Journal of Molecular Diagnostics最新文献

Next-generation sequencing (NGS) has proven clinical utility on disease management and serves as an important tool for genomic surveillance. Currently, hurdles surrounding its implementation, namely the complex and demanding analytical workflows, have impeded its widespread use in many laboratories. To address this challenge, the UCLA Molecular Microbiology and Pathogen Genomics Laboratory evaluated the performance of the Tecan MagicPrep NGS system, a commercial automated solution for library preparation for clinical whole-genome sequencing assays, against the Illumina Nextera DNA Flex Library Prep. Using 35 unique organisms (28 bacteria and 7 fungi) for various clinical applications, including microbial identification and genomic characterization, we compared the quantity and quality of the prepared libraries and the resulting sequences, and concordance of the overall results. We also assessed the impact of its implementation on laboratory workflow. The MagicPrep NGS produced higher library concentrations with smaller sizes, and correspondingly, higher molarity. Quality metrics of the sequences, however, demonstrated no significant impact on the overall results, producing 100% concordance with the reference method. Importantly, workflow analysis showed 5 hours less hands-on time per run with more flexibility. This evaluation study indicates that performance of the MagicPrep NGS is comparable to the Nextera DNA Flex with the added benefit of improving workflow efficiency and reducing labor for performing routine clinical microbial whole-genome sequencing tests.

This study describes the validation of a clinical RNA expression panel with evaluation of concordance between gene copy gain by a next-generation sequencing (NGS) assay and high gene expression by an RNA expression panel. The RNA Salah Targeted Expression Panel (RNA STEP) was designed with input from oncologists to include 204 genes with utility for clinical trial prescreening and therapy selection. RNA STEP was validated with the nanoString platform using remnant formalin-fixed, paraffin-embedded–derived RNA from 102 patients previously tested with a validated clinical NGS panel. The repeatability, reproducibility, and concordance of RNA STEP results with NGS results were evaluated. RNA STEP demonstrated high repeatability and reproducibility, with excellent correlation (r > 0.97, P < 0.0001) for all comparisons. Comparison of RNA STEP high gene expression (log2 ratio ≥ 2) versus NGS DNA-based gene copy number gain (copies ≥ 5) for 38 mutually covered genes revealed an accuracy of 93.0% with a positive percentage agreement of 69.4% and negative percentage agreement of 93.8%. Moderate correlation was observed between platforms (r = 0.53, P < 0.0001). Concordance between high gene expression and gene copy number gain varied by specific gene, and some genes had higher accuracy between assays. Clinical implementation of RNA STEP provides gene expression data complementary to NGS and offers a tool for prescreening patients for clinical trials.

Gene expression analysis is pivotal in cancer research and clinical practice. Although traditional methods lack spatial context, RNA in situ hybridization (RNA-ISH) is a powerful technique that retains spatial tissue information. Here, RNAscope score, RT–droplet digital PCR, and automated QuantISH and QuPath were used for quantifying RNA-ISH expression values from formalin-fixed, paraffin-embedded samples. The methods were compared using high-grade serous ovarian carcinoma samples, focusing on CCNE1, WFDC2, and PPIB genes. The findings demonstrate good concordance between automated methods and RNAscope, with RT–droplet digital PCR showing less concordance. Additionally, QuantISH exhibits robust performance, even for low-expressed genes like CCNE1, showcasing its modular design and enhancing accessibility as a viable alternative for gene expression analysis.

Molecular tests have an inherent limit of detection (LOD) and, therefore, require samples with sufficiently high percentages of neoplastic cells. Many laboratories use tissue dissection; however, optimal procedures for dissection and quality assurance measures have not been established. In this study, several modifications to tissue dissection procedures and workflow were introduced over 4 years. Each modification resulted in a significant improvement in one or more quality assurance measures. The review of materials following dissection resulted in a 90% reduction in KRAS mutations below the stated LOD (P = 0.004). Mutation allele frequencies correlated best with estimated tumor percentages for pathologists with more experience in this process. The direct marking of unstained slides, use of a stereomicroscope, validation of extraction from diagnostic slides, and use of a robust, targeted next-generation sequencing platform all resulted in reduction of quantity not sufficient specimens from 20% to 25% to nearly 0%, without a significant increase in test failures or mutations below the LOD. These data indicate that post-dissection review of unstained slides and monitoring quantity not sufficient rate, test failure rate, and mutation allele frequencies are important tumor dissection quality assurance measures that should be considered by laboratories performing tissue dissections. The amendments to tissue dissection procedures enacted during this study resulted in a measurable improvement in the quality and reliability of this process based on these metrics.

The potential of precision medicine to transform complex autoimmune disease treatment is often challenged by limited data availability and inadequate sample size when compared with the number of molecular features found in high-throughput multi-omics data sets. To address this issue, the novel framework PRoBeNet (Predictive Response Biomarkers using Network medicine) was developed. PRoBeNet operates under the hypothesis that the therapeutic effect of a drug propagates through a protein-protein interaction network to reverse disease states. PRoBeNet prioritizes biomarkers by considering i) therapy-targeted proteins, ii) disease-specific molecular signatures, and iii) an underlying network of interactions among cellular components (the human interactome). PRoBeNet helped discover biomarkers predicting patient responses to both an established autoimmune therapy (infliximab) and an investigational compound (a mitogen-activated protein kinase 3/1 inhibitor). The predictive power of PRoBeNet biomarkers was validated with retrospective gene-expression data from patients with ulcerative colitis and rheumatoid arthritis and prospective data from tissues from patients with ulcerative colitis and Crohn disease. Machine-learning models using PRoBeNet biomarkers significantly outperformed models using either all genes or randomly selected genes, especially when data were limited. These results illustrate the value of PRoBeNet in reducing features and for constructing robust machine-learning models when data are limited. PRoBeNet may be used to develop companion and complementary diagnostic assays, which may help stratify suitable patient subgroups in clinical trials and improve patient outcomes.

Osteogenesis imperfecta (OI) is the most common inherited connective tissue disease of the bone, characterized by recurrent fractures and deformities. In patients displaying the OI phenotype, genotype–phenotype correlation is used to screen multiple genes swiftly, identify new variants, and distinguish between differential diagnoses and mild subtypes. This study evaluated variants identified through next-generation sequencing in 58 patients with clinical characteristics indicative of OI. The cohort included 18 adults, 37 children, and 3 fetuses. Clinical classification revealed 25 patients as OI type I, three patients as OI type II, 18 as OI type III, and 10 as OI type IV. Fifteen variants in COL1A1 were detected in 19 patients, 9 variants in COL1A2 (n = 19), 5 variants in LEPRE1/P3H1 (n = 7), 3 variants in FKBP10 (n = 4), 3 variants in SERPINH1 (n = 2), 1 variant in IFITM5 (n = 1), and 1 variant in PLS3 (n = 1). In total, 37 variants (18 pathogenic, 14 likely pathogenic, and 5 variants of uncertain significance), including 16 novel variants, were identified in 43 (37 probands, 6 family members) of the 58 patients analyzed. This study highlights the efficacy of panel testing in the molecular diagnosis of OI, the significance of the next-generation sequencing technique, and the importance of genotype–phenotype correlation.

Fragile X syndrome is the leading genetic cause of intellectual disability and autism spectrum disorders. Female premutation carriers exhibit no obvious symptoms during reproductive age, but the premutation allele can expand to full mutation when transmitted to the fetus. Given the relatively low prevalence but large population, the distinct health care system, the middle-income economic status, and low awareness among public and medical professionals, the optimal genetic screening strategy remains unknown. We conducted a pilot study of Fragile X carrier screening in China, involving 22,245 pregnant women and women with childbearing intentions, divided into control and pilot groups. The prevalence of Fragile X carriers in the control group was 1 of 850, similar to East Asian populations. Strikingly, the prevalence of Fragile X carriers in the pilot group was 1 of 356, which can be attributed to extensive medical training, participant education, and rigorous genetic counseling and testing protocols. Cost-effectiveness analyses of four strategies—no screening, population-based screening, targeted screening, and our pilot screening—indicated that our pilot screening was the most cost-effective option. A follow-up survey revealed that 55% of respondents reported undergoing screening because of their family history. We have successfully established a standardized system, addressing the challenges of low prevalence, limited awareness, and genetic testing complexities. Our study provides practical recommendations for implementing Fragile X carrier screening in China.

The goals of the Association for Molecular Pathology Clinical Practice Committee's Pharmacogenomics (PGx) Working Group are to define the key attributes of pharmacogenetic alleles recommended for clinical testing and a minimum set of variants that should be included in clinical PGx genotyping assays. This document series provides recommendations for a minimum set of variant alleles (tier 1) and an extended list of variant alleles (tier 2) that will aid clinical laboratories when designing assays for PGx testing. The Association for Molecular Pathology PGx Working Group considered the functional impact of the variant alleles, allele frequencies in multiethnic populations, the availability of reference materials, and other technical considerations for PGx testing when developing these recommendations. The goal of this Working Group is to promote standardization of PGx testing across clinical laboratories. This document will focus on clinical DPYD PGx testing that may be applied to all dihydropyrimidine dehydrogenase–related medications. These recommendations are not to be interpreted as prescriptive but to provide a reference guide.

Prenatal diagnostic testing of amniotic fluid, chorionic villi, or more rarely, fetal cord blood is recommended following a positive or unreportable noninvasive cell-free fetal DNA test, abnormal maternal biochemical serum screen, abnormal ultrasound, or increased genetic risk for a cytogenomic abnormality based on family history. Although chromosomal microarray is recommended as the first-tier prenatal diagnostic test, in practice, multiple assays are often assessed in concert to achieve a final diagnostic result. The use of multiple methodologies is costly, time consuming, and labor intensive. Optical genome mapping (OGM) is an emerging technique with application for prenatal diagnosis because of its ability to detect and resolve, in a single assay, all classes of pathogenic cytogenomic aberrations. In an effort to characterize the potential of OGM as a novel alternative to traditional standard of care (SOC) testing of prenatal samples, OGM was performed on a total of 200 samples representing 123 unique cases, which were previously tested with SOC methods (92/123 = 74.7% cases tested with at least two SOCs). OGM demonstrated an overall accuracy of 99.6% when compared with SOC methods, a positive predictive value of 100%, and 100% reproducibility between sites, operators, and instruments. The standardized workflow, cost-effectiveness, and high-resolution cytogenomic analysis demonstrate the potential of OGM to serve as a first-tier test for prenatal diagnosis.