BackgroundAccurate fusion transcript detection is essential for comprehensive characterization of cancer transcriptomes. Over the last decade, multiple bioinformatic tools have been developed to predict fusions from RNA-seq, based on either read mapping or de novo fusion transcript assembly.ResultsWe benchmark 23 different methods including applications we develop, STAR-Fusion and TrinityFusion, leveraging both simulated and real RNA-seq. Overall, STAR-Fusion, Arriba, and STAR-SEQR are the most accurate and fastest for fusion detection on cancer transcriptomes.ConclusionThe lower accuracy of de novo assembly-based methods notwithstanding, they are useful for reconstructing fusion isoforms and tumor viruses, both of which are important in cancer research.

Highlights d Tim-1 + B cells are required for maintaining immune tolerance d Tim-1 + B cells differentially express TIGIT and other coinhibitory molecules d B cell expression of TIGIT and many other regulators requires Tim-1 signaling d B cell TIGIT expression is preferentially required for maintaining CNS tolerance

IL-17–producing Th17 cells are implicated in the pathogenesis of rheumatoid arthritis (RA) and TNF-α, a proinflammatory cytokine in the rheumatoid joint, facilitates Th17 differentiation. Anti-TNF therapy ameliorates disease in many patients with rheumatoid arthritis (RA). However, a significant proportion of patients do not respond to this therapy. The impact of anti-TNF therapy on Th17 responses in RA is not well understood. We conducted high-throughput gene expression analysis of Th17-enriched CCR6+CXCR3−CD45RA−CD4+T (CCR6+T) cells isolated from anti-TNF–treated RA patients classified as responders or nonresponders to therapy. CCR6+T cells from responders and nonresponders had distinct gene expression profiles. Proinflammatory signaling was elevated in the CCR6+T cells of nonresponders, and pathogenic Th17 signature genes were up-regulated in these cells. Gene set enrichment analysis on these signature genes identified transcription factor USF2 as their upstream regulator, which was also increased in nonresponders. Importantly, short hairpin RNA targetingUSF2in pathogenic Th17 cells led to reduced expression of proinflammatory cytokines IL-17A, IFN-γ, IL-22, and granulocyte-macrophage colony-stimulating factor (GM-CSF) as well as transcription factor T-bet. Together, our results revealed inadequate suppression of Th17 responses by anti-TNF in nonresponders, and direct targeting of the USF2-signaling pathway may be a potential therapeutic approach in the anti-TNF refractory RA.

Mutations in NHE6 (also termed SLC9A6) cause the X-linked neurological disorder Christianson syndrome (CS) in males. The purpose of this study was to examine the phenotypic spectrum of female carriers of NHE6 mutations. Twenty female carriers from 9 pedigrees were enrolled, ranging from approximately age 2 to 65. A subset of female carriers was assessed using standardized neuropsychological measures. Also, the association of NHE6 expression with markers of brain age was evaluated using 740 participants in the Religious Orders Study (ROS) and Rush Memory and Aging Project (MAP). A majority, but not all, female carriers demonstrated a deficit in at least one neurocognitive domain (85%). A recognizable neuropsychological profile emerged, revealing impairments in visuospatial function, attention, and executive function. Common neuropsychiatric diagnoses included: intellectual disability/developmental delay (20%), learning difficulties (31%), speech/language delays (30%), and attention-deficit/hyperactivity disorder (20%). Notable neurological diagnoses in aging CS female carriers include corticobasal degeneration and atypical parkinsonism. In postmortem brains from the ROS/MAP dataset of normal and pathological aging, decreased NHE6 expression was correlated with greater tau deposition. Our study provides an examination of the phenotypic range in female carriers of NHE6 mutations. The findings indicate that NHE6-related disease in females represents a new neurogenetic condition.

PURPOSE The availability of increasing volumes of multiomics, imaging, and clinical data in complex diseases such as cancer opens opportunities for the formulation and development of computational imaging genomics methods that can link multiomics, imaging, and clinical data. METHODS Here, we present the Imaging-AMARETTO algorithms and software tools to systematically interrogate regulatory networks derived from multiomics data within and across related patient studies for their relevance to radiography and histopathology imaging features predicting clinical outcomes. RESULTS To demonstrate its utility, we applied Imaging-AMARETTO to integrate three patient studies of brain tumors, specifically, multiomics with radiography imaging data from The Cancer Genome Atlas (TCGA) glioblastoma multiforme (GBM) and low-grade glioma (LGG) cohorts and transcriptomics with histopathology imaging data from the Ivy Glioblastoma Atlas Project (IvyGAP) GBM cohort. Our results show that Imaging-AMARETTO recapitulates known key drivers of tumor-associated microglia and macrophage mechanisms, mediated by STAT3, AHR, and CCR2, and neurodevelopmental and stemness mechanisms, mediated by OLIG2. Imaging-AMARETTO provides interpretation of their underlying molecular mechanisms in light of imaging biomarkers of clinical outcomes and uncovers novel master drivers, THBS1 and MAP2, that establish relationships across these distinct mechanisms. CONCLUSION Our network-based imaging genomics tools serve as hypothesis generators that facilitate the interrogation of known and uncovering of novel hypotheses for follow-up with experimental validation studies. We anticipate that our Imaging-AMARETTO imaging genomics tools will be useful to the community of biomedical researchers for applications to similar studies of cancer and other complex diseases with available multiomics, imaging, and clinical data.

Deep exome resequencing is a powerful approach for delineating patterns of protein-coding variation among genes, pathways, individuals and populations. We analyzed exome data from 2,440 individuals of European and African ancestry as part of the National Heart, Lung, and Blood Institute's Exome Project, the aim of which is to discover novel genes and mechanisms that contribute to heart, lung and blood disorders. Each exome was sequenced to a mean coverage of 116×, allowing detailed inferences about the population genomic patterns of both common variation and rare coding variation. We identifi ed more than 500,000 single nucleotide variations, the majority of which were novel and rare (76% of variants had a minor allele frequency of less than 0.1%), refl ecting the recent dramatic increase in the size of the human population. The unprecedented magnitude of this dataset allowed us to rigorously characterize the large variation in nucleotide diversity among genes (ranging from 0 to 1.32%), as well as the role of positive and purifying selection in shaping patterns of proteincoding variation and the diff erential signatures of population structure from rare and common variation. This dataset provides a framework for personal genomics and is an important resource that will allow inferences of broad importance to human evolution and health.

Hepatocellular carcinoma (HCC) is a leading cause of death among cirrhotic patients, for which chemopreventive strategies are lacking. Recently, we developed a simple human cell-based system modeling a clinical prognostic liver signature (PLS) predicting liver disease progression and HCC risk. In a previous study, we applied our cell-based system for drug discovery and identified captopril, an approved angiotensin converting enzyme (ACE) inhibitor, as a candidate compound for HCC chemoprevention. Here, we explored ACE as a therapeutic target for HCC chemoprevention. Captopril reduced liver fibrosis and effectively prevented liver disease progression toward HCC development in a diethylnitrosamine (DEN) rat cirrhosis model and a diet-based rat model for nonalcoholic steatohepatitis–induced (NASH-induced) hepatocarcinogenesis. RNA-Seq analysis of cirrhotic rat liver tissues uncovered that captopril suppressed the expression of pathways mediating fibrogenesis, inflammation, and carcinogenesis, including epidermal growth factor receptor (EGFR) signaling. Mechanistic data in liver disease models uncovered a cross-activation of the EGFR pathway by angiotensin. Corroborating the clinical translatability of the approach, captopril significantly reversed the HCC high-risk status of the PLS in liver tissues of patients with advanced fibrosis. Captopril effectively prevents fibrotic liver disease progression toward HCC development in preclinical models and is a generic and safe candidate drug for HCC chemoprevention.

Chronic hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC) world-wide. The molecular mechanisms of viral hepatocarcinogenesis are still partially understood. Here, we applied two complementary single-cell RNA-sequencing protocols to investigate HBV–HCC host cell interactions at the single cell level of patient-derived HCC. Computational analyses revealed a marked HCC heterogeneity with a robust and significant correlation between HBV reads and cancer cell differentiation. Viral reads significantly correlated with the expression of HBV-dependency factors such as HLF in different tumor compartments. Analyses of virus-induced host responses identified previously undiscovered pathways mediating viral carcinogenesis, such as E2F- and MYC targets as well as adipogenesis. Mapping of fused HBV–host cell transcripts allowed the characterization of integration sites in individual cancer cells. Collectively, single-cell RNA-Seq unravels heterogeneity and compartmentalization of both, virus and cancer identifying new candidate pathways for viral hepatocarcinogenesis. The perturbation of pro-carcinogenic gene expression even at low HBV levels highlights the need of HBV cure to eliminate HCC risk.