Validation of an Enhanced Version of a Single-Nucleotide Polymorphism-Based Noninvasive Prenatal Test for Detection of Fetal Aneuploidies.

OBJECTIVE: To validate an updated version (Version 2) of a single-nucleotide polymorphism (SNP)-based noninvasive prenatal test (NIPT) and to determine the likelihood of success when testing for fetal aneuploidies following a redraw. METHODS: Version 2 was analytically validated using 587 plasma samples with known genotype (184 trisomy 21, 37 trisomy 18, 15 trisomy 13, 9 monosomy X, 4 triploidy and 338 euploid). Sensitivity, specificity and no-call rate were calculated, and a fetal-fraction adjustment was applied to enable projection of these values in a commercial distribution. Likelihood of success of a second blood draw was computed based on fetal fraction and maternal weight from the first draw. RESULTS: Validation of this methodology yielded high sensitivities (≥99.4%) and specificities (100%) for all conditions tested with an observed no-call rate of 2.3%. The no-call threshold for sample calling was reduced to 2.8% fetal fraction. The redraw success rate was driven by higher initial fetal fractions and lower maternal weights, with the fetal fraction being the more significant variable. CONCLUSIONS: The enhanced version of this SNP-based NIPT method showed a reduced no-call rate and a reduced fetal-fraction threshold for sample calling in comparison to the earlier version, while maintaining high sensitivity and specificity.

Use of cell-free signals as biomarkers for early and easy prediction of preeclampsia.

Introduction: Preeclampsia (PE) is a leading cause of maternal and perinatal morbidity worldwide. However, current methods of screening are complicated and require special skill sets. In this observational study of prospectively collected samples, we wanted to evaluate if cell-free (cf) DNA could be an efficient biomarker for identification of at-risk patients. Methods: One hundred patients attending a private prenatal clinic in Canada were enrolled in their first trimester of pregnancy and a blood draw was carried out at 11 + 0 to 14 + 2 weeks' (timepoint A) and 17 + 6 to 25 + 5 weeks of gestation (timepoint B). CfDNA signals, namely concentration, fetal fraction, and fragment size distribution, were correlated with clinical outcomes in the test population to develop the logistic regression model. Results: Twelve patients developed PE-four early-stage and eight late-stage PE. Significant differences were observed between PE patients and control cases for all three cfDNA signals at timepoint A, while both fetal fraction and concentration were significantly different between PE patients and control cases at timepoint B. Overall, the model had a sensitivity of up to 100% and specificity of up to 87.5% at Timepoint A. Conclusion: This proof-of-principle study showed that use of this logistic regression model could identify patients at risk of preeclampsia in the first trimester of pregnancy.

Detection of Clonal and Subclonal Copy-Number Variants in Cell-Free DNA from Patients with Breast Cancer Using a Massively Multiplexed PCR Methodology.

We demonstrate proof-of-concept for the use of massively multiplexed PCR and next-generation sequencing (mmPCR-NGS) to identify both clonal and subclonal copy-number variants (CNVs) in circulating tumor DNA. This is the first report of a targeted methodology for detection of CNVs in plasma. Using an in vitro model of cell-free DNA, we show that mmPCR-NGS can accurately detect CNVs with average allelic imbalances as low as 0.5%, an improvement over previously reported whole-genome sequencing approaches. Our method revealed differences in the spectrum of CNVs detected in tumor tissue subsections and matching plasma samples from 11 patients with stage II breast cancer. Moreover, we showed that liquid biopsies are able to detect subclonal mutations that may be missed in tumor tissue biopsies. We anticipate that this mmPCR-NGS methodology will have broad applicability for the characterization, diagnosis, and therapeutic monitoring of CNV-enriched cancers, such as breast, ovarian, and lung cancer.