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
¹¹ The Department of Obstetrics, Foshan First People's Hospital, Foshan, China.
¹² School of Basic Medicine, Qingdao University, Qingdao, China.
¹³ College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
¹⁴ Department of Prenatal Diagnosis Center, Women and Children's Hospital, School of Medicine, Xiamen University, Xiamen, Fujian province, China.
¹⁵ Clinical Experimental Center, Jiangmen Central Hospital, Jiangmen, Guangdong province, China.
¹⁶ School of Medicine, South China University of Technology, Guangzhou, China.

Abstract

Background: The existence of maternal malignancy may cause false-positive results or failed tests of NIPT. Though recent studies have shown multiple chromosomal aneuploidies (MCA) are associated with malignancy, there is still no effective solution to identify maternal cancer patients from pregnant women with MCA results using NIPT. We aimed to develop a new method to effectively detect maternal cancer in pregnant women with MCA results using NIPT and a random forest classifier to identify the tissue origin of common maternal cancer types. Methods: For examination, 496 participants with MCA results via NIPT were enrolled from January 2016 to June 2019 at BGI. Cancer and non-cancer participants were confirmed through the clinical follow-up. The cohort comprising 42 maternal cancer cases and 294 non-cancer cases enrolled from January 2016 to December 2017 was utilized to develop a method named mean of the top five chromosome z scores (MTOP5Zscores). The remaining 160 participants enrolled from January 2018 to June 2019 were used to validate the performance of MTOP5Zscores. We established a random forest model to classify three common cancer types using normalized Pearson correlation coefficient (NPCC) values, z scores of 22 chromosomes, and seven plasma tumor markers (PTMs) as predictor variables. Results: 62 maternal cancer cases were confirmed with breast cancer, liver cancer, and lymphoma, the most common cancer types. MTOP5Zscores showed a sensitivity of 85% (95% confidence interval (CI), 62.11-96.79%) and specificity of 80% (95% CI, 72.41-88.28%) in the detection of maternal cancer among pregnant women with MCA results. The sensitivity of the classifier was 93.33, 66.67, and 50%, while specificity was 66.67, 90, and 97.06%, and positive predictive value (PPV) was 60.87, 72.73, and 80% for the prediction of breast cancer, liver cancer, and lymphoma, respectively. Conclusion: This study presents a solution to identify maternal cancer patients from pregnant women with MCA results using NIPT, indicating it as a value-added application of NIPT in the detection of maternal malignancies in addition to screening for fetal aneuploidies with no extra cost.

Keywords: cell-free DNA; classifier; maternal malignancy; non-invasive prediction; random forest.