PRDM6 promotes medulloblastoma by repressing chromatin accessibility and altering gene expression

SNCAIP duplication may promote Group 4 medulloblastoma via induction of PRDM6, a poorly characterized member of the PRDF1 and RIZ1 homology domain-containing (PRDM) family of transcription factors. Here, we investigated the function of PRDM6 in human hindbrain neuroepithelial stem cells and tested PRDM6 as a driver of Group 4 medulloblastoma. We report that human PRDM6 localizes predominantly to the nucleus, where it causes widespread repression of chromatin accessibility and complex alterations of gene expression patterns. Genome-wide mapping of PRDM6 binding reveals that PRDM6 binds to chromatin regions marked by histone H3 lysine 27 trimethylation that are located within, or proximal to, genes. Moreover, we show that PRDM6 expression in neuroepithelial stem cells promotes medulloblastoma. Surprisingly, medulloblastomas derived from PRDM6-expressing neuroepithelial stem cells match human Group 3, but not Group 4, medulloblastoma. We conclude that PRDM6 expression has oncogenic potential but is insufficient to drive Group 4 medulloblastoma from neuroepithelial stem cells. We propose that both PRDM6 and additional factors, such as specific cell-of-origin features, are required for Group 4 medulloblastoma. Given the lack of PRDM6 expression in normal tissues and its oncogenic potential shown here, we suggest that PRDM6 inhibition may have therapeutic value in PRDM6-expressing medulloblastomas.

Unified rhombic lip origins of group 3 and group 4 medulloblastoma

Medulloblastoma, a malignant childhood cerebellar tumour, segregates molecularly into biologically distinct subgroups, suggesting that a personalized approach to therapy would be beneficial1. Mouse modelling and cross-species genomics have provided increasing evidence of discrete, subgroup-specific developmental origins2. However, the anatomical and cellular complexity of developing human tissues3-particularly within the rhombic lip germinal zone, which produces all glutamatergic neuronal lineages before internalization into the cerebellar nodulus-makes it difficult to validate previous inferences that were derived from studies in mice. Here we use multi-omics to resolve the origins of medulloblastoma subgroups in the developing human cerebellum. Molecular signatures encoded within a human rhombic-lip-derived lineage trajectory aligned with photoreceptor and unipolar brush cell expression profiles that are maintained in group 3 and group 4 medulloblastoma, suggesting a convergent basis. A systematic diagnostic-imaging review of a prospective institutional cohort localized the putative anatomical origins of group 3 and group 4 tumours to the nodulus. Our results connect the molecular and phenotypic features of clinically challenging medulloblastoma subgroups to their unified beginnings in the rhombic lip in the early stages of human development.

A genetic mouse model with postnatal Nf1 and p53 loss recapitulates the histology and transcriptome of human malignant peripheral nerve sheath tumor

BACKGROUND

Malignant peripheral nerve sheath tumors (MPNST) are aggressive sarcomas. Somatic inactivation of NF1 and cooperating tumor suppressors, including CDKN2A/B, PRC2, and p53, is found in most MPNST. Inactivation of LATS1/2 of the Hippo pathway was recently shown to cause tumors resembling MPNST histologically, although Hippo pathway mutations are rarely found in MPNST. Because existing genetically engineered mouse (GEM) models of MPNST do not recapitulate some of the key genetic features of human MPNST, we aimed to establish a GEM-MPNST model that recapitulated the human disease genetically, histologically, and molecularly.

METHODS

We combined 2 genetically modified alleles, an Nf1;Trp53 cis-conditional allele and an inducible Plp-CreER allele (NP-Plp), to model the somatic, possibly postnatal, mutational events in human MPNST. We also generated conditional Lats1;Lats2 knockout mice. We performed histopathologic analyses of mouse MPNST models and transcriptomic comparison of mouse models and human nerve sheath tumors.

RESULTS

Postnatal Nf1;Trp53 cis-deletion resulted in GEM-MPNST that were histologically more similar to human MPNST than the widely used germline Nf1;Trp53 cis-heterozygous (NPcis) model and showed partial loss of H3K27me3. At the transcriptome level, Nf1;p53-driven GEM-MPNST were distinct from Lats-driven GEM-MPNST and resembled human MPNST more closely than do Lats-driven tumors.

CONCLUSIONS

The NP-Plp model recapitulates human MPNST genetically, histologically, and molecularly.

MBCL-21. GERMLINE ELONGATOR MUTATIONS IN SONIC HEDGEHOG MEDULLOBLASTOMA

BACKGROUND

Our previous analysis of established cancer predisposition genes in medulloblastoma (MB) identified pathogenic germline variants in ~5% of all patients. Here, we extended our analysis to include all protein-coding genes.

METHODS

Case-control analysis performed on 795 MB patients against >118,000 cancer-free children and adults was performed to identify an association between rare germline variants and MB.

RESULTS

Germline loss-of-function variants of Elongator Complex Protein 1 (ELP1; 9q31.3) were strongly associated with SHH subgroup (MB_SHH). ELP1-associated-MBs accounted for ~15% (29/202) of pediatric MB_SHH cases and were restricted to the SHHα subtype. ELP1-associated-MBs demonstrated biallelic inactivation of ELP1 due to somatic chromosome 9q loss and most tumors exhibited co-occurring somatic PTCH1 (9q22.32) alterations. Inheritance was verified by parent-offspring sequencing (n=3) and pedigree analysis identified two families with a history of pediatric MB. ELP1-associated-MB_SHH were characterized by desmoplastic/nodular histology (76%; 13/17) and demonstrated a favorable clinical outcome when compared to TP53-associated-MB_SHH (5-yr OS 92% vs 20%; p-value=1.3e-6) despite both belonging to the SHHα subtype. ELP1 is a subunit of the Elongator complex, that promotes efficient translational elongation through tRNA modifications at the wobble (U₃₄) position. Biochemical, transcriptional, and proteomic analyses revealed ELP1-associated-MBs exhibit destabilization of the core Elongator complex, loss of tRNA wobble modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response.

CONCLUSIONS

We identified ELP1 as the most common MB predisposition gene, increasing the total genetic predisposition for pediatric MB_SHH to 40%. These results mark MB_SHH as an overwhelmingly genetically-predisposed disease and implicate disruption of protein homeostasis in MB_SHH development.

Functional loss of a noncanonical BCOR-PRC1.1 complex accelerates SHH-driven medulloblastoma formation

In this study, Kutscher et al. investigated the transcriptional corepressor BCOR as a putative tumor suppressor and used a genetically engineered mouse model to delete exons 9/10 of Bcor in GNPs during development. Their data suggest that BCOR–PRC1.1 disruption leads to Igf2 overexpression, which transforms preneoplastic cells to malignant tumors.

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 (Bcor^ΔE9–10) in GNPs during development. This mutation leads to reduced expression of C-terminally truncated BCOR (BCOR^ΔE9–10). While Bcor^ΔE9–10 alone did not promote tumorigenesis or affect GNP differentiation, Bcor^ΔE9–10 combined with loss of the SHH receptor gene Ptch1 resulted in fully penetrant medulloblastomas. In Ptch1^+/−;Bcor^ΔE9–10 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^+/−;Bcor^ΔE9–10 tumors. Overall, our data suggests that BCOR–PRC1.1 disruption leads to Igf2 overexpression, which transforms preneoplastic cells to malignant tumors.

Germline Elongator mutations in Sonic Hedgehog medulloblastoma

Cancer genomics has revealed many genes and core molecular processes that contribute to human malignancies, but the genetic and molecular bases of many rare cancers remains unclear. Genetic predisposition accounts for 5 to 10% of cancer diagnoses in children1,2, and genetic events that cooperate with known somatic driver events are poorly understood. Pathogenic germline variants in established cancer predisposition genes have been recently identified in 5% of patients with the malignant brain tumour medulloblastoma3. Here, by analysing all protein-coding genes, we identify and replicate rare germline loss-of-function variants across ELP1 in 14% of paediatric patients with the medulloblastoma subgroup Sonic Hedgehog (MBSHH). ELP1 was the most common medulloblastoma predisposition gene and increased the prevalence of genetic predisposition to 40% among paediatric patients with MBSHH. Parent-offspring and pedigree analyses identified two families with a history of paediatric medulloblastoma. ELP1-associated medulloblastomas were restricted to the molecular SHHα subtype4 and characterized by universal biallelic inactivation of ELP1 owing to somatic loss of chromosome arm 9q. Most ELP1-associated medulloblastomas also exhibited somatic alterations in PTCH1, which suggests that germline ELP1 loss-of-function variants predispose individuals to tumour development in combination with constitutive activation of SHH signalling. ELP1 is the largest subunit of the evolutionarily conserved Elongator complex, which catalyses translational elongation through tRNA modifications at the wobble (U34) position5,6. Tumours from patients with ELP1-associated MBSHH were characterized by a destabilized Elongator complex, loss of Elongator-dependent tRNA modifications, codon-dependent translational reprogramming, and induction of the unfolded protein response, consistent with loss of protein homeostasis due to Elongator deficiency in model systems7-9. Thus, genetic predisposition to proteome instability may be a determinant in the pathogenesis of paediatric brain cancers. These results support investigation of the role of protein homeostasis in other cancer types and potential for therapeutic interference.

Genomic data mining for functional annotation of human long noncoding RNAs

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.

Prediction of LncRNA Subcellular Localization with Deep Learning from Sequence Features

Long non-coding RNAs are involved in biological processes throughout the cell including the nucleus, chromatin and cytosol. However, most lncRNAs remain unannotated and functional annotation of lncRNAs is difficult due to their low conservation and their tissue and developmentally specific expression. LncRNA subcellular localization is highly informative regarding its biological function, although it is difficult to discover because few prediction methods currently exist. While protein subcellular localization prediction is a well-established research field, lncRNA localization prediction is a novel research problem. We developed DeepLncRNA, a deep learning algorithm which predicts lncRNA subcellular localization directly from lncRNA transcript sequences. We analyzed 93 strand-specific RNA-seq samples of nuclear and cytosolic fractions from multiple cell types to identify differentially localized lncRNAs. We then extracted sequence-based features from the lncRNAs to construct our DeepLncRNA model, which achieved an accuracy of 72.4%, sensitivity of 83%, specificity of 62.4% and area under the receiver operating characteristic curve of 0.787. Our results suggest that primary sequence motifs are a major driving force in the subcellular localization of lncRNAs.

Integrative genomic analyses for identification and prioritization of long non-coding RNAs associated with autism

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.

Gene Coexpression Networks in Human Brain Developmental Transcriptomes Implicate the Association of Long Noncoding RNAs with Intellectual Disability

The advent of next-generation sequencing for genetic diagnoses of complex developmental disorders, such as intellectual disability (ID), has facilitated the identification of hundreds of predisposing genetic variants. However, there still exists a vast gap in our knowledge of causal genetic factors for ID as evidenced by low diagnostic yield of genetic screening, in which identifiable genetic causes are not found for the majority of ID cases. Most methods of genetic screening focus on protein-coding genes; however, noncoding RNAs may outnumber protein-coding genes and play important roles in brain development. Long noncoding RNAs (lncRNAs) specifically have been shown to be enriched in the brain and have diverse roles in gene regulation at the transcriptional and posttranscriptional levels. LncRNAs are a vastly uncharacterized group of noncoding genes, which could function in brain development and harbor ID-predisposing genetic variants. We analyzed lncRNAs for coexpression with known ID genes and affected biological pathways within a weighted gene coexpression network derived from RNA-sequencing data spanning human brain development. Several ID-associated gene modules were found to be enriched for lncRNAs, known ID genes, and affected biological pathways. Utilizing a list of de novo and pathogenic copy number variants detected in ID probands, we identified lncRNAs overlapping these genetic structural variants. By integrating our results, we have made a prioritized list of potential ID-associated lncRNAs based on the developing brain gene coexpression network and genetic structural variants found in ID probands.