Large-scale molecular profiling studies in recent years have shown that central nervous system (CNS) tumors display a much greater heterogeneity in terms of molecularly distinct entities, cellular origins and genetic drivers than anticipated from histological assessment. DNA methylation profiling has emerged as a useful tool for robust tumor classification, providing new insights into these heterogeneous molecular classes. This is particularly true for rare CNS tumors with a broad morphological spectrum, which are not possible to assign as separate entities based on histological similarity alone. Here, we describe a molecularly distinct subset of predominantly pediatric CNS neoplasms (n = 60) that harbor PATZ1 fusions. The original histological diagnoses of these tumors covered a wide spectrum of tumor types and malignancy grades. While the single most common diagnosis was glioblastoma (GBM), clinical data of the PATZ1-fused tumors showed a better prognosis than typical GBM, despite frequent relapses. RNA sequencing revealed recurrent MN1:PATZ1 or EWSR1:PATZ1 fusions related to (often extensive) copy number variations on chromosome 22, where PATZ1 and the two fusion partners are located. These fusions have individually been reported in a number of glial/glioneuronal tumors, as well as extracranial sarcomas. We show here that they are more common than previously acknowledged, and together define a biologically distinct CNS tumor type with high expression of neural development markers such as PAX2, GATA2 and IGF2. Drug screening performed on the MN1:PATZ1 fusion-bearing KS-1 brain tumor cell line revealed preliminary candidates for further study. In summary, PATZ1 fusions define a molecular class of histologically polyphenotypic neuroepithelial tumors, which show an intermediate prognosis under current treatment regimens.

Protein-coding gene annotation. To search for homologous genes, the protein sequences from all ferns and lycophytes transcriptomes in the OneKP project 1 were retrieved and aligned to the A. capillus-veneris genome, using GeneWise 2 . For transcriptome-based prediction, nineteen transcriptomes covering the entire life cycle of A. capillus-veneris were generated in this study (Supplementary Table 8). RNA was extracted using the Qiagen RNeasy protocol and sequenced on an Illumina HiSeq 4000 with a 300 bp insert size. For transcriptome-based prediction, the HISAT2 3 and StringTie 4 programs were used for transcript assembly 5 . The program PASA (http://pasapipeline.github.io) was used to align spliced transcripts and annotate candidate genes. Ab initio prediction was performed with AUGUSTUS 6 , GlimmerHMM 7 , and SNAP 8 . Finally, nonredundant gene models were obtained with EVidenceModeler (version 1.1.0) 9 to integrate the gene models developed by different datasets.To validate the assembly quality, RNA-seq reads from nineteen tissues (Supplementary Table 8), together with publicly available EST sequences from the NCBI database (downloaded from http://togodb.dbcls.jp/library), were mapped to the A. capillus-veneris genome using HISAT2 3 and BLAT 10 with default parameters, respectively. The BLAT results were filtered with an identity and coverage cutoff of 0.9.Identification of noncoding RNAs. We used tRNAscan-SE (version 2.0rc2) 11 , with default parameters, to search for tRNAs in the A. capillus-veneris genome. A total of 1,624 tRNAs were found. Moreover, the Rfam14.0 database 12 , including 3,445 noncoding RNA families, was used to annotate additional noncoding RNAs (ncRNAs), including miRNAs, snRNAs, and tRNAs, using INFERNAL (version 1.1.2) 13 program.We predicted rRNA (5S, 5.8S, 28S, 18S) by using HMM searching based rRNA predicator Barrnap (version 0.9, https://github.com/tseemann/barrnap#barrnap), with default parameters. We finally identified 145 5S, 75 5.8S, 155 28S, and 165 18S sequences and their locations within the genome assembly of A. capillus-veneris.