We analyzed maternal plasma cell-free DNA samples from twin pregnancies in a prospective blinded study to validate a single-nucleotide polymorphism (SNP)-based non-invasive prenatal test (NIPT) for zygosity, fetal sex, and aneuploidy. Zygosity was evaluated by looking for either one or two fetal genome complements, fetal sex was evaluated by evaluating Y-chromosome loci, and aneuploidy was assessed through SNP ratios. Zygosity was correctly predicted in 100% of cases (93/93; 95% confidence interval (CI) 96.1%–100%). Individual fetal sex for both twins was also called with 100% accuracy (102/102; 95% weighted CI 95.2%–100%). All cases with copy number truth were also correctly identified. The dizygotic aneuploidy sensitivity was 100% (10/10; 95% CI 69.2%–100%), and overall specificity was 100% (96/96; 95% weighted CI, 94.8%–100%). The mean fetal fraction (FF) of monozygotic twins (n = 43) was 13.0% (standard deviation (SD), 4.5%); for dizygotic twins (n = 79), the mean lower FF was 6.5% (SD, 3.1%) and the mean higher FF was 8.1% (SD, 3.5%). We conclude SNP-based NIPT for zygosity is of value when chorionicity is uncertain or anomalies are identified. Zygosity, fetal sex, and aneuploidy are complementary evaluations that can be carried out on the same specimen as early as 9 weeks’ gestation.

Original Clinical Science-General Background. Early detection of rejection in kidney transplant recipients holds the promise to improve clinical outcomes. Development and implementation of more accurate, noninvasive methods to detect allograft rejection remain an ongoing challenge. The limitations of existing allograft surveillance methods present an opportunity for donor-derived cell-free DNA (dd-cfDNA), which can accurately and rapidly differentiate patients with allograft rejection from patients with stable organ function. Methods. This study evaluated the analytical performance of a massively multiplexed polymerase chain reaction assay that targets 13 962 single-nucleotide polymorphisms, characterized and validated using 66 unique samples with 1064 replicates, including cell line-derived reference samples, plasma-derived mixtures, and transplant patient samples. The dd-cfDNA fraction was quantified in both related and unrelated donor-recipient pairs. Results. The dd-cfDNA assay showed a limit of blank of 0.11%, a limit of detection and limit of quantitation of 0.15% for unrelated donors, and limit of blank of 0.23%, a limit of detection and limit of quantitation of 0.29% for related donors. All other metrics (linearity, accuracy, and precision) were observed to be equivalent between unrelated and related donors. The measurement precision of coefficient of variation was 1.8% (repeatability, 0.6% dd-cfDNA) and was <5% for all the different reproducibility measures. Conclusions. This study validates the performance of a single-nucleotide polymorphism-based massively multiplexed polymerase chain reaction assay to detect the dd-cfDNA fraction with improved precision over currently available tests, regardless of donorrecipient relationships.

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Early detection of allograft rejection is critical to the successful management of transplant recipients. Tissue biopsy has been the "gold standard" for diagnosis of active rejection (AR), but is invasive and has poor reproducibility. 1 Conventional noninvasive biomarkers, such as changes in serum creatinine, are available for detecting AR, but are limited due to low sensitivity and specificity. 2 Thus, there is a need for new noninvasive markers that have high accuracy for detecting AR.Donor-derived cell-free DNA fraction (dd-cfDNA[%]) is a promising noninvasive biomarker for detecting allograft rejection. However, dd-cfDNA(%) can be artificially depressed by high levels of circulating cfDNA, which can occur in patients who are obese, have had recent surgery, have medical complications, or received certain medications. 3 This can potentially lead to false negative results.Recently, two studies provided preliminary evidence indicating that the absolute quantity of dd-cfDNA may show better performance for detecting AR than dd-cfDNA(%). 4,5 In this study we present our results from an assay that utilizes a new two-threshold algorithm that combines both dd-cfDNA(%) and absolute quantity of dd-cfDNA (copies/ml), with the goal of increasing test sensitivity, particularly through improved detection in patients where cfDNA levels are high.

Background. Donor-derived cell-free DNA (dd-cfDNA) fraction and quantity have both been shown to be associated with allograft rejection. The present study compared the relative predictive power of each of these variables to the combination of the two, and developed an algorithm incorporating both variables to detect active rejection in renal allograft biopsies. Methods. The first 426 sequential indication biopsy samples collected from the Trifecta study ( ClinicalTrials.gov # NCT04239703) with microarray-derived gene expression and dd-cfDNA results were included. After exclusions to simulate intended clinical use, 367 samples were analyzed. Biopsies were assessed using the molecular microscope diagnostic system and histology (Banff 2019). Logistic regression analysis examined whether combining dd-cfDNA fraction and quantity adds predictive value to either alone. The first 149 sequential samples were used to develop a two-threshold algorithm and the next 218 to validate the algorithm. Results. In regression, the combination of dd-cfDNA fraction and quantity was found to be significantly more predictive than either variable alone ( P = 0.009 and P < 0.0001). In the test set, the area under the receiver operating characteristic curve of the two-variable system was 0.88, and performance of the two-threshold algorithm showed a sensitivity of 83.1% and specificity of 81.0% for molecular diagnoses and a sensitivity of 73.5% and specificity of 80.8% for histology diagnoses. Conclusions. This prospective, biopsy-matched, multisite dd-cfDNA study in kidney transplant patients found that the combination of dd-cfDNA fraction and quantity was more powerful than either dd-cfDNA fraction or quantity alone and validated a novel two-threshold algorithm incorporating both variables.