Functional Copy-Number Alterations as Diagnostic and Prognostic Biomarkers in Neuroendocrine Tumors.

Functional copy-number alterations (fCNAs) are DNA copy-number changes with concordant differential gene expression. These are less likely to be bystander genetic lesions and could serve as robust and reproducible tumor biomarkers. To identify candidate fCNAs in neuroendocrine tumors (NETs), we integrated chromosomal microarray (CMA) and RNA-seq differential gene-expression data from 31 pancreatic (pNETs) and 33 small-bowel neuroendocrine tumors (sbNETs). Tumors were resected from 47 early-disease-progression (<24 months) and 17 late-disease-progression (>24 months) patients. Candidate fCNAs that accurately differentiated these groups in this discovery cohort were then replicated using fluorescence in situ hybridization (FISH) on formalin-fixed, paraffin-embedded (FFPE) tissues in a larger validation cohort of 60 pNETs and 82 sbNETs (52 early- and 65 late-disease-progression samples). Logistic regression analysis revealed the predictive ability of these biomarkers, as well as the assay-performance metrics of sensitivity, specificity, and area under the curve. Our results indicate that copy-number changes at chromosomal loci 4p16.3, 7q31.2, 9p21.3, 17q12, 18q21.2, and 19q12 may be used as diagnostic and prognostic NET biomarkers. This involves a rapid, cost-effective approach to determine the primary tumor site for patients with metastatic liver NETs and to guide risk-stratified therapeutic decisions.

Prioritization of Fluorescence In Situ Hybridization (FISH) Probes for Differentiating Primary Sites of Neuroendocrine Tumors with Machine Learning.

Determining neuroendocrine tumor (NET) primary sites is pivotal for patient care as pancreatic NETs (pNETs) and small bowel NETs (sbNETs) have distinct treatment approaches. The diagnostic power and prioritization of fluorescence in situ hybridization (FISH) assay biomarkers for establishing primary sites has not been thoroughly investigated using machine learning (ML) techniques. We trained ML models on FISH assay metrics from 85 sbNET and 59 pNET samples for primary site prediction. Exploring multiple methods for imputing missing data, the impute-by-median dataset coupled with a support vector machine model achieved the highest classification accuracy of 93.1% on a held-out test set, with the top importance variables originating from the ERBB2 FISH probe. Due to the greater interpretability of decision tree (DT) models, we fit DT models to ten dataset splits, achieving optimal performance with k-nearest neighbor (KNN) imputed data and a transformation to single categorical biomarker probe variables, with a mean accuracy of 81.4%, on held-out test sets. ERBB2 and MET variables ranked as top-performing features in 9 of 10 DT models and the full dataset model. These findings offer probabilistic guidance for FISH testing, emphasizing the prioritization of the ERBB2, SMAD4, and CDKN2A FISH probes in diagnosing NET primary sites.

Assessing variants of uncertain significance implicated in hearing loss using a comprehensive deafness proteome.

Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are "variants of uncertain significance" (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆GFold) for all DVD missense variants. We find that 5772 VUSs have a large, destabilizing ∆∆GFold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3456 VUSs are likely pathogenic at a probability of 99.0%. Of the 224 genes in the DVD, 166 genes (74%) exhibit one or more missense variants predicted to cause a pathogenic change in protein folding stability. The VUSs prioritized here affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients.

Assessing Variants of Uncertain Significance Implicated in Hearing Loss Using a Comprehensive Deafness Proteome.

Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6,328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are "variants of uncertain significance" (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆G Fold ) for all DVD missense variants. We find that 5,772 VUSs have a large, destabilizing ∆∆G Fold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3,456 VUSs are likely pathogenic at a probability of 99.0%. These VUSs affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients.

A splice site mutation in the TSEN2 causes a new syndrome with craniofacial and central nervous system malformations, and atypical hemolytic uremic syndrome.

Recessive mutations in the genes encoding the four subunits of the tRNA splicing endonuclease complex (TSEN54, TSEN34, TSEN15, and TSEN2) cause various forms of pontocerebellar hypoplasia, a disorder characterized by hypoplasia of the cerebellum and the pons, microcephaly, dysmorphisms, and other variable clinical features. Here, we report an intronic recessive founder variant in the gene TSEN2 that results in abnormal splicing of the mRNA of this gene, in six individuals from four consanguineous families affected with microcephaly, multiple craniofacial malformations, radiological abnormalities of the central nervous system, and cognitive retardation of variable severity. Remarkably, unlike patients with previously described mutations in the components of the TSEN complex, all the individuals that we report developed atypical hemolytic uremic syndrome (aHUS) with thrombotic microangiopathy, microangiopathic hemolytic anemia, thrombocytopenia, proteinuria, severe hypertension, and end-stage kidney disease (ESKD) early in life. Bulk RNA sequencing of peripheral blood cells of four affected individuals revealed abnormal tRNA transcripts, indicating an alteration of the tRNA biogenesis. Morpholino-mediated skipping of exon 10 of tsen2 in zebrafish produced phenotypes similar to human patients. Thus, we have identified a novel syndrome accompanied by aHUS suggesting the existence of a link between tRNA biology and vascular endothelium homeostasis, which we propose to name with the acronym TRACK syndrome (TSEN2 Related Atypical hemolytic uremic syndrome, Craniofacial malformations, Kidney failure).

Creation and validation of models to predict response to primary treatment in serous ovarian cancer.

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case-control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p < 0.05) were included in multivariate lasso regression prediction models. Initial models included only one variable. Variables were then combined to create complex models. Model performance was measured with area under the curve (AUC). Validation of all models was performed using TCGA HGSC database. By integrating clinical and genomic variables, we achieved prediction performances of over 95% in AUC. Most performances in the validation set did not differ from the training set. Models with DNA methylation or lncRNA underperformed in the validation set. Integrating comprehensive clinical and genomic data from patients with HGSC results in accurate and robust prediction models of treatment response.

Presacral neuroendocrine tumors associated with the Currarino syndrome.

Currarino syndrome (CS) is an autosomal dominant syndrome caused by mutations in MNX1 and characterized by anorectal abnormalities, partial sacral agenesis, and presacral masses. The presacral masses are typically benign; however, malignant degeneration can occur, and presacral neuroendocrine tumors (NETs) have been reported in six cases. We report three individuals from two families affected by CS in which multiple individuals developed presacral NETs. The first family, 491, had six members with features of CS, including two siblings who presented with presacral, Grade 2 NETs, one of which had metastasized to bone and lymph nodes. A germline c.874C>T (p.Arg292Trp) mutation was found in a highly conserved region of MNX1 in three affected members who underwent sequencing. A second somatic variant/deletion in MNX1 was not detected in either patient's tumor. In the second family, 342, the proband presented with an incidentally discovered presacral NET. The proband's father had previously undergone resection of a presacral NET, and so genetic testing was performed, which did not reveal an MNX1 mutation or copy number variants. The lack of a second, somatic mutation in the tumors from family 491 argues against MNX1 acting as a tumor suppressor, and the absence of a germline MNX1 mutation in family 342 suggests that other genetic and anatomic factors contribute to the development of presacral NETs. These cases highlight the variable presentation of CS, and the potential for malignancy in these patients.

A comparative analysis of genetic hearing loss phenotypes in European/American and Japanese populations.

We present detailed comparative analyses to assess population-level differences in patterns of genetic deafness between European/American and Japanese cohorts with non-syndromic hearing loss. One thousand eighty-three audiometric test results (921 European/American and 162 Japanese) from members of 168 families (48 European/American and 120 Japanese) with non-syndromic hearing loss secondary to pathogenic variants in one of three genes (KCNQ4, TECTA, WFS1) were studied. Audioprofile characteristics, specific mutation types, and protein domains were considered in the comparative analyses. Our findings support differences in audioprofiles driven by both mutation type (non-truncating vs. truncating) and ethnic background. The former finding confirms data that ascribe a phenotypic consequence to different mutation types in KCNQ4; the latter finding suggests that there are ethnic-specific effects (genetic and/or environmental) that impact gene-specific audioprofiles for TECTA and WFS1. Identifying the drivers of ethnic differences will refine our understanding of phenotype-genotype relationships and the biology of hearing and deafness.

Gene Expression Signatures Identify Novel Therapeutics for Metastatic Pancreatic Neuroendocrine Tumors.

PURPOSE: Pancreatic neuroendocrine tumors (pNETs) are uncommon malignancies noted for their propensity to metastasize and comparatively favorable prognosis. Although both the treatment options and clinical outcomes have improved in the past decades, most patients will die of metastatic disease. New systemic therapies are needed. EXPERIMENTAL DESIGN: Tissues were obtained from 43 patients with well-differentiated pNETs undergoing surgery. Gene expression was compared between primary tumors versus liver and lymph node metastases using RNA-Seq. Genes that were selectively elevated at only one metastatic site were filtered out to reduce tissue-specific effects. Ingenuity pathway analysis (IPA) and the Connectivity Map (CMap) identified drugs likely to antagonize metastasis-specific targets. The biological activity of top identified agents was tested in vitro using two pNET cell lines (BON-1 and QGP-1). RESULTS: A total of 902 genes were differentially expressed in pNET metastases compared with primary tumors, 626 of which remained in the common metastatic profile after filtering. Analysis with IPA and CMap revealed altered activity of factors involved in survival and proliferation, and identified drugs targeting those pathways, including inhibitors of mTOR, PI3K, MEK, TOP2A, protein kinase C, NF-kB, cyclin-dependent kinase, and histone deacetylase. Inhibitors of MEK and TOP2A were consistently the most active compounds. CONCLUSIONS: We employed a complementary bioinformatics approach to identify novel therapeutics for pNETs by analyzing gene expression in metastatic tumors. The potential utility of these drugs was confirmed by in vitro cytotoxicity assays, suggesting drugs targeting MEK and TOP2A may be highly efficacious against metastatic pNETs. This is a promising strategy for discovering more effective treatments for patients with pNETs.

A TFAP2C Gene Signature Is Predictive of Outcome in HER2-Positive Breast Cancer.

The AP-2γ transcription factor, encoded by the TFAP2C gene, regulates the expression of estrogen receptor-alpha (ERα) and other genes associated with hormone response in luminal breast cancer. Little is known about the role of AP-2γ in other breast cancer subtypes. A subset of HER2+ breast cancers with amplification of the TFAP2C gene locus becomes addicted to AP-2γ. Herein, we sought to define AP-2γ gene targets in HER2+ breast cancer and identify genes accounting for physiologic effects of growth and invasiveness regulated by AP-2γ. Comparing HER2+ cell lines that demonstrated differential response to growth and invasiveness with knockdown of TFAP2C, we identified a set of 68 differentially expressed target genes. CDH5 and CDKN1A were among the genes differentially regulated by AP-2γ and that contributed to growth and invasiveness. Pathway analysis implicated the MAPK13/p38δ and retinoic acid regulatory nodes, which were confirmed to display divergent responses in different HER2+ cancer lines. To confirm the clinical relevance of the genes identified, the AP-2γ gene signature was found to be highly predictive of outcome in patients with HER2+ breast cancer. We conclude that AP-2γ regulates a set of genes in HER2+ breast cancer that drive cancer growth and invasiveness. The AP-2γ gene signature predicts outcome of patients with HER2+ breast cancer and pathway analysis predicts that subsets of patients will respond to drugs that target the MAPK or retinoic acid pathways. IMPLICATIONS: A set of genes regulated by AP-2γ in HER2+ breast cancer that drive proliferation and invasion were identified and provided a gene signature that is predictive of outcome in HER2+ breast cancer.

A novel, wearable, electronic visual aid to assist those with reduced peripheral vision.

PURPOSE: To determine whether visual-tactile sensory substitution utilizing the Low-vision Enhancement Optoelectronic (LEO) Belt prototype is suitable as a new visual aid for those with reduced peripheral vision by assessing mobility performance and user opinions. METHODS: Sighted subjects (n = 20) and subjects with retinitis pigmentosa (RP) (n = 6) were recruited. The LEO Belt was evaluated on two cohorts: normally sighted subjects wearing goggles to artificially reduce peripheral vision to simulate stages of RP progression, and subjects with advanced visual field limitation from RP. Mobility speed and accuracy was assessed using simple mazes, with and without the LEO Belt, to determine its usefulness across disease severities and lighting conditions. RESULTS: Sighted subjects wearing most narrowed field goggles simulating most advanced RP had increased mobility accuracy (44% mean reduction in errors, p = 0.014) and self-reported confidence (77% mean increase, p = 0.004) when using the LEO Belt. Additionally, use of LEO doubled mobility accuracy for RP subjects with remaining visual fields between 10° and 20°. Further, in dim lighting, confidence scores for this group also doubled. By patient reported outcomes, subjects largely deemed the device comfortable (100%), easy to use (92.3%) and thought it had potential future benefit as a visual aid (96.2%). However, regardless of severity of vision loss or simulated vision loss, all subjects were slower to complete the mazes using the device. CONCLUSIONS: The LEO Belt improves mobility accuracy and therefore confidence in those with severely restricted peripheral vision. The LEO Belt's positive user feedback suggests it has potential to become the next generation of visual aid for visually impaired individuals. Given the novelty of this approach, we expect navigation speeds may improve with experience.

Structural Insights into Hearing Loss Genetics from Polarizable Protein Repacking.

Hearing loss is associated with ∼8100 mutations in 152 genes, and within the coding regions of these genes are over 60,000 missense variants. The majority of these variants are classified as "variants of uncertain significance" to reflect our inability to ascribe a phenotypic effect to the observed amino acid change. A promising source of pathogenicity information is biophysical simulation, although input protein structures often contain defects because of limitations in experimental data and/or only distant homology to a template. Here, we combine the polarizable atomic multipole optimized energetics for biomolecular applications force field, many-body optimization theory, and graphical processing unit acceleration to repack all deafness-associated proteins and thereby improve average structure MolProbity score from 2.2 to 1.0. We then used these optimized wild-type models to create over 60,000 structures for missense variants in the Deafness Variation Database, which are being incorporated into the Deafness Variation Database to inform deafness pathogenicity prediction. Finally, this work demonstrates that advanced polarizable atomic multipole force fields are efficient enough to repack the entire human proteome.

Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality.

BACKGROUND: In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expression profiles, such a wide variety of data presents a multitude of challenges. Large clinical datasets are subject to sparsely and/or inconsistently populated fields. Corresponding sequencing profiles can suffer from the problem of high-dimensionality, where making useful inferences can be difficult without correspondingly large numbers of instances. In this paper we report a novel deployment of machine learning techniques to handle data sparsity and high dimensionality, while evaluating potential biomarkers in the form of unsupervised transformations of RNA data. We apply preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usability of more than 500 patient cases from the TCGA-HNSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcinoma (HNSCC). RESULTS: Imputation was shown to improve prognostic ability of sparse clinical treatment variables. SPCA transformation of RNA expression variables reduced runtime for RNA-based models, though changes to classifier performance were not significant. Gene ontology enrichment analysis of gene sets associated with individual sparse principal components (SPCs) are also reported, showing that both high- and low-importance SPCs were associated with cell death pathways, though the high-importance gene sets were found to be associated with a wider variety of cancer-related biological processes. CONCLUSIONS: MICE imputation allowed us to impute missing values for clinically informative features, improving their overall importance for predicting two-year recurrence-free survival by incorporating variance from other clinical variables. Dimensionality reduction of RNA expression profiles via SPCA reduced both computation cost and model training/evaluation time without affecting classifier performance, allowing researchers to obtain experimental results much more quickly. SPCA simultaneously provided a convenient avenue for consideration of biological context via gene ontology enrichment analysis.

RABL6A inhibits tumor-suppressive PP2A/AKT signaling to drive pancreatic neuroendocrine tumor growth.

Hyperactivated AKT/mTOR signaling is a hallmark of pancreatic neuroendocrine tumors (PNETs). Drugs targeting this pathway are used clinically, but tumor resistance invariably develops. A better understanding of factors regulating AKT/mTOR signaling and PNET pathogenesis is needed to improve current therapies. We discovered that RABL6A, a new oncogenic driver of PNET proliferation, is required for AKT activity. Silencing RABL6A caused PNET cell-cycle arrest that coincided with selective loss of AKT-S473 (not T308) phosphorylation and AKT/mTOR inactivation. Restoration of AKT phosphorylation rescued the G1 phase block triggered by RABL6A silencing. Mechanistically, loss of AKT-S473 phosphorylation in RABL6A-depleted cells was the result of increased protein phosphatase 2A (PP2A) activity. Inhibition of PP2A restored phosphorylation of AKT-S473 in RABL6A-depleted cells, whereas PP2A reactivation using a specific small-molecule activator of PP2A (SMAP) abolished that phosphorylation. Moreover, SMAP treatment effectively killed PNET cells in a RABL6A-dependent manner and suppressed PNET growth in vivo. The present work identifies RABL6A as a new inhibitor of the PP2A tumor suppressor and an essential activator of AKT in PNET cells. Our findings offer what we believe is a novel strategy of PP2A reactivation for treatment of PNETs as well as other human cancers driven by RABL6A overexpression and PP2A inactivation.

Genomic Landscape and Mutational Signatures of Deafness-Associated Genes.

The classification of genetic variants represents a major challenge in the post-genome era by virtue of their extraordinary number and the complexities associated with ascribing a clinical impact, especially for disorders exhibiting exceptional phenotypic, genetic, and allelic heterogeneity. To address this challenge for hearing loss, we have developed the Deafness Variation Database (DVD), a comprehensive, open-access resource that integrates all available genetic, genomic, and clinical data together with expert curation to generate a single classification for each variant in 152 genes implicated in syndromic and non-syndromic deafness. We evaluate 876,139 variants and classify them as pathogenic or likely pathogenic (more than 8,100 variants), benign or likely benign (more than 172,000 variants), or of uncertain significance (more than 695,000 variants); 1,270 variants are re-categorized based on expert curation and in 300 instances, the change is of medical significance and impacts clinical care. We show that more than 96% of coding variants are rare and novel and that pathogenicity is driven by minor allele frequency thresholds, variant effect, and protein domain. The mutational landscape we define shows complex gene-specific variability, making an understanding of these nuances foundational for improved accuracy in variant interpretation in order to enhance clinical decision making and improve our understanding of deafness biology.

Correction to: Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

It has been highlighted that in the original manuscript [1] Table S3 'An example of the predictive computational modeling process. Specific details on an annexure section of the PD-L1 pathway show the step-by-step reactions, mechanisms, and reaction equations that occur. Such reactions also occurred in all of the other pathways' was omitted and did not appear in the Additional files and that the Additional files were miss-numbered thereafter. This Correction shows the correct and incorrect Additional files. The original article has been updated.

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

BACKGROUND: Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. METHODS: We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. RESULTS: Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. CONCLUSIONS: Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

Changes in gene expression in small bowel neuroendocrine tumors associated with progression to metastases.

BACKGROUND: Small bowel neuroendocrine tumors (SBNETs) present frequently with metastases, yet little is known about the molecular basis of this progression. This study sought to identify the serial differential expression of genes between normal small bowel, primary small bowel neuroendocrine tumors, and liver metastases. METHODS: RNA isolated from matched normal small bowel tissue, primary small bowel neuroendocrine tumors, and liver metastases in 12 patients was analyzed with whole transcriptome expression microarrays and RNA-Seq. Changes in gene expression between primary small bowel neuroendocrine tumors and normal small bowels, and liver metastases versus primary small bowel neuroendocrine tumors were calculated. Common genes that were differentially expressed serially (increasing or decreasing from normal small bowel to primary small bowel neuroendocrine tumors to liver metastases) were identified, and 10 were validated using qPCR. RESULTS: Use of 2 transcriptome platforms allowed for a robust discrimination of genes important in small bowel neuroendocrine tumors progression. Serial differential expression was validated in 7/10 genes, all of which had been described previously in abdominal cancers, and with several interacting with members of the AKT, MYC, or MAPK3 pathways. Liver metastases had consistent underexpression of PMP22, while high expression of SERPINA10 and SYT13 was characteristic of both pSBTs and liver metastases. CONCLUSION: Identification of the serial differential expression of genes from normal tissues to primary tumors to metastases lends insight into important pathways for SBNETs progression. Differential expression of various genes, including PMP22, SYT13 and SERPINA10, are associated with the progression of SBNETs and warrant further investigation.

Clinically Focused Molecular Investigation of 1000 Consecutive Families with Inherited Retinal Disease.

PURPOSE: To devise a comprehensive multiplatform genetic testing strategy for inherited retinal disease and to describe its performance in 1000 consecutive families seen by a single clinician. DESIGN: Retrospective series. PARTICIPANTS: One thousand consecutive families seen by a single clinician. METHODS: The clinical records of all patients seen by a single retina specialist between January 2010 and June 2016 were reviewed, and all patients who met the clinical criteria for a diagnosis of inherited retinal disease were included in the study. Each patient was assigned to 1 of 62 diagnostic categories, and this clinical diagnosis was used to define the scope and order of the molecular investigations that were performed. The number of nucleotides evaluated in a given subject ranged from 2 to nearly 900 000. MAIN OUTCOME MEASURES: Sensitivity and false genotype rate. RESULTS: Disease-causing genotypes were identified in 760 families (76%). These genotypes were distributed across 104 different genes. More than 75% of these 104 genes have coding sequences small enough to be packaged efficiently into an adeno-associated virus. Mutations in ABCA4 were the most common cause of disease in this cohort (173 families), whereas mutations in 80 genes caused disease in 5 or fewer families (i.e., 0.5% or less). Disease-causing genotypes were identified in 576 of the families without next-generation sequencing (NGS). This included 23 families with mutations in the repetitive region of RPGR exon 15 that would have been missed by NGS. Whole-exome sequencing of the remaining 424 families revealed mutations in an additional 182 families, and whole-genome sequencing of 4 of the remaining 242 families revealed 2 additional genotypes that were invisible by the other methods. Performing the testing in a clinically focused tiered fashion would be 6.1% more sensitive and 17.7% less expensive and would have a significantly lower average false genotype rate than using whole-exome sequencing to assess more than 300 genes in all patients (7.1% vs. 128%; P < 0.001). CONCLUSIONS: Genetic testing for inherited retinal disease is now more than 75% sensitive. A clinically directed tiered testing strategy can increase sensitivity and improve statistical significance without increasing cost.

A Comparison of Gene Expression Profiles between Glucocorticoid Responder and Non-Responder Bovine Trabecular Meshwork Cells Using RNA Sequencing.

The most common ocular side effect of glucocorticoid (GC) therapy is GC-induced ocular hypertension (OHT) and GC-induced glaucoma (GIG). GC-induced OHT occurs in about 40% of the general population, while the other 60% are resistant. This study aims to determine the genes and pathways involved in differential GC responsiveness in the trabecular meshwork (TM). Using paired bovine eyes, one eye was perfusion-cultured with 100nM dexamethasone (DEX), while the fellow eye was used to establish a bovine TM (BTM) cell strain. Based on maximum IOP change in the perfused eye, the BTM cell strain was identified as a DEX-responder or non-responder strain. Three responder and three non-responder BTM cell strains were cultured, treated with 0.1% ethanol or 100nM DEX for 7 days. RNA and proteins were extracted for RNA sequencing (RNAseq), qPCR, and Western immunoblotting (WB), respectively. Data were analyzed using the human and bovine genome databases as well as Tophat2 software. Genes were grouped and compared using Student's t-test. We found that DEX induced fibronectin expression in responder BTM cells but not in non-responder cells using WB. RNAseq showed between 93 and 606 differentially expressed genes in different expression groups between responder and non-responder BTM cells. The data generated by RNAseq were validated using qPCR. Pathway analyses showed 35 pathways associated with differentially expressed genes. These genes and pathways may play important roles in GC-induced OHT and will help us to better understand differential ocular responsiveness to GCs.