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RNA-sequencing data is widely used to identify disease biomarkers and therapeutic targets using numerical methods such as clustering, classification, regression, and differential expression analysis. Such approaches rely on the assumption that mRNA abundance estimates from RNA-seq are reliable estimates of true expression levels. Here, using data from five RNA-seq processing pipelines applied to 6,690 human tumor and normal tissues, we show that nearly 88% of protein-coding genes have similar gene expression profiles across all pipelines. However, for >12% of protein-coding genes, current best-in-class RNA-seq processing pipelines differ in their abundance estimates by more than four-fold when applied to exactly the same samples and the same set of RNA-seq reads. Expression fold changes are similarly affected. Many of the impacted genes are widely studied disease-associated genes. We show that impacted genes exhibit diverse patterns of discordance among pipelines, suggesting that many inter-pipeline differences contribute to overall uncertainty in mRNA abundance estimates. A concerted, community-wide effort will be needed to develop gold-standards for estimating the mRNA abundance of the discordant genes reported here. In the meantime, our list of discordantly evaluated genes provides an important resource for robust marker discovery and target selection.

A comprehensive molecular analysis of almost 1,000 pediatric subjects with acute myeloid leukemia (AML) uncovers widespread differences in pediatric AML as compared to adult AML, including a higher frequency of structural variants and different mutational patterns and epigenetic signatures. Future studies are needed to characterize the functional relevance of these alterations and to explore age-tailored therapies to improve disease control in younger patients. We present the molecular landscape of pediatric acute myeloid leukemia (AML) and characterize nearly 1,000 participants in Children's Oncology Group (COG) AML trials. The COG–National Cancer Institute (NCI) TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA and microRNA sequencing and CpG methylation profiling. Validated DNA variants corresponded to diverse, infrequent mutations, with fewer than 40 genes mutated in >2% of cases. In contrast, somatic structural variants, including new gene fusions and focal deletions of MBNL1 , ZEB2 and ELF1 , were disproportionately prevalent in young individuals as compared to adults. Conversely, mutations in DNMT3A and TP53 , which were common in adults, were conspicuously absent from virtually all pediatric cases. New mutations in GATA2 , FLT3 and CBL and recurrent mutations in MYC -ITD, NRAS , KRAS and WT1 were frequent in pediatric AML. Deletions, mutations and promoter DNA hypermethylation convergently impacted Wnt signaling, Polycomb repression, innate immune cell interactions and a cluster of zinc finger–encoding genes associated with KMT2A rearrangements. These results highlight the need for and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.

Independent scientific achievements have led to the discovery of aberrant splicing patterns in oncogenesis, while more recent advances have uncovered novel gene fusions involving neurotrophic tyrosine receptor kinases (NTRKs) in gliomas. The exploration of NTRK splice variants in normal and neoplastic brain provides an intersection of these two rapidly evolving fields. Tropomyosin receptor kinase B (TrkB), encoded NTRK2 , is known for critical roles in neuronal survival, differentiation, molecular properties associated with memory, and exhibits intricate splicing patterns and post-translational modifications. Here, we show a role for a truncated NTRK2 splice variant, TrkB.T1, in human glioma. TrkB.T1 enhances PDGF-driven gliomas in vivo, augments PDGF-induced Akt and STAT3 signaling in vitro, while next generation sequencing broadly implicates TrkB.T1 in the PI3K signaling cascades in a ligand-independent fashion. These TrkB.T1 findings highlight the importance of expanding upon whole gene and gene fusion analyses to include splice variants in basic and translational neuro-oncology research. Tropomyosin receptor kinase B (TrkB), encoded by the neurotrophic tyrosine receptor kinase 2 ( NTRK2 ) gene, exhibits intricate splicing patterns and post-translational modifications. Here, the authors perform whole gene and transcript-level analyses and report the TrkB.T1 splice variant enhances PDGF-driven gliomas in vivo and augments PI3K signaling cascades in vitro.

Single‐cell RNA sequencing has emerged as a powerful tool for resolving cellular states associated with normal and maligned developmental processes. Here, we used scRNA‐seq to examine the cell cycle states of expanding human neural stem cells (hNSCs). From these data, we constructed a cell cycle classifier that identifies traditional cell cycle phases and a putative quiescent‐like state in neuroepithelial‐derived cell types during mammalian neurogenesis and in gliomas. The Neural G0 markers are enriched with quiescent NSC genes and other neurodevelopmental markers found in non‐dividing neural progenitors. Putative glioblastoma stem‐like cells were significantly enriched in the Neural G0 cell population. Neural G0 cell populations and gene expression are significantly associated with less aggressive tumors and extended patient survival for gliomas. Genetic screens to identify modulators of Neural G0 revealed that knockout of genes associated with the Hippo/Yap and p53 pathways diminished Neural G0 in vitro, resulting in faster G1 transit, down‐regulation of quiescence‐associated markers, and loss of Neural G0 gene expression. Thus, Neural G0 represents a dynamic quiescent‐like state found in neuroepithelial‐derived cells and gliomas. SYNOPSIS scRNA‐seq and functional genomics are applied to human neural stem cells to characterize cell cycle phases and factors increasing proliferative rate. A new cell cycle state “Neural G0” is discovered, offering insights into glioma patient tumors and patient survival. Unsupervised cell clustering reveals eight distinct cell cycle phases in human neural stem cells. A novel Neural G0 cell cycle phase is characterized by up‐regulation of genes with key roles in neural development. Increased expression of a GBM‐specific Neural G0 gene signature is associated with better prognosis for glioma patients, independent of tumor grade and IDH1/2 mutation. A functional‐genomics screen identifies genes that alter the time spent in G0/G1‐like states thereby increasing the proliferative rate of neural stem cells. Graphical Abstract scRNA‐seq and functional genomics are applied to human neural stem cells to characterize cell cycle phases and factors increasing proliferative rate. A new cell cycle state “Neural G0” is discovered, offering insights into glioma patient tumors and patient survival.

Recent updating of the World Health Organization (WHO) classification of central nervous system (CNS) tumors in 2016 demonstrates the first organized effort to restructure brain tumor classification by incorporating histomorphologic features with recurrent molecular alterations. Revised CNS tumor diagnostic criteria also attempt to reduce interobserver variability of histological interpretation and provide more accurate stratification related to clinical outcome. As an example, diffuse gliomas (WHO grades II–IV) are now molecularly stratified based upon isocitrate dehydrogenase 1 or 2 (IDH) mutational status, with gliomas of WHO grades II and III being substratified according to 1p/19q codeletion status. For now, grading of diffuse gliomas is still dependent upon histological parameters. Independent of WHO classification criteria, multidimensional scaling analysis of molecular signatures for diffuse gliomas from The Cancer Genome Atlas (TCGA) has identified distinct molecular subgroups, and allows for their visualization in 2-dimensional (2D) space. Using the web-based platform Oncoscape as a tool, we applied multidimensional scaling-derived molecular groups to the 2D visualization of the 2016 WHO classification of diffuse gliomas. Here we show that molecular multidimensional scaling of TCGA data provides 2D clustering that represents the 2016 WHO classification of diffuse gliomas. Additionally, we used this platform to successfully identify and define novel copy-number alteration-based molecular subtypes, which are independent of WHO grading, as well as predictive of clinical outcome. The prognostic utility of these molecular subtypes was further validated using an independent data set of the German Glioma Network prospective glioblastoma patient cohort.

High levels of the inflammatory cytokine IL-6 in the bone marrow are associated with poor outcomes in pediatric acute myeloid leukemia (pAML), but its etiology remains unknown. Using RNA-seq data from pre-treatment bone marrows of 1489 children with pAML, we show that > 20% of patients have concurrent IL-6, IL-1, IFNα/β, and TNFα signaling activity and poorer outcomes. Targeted sequencing of pre-treatment bone marrow samples from affected patients ( n  = 181) revealed 5 highly recurrent patterns of somatic mutation. Using differential expression analyses of the most common genomic subtypes (~60% of total), we identify high expression of multiple potential drivers of inflammation-related treatment resistance. Regardless of genomic subtype, we show that JAK1/2 inhibition reduces receptor-mediated inflammatory signaling by leukemic cells in-vitro. The large number of high-risk pAML genomic subtypes presents an obstacle to the development of mutation-specific therapies. Our findings suggest that therapies targeting inflammatory signaling may be effective across multiple genomic subtypes of pAML. IL6 expression in the bone marrow is associated with reduced survival in paediatric AML. Here, the authors used RNA-seq to identify treatment resistance-associated co-occurring inflammatory signalling in leukemic cells.

Infant Acute Myeloid Leukemia (AML) is a poorly-addressed, heterogeneous malignancy distinguished by surprisingly few mutations per patient but accompanied by myriad age-specific translocations. These characteristics make treatment of infant AML challenging. While infant AML is a relatively rare disease, it has enormous impact on families, and in terms of life-years-lost and life limiting morbidities. To better understand the mechanisms that drive infant AML, we performed integrative analyses of genome-wide mRNA, miRNA, and DNA-methylation data in diagnosis-stage patient samples. Here, we report the activation of an onco-fetal B-cell developmental gene regulatory network in infant AML. AML in infants is genomically distinct from AML in older children/adults in that it has more structural genomic aberrations and fewer mutations. Differential expression analysis of ~1500 pediatric AML samples revealed a large number of infant-specific genes, many of which are associated with B cell development and function. 18 of these genes form a well-studied B-cell gene regulatory network that includes the epigenetic regulators BRD4 and POU2AF1 , and their onco-fetal targets LIN28B and IGF2BP3 . All four genes are hypo-methylated in infant AML. Moreover, micro-RNA Let7a-2 is expressed in a mutually exclusive manner with its target and regulator LIN28B . These findings suggest infant AML may respond to bromodomain inhibitors and immune therapies targeting CD19, CD20, CD22, and CD79A.

Failure to clear antigens causes CD8 + T cells to become increasingly hypo-functional, a state known as exhaustion. We combined manually extracted information from published literature with gene expression data from diverse model systems to infer a set of molecular regulatory interactions that underpin exhaustion. Topological analysis and simulation modeling of the network suggests CD8 + T cells undergo 2 major transitions in state following stimulation. The time cells spend in the earlier pro-memory/proliferative (PP) state is a fixed and inherent property of the network structure. Transition to the second state is necessary for exhaustion. Combining insights from network topology analysis and simulation modeling, we predict the extent to which each node in our network drives cells towards an exhausted state. We demonstrate the utility of our approach by experimentally testing the prediction that drug-induced interference with EZH2 function increases the proportion of pro-memory/proliferative cells in the early days post-activation.

Different experimental technologies measure different aspects of a system and to differing depth and breadth. High-throughput assays have inherently high false-positive and false-negative rates. Moreover, each technology includes systematic biases of a different nature. These differences make network reconstruction from multiple data sets difficult and error-prone. Additionally, because of the rapid rate of progress in biotechnology, there is usually no curated exemplar data set from which one might estimate data integration parameters. To address these concerns, we have developed data integration methods that can handle multiple data sets differing in statistical power, type, size, and network coverage without requiring a curated training data set. Our methodology is general in purpose and may be applied to integrate data from any existing and future technologies. Here we outline our methods and then demonstrate their performance by applying them to simulated data sets. The results show that these methods select true-positive data elements much more accurately than classical approaches. In an accompanying companion paper, we demonstrate the applicability of our approach to biological data. We have integrated our methodology into a free open source software package named POINTILLIST.