Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

Radiation-induced high-grade gliomas (RIGs) are an incurable late complication of cranial radiation therapy. We performed DNA methylation profiling, RNA-seq, and DNA sequencing on 32 RIG tumors and an in vitro drug screen in two RIG cell lines. We report that based on DNA methylation, RIGs cluster primarily with the pediatric receptor tyrosine kinase I high-grade glioma subtype. Common copy-number alterations include Chromosome (Ch.) 1p loss/1q gain, and Ch. 13q and Ch. 14q loss; focal alterations include PDGFRA and CDK4 gain and CDKN2A and BCOR loss. Transcriptomically, RIGs comprise a stem-like subgroup with lesser mutation burden and Ch. 1p loss and a pro-inflammatory subgroup with greater mutation burden and depleted DNA repair gene expression. Chromothripsis in several RIG samples is associated with extrachromosomal circular DNA-mediated amplification of PDGFRA and CDK4. Drug screening suggests microtubule inhibitors/stabilizers, DNA-damaging agents, MEK inhibition, and, in the inflammatory subgroup, proteasome inhibitors, as potentially effective therapies.

DICER1 syndrome is a tumor predisposition syndrome that is associated with up to 30 different neoplastic lesions, usually affecting children and adolescents. Here we identify a group of mesenchymal tumors which is highly associated with DICER1 syndrome, and molecularly distinct from other DICER1-associated tumors. This group of DICER1-associated mesenchymal tumors encompasses multiple well-established clinicopathological tumor entities and can be further divided into three clinically meaningful classes designated "low-grade mesenchymal tumor with DICER1 alteration" (LGMT DICER1), "sarcoma with DICER1 alteration" (SARC DICER1), and primary intracranial sarcoma with DICER1 alteration (PIS DICER1). Our study not only provides a combined approach to classify DICER1-associated neoplasms for improved clinical management but also suggests a role for global hypomethylation and other recurrent molecular events in sarcomatous differentiation in mesenchymal tumors with DICER1 alteration. Our results will facilitate future investigations into prognostication and therapeutic approaches for affected patients.

Sonic hedgehog medulloblastoma encompasses a clinically and molecularly diverse group of cancers of the developing central nervous system. Here, we use unbiased sequencing of the transcriptome across a large cohort of 250 tumors to reveal differences among molecular subtypes of the disease, and demonstrate the previously unappreciated importance of non-coding RNA transcripts. We identify alterations within the cAMP dependent pathway (GNAS, PRKAR1A) which converge on GLI2 activity and show that 18% of tumors have a genetic event that directly targets the abundance and/or stability of MYCN. Furthermore, we discover an extensive network of fusions in focally amplified regions encompassing GLI2, and several loss-of-function fusions in tumor suppressor genes PTCH1, SUFU and NCOR1. Molecular convergence on a subset of genes by nucleotide variants, copy number aberrations, and gene fusions highlight the key roles of specific pathways in the pathogenesis of Sonic hedgehog medulloblastoma and open up opportunities for therapeutic intervention.

Rhabdoid tumors (RT) are rare and highly aggressive pediatric neoplasms. Their epigenetically-driven intertumoral heterogeneity is well described; however, the cellular origin of RT remains an enigma. Here, we establish and characterize different genetically engineered mouse models driven under the control of distinct promoters and being active in early progenitor cell types with diverse embryonic onsets. From all models only Sox2-positive progenitor cells give rise to murine RT. Using single-cell analyses, we identify distinct cells of origin for the SHH and MYC subgroups of RT, rooting in early stages of embryogenesis. Intra- and extracranial MYC tumors harbor common genetic programs and potentially originate from fetal primordial germ cells (PGCs). Using PGC specific Smarcb1 knockout mouse models we validate that MYC RT originate from these progenitor cells. We uncover an epigenetic imbalance in MYC tumors compared to PGCs being sustained by epigenetically-driven subpopulations. Importantly, treatments with the DNA demethylating agent decitabine successfully impair tumor growth in vitro and in vivo. In summary, our work sheds light on the origin of RT and supports the clinical relevance of DNA methyltransferase inhibitors against this disease.

The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent potentially previously misclassified adenoid cystic carcinomas. Our findings can aid in improving the diagnostic classification of sinonasal tumors and could help to change the current perception of SNUCs.

Pediatric high-grade gliomas of the subclass MYCN (HGG-MYCN) are highly aggressive tumors frequently carrying MYCN amplifications, TP53 mutations, or both alterations. Due to their rarity, such tumors have only recently been identified as a distinct entity, and biological as well as clinical characteristics have not been addressed specifically. To gain insights into tumorigenesis and molecular profiles of these tumors, and to ultimately suggest alternative treatment options, we generated a genetically engineered mouse model by breeding hGFAP-cre::Trp53Fl/Fl::lsl-MYCN mice. All mice developed aggressive forebrain tumors early in their lifetime that mimic human HGG-MYCN regarding histology, DNA methylation, and gene expression. Single-cell RNA sequencing revealed a high intratumoral heterogeneity with neuronal and oligodendroglial lineage signatures. High-throughput drug screening using both mouse and human tumor cells finally indicated high efficacy of Doxorubicin, Irinotecan, and Etoposide as possible therapy options that children with HGG-MYCN might benefit from.