PREFACE: In silico pipeline for accurate cell-free fetal DNA fraction prediction

Phenome-wide association study in 25,639 pregnant Chinese women reveals loci associated with maternal comorbidities and child health

Phenome-wide association studies (PheWAS) have been less focused on maternal diseases and maternal-newborn comorbidities, especially in the Chinese population. To enhance our understanding of the genetic basis of these related diseases, we conducted a PheWAS on 25,639 pregnant women and 14,151 newborns in the Chinese Han population using ultra-low-coverage whole-genome sequence (ulcWGS). We identified 2,883 maternal trait-associated SNPs associated with 26 phenotypes, among which 99.5% were near established genome-wide association study (GWAS) loci. Further refinement delineated these SNPs to 442 unique trait-associated loci (TALs) predicated on linkage disequilibrium R2 > 0.8, revealing that 75.6% demonstrated pleiotropy and 50.9% were located in genes implicated in analogous phenotypes. Notably, we discovered 21 maternal SNPs associated with 35 neonatal phenotypes, including two SNPs associated with identical complications in both mothers and children. These findings underscore the importance of integrating ulcWGS data to enrich the discoveries derived from traditional PheWAS approaches.

Noninvasive prenatal screening in a pregnant woman with a history of stem cell transplant from a male donor: A case report and literature review

BACKGROUND

As a screening method, inaccuracies in noninvasive prenatal screening (NIPS) exist, which are often attributable to biological factors. One such factor is the history of transplantation. However, there are still limited reports on such NIPS cases.

METHODS

We report an NIPS case of a pregnant woman who had received a stem cell transplant from a male donor. To determine the karyotype in the woman's original cell, we performed chromosome microarray analysis (CMA) on her postnatal blood and oral mucosa. To comprehensively estimate the cell-free DNA (cfDNA) composition, we further performed standard NIPS procedures on the postnatal plasma. Moreover, we reviewed all published relevant NIPS case reports about pregnant women with transplantation history.

RESULTS

NIPS showed a low-risk result for common trisomies with a fetal fraction of 65.80%. CMA on maternal white blood cells showed a nonmosaic male karyotype, while the oral mucosa showed a nonmosaic female karyotype. The proportion of donor's cfDNA in postnatal plasma was 94.73% based on the Y-chromosome reads ratio. The composition of cfDNA in maternal plasma was estimated as follows: prenatally, 13.60% maternal, 65.80% donor, and 20.60% fetal/placental, whereas postnatally, 5.27% maternal and 94.73% donor.

CONCLUSIONS

This study expanded our understanding of the influence of stem cell transplantation on NIPS, allowing us to optimize NIPS management for these women.

Artificial intelligence for prenatal chromosome analysis

This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.

Development and performance evaluation of an artificial intelligence algorithm using cell-free DNA fragment distance for non-invasive prenatal testing (aiD-NIPT)

With advances in next-generation sequencing technology, non-invasive prenatal testing (NIPT) has been widely implemented to detect fetal aneuploidies, including trisomy 21, 18, and 13 (T21, T18, and T13). Most NIPT methods use cell-free DNA (cfDNA) fragment count (FC) in maternal blood. In this study, we developed a novel NIPT method using cfDNA fragment distance (FD) and convolutional neural network-based artificial intelligence algorithm (aiD-NIPT). Four types of aiD-NIPT algorithm (mean, median, interquartile range, and its ensemble) were developed using 2,215 samples. In an analysis of 17,678 clinical samples, all algorithms showed >99.40% accuracy for T21/T18/T13, and the ensemble algorithm showed the best performance (sensitivity: 99.07%, positive predictive value (PPV): 88.43%); the FC-based conventional Z-score and normalized chromosomal value showed 98.15% sensitivity, with 40.77% and 36.81% PPV, respectively. In conclusion, FD-based aiD-NIPT was successfully developed, and it showed better performance than FC-based NIPT methods.

Shallow whole-genome sequencing: a useful, easy to apply molecular technique for CNA detection on FFPE tumor tissue-a glioma-driven study

Copy number alterations (CNAs) have increasingly become part of the diagnostic algorithm of glial tumors. Alterations such as homozygous deletion of CDKN2A/B, 7 +/ 10 - chromosome copy number changes or EGFR amplification are predictive of a poor prognosis. The codeletion of chromosome arms 1p and 19q, typically associated with oligodendroglioma, implies a more favorable prognosis. Detection of this codeletion by the current diagnostic standard, being fluorescence in situ hybridization (FISH), is sometimes however subject to technical and interpretation problems. In this study, we evaluated CNA detection by shallow whole-genome sequencing (sWGS) as an inexpensive, complementary molecular technique. A cohort of 36 glioma tissue samples, enriched with "difficult" and "ambiguous" cases, was analyzed by sWGS. sWGS results were compared with FISH assays of chromosomes 1p and 19q. In addition, CNAs relevant to glioblastoma diagnosis were explored. In 4/36 samples, EGFR (7p11.2) amplifications and homozygous loss of CDKN2A/B were identified by sWGS. Six out of 8 IDH-wild-type glioblastomas demonstrated a prognostic chromosome 7/chromosome 10 signature. In 11/36 samples, local interstitial and terminal 1p/19q alterations were detected by sWGS, implying that FISH's targeted nature might promote false arm-level extrapolations. In this cohort, differences in overall survival between patients with and without codeletion were better pronounced by the sequencing-based distinction (likelihood ratio of 7.48) in comparison to FISH groupings (likelihood ratio of 0.97 at diagnosis and 1.79 ± 0.62 at reobservation), suggesting sWGS is more accurate than FISH. We recognized adverse effects of tissue block age on FISH signals. In addition, we show how sWGS reveals relevant aberrations beyond the 1p/19q state, such as EGFR amplification, combined gain of chromosome 7 and loss of chromosome 10, and homozygous loss of CDKN2A/B. The findings presented by this study might stimulate implementation of sWGS as a complementary, easy to apply technique for copy number detection.

Factors Affecting the Fetal Fraction in Noninvasive Prenatal Screening: A Review

A paradigm shift in noninvasive prenatal screening has been made with the discovery of cell-free fetal DNA in maternal plasma. Noninvasive prenatal screening is primarily used to screen for fetal aneuploidies, and has been used globally. Fetal fraction, an important parameter in the analysis of noninvasive prenatal screening results, is the proportion of fetal cell-free DNA present in the total maternal plasma cell-free DNA. It combines biological factors and bioinformatics algorithms to interpret noninvasive prenatal screening results and is an integral part of quality control. Maternal and fetal factors may influence fetal fraction. To date, there is no broad consensus on the factors that affect fetal fraction. There are many different approaches to evaluate this parameter, each with its advantages and disadvantages. Different fetal fraction calculation methods may be used in different testing platforms or laboratories. This review includes numerous publications that focused on the understanding of the significance, influencing factors, and interpretation of fetal fraction to provide a deeper understanding of this parameter.

Estimation of cell-free fetal DNA fraction from maternal plasma based on linkage disequilibrium information

Cell-free fetal DNA fraction (FF) in maternal plasma is a key parameter affecting the performance of noninvasive prenatal testing (NIPT). Accurate quantitation of FF plays a pivotal role in these tests. However, there are few methods that could determine FF with high accuracy using shallow-depth whole-genome sequencing data. In this study, we hypothesized that the actual FF in maternal plasma should be proportional to the discrepancy rate between the observed genotypes and inferred genotypes based on the linkage disequilibrium rule in certain polymorphism sites. Based on this hypothesis, we developed a method named Linkage Disequilibrium information-based cell-free Fetal DNA Fraction (LDFF) to accurately quantify FF in maternal plasma. This method achieves a high performance and outperforms existing methods in the fetal DNA fraction estimation. As LDFF is a gender-independent method and developed on shallow-depth samples, it can be easily incorporated into routine NIPT test and may enhance the current NIPT performance.

Total number of reads affects the accuracy of fetal fraction estimates in NIPT

Background

Sufficient fetal fraction (FF) is crucial for quality control of NIPT (Non‐Invasive Prenatal Test) results. Different factors influencing bioinformatic estimation of FF should be considered when implementing NIPT. To what extent the total number of sequencing reads influences FF estimate has been unexplored. In this study, to test the robustness of SeqFF FF estimation and provide additional recommendations for NIPT analysis quality control, we compared the SeqFF FF estimates with two other methods and investigated how the number of sequencing reads and FF level affects the accuracy and precision of FF estimates.

Methods

WGS data of 516 NIPT samples from a prenatal screening program was obtained. Sample data were randomly downsampled by the read count, and FF was calculated by SeqFF software. Then, the outcome was compared with FF estimates from SNP‐ and chrY‐based methods. FF estimated with different read counts and FF levels were compared with FF at 30 M reads as a reference.

Results

SeqFF FF highly correlates with SNP‐ and chrY‐based FF estimates. Raising read count from 2 M to 10 M drastically increased the accuracy of FF estimates. After adding more reads, we saw a further improvement in FF accuracy, reaching a plateau at 20 M reads. Precision of SeqFF FF estimate is independent of FF level in the sample.

Conclusion

SeqFF is a robust method for FF estimation for both genders and for any FF level in range 2–13%. Accuracy of FF estimates highly depends on the read count. We recommend using no less than 10 M reads to achieve accurate FF estimates for NIPT analysis in clinical settings.

Shallow-depth sequencing of cell-free DNA for Hodgkin and diffuse large B-cell lymphoma (differential) diagnosis: a standardized approach with underappreciated potential

Shallow-depth sequencing of cell-free DNA, an inexpensive and standardized approach to obtain molecular information on tumors non-invasively, has been insufficiently explored for the diagnosis of lymphoma and disease follow-up. This study collected 318 samples, including longitudinal liquid and paired solid biopsies, from a prospectively- recruited cohort of 38 Hodgkin lymphoma (HL) and 85 aggressive B-cell non-HL patients, represented by 81 diffuse large B-cell lymphoma (DLBCL) cases. Following sequencing, copy number alterations and viral read fractions were derived and analyzed. At diagnosis, liquid biopsies showed detectable copy number alterations in 84.2% of HL patients (88.6% for classical HL) and 74.1% of DLBCL patients. Of the DLBCL patients, copy number profiles between liquid-solid pairs were highly concordant (r=0.815±0.043); and, compared to tissue, HL liquid biopsies had abnormalities with higher amplitudes (P=0.010). This implies that tumor DNA is more abundant in plasma. Additionally, 39.5% of HL and 13.6% of DLBCL cases had a significantly elevated number of plasma Epstein-Barr virus DNA fragments, achieving a sensitivity of 100% compared to the current standard. A longitudinal analysis determined that, when detectable, copy number patterns were similar across (re)staging moments in refractory or relapsed patients. Further, the overall profile anomaly correlated highly with the total metabolic tumor volume (P<0.001). To conclude, as a proof of principle, we demonstrate that liquid biopsy-derived copy numbers can aid diagnosis: e.g., by differentiating HL from DLBCL, random forest modeling is represented by an area under the receiver operating characteristic curve of 0.967. This application is potentially useful when tissue is difficult to obtain or when biopsies are small and inconclusive.

Technical and Methodological Aspects of Cell-Free Nucleic Acids Analyzes

Analyzes of cell-free nucleic acids (cfNAs) have shown huge potential in many biomedical applications, gradually entering several fields of research and everyday clinical care. Many biological properties of cfNAs can be informative to gain deeper insights into the function of the organism, such as their different types (DNA, RNAs) and subtypes (gDNA, mtDNA, bacterial DNA, miRNAs, etc.), forms (naked or vesicle bound NAs), fragmentation profiles, sequence composition, epigenetic modifications, and many others. On the other hand, the workflows of their analyzes comprise many important steps, from sample collection, storage and transportation, through extraction and laboratory analysis, up to bioinformatic analyzes and statistical evaluations, where each of these steps has the potential to affect the outcome and informational value of the performed analyzes. There are, however, no universal or standard protocols on how to exactly proceed when analyzing different cfNAs for different applications, at least according to our best knowledge. We decided therefore to prepare an overview of the available literature and products commercialized for cfNAs processing, in an attempt to summarize the benefits and limitations of the currently available approaches, devices, consumables, and protocols, together with various factors influencing the workflow, its processes, and outcomes.

Fetal fraction and noninvasive prenatal testing: What clinicians need to know

The fetal fraction (FF) is a function of both biological factors and bioinformatics algorithms used to interpret DNA sequencing results. It is an essential quality control component of noninvasive prenatal testing (NIPT) results. Clinicians need to understand the biological influences on FF to be able to provide optimal post-test counseling and clinical management. There are many different technologies available for the measurement of FF. Clinicians do not need to know the details behind the bioinformatics algorithms of FF measurements, but they do need to appreciate the significant variations between the different sequencing technologies used by different laboratories. There is no universal FF threshold that is applicable across all platforms and there have not been any differences demonstrated in NIPT performance by sequencing platform or method of FF calculation. Importantly, while FF should be routinely measured, there is not yet a consensus as to whether it should be routinely reported to the clinician. The clinician should know what to expect from a standard test report and whether reasons for failed NIPT results are revealed. Emerging solutions to the challenges of samples with low FF should reduce rates of failed NIPT in the future. In the meantime, having a "plan B" prepared for those patients for whom NIPT is unsuccessful is essential in today's clinical practice.

PREFACE: In silico pipeline for accurate cell-free fetal DNA fraction prediction

Objective

During routine noninvasive prenatal testing (NIPT), cell‐free fetal DNA fraction is ideally derived from shallow‐depth whole‐genome sequencing data, preventing the need for additional experimental assays. The fraction of aligned reads to chromosome Y enables proper quantification for male fetuses, unlike for females, where advanced predictive procedures are required. This study introduces PREdict FetAl ComponEnt (PREFACE), a novel bioinformatics pipeline to establish fetal fraction in a gender‐independent manner.

Methods

PREFACE combines the strengths of principal component analysis and neural networks to model copy number profiles.

Results

For sets of roughly 1100 male NIPT samples, a cross‐validated Pearson correlation of 0.9 between predictions and fetal fractions according to Y chromosomal read counts was noted. PREFACE enables training with both male and unlabeled female fetuses. Using our complete cohort (n_female = 2468, n_male = 2723), the correlation metric reached 0.94.

Conclusions

Allowing individual institutions to generate optimized models sidelines between‐laboratory bias, as PREFACE enables user‐friendly training with a limited amount of retrospective data. In addition, our software provides the fetal fraction based on the copy number state of chromosome X. We show that these measures can predict mixed multiple pregnancies, sex chromosomal aneuploidies, and the source of observed aberrations.

What's already known about this topic?

Cell‐free fetal DNA fraction is an important estimate during noninvasive prenatal testing (NIPT).

Most techniques to establish fetal fraction require experimental procedures, which impede routine execution.

What does this study add?

PREFACE is a novel software to accurately predict fetal fraction based on solely shallow‐depth whole‐genome sequencing data, the fundamental base of a default NIPT assay.

In contrast to previous efforts, PREFACE enables user‐friendly model training with a limited amount of retrospective data.