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

Effective Identification of Maternal Malignancies in Pregnancies Undergoing Noninvasive Prenatal Testing

Jia Li  1   2 , Jia Ju  3   4 , Qiang Zhao  5   6 , Weiqiang Liu  7 , Yuying Yuan  1 , Qiang Liu  1 , Lijun Zhou  1 , Yuan Han  8 , Wen Yuan  8 , Yonghua Huang  5 , Yingjun Xie  9 , Zhihua Li  10 , Jingsi Chen  10 , Shuyu Huang  11 , Rufang Chen  11 , Wei Li  1 , Meihua Tan  1   4 , Danchen Wang  12 , Si Zhou  1   2   13 , Jian Zhang  14 , Fanwei Zeng  1 , Nan Yu  1 , Fengxia Su  3 , Min Chen  10 , Yunsheng Ge  14 , Yanming Huang  15 , Xin Jin  3   16
Affiliations

Effective Identification of Maternal Malignancies in Pregnancies Undergoing Noninvasive Prenatal Testing

Jia Li et al. Front Genet. .

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.

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Conflict of interest statement

JL was employed by Shijiazhuang BGI Genomics Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Characterization of 62 maternal cancer cases. (A) The number of cancer cases for each cancer type. (B) The distribution of cancer stages at diagnosis. NA: not available.
FIGURE 2
FIGURE 2
The frequencies of chromosomal amplifications (z score >3) and deletions (z score < −3) in breast cancer, liver cancer, lymphoma, and gastric cancer (A–D).
FIGURE 3
FIGURE 3
MTOP5Zscore analyses of 62 cancer cases in this study. (A) The comparison of MTOP5Zscores between 62 maternal cancer cases and 434 non-cancer participants; the red line represents the cutoff of 5.94 to determine MTOP5Zscore-positive. (B) The ROCs for MTOP5Zscores in the training and validation sets. (C) The comparison of MTOP5Zscores in maternal cancer patients at different cancer stages. (D) The Kaplan–Meier plot shows non-cancer rates for MCA-positive, MTOP5Zscore-positive, and MTOP5Zscore-negative groups.
FIGURE 4
FIGURE 4
The tumor tissue origin classifier. (A) The performances of the random forest classifier estimated by leave-one-out cross validation. (B) The importance of different features in the random forest classifier. MeanDecreaseGini is the variable’s total decrease in node impurity measured by the Gini impurity criterion.
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