PURPOSE: Historically, cancer predisposition syndromes (CPSs) were rarely established for children with cancer. This nationwide, population-based study investigated how frequently children with cancer had or were likely to have a CPS. METHODS: Children (0–17 years) in Denmark with newly diagnosed cancer were invited to participate in whole-genome sequencing of germline DNA. Suspicion of CPS was assessed according to Jongmans’/McGill Interactive Pediatric OncoGenetic Guidelines (MIPOGG) criteria and familial cancer diagnoses were verified using population-based registries. RESULTS: 198 of 235 (84.3%) eligible patients participated, of whom 94/198 (47.5%) carried pathogenic variants (PVs) in a CPS gene or had clinical features indicating CPS. Twenty-nine of 198 (14.6%) patients harbored a CPS, of whom 21/198 (10.6%) harbored a childhood-onset and 9/198 (4.5%) an adult-onset CPS. In addition, 23/198 (11.6%) patients carried a PV associated with biallelic CPS. Seven of the 54 (12.9%) patients carried two or more variants in different CPS genes. Seventy of 198 (35.4%) patients fulfilled the Jongmans’ and/or MIPOGG criteria indicating an underlying CPS, including two of the 9 (22.2%) patients with an adult-onset CPS versus 18 of the 21 (85.7%) patients with a childhood-onset CPS (p = 0.0022), eight of the additional 23 (34.8%) patients with a heterozygous PV associated with biallelic CPS, and 42 patients without PVs. Children with a central nervous system (CNS) tumor had family members with CNS tumors more frequently than patients with other cancers (11/44, p = 0.04), but 42 of 44 (95.5%) cases did not have a PV in a CPS gene. CONCLUSION: These results demonstrate the value of systematically screening pediatric cancer patients for CPSs and indicate that a higher proportion of childhood cancers may be linked to predisposing germline variants than previously supposed.

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and proteinprotein interaction. Advances in experimental mutational scans allow highthroughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-Valentina Sora and Adrian Otamendi Laspiur equally contributed to this study.

Ependymoma is the second most common malignant brain tumor in children. The etiology is largely unknown and germline DNA sequencing studies focusing on childhood ependymoma are limited. We therefore performed germline whole-genome sequencing on a population-based cohort of children diagnosed with ependymoma in Denmark over the past 20 years (n = 43). Single nucleotide and structural germline variants in 457 cancer related genes and 2986 highly evolutionarily constrained genes were assessed in 37 children with normal tissue available for sequencing. Molecular ependymoma classification was performed using DNA methylation profiling for 39 children with available tumor tissue. Pathogenic germline variants in known cancer predisposition genes were detected in 11% (4/37; NF2, LZTR1, NF1 & TP53). However, DNA methylation profiling resulted in revision of the histopathological ependymoma diagnosis to non-ependymoma tumor types in 8% (3/39). This included the two children with pathogenic germline variants in TP53 and NF1 whose tumors were reclassified to a diffuse midline glioma and a rosette-forming glioneuronal tumor, respectively. Consequently, 50% (2/4) of children with pathogenic germline variants in fact had other tumor types. A meta-analysis combining our findings with pediatric pan-cancer germline sequencing studies showed an overall frequency of pathogenic germline variants of 3.4% (7/207) in children with ependymoma. In summary, less than 4% of childhood ependymoma is explained by genetic predisposition, virtually restricted to pathogenic variants in NF2 and NF1. For children with other cancer predisposition syndromes, diagnostic reconsideration is recommended for ependymomas without molecular classification. Additionally, LZTR1 is suggested as a novel putative ependymoma predisposition gene.

Reliable prediction of free energy changes upon amino acidic substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Moreover, advances in experimental mutational scans allow high-throughput studies thanks to sophisticated multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput calculations of ΔΔGs. In this context, the Rosetta modeling suite implements effective approaches to predict the change in the folding free energy in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. Their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. RosettaDDGPrediction assists with checking whether the runs are completed successfully aggregates raw data for multiple variants, and generates publication-ready graphics. We showed the potential of the tool in selected case studies, including variants of unknown significance found in children who developed cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and a disordered functional motif, and phospho-mimetic variants. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.