Identification of skewed X chromosome inactivation using exome and transcriptome sequencing in patients with suspected rare genetic disease.

BACKGROUND: X-chromosome inactivation (XCI) is an epigenetic process that occurs during early development in mammalian females by randomly silencing one of two copies of the X chromosome in each cell. The preferential inactivation of either the maternal or paternal copy of the X chromosome in a majority of cells results in a skewed or non-random pattern of X inactivation and is observed in over 25% of adult females. Identifying skewed X inactivation is of clinical significance in patients with suspected rare genetic diseases due to the possibility of biased expression of disease-causing genes present on the active X chromosome. The current clinical test for the detection of skewed XCI relies on the methylation status of the methylation-sensitive restriction enzyme (Hpall) binding site present in proximity of short tandem polymorphic repeats on the androgen receptor (AR) gene. This approach using one locus results in uninformative or inconclusive data for 10-20% of tests. Further, recent studies have shown inconsistency between methylation of the AR locus and the state of inactivation of the X chromosome. Herein, we develop a method for estimating X inactivation status, using exome and transcriptome sequencing data derived from blood in 227 female samples. We built a reference model for evaluation of XCI in 135 females from the GTEx consortium. We tested and validated the model on 11 female individuals with different types of undiagnosed rare genetic disorders who were clinically tested for X-skew using the AR gene assay and compared results to our outlier-based analysis technique. RESULTS: In comparison to the AR clinical test for identification of X inactivation, our method was concordant with the AR method in 9 samples, discordant in 1, and provided a measure of X inactivation in 1 sample with uninformative clinical results. We applied this method on an additional 81 females presenting to the clinic with phenotypes consistent with different hereditary disorders without a known genetic diagnosis. CONCLUSIONS: This study presents the use of transcriptome and exome sequencing data to provide an accurate and complete estimation of X-inactivation and skew status in a cohort of female patients with different types of suspected rare genetic disease.

Semiautomated approach focused on new genomic information results in time and effort-efficient reannotation of negative exome data.

Most rare disease patients (75-50%) undergoing genomic sequencing remain unsolved, often due to lack of information about variants identified. Data review over time can leverage novel information regarding disease-causing variants and genes, increasing this diagnostic yield. However, time and resource constraints have limited reanalysis of genetic data in clinical laboratories setting. We developed RENEW, (REannotation of NEgative WES/WGS) an automated reannotation procedure that uses relevant new information in on-line genomic databases to enable rapid review of genomic findings. We tested RENEW in an unselected cohort of 1066 undiagnosed cases with a broad spectrum of phenotypes from the Mayo Clinic Center for Individualized Medicine using new information in ClinVar, HGMD and OMIM between the date of previous analysis/testing and April of 2022. 5741 variants prioritized by RENEW were rapidly reviewed by variant interpretation specialists. Mean analysis time was approximately 20 s per variant (32 h total time). Reviewed cases were classified as: 879 (93.0%) undiagnosed, 63 (6.6%) putatively diagnosed, and 4 (0.4%) definitively diagnosed. New strategies are needed to enable efficient review of genomic findings in unsolved cases. We report on a fast and practical approach to address this need and improve overall diagnostic success in patient testing through a recurrent reannotation process.

Complex balanced intrachromosomal rearrangement involving PITX2 identified as a cause of Axenfeld-Rieger Syndrome.

Axenfeld-Rieger Syndrome (ARS) type 1 is a rare autosomal dominant condition characterized by anterior chamber anomalies, umbilical defects, dental hypoplasia, and craniofacial anomalies, with Meckel's diverticulum in some individuals. Here, we describe a clinically ascertained female of childbearing age with ARS for whom clinical targeted sequencing and deletion/duplication analysis followed by clinical exome and genome sequencing resulted in no pathogenic variants or variants of unknown significance in PITX2 or FOXC1. Advanced bioinformatic analysis of the genome data identified a complex, balanced rearrangement disrupting PITX2. This case is the first reported intrachromosomal rearrangement leading to ARS, illustrating that for patients with compelling clinical phenotypes but negative genomic testing, additional bioinformatic analysis are essential to identify subtle genomic abnormalities in target genes.

The generality of the attentional boost effect for famous, unfamiliar, and inverted faces.

Familiarity and face inversion not only affect face recognition and memory but also influence attention. Face processing is less attention-demanding for familiar than for unfamiliar faces and for upright than for inverted faces. The automaticity raises the question of how face processing interacts with an increase in attention. Using a dual-task paradigm, we tested the interaction between attention and face familiarity and orientation. Participants encoded a series of faces to memory while simultaneously monitoring a stream of colored squares, pressing the space bar for target-colored squares and making no response to distractor-colored squares. Replicating previous findings of the attentional boost effect (ABE), we found that faces encoded with target squares were better remembered than faces encoded with distractor squares. If the automatic nature of familiar (or upright) face processing makes attention unnecessary, then the attentional boost should be attenuated for familiar relative to unfamiliar faces and for upright relative to inverted faces. Data from three experiments showed, however, that the ABE was the same for all types of faces. These results suggest that target detection did not simply elevate attention in an early encoding phase. Rather, selecting targets and rejecting distractors in the color task may have led to yoked temporal selection of target-concurrent faces for entry into memory.

SeekFusion - A Clinically Validated Fusion Transcript Detection Pipeline for PCR-Based Next-Generation Sequencing of RNA.

Detecting gene fusions involving driver oncogenes is pivotal in clinical diagnosis and treatment of cancer patients. Recent developments in next-generation sequencing (NGS) technologies have enabled improved assays for bioinformatics-based gene fusions detection. In clinical applications, where a small number of fusions are clinically actionable, targeted polymerase chain reaction (PCR)-based NGS chemistries, such as the QIAseq RNAscan assay, aim to improve accuracy compared to standard RNA sequencing. Existing informatics methods for gene fusion detection in NGS-based RNA sequencing assays traditionally use a transcriptome-based spliced alignment approach or a de-novo assembly approach. Transcriptome-based spliced alignment methods face challenges with short read mapping yielding low quality alignments. De-novo assembly-based methods yield longer contigs from short reads that can be more sensitive for genomic rearrangements, but face performance and scalability challenges. Consequently, there exists a need for a method to efficiently and accurately detect fusions in targeted PCR-based NGS chemistries. We describe SeekFusion, a highly accurate and computationally efficient pipeline enabling identification of gene fusions from PCR-based NGS chemistries. Utilizing biological samples processed with the QIAseq RNAscan assay and in-silico simulated data we demonstrate that SeekFusion gene fusion detection accuracy outperforms popular existing methods such as STAR-Fusion, TOPHAT-Fusion and JAFFA-hybrid. We also present results from 4,484 patient samples tested for neurological tumors and sarcoma, encompassing details on some novel fusions identified.

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment. OBJECTIVE: Here, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population. METHODS: We retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result. RESULTS: The GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106). CONCLUSIONS: Our deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

P2T2: Protein Panoramic annoTation Tool for the interpretation of protein coding genetic variants.

MOTIVATION: Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms. MATERIALS AND METHODS: P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome. RESULTS: Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies. CONCLUSION: The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.

LPCAT1-TERT fusions are uniquely recurrent in epithelioid trophoblastic tumors and positively regulate cell growth.

Gestational trophoblastic disease (GTD) is a heterogeneous group of lesions arising from placental tissue. Epithelioid trophoblastic tumor (ETT), derived from chorionic-type trophoblast, is the rarest form of GTD with only approximately 130 cases described in the literature. Due to its morphologic mimicry of epithelioid smooth muscle tumors and carcinoma, ETT can be misdiagnosed. To date, molecular characterization of ETTs is lacking. Furthermore, ETT is difficult to treat when disease spreads beyond the uterus. Here using RNA-Seq analysis in a cohort of ETTs and other gestational trophoblastic lesions we describe the discovery of LPCAT1-TERT fusion transcripts that occur in ETTs and coincide with underlying genomic deletions. Through cell-growth assays we demonstrate that LPCAT1-TERT fusion proteins can positively modulate cell proliferation and therefore may represent future treatment targets. Furthermore, we demonstrate that TERT upregulation appears to be a characteristic of ETTs, even in the absence of LPCAT1-TERT fusions, and that it appears linked to copy number gains of chromosome 5. No evidence of TERT upregulation was identified in other trophoblastic lesions tested, including placental site trophoblastic tumors and placental site nodules, which are thought to be the benign chorionic-type trophoblast counterpart to ETT. These findings indicate that LPCAT1-TERT fusions and copy-number driven TERT activation may represent novel markers for ETT, with the potential to improve the diagnosis, treatment, and outcome for women with this rare form of GTD.

Impact of integrated translational research on clinical exome sequencing.

PURPOSE: Exome sequencing often identifies pathogenic genetic variants in patients with undiagnosed diseases. Nevertheless, frequent findings of variants of uncertain significance necessitate additional efforts to establish causality before reaching a conclusive diagnosis. To provide comprehensive genomic testing to patients with undiagnosed disease, we established an Individualized Medicine Clinic, which offered clinical exome testing and included a Translational Omics Program (TOP) that provided variant curation, research activities, or research exome sequencing. METHODS: From 2012 to 2018, 1101 unselected patients with undiagnosed diseases received exome testing. Outcomes were reviewed to assess impact of the TOP and patient characteristics on diagnostic rates through descriptive and multivariate analyses. RESULTS: The overall diagnostic yield was 24.9% (274 of 1101 patients), with 174 (15.8% of 1101) diagnosed on the basis of clinical exome sequencing alone. Four hundred twenty-three patients with nondiagnostic or without access to clinical exome sequencing were evaluated by the TOP, with 100 (9% of 1101) patients receiving a diagnosis, accounting for 36.5% of the diagnostic yield. The identification of a genetic diagnosis was influenced by the age at time of testing and the disease phenotype of the patient. CONCLUSION: Integration of translational research activities into clinical practice of a tertiary medical center can significantly increase the diagnostic yield of patients with undiagnosed disease.

Interpretation challenges of novel dual-class missense and splice-impacting variant in POLR3A-related late-onset hereditary spastic ataxia.

BACKGROUND: RNA polymerase III (Pol III)-related disorders are autosomal recessive neurodegenerative disorders caused by variants in POLR3A or POLR3B. Recently, a novel phenotype of adult-onset spastic ataxia was identified in individuals with the c.1909+22G>A POLR3A variant in compound heterozygosity. METHODS: Whole-exome sequencing was performed in the proband and parents. Variants were confirmed by Sanger sequencing. RNA sequencing was performed to evaluate splicing implications. RESULTS: A 42-year-old female was evaluated for unexplained neurological findings with a slow progressive decline in gait and walking speed since adolescence. WES revealed a novel missense variant (c.3593A>C, p.Lys1198Arg) in exon 27 of POLR3A in compound heterozygosity with the c.1909+22G>A variant. Summary of previously reported clinical features from individuals with pathogenic biallelic alterations in POLR3A and adult-onset phenotype is consistent with our findings. RNA analysis revealed c.3593A>G drives the production of four RNA transcript products each with different functional impacts. CONCLUSION: The novel dual-class c.3593A>C variant in POLR3A causes an amino acid substitution and complex disruption of splicing. Our report supports the need to investigate variants near splice junctions for proper interpretation. Current interpretation guidelines need to address best practices for inclusion of predicted or measured transcriptional disruption pending functional activity or reliable transcript abundance estimates.

LeafCutterMD: an algorithm for outlier splicing detection in rare diseases.

MOTIVATION: Next-generation sequencing is rapidly improving diagnostic rates in rare Mendelian diseases, but even with whole genome or whole exome sequencing, the majority of cases remain unsolved. Increasingly, RNA sequencing is being used to solve many cases that evade diagnosis through sequencing alone. Specifically, the detection of aberrant splicing in many rare disease patients suggests that identifying RNA splicing outliers is particularly useful for determining causal Mendelian disease genes. However, there is as yet a paucity of statistical methodologies to detect splicing outliers. RESULTS: We developed LeafCutterMD, a new statistical framework that significantly improves the previously published LeafCutter in the context of detecting outlier splicing events. Through simulations and analysis of real patient data, we demonstrate that LeafCutterMD has better power than the state-of-the-art methodology while controlling false-positive rates. When applied to a cohort of disease-affected probands from the Mayo Clinic Center for Individualized Medicine, LeafCutterMD recovered all aberrantly spliced genes that had previously been identified by manual curation efforts. AVAILABILITY AND IMPLEMENTATION: The source code for this method is available under the opensource Apache 2.0 license in the latest release of the LeafCutter software package available online at http://davidaknowles.github.io/leafcutter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Computational Detection of Known Pathogenic Gene Fusions in a Normal Tissue Database and Implications for Genetic Disease Research.

Several recent studies have demonstrated the utility of RNA-Seq in the diagnosis of rare inherited disease. Diagnostic rates 35% higher than those previously achievable with DNA-Seq alone have been attained. These studies have primarily profiled gene expression and splicing defects, however, some have also shown that fusion transcripts are diagnostic or phenotypically relevant in patients with constitutional disorders. Fusion transcripts have traditionally been studied as oncogenic phenomena, with relevance only to cancer testing. Consequently, fusion detection algorithms were biased toward the detection of well-known oncogenic fusions, hindering their application to rare Mendelian genetic disease studies. A recent methodology published by the authors successfully tailored a traditional algorithm to the detection of pathogenic fusion events in inherited disease. A key mechanism of decreasing false positive or biologically benign events was comparison to a database of events detected in normal tissues. This approach is akin to population frequency-based filtering of genetic variants. It is predicated on the idea that pathogenic fusion transcripts are absent from normal tissue. We report on an analysis of RNA-Seq data from the genotype-tissue expression (GTEx) project in which known pathogenic fusions are computationally detected at low levels in normal tissues unassociated with the disease phenotype. Examples include archetypal cancer fusion transcripts, as well as fusions responsible for rare inherited disease. We consider potential explanations for the detectability of such transcripts and discuss the bearing such results have on the future profiling of genetic disease patients for pathogenic gene fusions.

An intragenic duplication of TRPS1 leading to abnormal transcripts and causing trichorhinophalangeal syndrome type I.

Trichorhinophalangeal syndrome type I (TRPSI) is a rare disorder that causes distinctive ectodermal, facial, and skeletal features affecting the hair (tricho-), nose (rhino-), and fingers and toes (phalangeal) and is inherited in an autosomal dominant pattern. TRPSI is caused by loss of function variants in TRPS1, involved in the regulation of chondrocyte and perichondrium development. Pathogenic variants in TRPS1 include missense mutations and deletions with variable breakpoints, with only a single instance of an intragenic duplication reported to date. Here we report an affected individual presenting with a classic TRPSI phenotype who is heterozygous for a de novo intragenic ∼36.3-kbp duplication affecting exons 2-4 of TRPS1 Molecular analysis revealed the duplication to be in direct tandem orientation affecting the splicing of TRPS1 The aberrant transcripts are predicted to produce a truncated TRPS1 missing the nuclear localization signal and the GATA and IKAROS-like zinc-finger domains resulting in functional TRPS1 haploinsufficiency. Our study identifies a novel intragenic tandem duplication of TRPS1 and highlights the importance of molecular characterization of intragenic duplications.

A tailored approach to fusion transcript identification increases diagnosis of rare inherited disease.

BACKGROUND: RNA sequencing has been proposed as a means of increasing diagnostic rates in studies of undiagnosed rare inherited disease. Recent studies have reported diagnostic improvements in the range of 7.5-35% by profiling splicing, gene expression quantification and allele specific expression. To-date however, no study has systematically assessed the presence of gene-fusion transcripts in cases of germline disease. Fusion transcripts are routinely identified in cancer studies and are increasingly recognized as having diagnostic, prognostic or therapeutic relevance. Isolated reports exist of fusion transcripts being detected in cases of developmental and neurological phenotypes, and thus, systematic application of fusion detection to germline conditions may further increase diagnostic rates. However, current fusion detection methods are unsuited to the investigation of germline disease due to performance biases arising from their development using tumor, cell-line or in-silico data. METHODS: We describe a tailored approach to fusion candidate identification and prioritization in a cohort of 47 undiagnosed, suspected inherited disease patients. We modify an existing fusion transcript detection algorithm by eliminating its cell line-derived filtering steps, and instead, prioritize candidates using a custom workflow that integrates genomic and transcriptomic sequence alignment, biological and technical annotations, customized categorization logic, and phenotypic prioritization. RESULTS: We demonstrate that our approach to fusion transcript identification and prioritization detects genuine fusion events excluded by standard analyses and efficiently removes phenotypically unimportant candidates and false positive events, resulting in a reduced candidate list enriched for events with potential phenotypic relevance. We describe the successful genetic resolution of two previously undiagnosed disease cases through the detection of pathogenic fusion transcripts. Furthermore, we report the experimental validation of five additional cases of fusion transcripts with potential phenotypic relevance. CONCLUSIONS: The approach we describe can be implemented to enable the detection of phenotypically relevant fusion transcripts in studies of rare inherited disease. Fusion transcript detection has the potential to increase diagnostic rates in rare inherited disease and should be included in RNA-based analytical pipelines aimed at genetic diagnosis.

RNA-Seq detects a SAMD12-EXT1 fusion transcript and leads to the discovery of an EXT1 deletion in a child with multiple osteochondromas.

BACKGROUND: We describe a patient presenting with pachygyria, epilepsy, developmental delay, short stature, failure to thrive, facial dysmorphisms, and multiple osteochondromas. METHODS: The patient underwent extensive genetic testing and analysis in an attempt to diagnose the cause of his condition. Clinical testing included metaphase karyotyping, array comparative genomic hybridization, direct sequencing and multiplex ligation-dependent probe amplification and trio-based exome sequencing. Subsequently, research-based whole transcriptome sequencing was conducted to determine whether it might shed light on the undiagnosed phenotype. RESULTS: Clinical exome sequencing of patient and parent samples revealed a maternally inherited splice-site variant in the doublecortin (DCX) gene that was classified as likely pathogenic and diagnostic of the patient's neurological phenotype. Clinical array comparative genome hybridization analysis revealed a 16p13.3 deletion that could not be linked to the patient phenotype based on affected genes. Further clinical testing to determine the cause of the patient's multiple osteochondromas was unrevealing despite extensive profiling of the most likely causative genes, EXT1 and EXT2, including mutation screening by direct sequence analysis and multiplex ligation-dependent probe amplification. Whole transcriptome sequencing identified a SAMD12-EXT1 fusion transcript that could have resulted from a chromosomal deletion, leading to the loss of EXT1 function. Re-review of the clinical array comparative genomic hybridization results indicated a possible unreported mosaic deletion affecting the SAMD12 and EXT1 genes that corresponded precisely to the introns predicted to be affected by a fusion-causing deletion. The existence of the mosaic deletion was subsequently confirmed clinically by an increased density copy number array and orthogonal methodologies CONCLUSIONS: While mosaic mutations and deletions of EXT1 and EXT2 have been reported in the context of multiple osteochondromas, to our knowledge, this is the first time that transcriptomics technologies have been used to diagnose a patient via fusion transcript analysis in the congenital disease setting.

Assessing Human Genetic Variations in Glucose Transporter SLC2A10 and Their Role in Altering Structural and Functional Properties.

Purpose: Demand is increasing for clinical genomic sequencing to provide diagnoses for patients presenting phenotypes indicative of genetic diseases, but for whom routine genetic testing failed to yield a diagnosis. DNA-based testing using high-throughput technologies often identifies variants with insufficient evidence to determine whether they are disease-causal or benign, leading to categorization as variants of uncertain significance (VUS). Methods: We used molecular modeling and simulation to generate specific hypotheses for the molecular effects of variants in the human glucose transporter, GLUT10 (SLC2A10). Similar to many disease-relevant membrane proteins, no experimentally derived 3D structure exists. An atomic model was generated and used to evaluate multiple variants, including pathogenic, benign, and VUS. Results: These analyses yielded detailed mechanistic data, not currently predictable from sequence, including altered protein stability, charge distribution of ligand binding surfaces, and shifts toward or away from transport-competent conformations. Consideration of the two major conformations of GLUT10 was important as variants have conformation-specific effects. We generated detailed molecular hypotheses for the functional impact of variants in GLUT10 and propose means to determine their pathogenicity. Conclusion: The type of workflow we present here is valuable for increasing the throughput and resolution with which VUS effects can be assessed and interpreted.

Characterization of three ciliopathy pedigrees expands the phenotype associated with biallelic C2CD3 variants.

Whole exome sequencing (WES) is utilized in diagnostic odyssey cases to identify the underlying genetic cause associated with complex phenotypes. Recent publications suggest that WES reveals the genetic cause in ~25% of these cases and is most successful when applied to children with neurological disease. The residual 75% of cases remain genetically elusive until more information becomes available in the literature or functional studies are pursued. WES performed on three families with presumed ciliopathy diagnoses, including orofaciodigital (OFD) syndrome, fetal encephalocele, or Joubert-related disorder, identified compound heterozygous variants in C2CD3. Biallelic variants in C2CD3 have previously been associated with ciliopathies, including OFD syndrome type 14 (OFD14; MIM: 615948). As three of the six identified variants were predicted to affect splicing, exon-skipping analysis using either RNA sequencing or PCR-based methods were completed to determine the pathogenicity of these variants, and showed that each of the splicing variants led to a frameshifted protein product. Using these studies in combination with the 2015 ACMG guidelines, each of the six identified variants were classified as either pathogenic or likely pathogenic, and are therefore likely responsible for our patients' phenotypes. Each of the families had a distinct clinical phenotype and severity of disease, extending from lethal to viable. These findings highlight that there is a broad phenotypic spectrum associated with C2CD3-mediated disease and not all patients present with the typical features of OFD14.

Development and Verification of an RNA Sequencing (RNA-Seq) Assay for the Detection of Gene Fusions in Tumors.

We assessed the performance characteristics of an RNA sequencing (RNA-Seq) assay designed to detect gene fusions in 571 genes to help manage patients with cancer. Polyadenylated RNA was converted to cDNA, which was then used to prepare next-generation sequencing libraries that were sequenced on an Illumina HiSeq 2500 instrument and analyzed with an in-house developed bioinformatic pipeline. The assay identified 38 of 41 gene fusions detected by another method, such as fluorescence in situ hybridization or RT-PCR, for a sensitivity of 93%. No false-positive gene fusions were identified in 15 normal tissue specimens and 10 tumor specimens that were negative for fusions by RNA sequencing or Mate Pair NGS (100% specificity). The assay also identified 22 fusions in 17 tumor specimens that had not been detected by other methods. Eighteen of the 22 fusions had not previously been described. Good intra-assay and interassay reproducibility was observed with complete concordance for the presence or absence of gene fusions in replicates. The analytical sensitivity of the assay was tested by diluting RNA isolated from gene fusion-positive cases with fusion-negative RNA. Gene fusions were generally detectable down to 12.5% dilutions for most fusions and as little as 3% for some fusions. This assay can help identify fusions in patients with cancer; these patients may in turn benefit from both US Food and Drug Administration-approved and investigational targeted therapies.