Identifying occult maternal malignancies from 1.93 million pregnant women undergoing noninvasive prenatal screening tests
Menée à partir de données portant sur 639 femmes enceintes présentant de multiples aneuploïdies chromosomiques détectées lors de tests prénataux non invasifs, cette étude évalue la performance d'un algorithme, basé sur les variations du nombre de copies de gènes, pour identifier une lésion maligne maternelle occulte
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.
Genetics in Medicine , résumé, 2019