Microarray-Based DNA Methylation Profiling: Validation Considerations for Clinical Testing.

Microarray-based methylation profiling has emerged as a valuable tool for refining diagnoses and revealing novel tumor subtypes, particularly in central nervous system tumors. Despite the increasing adoption of this technique in clinical genomic laboratories, no technical standards have been published in establishing minimum criteria for test validation. A working group with experience and expertise in DNA-based methylation profiling tests on central nervous system tumors collaborated to develop practical discussion points and focus on important considerations for validating this test in clinical laboratory settings. The experience in validating this methodology in a clinical setting is summarized. Specifically, the advantages and challenges associated with utilizing an in-house classifier compared with a third-party classifier are highlighted. Additionally, experiences in demonstrating the assay's sensitivity and specificity, establishing minimum sample criteria, and implementing quality control metrics are described. As methylation profiling for tumor classification expands to other tumor types and continues to evolve for various other applications, the critical considerations described here are expected to serve as a guidance for future efforts in establishing professional guidelines for this assay.

The performance of common SNP arrays in assigning African mitochondrial haplogroups.

BACKGROUND: Mitochondrial haplogroup assignment is an important tool for forensics and evolutionary genetics. African populations are known to display a high diversity of mitochondrial haplogroups. In this research we explored mitochondrial haplogroup assignment in African populations using commonly used genome-wide SNP arrays. RESULTS: We show that, from eight commonly used SNP arrays, two SNP arrays outperform the other arrays when it comes to the correct assignment of African mitochondrial haplogroups. One array enables the recognition of 81% of the African mitochondrial haplogroups from our compiled dataset of full mitochondrial sequences. Other SNP arrays were able to assign 4-62% of the African mitochondrial haplogroups present in our dataset. We also assessed the performance of available software for assigning mitochondrial haplogroups from SNP array data. CONCLUSIONS: These results provide the first cross-checked quantification of mitochondrial haplogroup assignment performance from SNP array data. Mitochondrial haplogroup frequencies inferred from most common SNP arrays used for human population analysis should be considered with caution.

A two-year prospective study assessing the performance of fetal chromosomal microarray analysis and next-generation sequencing in high-risk pregnancies.

BACKGROUND: Introduction of cell-free fetal DNA (cff-DNA) testing in maternal blood opened possibilities to improve the performance of combined first-trimester screening (cFTS) in terms of better detection of trisomies and lowering invasive testing rate. The use of new molecular methods, such as chromosomal microarray analysis (CMA) and next-generation sequencing (NGS), has shown benefits in prenatal diagnosis of chromosomal and genetic diseases, which are not detectable with cff-DNA screening, but require an invasive procedure. METHODS: The objective of this study was to evaluate prospectively during two years performance of CMA and NGS in high-risk pregnancies. Initially, we investigated 14,566 singleton pregnancies with cFTS. A total of 334 high-risk pregnancies were selected for CMA diagnostic performance evaluation and 28 cases of highly dysmorphic fetuses for NGS analysis. CMA study group was divided into two groups based on the indications for testing; group A patients with high-risk for trisomies after cFTS, but normal ultrasound and group B patients who met criteria for CMA as a first-tier diagnostic test. RESULTS: The diagnostic yield of CMA was overall 3.6% (1.6% in Group A and 6.0% in Group B). In NGS analysis group, we report diagnostic yield of 17.9%. CONCLUSION: The use of CMA in high-risk pregnancies is justified and provides relevant clinical information in 3.6% of the cases. NGS analysis in fetuses with multiple anomalies shows promising results, but more investigations are needed for a better understanding of practical applications of this molecular diagnosis method in prenatal settings.

Development and validation of a 1 K sika deer (Cervus nippon) SNP Chip.

BACKGROUND: China is the birthplace of the deer family and the country with the most abundant deer resources. However, at present, China's deer industry faces the problem that pure sika deer and hybrid deer cannot be easily distinguished. Therefore, the development of a SNP identification chip is urgently required. RESULTS: In this study, 250 sika deer, 206 red deer, 23 first-generation hybrid deer (F1), 20 s-generation hybrid deer (F2), and 20 third-generation hybrid deer (F3) were resequenced. Using the chromosome-level sika deer genome as the reference sequence, mutation detection was performed on all individuals, and a total of 130,306,923 SNP loci were generated. After quality control filtering was performed, the remaining 31,140,900 loci were confirmed. From molecular-level and morphological analyses, the sika deer reference population and the red deer reference population were established. The Fst values of all SNPs in the two reference populations were calculated. According to customized algorithms and strict screening principles, 1000 red deer-specific SNP sites were finally selected for chip design, and 63 hybrid individuals were determined to contain red deer-specific SNP loci. The results showed that the gene content of red deer gradually decreased in subsequent hybrid generations, and this decrease roughly conformed to the law of statistical genetics. Reaction probes were designed according to the screening sites. All candidate sites met the requirements of the Illumina chip scoring system. The average score was 0.99, and the MAF was in the range of 0.3277 to 0.3621. Furthermore, 266 deer (125 sika deer, 39 red deer, 56 F1, 29 F2,17 F3) were randomly selected for 1 K SNP chip verification. The results showed that among the 1000 SNP sites, 995 probes were synthesized, 4 of which could not be typed, while 973 loci were polymorphic. PCA, random forest and ADMIXTURE results showed that the 1 K sika deer SNP chip was able to clearly distinguish sika deer, red deer, and hybrid deer and that this 1 K SNP chip technology may provide technical support for the protection and utilization of pure sika deer species resources. CONCLUSION: We successfully developed a low-density identification chip that can quickly and accurately distinguish sika deer from their hybrid offspring, thereby providing technical support for the protection and utilization of pure sika deer germplasm resources.

Standardization of a SNP panel for parentage verification and identification in the domestic cat (Felis silvestris catus).

The domestic cat (Felis silvestris catus) is a valued companion animal throughout the world. Over 60 different cat breeds are accepted for competition by the cat fancy registries in different countries. Genetic markers, including short tandem repeats and SNPs, are available to evaluate and manage levels of inbreeding and genetic diversity, population and breed structure relationships, and individual identification for forensic and registration purposes. The International Society of Animal Genetics (ISAG) hosts the Applied Genetics in Companion Animals Workshop, which supports the standardization of genetic marker panels and genotyping for the identification of cats via comparison testing. SNP panels have been in development for many species, including the domestic cat. An ISAG approved core panel of SNPs for use in cat identification and parentage analyses is presented. SNPs (n = 121) were evaluated by different university-based and commercial laboratories using 20 DNA samples as part of the ISAG comparison testing procedures. Different SNP genotyping technologies were examined, including DNA arrays, genotyping-by-sequencing and mass spectroscopy, to select a robust and efficient panel of 101 SNPs as the ISAG core panel for cats. The SNPs are distributed across all chromosomes including two on the X chromosome and an XY pseudo-autosomal sexing marker (zinc-finger XY; ZFXY). A population study demonstrated that the markers have an average polymorphic information content of 0.354 and a power of exclusion greater than 0.9999. The SNP panel should keep testing affordable while also allowing for the development of additional panels to monitor health, phenotypic traits, hybrid cats and highly inbred cats.

A comparison of genotyping arrays.

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements.

Assessing Various Control Samples for Microarray Gene Expression Profiling of Laryngeal Squamous Cell Carcinoma.

Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared the results using multidimensional scaling and hierarchical clustering versus tumor samples and laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the analyzed cohorts separated according to two dominant factors: "malignancy", which separated controls from malignant samples and "cell culture-microenvironment" which reflected the differences between cultured and non-cultured samples. In conclusion, we advocate the use of cultured epithelial cells as controls for gene expression profiling of cancer cell lines. In contrast, comparisons of gene expression profiles of cancer cell lines versus surgical margin controls should be treated with caution, whereas fresh frozen surgical margins seem to be appropriate for gene expression profiling of tumor samples.

Comparison of methylation capture sequencing and Infinium MethylationEPIC array in peripheral blood mononuclear cells.

BACKGROUND: Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium MethylationEPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price. METHODS: Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000 ng), medium (300-1000 ng), and low (150 ng-300 ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array. RESULTS: After quality control, an average of 3,708,550 CpG sites per sample were detected by MC-seq with DNA quantity > 1000 ng. Reproducibility of DNA methylation in MC-seq-detected CpG sites was high among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98-0.99). However, methylation for a small proportion of CpGs (N = 235) differed significantly between the two platforms, with differences in beta values of greater than 0.5. CONCLUSIONS: Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

Content and Performance of the MiniMUGA Genotyping Array: A New Tool To Improve Rigor and Reproducibility in Mouse Research.

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of XO and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research.

Chromosomal Microarray Analysis for the Fetuses with Aortic Arch Abnormalities and Normal Karyotype.

BACKGROUND: Aortic arch abnormalities (AAA) are abnormal embryologic developments of the aorta and its branches. Their outcomes often depend on their association with other congenital diseases and genetic testing results. OBJECTIVE: This study aimed to evaluate the yield of chromosomal microarray analysis (CMA) in fetuses with different patterns of AAA and normal karyotype. METHODS: Data from 158 pregnancies referred for prenatal CMA testing due to fetal AAA were obtained between April 2016 and April 2019. Fetuses with isolated AAA, AAA accompanied by soft ultrasound markers, and AAA with other ultrasound malformations were classified into groups A, B, and C, respectively. Cases with detectable karyotype aberrations were excluded from the study. RESULTS: Twenty cases (12.7%) of submicroscopic anomalies were detected in 158 cases with normal karyotype, comprising 16 cases (10.1%) of clinically significant variants, two cases (1.3%) of variants of unknown significance, and two variants (1.3%) that were likely benign. Microdeletion of 22q11.2 accounted for 25% (4/16) of the clinically significant variants. The overall incremental yields by CMA in group A, group B, and group C were 1.8%, 2.3%, and 24.1%, respectively. Except for double aortic arch, the incremental yield of clinical significant findings for each type of AAA in group C was much higher than that in group A and group B. In group A, a clinically significant variant was only detected in one fetus with right aortic arch (RAA) (1.8%, 1/57). CONCLUSIONS: In addition to 22q11.2 microdeletion, many other clinically significant submicroscopic variants are present in fetuses with AAA, especially in fetuses with other ultrasound malformations. Although CMA is always recommended in the presence of any malformation in many countries, our results suggest insufficient evidence to recommend CMA in fetuses with isolated AAA, except for isolated RAA.

Clinical performance of the HPV DNA Array genotyping assay in detection of CIN2+ lesions with BS GP5+/6+ MPG Luminex tested cervical samples.

Human papillomavirus (HPV) detection is used for screening of cervical cancer and genotype-specific persistence has shown to be mandatory for dysplasia development. Aim of this study was to evaluate the clinical performance of HPV DNA Array for cervical intraepithelial neoplasia 2+ (CIN2+) lesion detection. HPV DNA Array is a polymerase chain reaction-based assay that targets E1 sequences of 29 HPV types (6, 11, 16, 18, 26, 31, 33, 35, 39, 40, 42, 44, 45, 51, 52, 53, 54, 56, 58, 59, 66, 67, 68, 69, 70, 73, 82, 85, and 97). The clinical evaluation was performed against the reference assay, BS-GP5+/6+ multiplex genotyping (MPG)-Luminex, with 600 cervical smear samples of a referral population. HPV DNA Array detected CIN2+ lesions with a sensitivity of 90.2%, identical to that of MPG-Luminex. Detection of CIN3+ lesions was with a sensitivity of 90.3%, as compared with 88.7% of MPG-Luminex. It demonstrated very good agreement for HPV detection, irrespective of type, of 91.5% (κ = 0.832). HPV DNA Array is a simple and robust assay, with a short protocol of 4 hours hands-on time and automated readout by ELISpot AiDot software. It permits testing of up to 96 samples in one run and may be considered for use in organized screening programs and low resource settings.

Copolymerization of single-cell nucleic acids into balls of acrylamide gel.

We show the use of 5'-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide gel (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications.

Testing single/combined clinical categories on 5110 Italian patients with developmental phenotypes to improve array-based detection rate.

BACKGROUND: Chromosomal microarray analysis (CMA) is nowadays widely used in the diagnostic path of patients with clinical phenotypes. However, there is no ascertained evidence to date on how to assemble single/combined clinical categories of developmental phenotypic findings to improve the array-based detection rate. METHODS: The Italian Society of Human Genetics coordinated a retrospective study which included CMA results of 5,110 Italian patients referred to 17 genetics laboratories for variable combined clinical phenotypes. RESULTS: Non-polymorphic copy number variants (CNVs) were identified in 1512 patients (30%) and 615 (32%) present in 552 patients (11%) were classified as pathogenic. CNVs were analysed according to type, size, inheritance pattern, distribution among chromosomes, and association to known syndromes. In addition, the evaluation of the detection rate of clinical subgroups of patients allowed to associate dysmorphisms and/or congenital malformations combined with any other single clinical sign to an increased detection rate, whereas non-syndromic neurodevelopmental signs and non-syndromic congenital malformations to a decreased detection rate. CONCLUSIONS: Our retrospective study resulted in confirming the high detection rate of CMA and indicated new clinical markers useful to optimize their inclusion in the diagnostic and rehabilitative path of patients with developmental phenotypes.

Chip-Based Digital PCR Approach Provides A Sensitive and Cost-Effective Single-Day Screening Tool for Common Fetal Aneuploidies-A Proof of Concept Study.

In the prenatal period, the copy number aberrations of chromosomes 13, 18, 21, X and Y account for over 80% of the clinically significant chromosome abnormalities. Classical cytogenetic analysis is the gold standard in invasive prenatal diagnostics but the long test waiting time affects its clinical utility. Several molecular rapid tests have been developed and employed in clinical practice, however all have substantial drawbacks. The aim of the study was to design and evaluate an optimized tool for rapid molecular detection of fetal aneuploidies. We established a novel single-day method using a chip-based platform, the QuantStudio 3D Digital PCR system. In order to assess the clinical usefulness of our screening test, we analyzed 133 prenatal samples. The difference in distributions of euploid and aneuploid samples identified the ploidy of each of the target chromosomes with high precision. The distribution of the chromosome ratio for euploid and aneuploid samples showed a statistically significant result (p = 0.003 for trisomy 13, p = 0.001 for trisomies 18 and 21, Mann-Whitney U test). Our results suggest that this novel chip-based approach provides a tool for rapid, technically simple, cost-effective screening for common fetal aneuploidies.

Comparison of Illumina 450K and EPIC arrays in placental DNA methylation.

Illumina HumanMethylation450 BeadChip (450K) has been commonly used to investigate DNA methylation in human tissues. Recently, it has been replaced by Illumina HumanMethylationEPIC BeadChip (EPIC) covering over 850,000 CpGs distributed genome-wide. Many consortia have now datasets coming from both arrays and aspire to analyze the two together. The placenta shows a high number of intermediate methylation levels and is often investigated for obstetric/birth outcomes, and potentially for long-term programming in offspring. We performed a systematic comparison between the two arrays using 108 duplicate placental samples from Gen3G birth cohort. We find that placenta shows a high per-sample correlation between the arrays, and higher median correlations at individual CpGs than those reported for blood. We identify 26,340 probes with absolute difference in per cent methylation >10%. We conclude that EPIC and 450K placental data can be combined, and we provide two lists of CpGs that should be excluded to avoid misleading results.

Expression Concordance of 325 Novel RNA Biomarkers between Data Generated by NanoString nCounter and Affymetrix GeneChip.

BACKGROUND: With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This "cancer in reverse" approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in "driver genes." Objective. To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms. METHODS AND RESULTS: We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with r = 0.995 for Affymetrix and r = 0.999 for NanoString. Correlation in multiple days and multiple users was for Affymetrix r = (0.962 - 0.999) and for NanoString r = (0.982 - 0.991). CONCLUSION: The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance. Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay.

A Hybrid Gene Selection Method Based on ReliefF and Ant Colony Optimization Algorithm for Tumor Classification.

For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy.

Analytical Validation of Multiplex Biomarker Assay to Stratify Colorectal Cancer into Molecular Subtypes.

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.

[Single nucleotide polymorphism microarray in prenatal diagnosis of fetuses with absent nasal bone].

OBJECTIVE: To assess the clinical application of single nucleotide polymorphism microarray (SNP array) in prenatal genetic diagnosis for fetuses with absent nasal bone. METHODS: Seventy four fetuses with absent nasal bone detected by prenatal ultrasound scanning were recruited from Women's Hospital, Zhejiang University School of Medicine during June 2015 and October 2018. The chromosome karyotypes analysis and SNP array were performed. The correlation between absent fetal nasal bone and chromosome copy number variants was analyzed. RESULTS: Among 74 fetuses, 19 were detected to have chromosomal abnormalities, including 16 cases of trisomy-21, 1 case of trisomy-18 and two cases of micro-deletion/duplication. Among 46 cases with isolated absence of nasal bone, 3 had trisomy-21, and 1 had a micro-duplication. Absence of nasal bone in association with nuchal translucency thickening had a higher rate of abnormal karyotypes compared with isolated absence of nasal bone (χ2=32.27,P<0.01). CONCLUSIONS: Fetuses with absent nasal bone and nuchal translucency thickening are likely to have chromosome abnormalities, and SNP array testing is recommended to exclude the chromosome abnormalities.

[Application of single nucleotide polymorphism microarray in clinical diagnosis of intellectual disability or retardation].

OBJECTIVE: To assess the clinical application of single nucleotide polymorphism microarray (SNP array) in patients with intellectual disability/developmental delay(ID/DD). METHODS: SNP array was performed to detect genome-wide DNA copy number variants (CNVs) for 145 patients with ID/DD in Women's Hospital, Zhejiang University School of Medicine from January 2013 to June 2018. The CNVs were analyzed by CHAS software and related databases. RESULTS: Among 145 patients, pathogenic chromosomal abnormalities were detected in 32 cases, including 26 cases of pathogenic CNVs and 6 cases of likely pathogenic CNVs. Meanwhile, 18 cases of uncertain clinical significance and 14 cases of likely benign were identified, no significant abnormalities were found in 81 cases (including benign). CONCLUSIONS: SNP array is effective for detecting chromosomal abnormalities in patients with ID/DD with high efficiency and resolution.