Improving the accuracy of noninvasive prenatal testing through size-selection between fetal and maternal cfDNA.

OBJECTIVES: In general, fetal cfDNA is shorter than maternal cfDNA, and accuracy of noninvasive prenatal testing (NIPT) results can be improved by selecting shorter cfDNA fragments to enrich fetal-derived cfDNA. This study investigated potential improvements in the accuracy of NIPT by performing classification and analysis based on differences in cfDNA size. METHODS: We performed paired-end sequencing to identify size ranges of fetal and maternal cfDNA from 62,374 pregnant women. We then developed a size-selection method to isolate and analyze both fetal and maternal cfDNA, defining fetal-derived cfDNA as less than 150 bp and maternal-derived cfDNA as greater than 180 bp. RESULTS: By implementing size-selection method, the accuracy of NIPT was improved, resulting in an increase in the overall positive predictive value for all aneuploidies from 89.57% to 97.1%. This was achieved by enriching both fetal and maternal-derived cfDNA, which increased fetal DNA fraction while the number of false positives for all aneuploidies was reduced by more than 70%. CONCLUSIONS: We identified the differences in read length between fetal and maternal-derived cfDNA, and selectively enriched both shorter and longer cfDNA fragments for subsequent analysis. Our approach can increase the detection accuracy of NIPT for detecting fetal aneuploidies and reduce the number of false positives caused by maternal chromosomal abnormalities.

Enhancing Lung Cancer Classification through Integration of Liquid Biopsy Multi-Omics Data with Machine Learning Techniques.

Early detection of lung cancer is crucial for patient survival and treatment. Recent advancements in next-generation sequencing (NGS) analysis enable cell-free DNA (cfDNA) liquid biopsy to detect changes, like chromosomal rearrangements, somatic mutations, and copy number variations (CNVs), in cancer. Machine learning (ML) analysis using cancer markers is a highly promising tool for identifying patterns and anomalies in cancers, making the development of ML-based analysis methods essential. We collected blood samples from 92 lung cancer patients and 80 healthy individuals to analyze the distinction between them. The detection of lung cancer markers Cyfra21 and carcinoembryonic antigen (CEA) in blood revealed significant differences between patients and controls. We performed machine learning analysis to obtain AUC values via Adaptive Boosting (AdaBoost), Multi-Layer Perceptron (MLP), and Logistic Regression (LR) using cancer markers, cfDNA concentrations, and CNV screening. Furthermore, combining the analysis of all multi-omics data for ML showed higher AUC values compared with analyzing each element separately, suggesting the potential for a highly accurate diagnosis of cancer. Overall, our results from ML analysis using multi-omics data obtained from blood demonstrate a remarkable ability of the model to distinguish between lung cancer and healthy individuals, highlighting the potential for a diagnostic model against lung cancer.

Advances in methylation analysis of liquid biopsy in early cancer detection of colorectal and lung cancer.

Methylation patterns in cell-free DNA (cfDNA) have emerged as a promising genomic feature for detecting the presence of cancer and determining its origin. The purpose of this study was to evaluate the diagnostic performance of methylation-sensitive restriction enzyme digestion followed by sequencing (MRE-Seq) using cfDNA, and to investigate the cancer signal origin (CSO) of the cancer using a deep neural network (DNN) analyses for liquid biopsy of colorectal and lung cancer. We developed a selective MRE-Seq method with DNN learning-based prediction model using demethylated-sequence-depth patterns from 63,266 CpG sites using SacII enzyme digestion. A total of 191 patients with stage I-IV cancers (95 lung cancers and 96 colorectal cancers) and 126 noncancer participants were enrolled in this study. Our study showed an area under the receiver operating characteristic curve (AUC) of 0.978 with a sensitivity of 78.1% for colorectal cancer, and an AUC of 0.956 with a sensitivity of 66.3% for lung cancer, both at a specificity of 99.2%. For colorectal cancer, sensitivities for stages I-IV ranged from 76.2 to 83.3% while for lung cancer, sensitivities for stages I-IV ranged from 44.4 to 78.9%, both again at a specificity of 99.2%. The CSO model's true-positive rates were 94.4% and 89.9% for colorectal and lung cancers, respectively. The MRE-Seq was found to be a useful method for detecting global hypomethylation patterns in liquid biopsy samples and accurately diagnosing colorectal and lung cancers, as well as determining CSO of the cancer using DNN analysis.Trial registration: This trial was registered at ClinicalTrials.gov (registration number: NCT04253509) for lung cancer on 5 February 2020, https://clinicaltrials.gov/ct2/show/NCT04253509 . Colorectal cancer samples were retrospectively registered at CRIS (Clinical Research Information Service, registration number: KCT0008037) on 23 December 2022, https://cris.nih.go.kr , https://who.init/ictrp . Healthy control samples were retrospectively registered.

Improving CNV Detection Performance in Microarray Data Using a Machine Learning-Based Approach.

Copy number variation (CNV) is a primary source of structural variation in the human genome, leading to several disorders. Therefore, analyzing neonatal CNVs is crucial for managing CNV-related chromosomal disabilities. However, genomic waves can hinder accurate CNV analysis. To mitigate the influences of the waves, we adopted a machine learning approach and developed a new method that uses a modified log R ratio instead of the commonly used log R ratio. Validation results using samples with known CNVs demonstrated the superior performance of our method. We analyzed a total of 16,046 Korean newborn samples using the new method and identified CNVs related to 39 genetic disorders were identified in 342 cases. The most frequently detected CNV-related disorder was Joubert syndrome 4. The accuracy of our method was further confirmed by analyzing a subset of the detected results using NGS and comparing them with our results. The utilization of a genome-wide single nucleotide polymorphism array with wave offset was shown to be a powerful method for identifying CNVs in neonatal cases. The accurate screening and the ability to identify various disease susceptibilities offered by our new method could facilitate the identification of CNV-associated chromosomal disease etiologies.

A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women.

Purpose: Changes in facial appearance are affected by various intrinsic and extrinsic factors, which vary from person to person. Therefore, each person needs to determine their skin condition accurately to care for their skin accordingly. Recently, genetic identification by skin-related phenotypes has become possible using genome-wide association studies (GWAS) and machine-learning algorithms. However, because most GWAS have focused on populations with American or European skin pigmentation, large-scale GWAS are needed for Asian populations. This study aimed to evaluate the correlation of facial phenotypes with candidate single-nucleotide polymorphisms (SNPs) to predict phenotype from genotype using machine learning. Materials and Methods: A total of 749 Korean women aged 30-50 years were enrolled in this study and evaluated for five facial phenotypes (melanin, gloss, hydration, wrinkle, and elasticity). To find highly related SNPs with each phenotype, GWAS analysis was used. In addition, phenotype prediction was performed using three machine-learning algorithms (linear, ridge, and linear support vector regressions) using five-fold cross-validation. Results: Using GWAS analysis, we found 46 novel highly associated SNPs (p < 1×10-05): 3, 20, 12, 6, and 5 SNPs for melanin, gloss, hydration, wrinkle, and elasticity, respectively. On comparing the performance of each model based on phenotypes using five-fold cross-validation, the ridge regression model showed the highest accuracy (r2 = 0.6422-0.7266) in all skin traits. Therefore, the optimal solution for personal skin diagnosis using GWAS was with the ridge regression model. Conclusion: The proposed facial phenotype prediction model in this study provided the optimal solution for accurately predicting the skin condition of an individual by identifying genotype information of target characteristics and machine-learning methods. This model has potential utility for the development of customized cosmetics.

Clinical Utility of Combined Circulating Tumor Cell and Circulating Tumor DNA Assays for Diagnosis of Primary Lung Cancer.

BACKGROUND/AIM: Although it has been suggested that circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) might be used in a complementary manner in lung cancer diagnosis, limited confirmatory data are available. In this prospective study, we evaluated the diagnostic performance of each assay separately and in combination. PATIENTS AND METHODS: From March 2018 to January 2019, patients with suspected primary lung cancer, who underwent routine lung cancer work-up and peripheral blood sampling, were prospectively enrolled in the study. Epithelial cell adhesion molecule and cytokeratin served as markers of CTCs. In terms of ctDNA analysis, single-nucleotide variants were evaluated via next-generation sequencing. RESULTS: We analyzed 111 patients, including 99 with primary lung cancer and 12 with benign pulmonary disease. The median number of CTCs in 10 ml of blood was 3. The most frequently detected single nucleotide variants of ctDNA were TP53, CDKN2A, and EGFR. The diagnostic sensitivity of conventional tumor marker (combination of carcinoembryonic antigen/CYFRA 21-1/neuron-specific enolase) was 66.7%, while those of the ctDNA and CTC assays were 72.7% and 65.7%, respectively. The sensitivity of the CTC/ctDNA combination (95.0%) was significantly greater than those of the CTC (p<0.001), ctDNA (p<0.001), or conventional tumor marker (p<0.001) alone. Subgroup analysis revealed that the sensitivity of the combination assay was greater than those of the CTC or ctDNA assays alone, regardless of tumor stage or histopathology type. CONCLUSION: The CTC/ctDNA combination assay enhanced the sensitivity of primary lung cancer diagnosis. The combination assay strategy may be clinically useful and could enhance the early detection of lung cancer (ClinicalTrials.gov number: NCT03479099).

Maternal nutrient restriction reduces concentrations of amino acids and polyamines in ovine maternal and fetal plasma and fetal fluids.

Amino acids and polyamines are essential for placental and fetal growth, but little is known about their availability in the conceptus in response to maternal undernutrition. We hypothesized that maternal nutrient restriction reduces concentrations of amino acids and polyamines in the ovine conceptus. This hypothesis was tested in nutrient-restricted ewes between Days 28 and 78 (experiment 1) and between Days 28 and 135 (experiment 2) of gestation. In both experiments, ewes were assigned randomly on Day 28 of gestation to a control group fed 100% of National Research Council (NRC) nutrient requirements and to an nutrient-restricted group fed 50% of NRC requirements. Every 7 days beginning on Day 28 of gestation, ewes were weighed and rations adjusted for changes in body weight. On Day 78 of gestation, blood samples were obtained from the uterine artery and umbilical vein for analysis. In experiment 2, nutrient-restricted ewes on Day 78 of gestation either continued to be fed 50% of NRC requirements or were realimented to 100% of NRC requirements until Day 135. Fetal weight was reduced in nutrient-restricted ewes at both Day 78 (32%) and Day 135 (15%) compared with controls. Nutritional restriction markedly reduced (P < 0.05) concentrations of total alpha-amino acids (particularly serine, arginine-family amino acids, and branched-chain amino acids) and polyamines in maternal and fetal plasma and in fetal allantoic and amniotic fluids at both mid and late gestation. Realimentation of nutrient-restricted ewes increased (P < 0.05) concentrations of total alpha-amino acids and polyamines in all the measured compartments and prevented intrauterine growth retardation. These novel findings demonstrate that 50% global nutrient restriction decreases concentrations of amino acids and polyamines in the ovine conceptus that could adversely impact key fetal functions. The results have important implications for understanding the mechanisms responsible for both intrauterine growth retardation and developmental origins of adult disease.

Developmental changes in nitric oxide synthesis in the ovine placenta.

Nitric oxide (NO), synthesized from l-arginine by NO synthase (NOS), is a key regulator of placental angiogenesis and growth during pregnancy. However, little is known about placental NO synthesis associated with ovine conceptus development. This study was conducted to test the hypothesis that placental NO synthesis is greatest during early gestation. Columbia cross-bred ewes were hysterectomized on Days 30, 40, 60, 80, 100, 120, or 140 of gestation (n = 4 per day) to obtain placentomes, intercotyledonary placenta, and intercaruncular endometrium. Tissues were analyzed for constitutive NOS (cNOS) and inducible NOS (iNOS) activities, NO synthesis, tetrahydrobiopterin (BH4) and NADPH (essential cofactors for NOS), and GTP-cyclohydrolase I (GTP-CH, a rate-controlling enzyme in de novo synthesis of BH4) activity using radiochemical and chromatographic methods. Marked changes in NO synthesis, cNOS and iNOS activities, GTP-CH activity, and concentrations of BH4 and NADPH occurred in all placental and endometrial tissues between Days 30 and 140 of gestation. NO synthesis peaked on Day 60 of gestation in both intercotyledonary placenta and placentomes and on Days 40-60 in intercaruncular endometrium. NO synthesis in placentomes increased 100% between Days 80 and 100 of gestation, when placental and uterine blood flows increase continuously. In all placental and endometrial tissues, NO synthesis was positively correlated with total NOS activity, GTP-CH activity, and concentrations of BH4 and NADPH. Importantly, these results indicate a high degree of metabolic coordination among the several integrated pathways that support high rates of NO synthesis in the conceptus and uterus and establish a new base of information for future studies to define the roles of NO in fetal-placental growth and development.

Developmental changes in polyamine levels and synthesis in the ovine conceptus.

Polyamines (putrescine, spermidine, and spermine) are essential for placental growth and angiogenesis. However, little is known about changes in polyamine synthesis associated with development of the ovine conceptus (embryo/fetus and associated placental membranes). We hypothesized that rates of placental polyamine synthesis were maximal during the rapid placental growth that occurs in the first half of pregnancy. This hypothesis was tested using ewes between Days 30 and 140 of gestation. Columbia cross-bred ewes were hysterectomized on Days 30, 40, 60, 80, 100, 120, or 140 of gestation (Day 0 = mating; n = 4 ewes/day) to obtain placentomes, intercotyledonary placenta, intercaruncular endometrium, and allantoic as well as amniotic fluids. The tissues were analyzed for ornithine decarboxylase (ODC) and arginase activities; arginine, ornithine, and polyamine concentrations; and polyamine synthesis using radiochemical and chromatographic methods. Maximal ODC and arginase activities and the highest rates of polyamine synthesis were observed in all tissues on Day 40 of gestation. Concentrations of ornithine and polyamines in placentomes and intercaruncular endometrium also peaked on Day 40 of gestation. In ovine allantoic and amniotic fluids, polyamines were most abundant during early (Days 40-60) and late (Days 100-140) gestation, respectively. Amniotic fluid spermine increased progressively with advancing gestation. Results of the present study indicate metabolic coordination among the several integrated pathways that support high rates of polyamine synthesis in the placenta and endometrium during early pregnancy. Our findings may have important implications for both intrauterine growth retardation and fetal origins of diseases in adults.