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The Swedish Childhood Tumor Biobank (BTB) is a nonprofit national infrastructure for collecting tissue samples and genomic data from pediatric patients diagnosed with central nervous system (CNS) and other solid tumors. The BTB is built on a multidisciplinary network established to provide the scientific community with standardized biospecimens and genomic data, thereby improving knowledge of the biology, treatment and outcome of childhood tumors. As of 2022, over 1100 fresh-frozen tumor samples are available for researchers. We present the workflow of the BTB from sample collection and processing to the generation of genomic data and services offered. To determine the research and clinical utility of the data, we performed bioinformatics analyses on next-generation sequencing (NGS) data obtained from a subset of 82 brain tumors and patient blood-derived DNA combined with methylation profiling to enhance the diagnostic accuracy and identified germline and somatic alterations with potential biological or clinical significance. The BTB procedures for collection, processing, sequencing, and bioinformatics deliver high-quality data. We observed that the findings could impact patient management by confirming or clarifying the diagnosis in 79 of the 82 tumors and detecting known or likely driver mutations in 68 of 79 patients. In addition to revealing known mutations in a broad spectrum of genes implicated in pediatric cancer, we discovered numerous alterations that may represent novel driver events and specific tumor entities. In summary, these examples reveal the power of NGS to identify a wide number of actionable gene alterations. Making the power of NGS available in healthcare is a challenging task requiring the integration of the work of clinical specialists and cancer biologists; this approach requires a dedicated infrastructure, as exemplified here by the BTB.

Glioblastoma (GBM) is a deadly disease with a need for deeper understanding and new therapeutic approaches. The microenvironment of glioblastoma has previously been shown to guide glioblastoma progression. In this study, astrocytes were investigated with regard to their effect on glioblastoma proliferation through correlative analyses of clinical samples and experimental in vitro and in vivo studies. Co-culture techniques were used to investigate the GBM growth enhancing potential of astrocytes. Cell sorting and RNA sequencing were used to generate a GBM-associated astrocyte signature and to investigate astrocyte-induced GBM genes. A NOD scid GBM mouse model was used for in vivo studies. A gene signature reflecting GBM-activated astrocytes was associated with poor prognosis in the TCGA GBM dataset. Two genes, periostin and serglycin, induced in GBM cells upon exposure to astrocytes were expressed at higher levels in cases with high "astrocyte signature score". Astrocytes were shown to enhance glioblastoma cell growth in cell lines and in a patient-derived culture, in a manner dependent on cell-cell contact and involving increased cell proliferation. Furthermore, co-injection of astrocytes with glioblastoma cells reduced survival in an orthotopic GBM model in NOD scid mice. In conclusion, this study suggests that astrocytes contribute to glioblastoma growth and implies this crosstalk as a candidate target for novel therapies.

The microenvironment and architecture of peritumoral tissue have been suggested to affect permissiveness for infiltration of malignant cells. Astrocytes constitute a heterogeneous population of cells and have been linked to proliferation, migration, and drug sensitivity of glioblastoma (GBM) cells. Through double-immunohistochemical staining for platelet-derived growth factor receptor α (PDGFRα) and glial fibrillary acidic protein (GFAP), this study explored the intercase variability among 45 human GBM samples regarding density of GFAP+ peritumoral astrocytes and a subset of GFAP+ peritumoral astrocyte-like cells also expressing PDGFRα. Large intercase variability regarding the total peritumoral astrocyte density and the density of PDGFRα+/GFAP+ peritumoral astrocyte-like cells was detected. DNA fluorescence in situ hybridization analyses for commonly altered genetic tumor markers supported the interpretation that these cells represented a genetically unaffected host cell subset referred to as PDGFRα+/GFAP+ peritumoral astrocytes. The presence of PDGFRα+/GFAP+ peritumoral astrocytes was significantly positively correlated to older patient age and peritumoral astrocyte density, but not to other established prognostic factors. Notably, presence of PDGFRα+/GFAP+ peritumoral astrocytes, but not peritumoral astrocyte density, was associated with significantly shorter patient overall survival. The prognostic association of PDGFRα+/GFAP+ peritumoral astrocytes was confirmed in multivariable analyses. This exploratory study thus demonstrates previously unrecognized intercase variability and prognostic significance of peritumoral abundance of a novel PDGFRα+ subset of GFAP+ astrocytes. Findings suggest clinically relevant roles of the microenvironment of peritumoral GBM tissue and encourage further characterization of the novel astrocyte subset with regard to origin, function, and potential as biomarker and drug target.

Whole-genome sequencing (WGS) is a fundamental technology for research to advance precision medicine, but the limited availability of portable and user-friendly workflows for WGS analyses poses a major challenge for many research groups and hampers scientific progress. Here we present Sarek, an open-source workflow to detect germline variants and somatic mutations based on sequencing data from WGS, whole-exome sequencing (WES), or gene panels. Sarek features (i) easy installation, (ii) robust portability across different computer environments, (iii) comprehensive documentation, (iv) transparent and easy-to-read code, and (v) extensive quality metrics reporting. Sarek is implemented in the Nextflow workflow language and supports both Docker and Singularity containers as well as Conda environments, making it ideal for easy deployment on any POSIX-compatible computers and cloud compute environments. Sarek follows the GATK best-practice recommendations for read alignment and pre-processing, and includes a wide range of software for the identification and annotation of germline and somatic single-nucleotide variants, insertion and deletion variants, structural variants, tumour sample purity, and variations in ploidy and copy number. Sarek offers easy, efficient, and reproducible WGS analyses, and can readily be used both as a production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups. The Sarek source code, documentation and installation instructions are freely available at https://github.com/nf-core/sarek and at https://nf-co.re/sarek/.

Background Glioblastomas display a high level of intratumoral heterogeneity with regard to both genetic and histological features. Within single tumors, subclones have been shown to communicate with each other to affect overall tumor growth. The aim of this study was to broaden the understanding of interclonal communication in glioblastoma. Methods We have used the U-343 model, consisting of U-343 MG, U-343 MGa, U-343 MGa 31L, and U-343 MGa Cl2:6, a set of distinct glioblastoma cell lines that have been derived from the same tumor. We characterized these with regard to temozolomide sensitivity, protein secretome, gene expression, DNA copy number, and cancer cell phenotypic traits. Furthermore, we performed coculture and conditioned media-based experiments to model cell-to-cell signaling in a setting of intratumoral heterogeneity. Results Temozolomide treatment of a coculture composed of all 4 U-343 cell lines presents a tumor relapse model where the least sensitive population, U-343 MGa 31L, outlives the others. Interestingly, the U-343 cell lines were shown to have distinct gene expression signatures and phenotypes although they were derived from a single tumor. The DNA copy number analysis revealed both common and unique alterations, indicating the evolutionary relationship between the cells. Moreover, these cells were found to communicate and affect each other’s proliferation, both via contact-dependent and -independent interactions, where NOTCH1, TGFBI, and ADAMTS1 signaling effects were involved, respectively. Conclusions These results provide insight into how complex the signaling events may prove to be in a setting of intratumoral heterogeneity in glioblastoma and provide a map for future studies.