Diagnostic yield of next-generation sequencing in fetuses with isolated increased nuchal translucency: systematic review and meta-analysis.

OBJECTIVE: To determine the diagnostic yield of exome or genome sequencing (ES/GS) over chromosomal microarray analysis (CMA) in fetuses with increased nuchal translucency (NT) and no concomitant anomalies. METHODS: This systematic review was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses criteria. PubMed, Scopus and Web of Science were searched for studies describing ES/GS in fetuses with isolated increased NT. Inclusion criteria were: (1) study written in English; (2) more than two fetuses with increased NT > 99th percentile and no concomitant anomalies; and (3) a negative CMA result considered as the reference standard. Only positive variants identified on ES/GS that were classified as likely pathogenic or pathogenic and determined to be causative of the fetal phenotype were considered. Risk was assessed as the pooled effect size by single-proportion analysis using random-effects modeling (weighted by inverse of variance). RESULTS: Eleven studies reporting on the diagnostic yield of ES/GS in fetuses with isolated increased NT > 99th percentile were identified and included 309 cases. All studies were high quality according to Standards for Reporting of Diagnostic Accuracy. Overall, a pathogenic or likely pathogenic variant was identified on ES/GS in 15 fetuses, resulting in a pooled incremental yield of 4% (95% CI, 2-6%). Six (40%) of these fetuses had NT of 5 mm or more. The observed inheritance pattern was autosomal dominant in 12 cases, including four fetuses with Noonan syndrome, autosomal recessive in two cases and X-linked in one case. CONCLUSIONS: There is a 4% incremental diagnostic yield of ES/GS over CMA in fetuses with increased NT > 99th percentile without a concomitant anomaly. It is unclear whether a NT cut-off higher than 3.5 mm may be more useful in case selection for ES/GS. © 2021 International Society of Ultrasound in Obstetrics and Gynecology.

Diagnostic accuracy and value of chromosomal microarray analysis for chromosomal abnormalities in prenatal detection: A prospective clinical study.

ABSTRACT: Chromosomal microarray analysis (CMA) has emerged as a primary diagnostic tool for the evaluation of developmental delay and structural malformations in children. The aim of this study was to compare the accuracy and value of CMA and karyotyping on diagnosis of chromosomal abnormalities in Fujian province of South China.In the study, 410 clinical samples were collected from pregnant women between March 2015 and December 2016, including 3 villus (0.73%, 3/410), 296 amniotic fluid (72.20%, 296/410), and 111 umbilical cord blood (27.07%, 111/410). All samples were screening for chromosomal abnormalities by both using CMA and karyotyping.The success rate of CMA and karyotyping was 100% (410/410) and 99.27% (407/410), respectively. Sixty-one (14.88%, 61/410) samples were presented with chromosomal abnormalities by using CMA, whereas 47 (11.55%, 47/407) samples were shown with chromosomal abnormalities by using karyotyping. Thirty-one (8.61%, 31/360) samples with normal karyotypes were found to exist chromosomal abnormalities by using CMA. Receiver operating characteristic analysis showed that the area under the curve of karyotyping on the diagnosis of chromosomal abnormalities was 0.90 (95% confidence interval: 0.87-0.93), the sensitivity and specificity was 87.56% and 91.22%, respectively. The area under the curve of CMA on the diagnosis of chromosomal abnormalities was 0.93 (95% confidence interval: 0.90-0.95), with 90.68% sensitivity and 94.40% specificity. Notably, the combination of CMA and karyotyping could improve the diagnosis of chromosomal abnormalities.CMA has a better diagnostic value for screening chromosomal abnormalities, especially for those pregnant women with normal karyotypes. This study has guiding value for prenatal diagnosis in Fujian province of South China.

Fetal hydrops and the Incremental yield of Next-generation sequencing over standard prenatal Diagnostic testing (FIND) study: prospective cohort study and meta-analysis.

OBJECTIVE: To determine the incremental yield of exome sequencing (ES) over chromosomal microarray analysis (CMA) or karyotyping in prenatally diagnosed non-immune hydrops fetalis (NIHF). METHODS: A prospective cohort study (comprising an extended group of the Prenatal Assessment of Genomes and Exomes (PAGE) study) was performed which included 28 cases of prenatally diagnosed NIHF undergoing trio ES following negative CMA or karyotyping. These cases were combined with data from a systematic review of the literature. MEDLINE, EMBASE, CINAHL and ClinicalTrials.gov databases were searched electronically (January 2000 to October 2020) for studies reporting on the incremental yield of ES over CMA or karyotyping in fetuses with prenatally detected NIHF. Inclusion criteria for the systematic review were: (i) at least two cases of NIHF undergoing sequencing; (ii) testing initiated based on prenatal ultrasound-based phenotype; and (iii) negative CMA or karyotyping result. The incremental diagnostic yield of ES was assessed in: (i) all cases of NIHF; (ii) isolated NIHF; (iii) NIHF associated with an additional fetal structural anomaly; and (iv) NIHF according to severity (i.e. two vs three or more cavities affected). RESULTS: In the extended PAGE study cohort, the additional diagnostic yield of ES over CMA or karyotyping was 25.0% (7/28) in all NIHF cases, 21.4% (3/14) in those with isolated NIHF and 28.6% (4/14) in those with non-isolated NIHF. In the meta-analysis, the pooled incremental yield based on 21 studies (306 cases) was 29% (95% CI, 24-34%; P < 0.00001; I2 = 0%) in all NIHF, 21% (95% CI, 13-30%; P < 0.00001; I2 = 0%) in isolated NIHF and 39% (95% CI, 30-49%; P < 0.00001; I2 = 1%) in NIHF associated with an additional fetal structural anomaly. In the latter group, congenital limb contractures were the most prevalent additional structural anomaly associated with a causative pathogenic variant, occurring in 17.3% (19/110) of cases. The incremental yield did not differ significantly according to hydrops severity. The most common genetic disorders identified were RASopathies, occurring in 30.3% (27/89) of cases with a causative pathogenic variant, most frequently due to a PTPN11 variant (44.4%; 12/27). The predominant inheritance pattern in causative pathogenic variants was autosomal dominant in monoallelic disease genes (57.3%; 51/89), with most being de novo (86.3%; 44/51). CONCLUSIONS: Use of prenatal next-generation sequencing in both isolated and non-isolated NIHF should be considered in the development of clinical pathways. Given the wide range of potential syndromic diagnoses and heterogeneity in the prenatal phenotype of NIHF, exome or whole-genome sequencing may prove to be a more appropriate testing approach than a targeted gene panel testing strategy. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Chromosomal microarray analysis in fetuses with high-risk prenatal indications: A retrospective study in China.

OBJECTIVE: The present study aimed to determine the diagnostic value of prenatal chromosomal microarray analysis (CMA) for fetuses with several indications of being at high risk for various conditions. MATERIALS AND METHODS: This retrospective analysis included 1256 pregnancies that were prenatally evaluated due to high-risk indications using invasive CMA. The indications for invasive prenatal diagnosis mainly included ultrasound anomalies, high-risk for maternal serum screening (MSS), high-risk for non-invasive prenatal tests (NIPT), family history of genetic disorders or birth defects, and advanced maternal age (AMA). The rate of clinically significant genomic imbalances between the different groups was compared. RESULTS: The overall prenatal diagnostic yield was 98 (7.8%) of 1256 pregnancies. Clinically significant genomic aberrations were identified in 2 (1.5%) of 132 patients with non-structural ultrasound anomalies, 36 (12.7%) of 283 with structural ultrasound anomalies, 2 (4.5%) of 44 at high-risk for MSS, 38 (26.6%) of 143 at high-risk for NIPT, 11 (3.8%) of 288 with a family history, and 7 (2.1%) of 328 with AMA. Submicroscopic findings were identified in 29 fetuses, 19 of whom showed structural ultrasound anomalies. CONCLUSION: The diagnostic yields of CMA for pregnancies with different indications greatly varied. CMA could serve as a first-tier test for structural anomalies, especially multiple anomalies, craniofacial dysplasia, urinary defects, and cardiac dysplasia. Our results have important implications for genetic counseling.

Dealing with uncertain results from chromosomal microarray and exome sequencing in the prenatal setting: An international cross-sectional study with healthcare professionals.

OBJECTIVES: To conduct qualitative interviews with healthcare providers working in different countries to understand their experiences of dealing with uncertain results from prenatal chromosome microarray analysis (CMA) and exome sequencing (ES). METHODS: Semi-structured interviews with 31 healthcare providers who report or return prenatal CMA and/or ES results (clinicians, genetic counsellors and clinical scientists) in six countries with differing healthcare systems; Australia (4), Denmark (5), Netherlands (6), Singapore (4), Sweden (6) and United Kingdom (6). The topic guide explored the main sources of uncertainty and their management. RESULTS: There was variation in reporting practices both between and across countries for variants of uncertain significance, however, there was broad agreement on reporting practices for incidental findings. There was also variation in who decides what results are reported (clinical scientists or clinicians). Technical limitations and lack of knowledge (to classify variants and of prenatal phenotypes) were significant challenges, as were turnaround times and lack of guidelines. CONCLUSION: Health professionals around the globe are dealing with similar sources of uncertainty, but managing them in different ways, Continued dialogue with international colleagues on ways of managing uncertain results is important to compare and contrast the benefits and limitations of the different approaches.

Microarray Data Mining and Preliminary Bioinformatics Analysis of Hepatitis D Virus-Associated Hepatocellular Carcinoma.

Several studies have demonstrated that chronic hepatitis delta virus (HDV) infection is associated with a worsening of hepatitis B virus (HBV) infection and increased risk of hepatocellular carcinoma (HCC). However, there is limited data on the role of HDV in the oncogenesis of HCC. This study is aimed at assessing the potential mechanisms of HDV-associated hepatocarcinogenesis, especially to screen and identify key genes and pathways possibly involved in the pathogenesis of HCC. We selected three microarray datasets: GSE55092 contains 39 cancer specimens and 81 paracancer specimens from 11 HBV-associated HCC patients, GSE98383 contains 11 cancer specimens and 24 paracancer specimens from 5 HDV-associated HCC patients, and 371 HCC patients with the RNA-sequencing data combined with their clinical data from the Cancer Genome Atlas (TCGA). Afterwards, 948 differentially expressed genes (DEGs) closely related to HDV-associated HCC were obtained using the R package and filtering with a Venn diagram. We then performed gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to determine the biological processes (BP), cellular component (CC), molecular function (MF), and KEGG signaling pathways most enriched for DEGs. Additionally, we performed Weighted Gene Coexpression Network Analysis (WGCNA) and protein-to-protein interaction (PPI) network construction with 948 DEGs, from which one module was identified by WGCNA and three modules were identified by the PPI network. Subsequently, we validated the expression of 52 hub genes from the PPI network with an independent set of HCC dataset stored in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, seven potential key genes were identified by intersecting with key modules from WGCNA, including 3 reported genes, namely, CDCA5, CENPH, and MCM7, and 4 novel genes, namely, CDC6, CDC45, CDCA8, and MCM4, which are associated with nucleoplasm, cell cycle, DNA replication, and mitotic cell cycle. The CDCA8 and stage of HCC were the independent factors associated with overall survival of HDV-associated HCC. All the related findings of these genes can help gain a better understanding of the role of HDV in the underlying mechanism of HCC carcinogenesis.

A Set of Markers Related to Viral Infection Has a Sex-sensitive Prognostic Value in Papillary Thyroid Carcinoma.

CONTEXT: Although the incidence of papillary thyroid carcinoma (PTC) is significantly higher in females than in males, the prognosis of male PTC is more unfavorable. However, the cause of higher malignancy of PTC in male patients remains unclear. OBJECTIVE: We conducted our analysis on microarrays datasets, tissue samples from PTC patients and the RNAseq datasets from TCGA with survival data. METHODS: We searched all publicly available microarray datasets and performed a genome-wide meta-analysis comparing PTC and normal samples. Gene Ontology analysis was then conducted. The candidate genes were tested by quantitative real-time polymerase chain reaction. The analysis of prognostic value of genes was performed with datasets from The Cancer Genome Atlas. RESULTS: After meta-analyses, 150 significantly differentially expressed genes (DEGs) were specifically found in male subjects. Gene Ontology analysis of these 150 genes revealed that the viral process was activated. Seven genes involved in the viral process in male patients showed a significantly differential expression between PTC and normal tissue. Survival analysis exhibited that the 7 genes, used in combination, were prognostically valuable and, of them, PSMB1 possessed a conspicuous prognostic value, especially in males. CONCLUSION: In this study, we searched all publicly available microarray datasets and conducted a comprehensive analysis to understand the male propensity for higher malignancy. We found that markers of viral infection showed significantly differential expression only in male patients compared with their female counterparts and had a sex-sensitive prognostic value in PTC.

Clinical utility of expanded non-invasive prenatal screening and chromosomal microarray analysis in high-risk pregnancy.

OBJECTIVE: To evaluate the utility of expanded non-invasive prenatal screening (NIPS), compared with chromosomal microarray analysis (CMA), for the detection of chromosomal abnormalities in high-risk pregnancies. METHODS: This was a multicenter retrospective study of singleton pregnancies at high risk for chromosomal abnormality. Patients who underwent expanded NIPS and CMA sequentially during pregnancy from 2015 to 2019 were included in the analysis. Pregnancies with a positive result for sex chromosome aneuploidy were excluded as the full details could not be retrieved. The utility of expanded NIPS and CMA for detection of chromosomal abnormalities in this cohort was compared by assessing the concordance between the results. RESULTS: Of the 774 included high-risk pregnancies, 550 (71.1%) had a positive NIPS result, while a positive CMA result was detected in 308 (39.8%) cases. The rate of full or partial concordance between NIPS and CMA was 82.2%, 59.6% and 25.0% for trisomies 21, 18 and 13, respectively. For rare aneuploidies and segmental imbalances, NIPS and CMA results were fully or partially concordant in 7.5% and 33.3% of cases, respectively. Copy-number variants < 5 Mb were detected more often by CMA, with an incidence of 7.9% (61/774) compared with 3.1% (24/774) by NIPS. A genetic aberration was detected by CMA in 1 in 17 (5.8%) high-risk pregnancies that had a negative or non-reportable NIPS result. CONCLUSION: CMA allows for comprehensive detection of genome-wide chromosomal abnormalities in high-risk pregnancies. CMA should be offered instead of expanded NIPS for high-risk pregnancies. Copyright © 2020 ISUOG. Published by John Wiley & Sons Ltd.

Universal chromosomal microarray analysis reveals high proportion of copy-number variants in low-risk pregnancies.

OBJECTIVES: To evaluate the yield and utility of the routine use of chromosomal microarray analysis (CMA) for prenatal genetic diagnosis in a large cohort of pregnancies with normal ultrasound (US) at the time of genetic testing, compared with pregnancies with abnormal US findings. METHODS: We reviewed all prenatal CMA results in our center between November 2013 and December 2018. The prevalence of different CMA results in pregnancies with normal US at the time of genetic testing ('low-risk pregnancies'), was compared with that in pregnancies with abnormal US findings ('high-risk pregnancies'). Medical records were searched in order to evaluate subsequent US follow-up and the outcome of pregnancies with a clinically relevant copy-number variant (CNV), i.e. a pathogenic or likely pathogenic CNV or a susceptibility locus for disease with > 10% penetrance, related to early-onset disease in the low-risk group. RESULTS: In a cohort of 6431 low-risk pregnancies that underwent CMA, the prevalence of a clinically significant CNV related to early-onset disease was 1.1% (72/6431), which was significantly lower than the prevalence in high-risk pregnancies (4.9% (65/1326)). Of the low-risk pregnancies, 0.4% (27/6431) had a pathogenic or likely pathogenic CNV, and another 0.7% (45/6431) had a susceptibility locus with more than 10% penetrance. Follow-up of the low-risk pregnancies with a clinically significant early-onset CNV revealed that 31.9% (23/72) were terminated, while outcome data were missing in 26.4% (19/72). In 16.7% (12/72) of low-risk pregnancies, an US abnormality was discovered later on in gestation, after genetic testing had been performed. CONCLUSION: Although the background risk of identifying a clinically significant early-onset abnormal CMA result in pregnancies with a low a-priori risk is lower than that observed in high-risk pregnancies, the risk is substantial and should be conveyed to all pregnant women. © 2020 International Society of Ultrasound in Obstetrics and Gynecology.

Risk of Clinically Significant Chromosomal Microarray Analysis Findings in Fetuses With Nuchal Translucency From 3.0 mm Through 3.4 mm.

OBJECTIVE: To examine the risk of clinically significant chromosomal microarray analysis findings in fetuses with nuchal translucency from 3.0-3.4 mm. In addition, we aimed to define the yield of noninvasive prenatal testing (NIPT) in such pregnancies. METHODS: This retrospective cohort study included results of all chromosomal microarray analysis tests performed owing to a nuchal translucency measurement from 3.0-3.4 mm, without ultrasonographic anomalies, retrieved from the Israeli Ministry of Health computerized database. Rates of clinically significant (pathogenic and likely pathogenic) microarray findings were compared with a previously published local control population, encompassing 2,752 fetuses with normal ultrasound findings and nuchal translucency less than 3.0 mm. RESULTS: Overall, 619 chromosomal microarray analyses were performed owing to isolated nuchal translucency from 3.0-3.4 mm. Of these, 29 (4.7%) cases had clinically significant copy number variants, a significantly higher risk compared with control-group pregnancies (relative risk 3.3, 95% CI 2.6-7.2). Divided by tenths of millimeters, the risk for abnormal chromosomal microarray analysis findings remained significantly increased, except for the subgroup of 198 fetuses with nuchal translucency measurements of 3.0 mm. Noninvasive prenatal testing for the five common chromosome aneuploidies would have missed 41.4% of the abnormal copy number variants-1.9% of overall cases, or 1 in 52 fetuses with nuchal translucency from 3-3.4 mm. Genome-wide NIPT, as well as traditional karyotyping, could have missed an abnormal finding in 9 of 619 (1.5%), or 1 in 69 fetuses. CONCLUSION: Our outcomes show that the rate of abnormal chromosomal microarray analysis findings in fetuses with nuchal translucency from 3.1-3.4 mm is significantly higher compared with fetuses with normal ultrasound findings.

Use of microarrays and MLPA for integrating diagnostics and personalizing treatment - Case report of a patient with Ph-like acute B-cell lymphoblastic leukemia.

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is the most common childhood cancer. A special subtype of high risk BCP-ALL is Philadelphia-like ALL (Ph-like ALL), in which the gene expression profile is similar to BCR-ABL1-positive leukemia; however, fusion of the mentioned genes does not occur. The unfavourable clinical course and incidence of 15% of cases means that the diagnosis and therapeutic strategy of Ph-like ALL must be carefully developed and implemented into clinical practice. The study presents the case of a patient with diagnosed Ph-like ALL. The use of molecular analytical techniques has made it possible to identify a patient who is likely to relapse and who may benefit from personalized therapy This study shows the advantages of using genomic analyses to identify therapeutic targets, which is especially important for patients with high-risk disease. This model of management could be extended to other cancer subtypes, allowing for tailored diagnosis.

Current approaches and developments in transcript profiling of the human placenta.

BACKGROUND: The placenta is the active interface between mother and foetus, bearing the molecular marks of rapid development and exposures in utero. The placenta is routinely discarded at delivery, providing a valuable resource to explore maternal-offspring health and disease in pregnancy. Genome-wide profiling of the human placental transcriptome provides an unbiased approach to study normal maternal-placental-foetal physiology and pathologies. OBJECTIVE AND RATIONALE: To date, many studies have examined the human placental transcriptome, but often within a narrow focus. This review aims to provide a comprehensive overview of human placental transcriptome studies, encompassing those from the cellular to tissue levels and contextualize current findings from a broader perspective. We have consolidated studies into overarching themes, summarized key research findings and addressed important considerations in study design, as a means to promote wider data sharing and support larger meta-analysis of already available data and greater collaboration between researchers in order to fully capitalize on the potential of transcript profiling in future studies. SEARCH METHODS: The PubMed database, National Center for Biotechnology Information and European Bioinformatics Institute dataset repositories were searched, to identify all relevant human studies using 'placenta', 'decidua', 'trophoblast', 'transcriptome', 'microarray' and 'RNA sequencing' as search terms until May 2019. Additional studies were found from bibliographies of identified studies. OUTCOMES: The 179 identified studies were classifiable into four broad themes: healthy placental development, pregnancy complications, exposures during pregnancy and in vitro placental cultures. The median sample size was 13 (interquartile range 8-29). Transcriptome studies prior to 2015 were predominantly performed using microarrays, while RNA sequencing became the preferred choice in more recent studies. Development of fluidics technology, combined with RNA sequencing, has enabled transcript profiles to be generated of single cells throughout pregnancy, in contrast to previous studies relying on isolated cells. There are several key study aspects, such as sample selection criteria, sample processing and data analysis methods that may represent pitfalls and limitations, which need to be carefully considered as they influence interpretation of findings and conclusions. Furthermore, several areas of growing importance, such as maternal mental health and maternal obesity are understudied and the profiling of placentas from these conditions should be prioritized. WIDER IMPLICATIONS: Integrative analysis of placental transcriptomics with other 'omics' (methylome, proteome and metabolome) and linkage with future outcomes from longitudinal studies is crucial in enhancing knowledge of healthy placental development and function, and in enabling the underlying causal mechanisms of pregnancy complications to be identified. Such understanding could help in predicting risk of future adversity and in designing interventions that can improve the health outcomes of both mothers and their offspring. Wider collaboration and sharing of placental transcriptome data, overcoming the challenges in obtaining sufficient numbers of quality samples with well-defined clinical characteristics, and dedication of resources to understudied areas of pregnancy will undoubtedly help drive the field forward.

The difference between karyotype analysis and chromosome microarray for mosaicism of aneuploid chromosomes in prenatal diagnosis.

OBJECTIVE: To compare karyotype and chromosomal microarray (CMA) analysis of aneuploid chromosome mosaicism in amniocentesis samples. MATERIALS AND METHODS: A total of 2091 amniocentesis samples from pregnant women were collected from March 1, 2019, to January 31, 2020. Karyotype analysis was performed using G-banding and CMA analysis used the Affymetrix CytoScan 750K SNP microarray. RESULT: Thirteen cases with aneuploid chromosome mosaicism were detected and compared between the karyotype and CMA methods. Seven of these cases were trisomic mosaicism, and the levels of mosaicism calculated from CMA were higher than those detected from karyotype analysis; noting three cases of trisomy mosaicism were not detected by karyotype analysis. Four cases exhibited monomeric mosaicism, and the levels of mosaicism detected in three of these cases were higher in karyotype compared with CMA analysis; one case had equivalent levels of monomeric mosaicism from both karyotype and CMA analysis. Two other cases from karyotype analysis were a mix of monosomic and trisomic mosaicism, whereas the CMA result was restricted to monosomic mosaicism for these cases. CONCLUSION: Both karyotype and CMA analysis can be used to detect aneuploid chromosome mosaicism. However, the two methods produced different results. CMA and karyotype analysis have their own advantages in detecting aneuploid mosaicism, and the combination of these methods provides a more rigorous diagnosis.

Multivariate permutation tests for two sample testing in presence of nondetects with application to microarray data.

Very often, data collected in medical research are characterized by censored observations and/or data with mass on the value zero. This happens for example when some measurements fall below the detection limits of the specific instrument used. This type of left censored observations is called "nondetects". Such a situation of an excessive number of zeros in a data set is also referred to as zero-inflated data. In the present work, we aim at comparing different multivariate permutation procedures in two-sample testing for data with nondetects. The effect of censoring is investigated with regard to the different values that may be attributed to nondetected values, both under the null hypothesis and under alternative. We motivate the problem using data from allergy research.

Information Women Choose to Receive About Prenatal Chromosomal Microarray Analysis.

OBJECTIVE: To examine the choices of women with both high-risk and low-risk pregnancies who are undergoing prenatal chromosomal microarray analysis in a clinical setting regarding three challenging types of findings: variants of uncertain clinical significance, susceptibility loci for neurodevelopmental disorders, and copy number variants associated with risks for adult-onset conditions. We assessed whether women's choices were associated with indications for testing or with one-on-one pretest genetic counseling. METHODS: In this cross-sectional study, medical records of women who underwent invasive prenatal chromosomal microarray analysis testing (N=1,070) at Hadassah Medical Center between June 2017 and February 2018 were examined for testing indications, choices regarding chromosomal microarray analysis findings, and type of pretest genetic counseling. Multivariable analyses to assess associations with testing indication and prior genetic counseling were carried out using logistic regression models. RESULTS: In total, 56% of women (n=593) chose to be informed of all three types of findings and 20% (n=218) chose not to be informed of any of the findings beyond high-penetrance childhood-onset pathogenic findings. Variants of uncertain clinical significance as a single choice was the least-selected finding (2.5%, n=27). Low-risk pregnancies (ie, those with normal biochemical screening and fetal ultrasound examinations) were associated with increased interest in receiving genetic information about adult-onset conditions (adjusted odds ratio [aOR] 1.7; 95% CI 1.18-2.33) and susceptibility loci (aOR 1.5; 95% CI 1.08-2.10). CONCLUSION: Women with both high-risk and low-risk pregnancies were generally more likely to choose to receive additional genetic information, albeit differences in preferences depend on testing indication and type of pretest counseling.

Simulation-selection-extrapolation: Estimation in high-dimensional errors-in-variables models.

Errors-in-variables models in high-dimensional settings pose two challenges in application. First, the number of observed covariates is larger than the sample size, while only a small number of covariates are true predictors under an assumption of model sparsity. Second, the presence of measurement error can result in severely biased parameter estimates, and also affects the ability of penalized methods such as the lasso to recover the true sparsity pattern. A new estimation procedure called SIMulation-SELection-EXtrapolation (SIMSELEX) is proposed. This procedure makes double use of lasso methodology. First, the lasso is used to estimate sparse solutions in the simulation step, after which a group lasso is implemented to do variable selection. The SIMSELEX estimator is shown to perform well in variable selection, and has significantly lower estimation error than naive estimators that ignore measurement error. SIMSELEX can be applied in a variety of errors-in-variables settings, including linear models, generalized linear models, and Cox survival models. It is furthermore shown in the Supporting Information how SIMSELEX can be applied to spline-based regression models. A simulation study is conducted to compare the SIMSELEX estimators to existing methods in the linear and logistic model settings, and to evaluate performance compared to naive methods in the Cox and spline models. Finally, the method is used to analyze a microarray dataset that contains gene expression measurements of favorable histology Wilms tumors.

ROC Curves for the Statistical Analysis of Microarray Data.

A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.

Integrated analysis of 34 microarray datasets reveals CBX3 as a diagnostic and prognostic biomarker in glioblastoma.

BACKGROUND: Glioblastomas have a high degree of malignancy, high recurrence rate, high mortality rate, and low cure rate. Searching for new markers of glioblastomas is of great significance for improving the diagnosis, prognosis and treatment of glioma. METHODS: Using the GEO public database, we combined 34 glioma microarray datasets containing 1893 glioma samples and conducted genetic data mining through statistical analysis, bioclustering, and pathway analysis. The results were validated in TCGA, CGGA, and internal cohorts. We further selected a gene for subsequent experiments and conducted cell proliferation and cell cycle analyses to verify the biological function of this gene. RESULTS: Eight glioblastoma-specific differentially expressed genes were screened using GEO. In the TCGA and CGGA cohorts, patients with high CBX3, BARD1, EGFR, or IFRD1 expression had significantly shorter survival but patients with high GUCY1A3 or MOBP expression had significantly longer survival than patients with lower expression of these genes. After reviewing the literature, we selected the CBX3 gene for further experiments. We confirmed that CBX3 was overexpressed in glioblastoma by immunohistochemical analysis of tissue microarrays and qPCR analysis of surgical specimens. The functional assay results showed that silencing CBX3 arrests the cell cycle in the G2/M phase, thereby weakening the cell proliferation ability. CONCLUSIONS: We used a multidisciplinary approach to analyze glioblastoma samples in 34 microarray datasets, revealing novel diagnostic and prognostic biomarkers in patients with glioblastoma and providing a new direction for screening tumor markers.

Gene selection for microarray data classification via adaptive hypergraph embedded dictionary learning.

Due to the rapid development of DNA microarray technology, a large number of microarray data come into being and classifying these data has been verified useful for cancer diagnosis, treatment and prevention. However, microarray data classification is still a challenging task since there are often a huge number of genes but a small number of samples in gene expression data. As a result, a computational method for reducing the dimension of microarray data is necessary. In this paper, we introduce a computational gene selection model for microarray data classification via adaptive hypergraph embedded dictionary learning (AHEDL). Specifically, a dictionary is learned from the feature space of original high dimensional microarray data, and this learned dictionary is used to represent original genes with a reconstruction coefficient matrix. Then we use a l2, 1-norm regularization to impose the row sparsity on the coefficient matrix for selecting discriminate genes. Meanwhile, in order to capture the localmanifold geometrical structure of original microarray data in a high-order manner, a hypergraph is adaptively learned and embedded into the model. An iterative updating algorithm is designed for solving the optimization problem. In order to validate the efficacy of the proposed model, we have conducted experiments on six publicly available microarray data sets and the results demonstrate that AHEDL outperforms other state-of-the-art methods in terms of microarray data classification. ABBREVIATIONS.