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During mammalian brain development, neural progenitor cells proliferate extensively but can ensure the production of correct numbers of various types of mature cells by balancing symmetric proliferative versus asymmetric differentiative cell divisions. This process of cell fate determination may be harnessed for developing cancer therapy. Here, we test this idea by targeting KIF20A, a mitotic kinesin crucial for the control of cell division modes, in a genetic model of medulloblastoma (MB) and human MB cells. Inducible Kif20a knockout in both normal and MB-initiating granule neuron progenitors (GNPs) causes early cell cycle exit and precocious neuronal differentiation without causing cytokinesis failure and suppresses the development of Sonic Hedgehog (SHH)-activated MB. Inducible KIF20A knockdown in human MB cells inhibits proliferation both in cultures and in growing tumors. Our results indicate that targeting the fate specification process of nascent daughter cells presents a novel avenue for developing anti-proliferation treatment for malignant brain tumors., Runxiang Qiu et al find that conditional knockout of Kif20a, a regulator of cytokinesis and neural progenitor cell fate, induces early cell cycle exit and precocious neuronal differentiation of cerebellar granule neuron progenitors. They show that Kif20a depletion suppresses tumour formation in genetic and xenograft mouse models of medulloblastoma, indicating the value of targeting daughter cell fate specification.

BackgroundRecent genomic studies have shed light on the biology and inter-tumoral heterogeneity underlying pineal parenchymal tumors, in particular pineoblastomas (PBs) and pineal parenchymal tumors of intermediate differentiation (PPTIDs). Previous reports, however, had modest sample sizes and lacked power to integrate molecular and clinical findings. The different proposed subgroup structures also highlighted a need to reach consensus on a robust and relevant classification system.MethodsWe performed a meta-analysis on 221 patients with molecularly characterized PBs and PPTIDs. DNA methylation profiles were analyzed through complementary bioinformatic approaches and molecular subgrouping was harmonized. Demographic, clinical and genomic features of patients and samples from these pineal tumor subgroups were annotated.FindingsFour clinically and biologically relevant consensus PB subgroups were defined: PB-miRNA1 (n=96), PB-miRNA2 (n=23), PB-MYC/FOXR2 (n=34) and PB-RB1 (n=25); with PPTID (n=43) remaining as a molecularly distinct entity. Genomic and transcriptomic profiling allowed the characterization of oncogenic drivers for individual subgroups, specifically, alterations in the microRNA processing pathway in PB-miRNA1/2, MYC amplification and FOXR2 overexpression in PB-MYC/FOXR2, RB1 alteration in PB-RB1, and KBTBD4 insertion in PPTID. Age at diagnosis, sex predilection and metastatic status varied significantly among tumor subgroups. While patients with PB-miRNA2 and PPTID had superior outcome, survival was intermediate for patients with PB-miRNA1, and dismal for those with PB-MYC/FOXR2 and PB-RB1.InterpretationWe systematically interrogated the clinical and molecular heterogeneity within pineal parenchymal tumors and proposed a consensus nomenclature for disease subgroups, laying the groundwork for future studies as well as routine use in tumor classification.

Pediatric high-grade glioma (pHGG) is a major contributor to cancer-related death in children. In vitro and in vivo disease models reflecting the intimate connection between developmental context and pathogenesis of pHGG are essential to advance understanding and identify therapeutic vulnerabilities. Here we report establishment of 21 patient-derived pHGG orthotopic xenograft (PDOX) models and eight matched cell lines from diverse groups of pHGG. These models recapitulate histopathology, DNA methylation signatures, mutations and gene expression patterns of the patient tumors from which they were derived, and include rare subgroups not well-represented by existing models. We deploy 16 new and existing cell lines for high-throughput screening (HTS). In vitro HTS results predict variable in vivo response to PI3K/mTOR and MEK pathway inhibitors. These unique new models and an online interactive data portal for exploration of associated detailed molecular characterization and HTS chemical sensitivity data provide a rich resource for pediatric brain tumor research., Patient-derived xenografts provide a resource for basic and translational cancer research. Here, the authors generate multiple pediatric high-grade glioma xenografts, use omics technologies to show that they are representative of primary tumours and use them to assess therapeutic response.

Cancer genomics has illuminated a wide spectrum of genes and core molecular processes contributing to human malignancy. Still, the genetic and molecular basis of many cancers remains only partially explained. Genetic predisposition accounts for 5-10% of cancer diagnoses(1,2) and genetic events cooperating with known somatic driver events are poorly understood. Analyzing established cancer predisposition genes in medulloblastoma (MB), a malignant childhood brain tumor, we recently identified pathogenic germline variants that account for 5% of all MB patients(3). Here, by extending our previous analysis to include all protein-coding genes, we discovered and replicated rare germline loss-of-function (LoF) variants across Elongator Complex Protein 1 (ELP1) on 9q31.3 in 15% of pediatric MB(SHH) cases, thus implicating ELP1 as the most common MB predisposition gene and increasing genetic predisposition to 40% for pediatric MB(SHH). Inheritance was verified based on parent-offspring and pedigree analysis, which identified two families with a history of pediatric MB. ELP1-associated MBs were restricted to the molecular SHHα subtype(4) and were characterized by universal biallelic inactivation of ELP1 due to somatic loss of chromosome 9q. The majority of ELP1-associated MBs exhibited co-occurring somatic PTCH1 (9q22.32) alterations, suggesting that ELP1-deficiency predisposes to tumor development in combination with constitutive activation of SHH signaling. ELP1 is an essential subunit of the evolutionary conserved Elongator complex, whose primary function is to enable efficient translational elongation through tRNA modifications at the wobble (U(34)) position(5,6). Biochemical, transcriptional, and proteomic analyses revealed that ELP1-associated MB(SHH) are characterized by a destabilized core Elongator complex, loss of Elongator-dependent tRNA wobble modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response (UPR), consistent with deregulation of protein homeostasis due to Elongator-deficiency in model systems(7–9). Our findings suggest that genetic predisposition to proteome instability is a previously underappreciated determinant in the pathogenesis of pediatric brain cancer. These results provide a strong rationale for further investigating the role of protein homeostasis in other cancer types and potential opportunities for novel therapeutic interference.

Medulloblastoma is a malignant childhood brain tumor arising from the developing cerebellum. In Sonic Hedgehog (SHH) subgroup medulloblastoma, aberrant activation of SHH signaling causes increased proliferation of granule neuron progenitors (GNPs), and predisposes these cells to tumorigenesis. A second, cooperating genetic hit is often required to push these hyperplastic cells to malignancy and confer mutation-specific characteristics associated with oncogenic signaling. Somatic loss-of-function mutations of the transcriptional corepressor BCOR are recurrent and enriched in SHH medulloblastoma. To investigate BCOR as a putative tumor suppressor, we used a genetically engineered mouse model to delete exons 9/10 of Bcor (BcorΔE9–10) in GNPs during development. This mutation leads to reduced expression of C-terminally truncated BCOR (BCORΔE9–10). While BcorΔE9–10 alone did not promote tumorigenesis or affect GNP differentiation, BcorΔE9–10 combined with loss of the SHH receptor gene Ptch1 resulted in fully penetrant medulloblastomas. In Ptch1+/−;BcorΔE9–10 tumors, the growth factor gene Igf2 was aberrantly up-regulated, and ectopic Igf2 overexpression was sufficient to drive tumorigenesis in Ptch1+/− GNPs. BCOR directly regulates Igf2, likely through the PRC1.1 complex; the repressive histone mark H2AK119Ub is decreased at the Igf2 promoter in Ptch1+/−;BcorΔE9–10 tumors. Overall, our data suggests that BCOR–PRC1.1 disruption leads to Igf2 overexpression, which transforms preneoplastic cells to malignant tumors.

Life may have begun in an RNA world, which is supported by increasing evidence of the vital role that RNAs perform in biological systems. In the human genome, most genes actually do not encode proteins; they are noncoding RNA genes. The largest class of noncoding genes is known as long noncoding RNAs (lncRNAs), which are transcripts greater in length than 200 nucleotides, but with no protein-coding capacity. While some lncRNAs have been demonstrated to be key regulators of gene expression and 3D genome organization, most lncRNAs are still uncharacterized. We thus propose several data mining and machine learning approaches for the functional annotation of human lncRNAs by leveraging the vast amount of data from genetic and genomic studies. Recent results from our studies and those of other groups indicate that genomic data mining can give insights into lncRNA functions and provide valuable information for experimental studies of candidate lncRNAs associated with human disease.

Genetic studies have identified many risk loci for autism spectrum disorder (ASD) although causal factors in the majority of cases are still unknown. Currently, known ASD risk genes are all protein-coding genes; however, the vast majority of transcripts in humans are non-coding RNAs (ncRNAs) which do not encode proteins. Recently, long non-coding RNAs (lncRNAs) were shown to be highly expressed in the human brain and crucial for normal brain development. We have constructed a computational pipeline for the integration of various genomic datasets to identify lncRNAs associated with ASD. This pipeline utilizes differential gene expression patterns in affected tissues in conjunction with gene co-expression networks in tissue-matched non-affected samples. We analyzed RNA-seq data from the cortical brain tissues from ASD cases and controls to identify lncRNAs differentially expressed in ASD. We derived a gene co-expression network from an independent human brain developmental transcriptome and detected a convergence of the differentially expressed lncRNAs and known ASD risk genes into specific co-expression modules. Co-expression network analysis facilitates the discovery of associations between previously uncharacterized lncRNAs with known ASD risk genes, affected molecular pathways and at-risk developmental time points. In addition, we show that some of these lncRNAs have a high degree of overlap with major CNVs detected in ASD genetic studies. By utilizing this integrative approach comprised of differential expression analysis in affected tissues and connectivity metrics from a developmental co-expression network, we have prioritized a set of candidate ASD-associated lncRNAs. The identification of lncRNAs as novel ASD susceptibility genes could help explain the genetic pathogenesis of ASD.

Long non-coding RNAs (lncRNAs), each with >200 nucleotides in length, constitute a large portion of the human transcriptome. Although recent studies indicate that lncRNAs play key roles in gene regulation, development and disease, the RNA functional motifs are still poorly understood. Most of the existing algorithms for motif finding are severely limited in scalability with regards to sequence and motif size. In this study, we propose a novel genetic algorithm for discriminative motif identification capable of handling large input sequences and motif sizes by utilizing genetic operators to learn and evolve in response to the input sequences. We utilize our method on long non-coding RNA (lncRNA) transcripts as a test case to identify functional motifs associated with subcellular localization. Our methodology shows high accuracy and the ability to identify functional motifs associated with subcellular localization in lncRNAs, which recapitulates a previous experimental study.