Abstract Despite improvements in cure rates, cancer is still the leading cause of disease-related deaths among children in high-income countries. As childhood cancers are rare in comparison to adult cancers, concerted efforts are needed to advance research and improve patient care. In The Netherlands, all childhood cancer care and most research has been concentrated in a single national center, the Princess Máxima Center for Pediatric Oncology. The institute’s centralized position enables uniform generation of data from patients across the country, that through federation with other initiatives can provide a unique contribution to tackle childhood cancer world-wide. Whole-exome sequencing, whole-genome sequencing and RNA-sequencing are routinely performed for diagnostic and research purposes, leading to a representative national dataset with little batch effects. Currently this data collection consists of ∼1000 patient samples with rich and uniform clinical data. This collection is expected to grow to ∼4,000 patient samples by 2028. Here, I will show how we use this rich, harmonized data resource to improve patient care and aid data-driven research into understanding the role of different types of somatic variation in tumor initiation and progression. First, by performing whole-exome sequencing and RNA-sequencing as standard-of-care we provide individualized diagnosis and treatment plans for precision medicine purposes. Second, by investigating complex genomic rearrangements in pediatric solid tumors we show that these occur in approximately half of the tumor samples and are likely highly pathogenic. Third, by performing integrative tumor driver identification, we identify events that are otherwise likely missed through more targeted approaches. Fourth, through the development of M&M, a pan-cancer RNA-seq based classifier we obtain an accuracy of ∼95% in predicting tumor (sub)types across the breadth of (rare) pediatric tumors. Ultimately, we anticipate that the data collection presented here will further facilitate pediatric cancer research and provide an invaluable resource for precision oncology. Citation Format: Joanna von Berg, Ianthe A.E.M. van Belzen, Fleur S.A. Wallis, Anastasia Spinou, Roula Farag, Victoria M. Cruz, Lennart A. Kester, Marco Koudijs, John L. Baker-Hernandez, Alex Janse, Shashi Badloe, Sam de Vos, Marcel Santoso, Eugene T.P. Verwiel, Mark van Tuil, Hindrik H.D. Kerstens, Jayne Y. Hehir-Kwa, Frank C.P. Holstege, Bastiaan B.J. Tops, Patrick Kemmeren. The Dutch childhood cancer genome project: Data-driven precision medicine and research [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B009.

In pediatric cancer, structural variants (SVs) and copy-number alterations contribute to cancer initiation as well as progression, thereby aiding diagnosis and treatment stratification. Although suggested to be of importance, the prevalence and biological relevance of complex genomic rearrangements (CGRs) across pediatric solid tumors is largely unexplored. In a cohort of 120 primary tumors, we systematically characterized patterns of extrachromosomal DNA, chromoplexy, and chromothripsis across five pediatric solid cancer types. CGRs were identified in 56 tumors (47%), and in 42 of these tumors, CGRs affect cancer driver genes or result in unfavorable chromosomal alterations. This demonstrates that CGRs are prevalent and pathogenic in pediatric solid tumors and suggests that selection likely contributes to the structural variation landscape. Moreover, carrying CGRs is associated with more adverse clinical events. Our study highlights the potential for CGRs to be incorporated in risk stratification or exploited for targeted treatments.

The increase in speed, reliability, and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS), and transcriptome analysis. WXS and RNA sequencing is now being implemented as the standard of care for patients and for patients included in clinical studies. To keep track of sample relationships and analyses, a platform is needed that can unify metadata for diverse sequencing strategies with sample metadata whilst supporting automated and reproducible analyses, in essence ensuring that analyses are conducted consistently and data are Findable, Accessible, Interoperable, and Reusable (FAIR).We present “Trecode”, a framework that records both clinical and research sample (meta) data and manages computational genome analysis workflows executed for both settings, thereby achieving tight integration between analysis results and sample metadata. With complete, consistent, and FAIR (meta) data management in a single platform, stacked bioinformatic analyses are performed automatically and tracked by the database, ensuring data provenance, reproducibility, and reusability, which is key in worldwide collaborative translational research. The Trecode data model, codebooks, NGS workflows, and client programs are publicly available. In addition, the complete software stack is coded in an Ansible playbook to facilitate automated deployment and adoption of Trecode by other users.

Chromosomal alterations have recurrently been identified in Wilms tumors (WTs) and some are associated with poor prognosis. Gain of 1q (1q+) is of special interest given its high prevalence and is currently actively studied for its prognostic value. However, the underlying mutational mechanisms and functional effects remain unknown. In a national unbiased cohort of 30 primary WTs, we integrated somatic SNVs, CNs and SVs with expression data and distinguished four clusters characterized by affected biological processes: muscle differentiation, immune system, kidney development and proliferation. Combined genome-wide CN and SV profiles showed that tumors profoundly differ in both their types of 1q+ and genomic stability and can be grouped into WTs with co-occurring 1p−/1q+, multiple chromosomal gains or CN neutral tumors. We identified 1q+ in eight tumors that differ in mutational mechanisms, subsequent rearrangements and genomic contexts. Moreover, 1q+ tumors were present in all four expression clusters reflecting activation of various biological processes, and individual tumors overexpress different genes on 1q. In conclusion, by integrating CNs, SVs and gene expression, we identified subgroups of 1q+ tumors reflecting differences in the functional effect of 1q gain, indicating that expression data is likely needed for further risk stratification of 1q+ WTs.