Characterization of aberrant splicing in pediatric central nervous system tumors reveals CLK1 as a candidate oncogenic dependency.

Pediatric brain cancer is the leading cause of disease-related mortality in children, and many aggressive tumors still lack effective treatment strategies. Despite extensive studies characterizing these tumor genomes, alternative transcriptional splicing patterns remain underexplored. Here, we systematically characterized aberrant alternative splicing across pediatric brain tumors, identifying pediatric high-grade gliomas (HGGs) among the most heterogeneous. Through integration with UniProt Knowledgebase annotations, we identified 12,145 splice events in 5,424 genes, leading to functional changes in protein activation, folding, and localization. We discovered that the master splicing factor and cell-cycle modulator, CDC-like kinase 1 (CLK1), is aberrantly spliced in HGGs to include exon 4, resulting in a gain of two phosphorylation sites and subsequent activation of CLK1. Inhibition of CLK1 with Cirtuvivint in the pediatric HGG KNS-42 cell line significantly decreased both cell viability and proliferation in a dose-dependent manner. Morpholino-mediated depletion of CLK1 exon 4 splicing reduced RNA expression, protein abundance, and cell viability. Notably, KNS-42 cells treated with the CLK1 exon 4 morpholino demonstrated differential expression 78 genes and differential splicing with loss or gain of a functional site in 193 genes annotated as oncogene or tumor suppressor genes (TSGs). These genes were enriched for cancer-associated pathways, with 20 identified as significant gene dependencies in pediatric HGGs. Our findings highlight a dependency of pediatric HGGs on CLK1 and its roles contributing to tumor splicing heterogeneity through transcriptional dysregulation of splicing factors and transcriptional modulation of oncogenes. Overall, aberrant splicing in HGGs and other pediatric brain tumors represents a potentially targetable oncogenic pathway contributing to tumor growth and maintenance.

The Open Pediatric Cancer Project.

Background: In 2019, the Open Pediatric Brain Tumor Atlas (OpenPBTA) was created as a global, collaborative open-science initiative to genomically characterize 1,074 pediatric brain tumors and 22 patient-derived cell lines. Here, we extend the OpenPBTA to create the Open Pediatric Cancer (OpenPedCan) Project, a harmonized open-source multi-omic dataset from 6,112 pediatric cancer patients with 7,096 tumor events across more than 100 histologies. Combined with RNA-Seq from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA), OpenPedCan contains nearly 48,000 total biospecimens (24,002 tumor and 23,893 normal specimens). Findings: We utilized Gabriella Miller Kids First (GMKF) workflows to harmonize WGS, WXS, RNA-seq, and Targeted Sequencing datasets to include somatic SNVs, InDels, CNVs, SVs, RNA expression, fusions, and splice variants. We integrated summarized CPTAC whole cell proteomics and phospho-proteomics data, miRNA-Seq data, and have developed a methylation array harmonization workflow to include m-values, beta-vales, and copy number calls. OpenPedCan contains reproducible, dockerized workflows in GitHub, CAVATICA, and Amazon Web Services (AWS) to deliver harmonized and processed data from over 60 scalable modules which can be leveraged both locally and on AWS. The processed data are released in a versioned manner and accessible through CAVATICA or AWS S3 download (from GitHub), and queryable through PedcBioPortal and the NCI's pediatric Molecular Targets Platform. Notably, we have expanded PBTA molecular subtyping to include methylation information to align with the WHO 2021 Central Nervous System Tumor classifications, allowing us to create research- grade integrated diagnoses for these tumors. Conclusions: OpenPedCan data and its reproducible analysis module framework are openly available and can be utilized and/or adapted by researchers to accelerate discovery, validation, and clinical translation.

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

SUMMARY: With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics-Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically focused classification of germline sequence variants in a research setting. AVAILABILITY AND IMPLEMENTATION: AutoGVP is an open source dockerized workflow implemented in R and freely available on GitHub at https://github.com/diskin-lab-chop/AutoGVP.

AutoGVP: a dockerized workflow integrating ClinVar and InterVar germline sequence variant classification.

With the increasing rates of exome and whole genome sequencing, the ability to classify large sets of germline sequencing variants using up-to-date American College of Medical Genetics - Association for Molecular Pathology (ACMG-AMP) criteria is crucial. Here, we present Automated Germline Variant Pathogenicity (AutoGVP), a tool that integrates germline variant pathogenicity annotations from ClinVar and sequence variant classifications from a modified version of InterVar (PVS1 strength adjustments, removal of PP5/BP6). This tool facilitates large-scale, clinically-focused classification of germline sequence variants in a research setting.

FBXW7ß isoform drives transcriptional activation of the proinflammatory TNF cluster in human pro-B cells.

Noncanonical exon usage plays many important roles in cellular phenotypes, but its contribution to human B-cell development remains sketchily understood. To fill this gap, we collected various B-cell fractions from bone marrow (BM) and tonsil donors, performed RNA sequencing, and examined transcript variants. We identified 150 genes that harbor local splicing variations in all pairwise comparisons. One of them encodes FBXW7, an E3 ubiquitin ligase implicated as a driver in several blood cancers. Surprisingly, we discovered that in normal human pro-B cells, the predominant transcript used an alternative first exon to produce the poorly characterized FBXW7ß isoform, previously thought to be restricted to neural tissues. The FBXW7ß transcript was also abundant in cell lines and primary samples of pediatric B-cell acute lymphoblastic leukemia (B-ALL), which originates in the BM. When overexpressed in a heterologous cell system, this transcript yielded the expected protein product, as judged by anti-FLAG immunoblotting and mass spectrometry. Furthermore, in REH B-ALL cells, FBXW7ß mRNA was the only FBXW7 isoform enriched in the polyribosome fraction. To shed light on possible functions of FBXW7ß, we used gain- and loss-of-function approaches and identified an FBXW7-dependent inflammatory gene signature, apparent in a subset of B-ALL with high FBXW7ß expression. This signature contained several members of the tumor necrosis factor superfamily, including those comprising the HLA Class III cluster (LTB, LST1, NCR3, LTA, and NFKBIL1). Our findings suggest that FBXW7ß expression drives proinflammatory responses, which could contribute to normal B-cell development, leukemogenesis, and responses to anticancer therapies.

Targeting CD123 in blastic plasmacytoid dendritic cell neoplasm using allogeneic anti-CD123 CAR T cells.

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare hematologic malignancy with poor outcomes with conventional therapy. Nearly 100% of BPDCNs overexpress interleukin 3 receptor subunit alpha (CD123). Given that CD123 is differentially expressed on the surface of BPDCN cells, it has emerged as an attractive therapeutic target. UCART123 is an investigational product consisting of allogeneic T cells expressing an anti-CD123 chimeric antigen receptor (CAR), edited with TALEN® nucleases. In this study, we examine the antitumor activity of UCART123 in preclinical models of BPDCN. We report that UCART123 have selective antitumor activity against CD123-positive primary BPDCN samples (while sparing normal hematopoietic progenitor cells) in the in vitro cytotoxicity and T cell degranulation assays; supported by the increased secretion of IFNγ by UCART123 cells when cultured in the presence of BPDCN cells. UCART123 eradicate BPDCN and result in long-term disease-free survival in a subset of primary patient-derived BPDCN xenograft mouse models. One potential challenge of CD123 targeting therapies is the loss of CD123 antigen through diverse genetic mechanisms, an event observed in one of three BPDCN PDX studied. In summary, these results provide a preclinical proof-of-principle that allogeneic UCART123 cells have potent anti-BPDCN activity.

Modulation of CD22 Protein Expression in Childhood Leukemia by Pervasive Splicing Aberrations: Implications for CD22-Directed Immunotherapies.

Downregulation of surface epitopes causes postimmunotherapy relapses in B-lymphoblastic leukemia (B-ALL). Here we demonstrate that mRNA encoding CD22 undergoes aberrant splicing in B-ALL. We describe the plasma membrane-bound CD22 Δex5-6 splice isoform, which is resistant to chimeric antigen receptor (CAR) T cells targeting the third immunoglobulin-like domain of CD22. We also describe splice variants skipping the AUG-containing exon 2 and failing to produce any identifiable protein, thereby defining an event that is rate limiting for epitope presentation. Indeed, forcing exon 2 skipping with morpholino oligonucleotides reduced CD22 protein expression and conferred resistance to the CD22-directed antibody-drug conjugate inotuzumab ozogamicin in vitro. Furthermore, among inotuzumab-treated pediatric patients with B-ALL, we identified one nonresponder in whose leukemic blasts Δex2 isoforms comprised the majority of CD22 transcripts. In a second patient, a sharp reduction in CD22 protein levels during relapse was driven entirely by increased CD22 exon 2 skipping. Thus, dysregulated CD22 splicing is a major mechanism of epitope downregulation and ensuing resistance to immunotherapy. SIGNIFICANCE: The mechanism(s) underlying downregulation of surface CD22 following CD22-directed immunotherapy remains underexplored. Our biochemical and correlative studies demonstrate that in B-ALL, CD22 expression levels are controlled by inclusion/skipping of CD22 exon 2. Thus, aberrant splicing of CD22 is an important driver/biomarker of de novo and acquired resistance to CD22-directed immunotherapies. See related commentary by Bourcier and Abdel-Wahab, p. 87. This article is highlighted in the In This Issue feature, p. 85.

annoFuse: an R Package to annotate, prioritize, and interactively explore putative oncogenic RNA fusions.

BACKGROUND: Gene fusion events are significant sources of somatic variation across adult and pediatric cancers and are some of the most clinically-effective therapeutic targets, yet low consensus of RNA-Seq fusion prediction algorithms makes therapeutic prioritization difficult. In addition, events such as polymerase read-throughs, mis-mapping due to gene homology, and fusions occurring in healthy normal tissue require informed filtering, making it difficult for researchers and clinicians to rapidly discern gene fusions that might be true underlying oncogenic drivers of a tumor and in some cases, appropriate targets for therapy. RESULTS: We developed annoFuse, an R package, and shinyFuse, a companion web application, to annotate, prioritize, and explore biologically-relevant expressed gene fusions, downstream of fusion calling. We validated annoFuse using a random cohort of TCGA RNA-Seq samples (N = 160) and achieved a 96% sensitivity for retention of high-confidence fusions (N = 603). annoFuse uses FusionAnnotator annotations to filter non-oncogenic and/or artifactual fusions. Then, fusions are prioritized if previously reported in TCGA and/or fusions containing gene partners that are known oncogenes, tumor suppressor genes, COSMIC genes, and/or transcription factors. We applied annoFuse to fusion calls from pediatric brain tumor RNA-Seq samples (N = 1028) provided as part of the Open Pediatric Brain Tumor Atlas (OpenPBTA) Project to determine recurrent fusions and recurrently-fused genes within different brain tumor histologies. annoFuse annotates protein domains using the PFAM database, assesses reciprocality, and annotates gene partners for kinase domain retention. As a standard function, reportFuse enables generation of a reproducible R Markdown report to summarize filtered fusions, visualize breakpoints and protein domains by transcript, and plot recurrent fusions within cohorts. Finally, we created shinyFuse for algorithm-agnostic interactive exploration and plotting of gene fusions. CONCLUSIONS: annoFuse provides standardized filtering and annotation for gene fusion calls from STAR-Fusion and Arriba by merging, filtering, and prioritizing putative oncogenic fusions across large cancer datasets, as demonstrated here with data from the OpenPBTA project. We are expanding the package to be widely-applicable to other fusion algorithms and expect annoFuse to provide researchers a method for rapidly evaluating, prioritizing, and translating fusion findings in patient tumors.

A transcriptome-based classifier to determine molecular subtypes in medulloblastoma.

Medulloblastoma is a highly heterogeneous pediatric brain tumor with five molecular subtypes, Sonic Hedgehog TP53-mutant, Sonic Hedgehog TP53-wildtype, WNT, Group 3, and Group 4, defined by the World Health Organization. The current mechanism for classification into these molecular subtypes is through the use of immunostaining, methylation, and/or genetics. We surveyed the literature and identified a number of RNA-Seq and microarray datasets in order to develop, train, test, and validate a robust classifier to identify medulloblastoma molecular subtypes through the use of transcriptomic profiling data. We have developed a GPL-3 licensed R package and a Shiny Application to enable users to quickly and robustly classify medulloblastoma samples using transcriptomic data. The classifier utilizes a large composite microarray dataset (15 individual datasets), an individual microarray study, and an RNA-Seq dataset, using gene ratios instead of gene expression measures as features for the model. Discriminating features were identified using the limma R package and samples were classified using an unweighted mean of normalized scores. We utilized two training datasets and applied the classifier in 15 separate datasets. We observed a minimum accuracy of 85.71% in the smallest dataset and a maximum of 100% accuracy in four datasets with an overall median accuracy of 97.8% across the 15 datasets, with the majority of misclassification occurring between the heterogeneous Group 3 and Group 4 subtypes. We anticipate this medulloblastoma transcriptomic subtype classifier will be broadly applicable to the cancer research and clinical communities.