An Opportunity to Fill a Gap for Newborn Screening of Neurodevelopmental Disorders.

Screening newborns using genome sequencing is being explored due to its potential to expand the list of conditions that can be screened. Previously, we proposed the need for large-scale pilot studies to assess the feasibility of screening highly penetrant genetic neurodevelopmental disorders. Here, we discuss the initial experience from the GUARDIAN study and the systemic gaps in clinical services that were identified in the early stages of the pilot study.

NBSTRN Tools to Advance Newborn Screening Research and Support Newborn Screening Stakeholders.

Rapid advances in the screening, diagnosis, and treatment of genetic disorders have increased the number of conditions that can be detected through universal newborn screening (NBS). However, the addition of conditions to the Recommended Uniform Screening Panel (RUSP) and the implementation of nationwide screening has been a slow process taking several years to accomplish for individual conditions. Here, we describe web-based tools and resources developed and implemented by the newborn screening translational research network (NBSTRN) to advance newborn screening research and support NBS stakeholders worldwide. The NBSTRN's tools include the Longitudinal Pediatric Data Resource (LPDR), the NBS Condition Resource (NBS-CR), the NBS Virtual Repository (NBS-VR), and the Ethical, Legal, and Social Issues (ELSI) Advantage. Research programs, including the Inborn Errors of Metabolism Information System (IBEM-IS), BabySeq, EarlyCheck, and Family Narratives Use Cases, have utilized NBSTRN's tools and, in turn, contributed research data to further expand and refine these resources. Additionally, we discuss ongoing tool development to facilitate the expansion of genetic disease screening in increasingly diverse populations. In conclusion, NBSTRN's tools and resources provide a trusted platform to enable NBS stakeholders to advance NBS research and improve clinical care for patients and their families.

Newborn screening for neurodevelopmental diseases: Are we there yet?

In the US, newborn screening (NBS) is a unique health program that supports health equity and screens virtually every baby after birth, and has brought timely treatments to babies since the 1960's. With the decreasing cost of sequencing and the improving methods to interpret genetic data, there is an opportunity to add DNA sequencing as a screening method to facilitate the identification of babies with treatable conditions that cannot be identified in any other scalable way, including highly penetrant genetic neurodevelopmental disorders (NDD). However, the lack of effective dietary or drug-based treatments has made it nearly impossible to consider NDDs in the current NBS framework, yet it is anticipated that any treatment will be maximally effective if started early. Hence there is a critical need for large scale pilot studies to assess if and how NDDs can be effectively screened at birth, if parents desire that information, and what impact early diagnosis may have. Here we attempt to provide an overview of the recent advances in NDD treatments, explore the possible framework of setting up a pilot study to genetically screen for NDDs, highlight key technical, practical, and ethical considerations and challenges, and examine the policy and health system implications.

Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia.

BACKGROUND: Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. METHODS: The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. RESULTS: RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

Analytical Performance of a Gene Expression Classifier for Medullary Thyroid Carcinoma.

BACKGROUND: The aim of this study was to demonstrate the analytical validity of an RNA classifier for medullary thyroid carcinoma (MTC). METHODS: Fresh-frozen tissue specimens were obtained from commercial sources, and MTC diagnoses were confirmed by histopathology review. De-identified patient fine-needle aspiration biopsies (FNABs) and whole blood from normal donors were obtained. Total RNA was extracted, amplified, and hybridized to custom microarrays for gene expression analysis. Gene expression data were normalized and classified via a machine learning algorithm. Positive control materials were produced from MTC tissues and tested across multiple experiments and laboratories. Twenty-seven MTC tissue specimens were used to evaluate the sensitivity of the MTC classifier. Gene expression data from tissues and FNABs were used to model classifier response to mixtures of MTC samples with normal thyroid tissue, a benign thyroid nodule, a Hürthle cell adenoma, and whole blood. Select mixture conditions were confirmed in vitro. Assay tolerance to RNA input variation (5-25 ng) and genomic DNA contamination (30% by mass) was evaluated. The intra- and inter-run reproducibility and inter-laboratory accuracy of MTC classifier results were characterized. RESULTS: The MTC classifier sensitivity of 96.3% [confidence interval 81.0-99.9%] was determined retrospectively using 27 MTC confirmed tissue specimens. One false-negative result in a necrotic tissue implicated sample necrosis in reduced classifier sensitivity. Dilution modeling of MTC samples with normal or benign tissues showed consistent detection of MTC down to 20% sample proportions, with in vitro confirmation of 20% analytical sensitivity. Classifier tolerance to RNA input variation (5-25 ng), genomic DNA contamination (30% by mass), and an interfering substance (blood) was demonstrated with 100% accurate classifier results under all tested conditions. The maximum observed run-to-run score difference for a single FNAB sample was ∼1 unit compared with the average score difference between 38 MTC and non-MTC FNABs of ∼32 units. MTC classifier results for 20 tissues processed from total RNA in two different laboratories showed 100% concordance. CONCLUSIONS: The MTC classifier, offered as part of the routine molecular testing of cytology-indeterminate thyroid nodules, demonstrates robust analytical sensitivity, specificity, accuracy, and reproducibility.

Analytical performance of a bronchial genomic classifier.

BACKGROUND: The current standard practice of lung lesion diagnosis often leads to inconclusive results, requiring additional diagnostic follow up procedures that are invasive and often unnecessary due to the high benign rate in such lesions (Chest 143:e78S-e92, 2013). The Percepta bronchial genomic classifier was developed and clinically validated to provide more accurate classification of lung nodules and lesions that are inconclusive by bronchoscopy, using bronchial brushing specimens (N Engl J Med 373:243-51, 2015, BMC Med Genomics 8:18, 2015). The analytical performance of the Percepta test is reported here. METHODS: Analytical performance studies were designed to characterize the stability of RNA in bronchial brushing specimens during collection and shipment; analytical sensitivity defined as input RNA mass; analytical specificity (i.e. potentially interfering substances) as tested on blood and genomic DNA; and assay performance studies including intra-run, inter-run, and inter-laboratory reproducibility. RESULTS: RNA content within bronchial brushing specimens preserved in RNAprotect is stable for up to 20 days at 4 °C with no changes in RNA yield or integrity. Analytical sensitivity studies demonstrated tolerance to variation in RNA input (157 ng to 243 ng). Analytical specificity studies utilizing cancer positive and cancer negative samples mixed with either blood (up to 10 % input mass) or genomic DNA (up to 10 % input mass) demonstrated no assay interference. The test is reproducible from RNA extraction through to Percepta test result, including variation across operators, runs, reagent lots, and laboratories (standard deviation of 0.26 for scores on > 6 unit scale). CONCLUSIONS: Analytical sensitivity, analytical specificity and robustness of the Percepta test were successfully verified, supporting its suitability for clinical use.

Machine learning from concept to clinic: reliable detection of BRAF V600E DNA mutations in thyroid nodules using high-dimensional RNA expression data.

The promise of personalized medicine will require rigorously validated molecular diagnostics developed on minimally invasive, clinically relevant samples. Measurement of DNA mutations is increasingly common in clinical settings but only higher-prevalence mutations are cost-effective. Patients with rare variants are at best ignored or, at worst, misdiagnosed. Mutations result in downstream impacts on transcription, offering the possibility of broader diagnosis for patients with rare variants causing similar downstream changes. Use of such signatures in clinical settings is rare as these algorithms are difficult to validate for commercial use. Validation on a test set (against a clinical gold standard) is necessary but not sufficient: accuracy must be maintained amidst interfering substances, across reagent lots and across operators. Here we report the development, clinical validation, and diagnostic accuracy of a pre-operative molecular test (Afirma BRAF) to identify BRAF V600E mutations using mRNA expression in thyroid fine needle aspirate biopsies (FNABs). FNABs were obtained prospectively from 716 nodules and more than 3,000 features measured using microarrays. BRAF V600E labels for training (n=181) and independent test (n=535) sets were established using a sensitive quantitative PCR (qPCR) assay. The resulting 128-gene linear support vector machine was compared to qPCR in the independent test set. Clinical sensitivity and specificity for malignancy were evaluated in a subset of test set samples (n=213) with expert-derived histopathology. We observed high positive- (PPA, 90.4%) and negative (NPA, 99.0%) percent agreement with qPCR on the test set. Clinical sensitivity for malignancy was 43.8% (consistent with published prevalence of BRAF V600E in this neoplasm) and specificity was 100%, identical to qPCR on the same samples. Classification was accurate in up to 60% blood. A double-mutant still resulting in the V600E amino acid change was negative by qPCR but correctly positive by Afirma BRAF. Non-diagnostic rates were lower (7.6%) for Afirma BRAF than for qPCR (24.5%), a further advantage of using RNA in small sample biopsies. Afirma BRAF accurately determined the presence or absence of the BRAF V600E DNA mutation in FNABs, a collection method directly relevant to solid tumor assessment, with performance equal to that of an established, highly sensitive DNA-based assay and with a lower non-diagnostic rate. This is the first such test in thyroid cancer to undergo sufficient analytical and clinical validation for real-world use in a personalized medicine context to frame individual patient risk and inform surgical choice.

Exon-level expression profiling: a comprehensive transcriptome analysis of oral fluids.

BACKGROUND: The application of global gene expression profiling to saliva samples is hampered by the presence of partially fragmented and degraded RNAs that are difficult to amplify and detect with the prevailing technologies. Moreover, the often limited volume of saliva samples is a challenge to quantitative PCR (qPCR) validation of multiple candidates. The aim of this study was to provide proof-of-concept data on the combination of a universal mRNA-amplification method with exon arrays for candidate selection and a multiplex preamplification method for easy validation. METHODS: We used a universal mRNA-specific linear-amplification strategy in combination with Affymetrix Exon Arrays to amplify salivary RNA from 18 healthy individuals on the nanogram scale. Multiple selected candidates were preamplified in one multiplex reverse transcription PCR reaction, cleaned up enzymatically, and validated by qPCR. RESULTS: We defined a salivary exon core transcriptome (SECT) containing 851 transcripts of genes that have highly similar expression profiles in healthy individuals. A subset of the SECT transcripts was verified by qPCR analysis. Informatics analysis of the SECT revealed several functional clusters and sequence motifs. Sex-specific salivary exon biomarkers were identified and validated in tests with samples from healthy individuals. CONCLUSIONS: It is feasible to use samples containing fragmented RNAs to conduct high-resolution expression profiling with coverage of the entire transcriptome and to validate multiple targets from limited amounts of sample.

Involvement of JNK/p73/NOXA in vitamin E analog-induced apoptosis of human breast cancer cells.

Microarray analyses of human MDA-MB-435 breast cancer cells treated with vitamin E analog 2,5,7,8-tetramethyl-2R-(4R,8R,12-trimethyltridecyl) chroman-6-yloxy acetic acid (alpha-TEA) showed over 400 genes to be modulated. Thirty-four genes deemed of interest based on potential involvement in anticancer activities of alpha-TEA fell into six categories: apoptosis related, signal transduction, cell cycle related, cell adhesion and motility, transcriptional regulators, and membrane traffic related. The gene (PMAIP1) for NOXA was studied further. NOXA mRNA and protein levels were elevated in a time and dose-dependent fashion following alpha-TEA treatment. Functional knockdowns using small interfering RNA (siRNA) showed NOXA to contribute to alpha-TEA-induced apoptosis. A correlation between alpha-TEA's ability to upregulate NOXA and induce apoptosis was seen among several human breast cancer cell lines. Efforts to identify upstream regulators of NOXA in alpha-TEA-induced apoptosis identified the necessity of both c-Jun N-terminal kinase (JNK) activation and p73 expression. Additionally, protein levels of full length p73 were decreased by JNK siRNA treatment, suggesting that the signal transduction module of JNK-p73-NOXA is involved in alpha-TEA induced apoptosis of human breast cancer cells. Taken together, these findings suggest a role for JNK activation in mediating full length p73 expression and add to our understanding of the mechanisms of anticancer actions of alpha-TEA, a potential chemotherapeutic agent.

Genetic reconstruction of a functional transcriptional regulatory network.

Although global analyses of transcription factor binding provide one view of potential transcriptional regulatory networks, regulation also occurs at levels distinct from transcription factor binding. Here, we use a genetic approach to identify targets of transcription factors in yeast and reconstruct a functional regulatory network. First, we profiled transcriptional responses in S. cerevisiae strains with individual deletions of 263 transcription factors. Then we used directed-weighted graph modeling and regulatory epistasis analysis to identify indirect regulatory relationships between these transcription factors, and from this we reconstructed a functional transcriptional regulatory network. The enrichment of promoter motifs and Gene Ontology annotations provide insight into the biological functions of the transcription factors.

The heme activator protein Hap1 represses transcription by a heme-independent mechanism in Saccharomyces cerevisiae.

The yeast heme activator protein Hap1 binds to DNA and activates transcription of genes encoding functions required for respiration and for controlling oxidative damage, in response to heme. Hap1 contains a DNA-binding domain with a C6 zinc cluster motif, a coiled-coil dimerization element, typical of the members of the yeast Gal4 family, and an acidic activation domain. The regulation of Hap1 transcription-activating activity is controlled by two classes of Hap1 elements, repression modules (RPM1-3) and heme-responsive motifs (HRM1-7). Previous indirect evidence indicates that Hap1 may repress transcription directly. Here we show, by promoter analysis, by chromatin immunoprecipitation, and by electrophoretic mobility shift assay, that Hap1 binds directly to DNA and represses transcription of its own gene by at least 20-fold. We found that Hap1 repression of the HAP1 gene occurs independently of heme concentrations. While DNA binding is required for transcriptional repression by Hap1, deletion of Hap1 activation domain and heme-regulatory elements has varying effects on repression. Further, we found that repression by Hap1 requires the function of Hsp70 (Ssa), but not Hsp90. These results show that Hap1 binds to its own promoter and represses transcription in a heme-independent but Hsp70-dependent manner.

Genome-wide analysis of the biology of stress responses through heat shock transcription factor.

Heat shock transcription factor (HSF) and the promoter heat shock element (HSE) are among the most highly conserved transcriptional regulatory elements in nature. HSF mediates the transcriptional response of eukaryotic cells to heat, infection and inflammation, pharmacological agents, and other stresses. While HSF is essential for cell viability in Saccharomyces cerevisiae, oogenesis and early development in Drosophila melanogaster, extended life span in Caenorhabditis elegans, and extraembryonic development and stress resistance in mammals, little is known about its full range of biological target genes. We used whole-genome analyses to identify virtually all of the direct transcriptional targets of yeast HSF, representing nearly 3% of the genomic loci. The majority of the identified loci are heat-inducibly bound by yeast HSF, and the target genes encode proteins that have a broad range of biological functions including protein folding and degradation, energy generation, protein trafficking, maintenance of cell integrity, small molecule transport, cell signaling, and transcription. This genome-wide identification of HSF target genes provides novel insights into the role of HSF in growth, development, disease, and aging and in the complex metabolic reprogramming that occurs in all cells in response to stress.