With many rare tumour types, acquiring the correct diagnosis is a challenging but crucial process in paediatric oncology. Historically, this is done based on histology and morphology of the disease. However, advances in genome wide profiling techniques such as RNA sequencing now allow the development of molecular classification tools. Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types. The RNA-seq based algorithm can classify 52 different tumour types (precision ∼99%, recall ∼80%), plus the underlying 96 tumour subtypes (precision ∼96%, recall ∼70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scoring labels. We then validated M&M on an internal dataset (precision 99%, recall 76%) and an external dataset from the KidsFirst initiative (precision 98%, recall 77%). Finally, we show that M&M has similar performance as existing disease or domain specific classification algorithms based on RNA sequencing or methylation data. M&M's pan-cancer setup allows for easy clinical implementation, requiring only one classifier for all incoming diagnostic samples, including samples from different tumour stages and treatment statuses. Simultaneously, its performance is comparable to existing tumour- and tissue-specific classifiers. The introduction of an extensive pan-cancer classifier in diagnostics has the potential to increase diagnostic accuracy for many paediatric cancer cases, thereby contributing towards optimal patient survival and quality of life. Financial support was provided by the Foundation Children Cancer Free (KiKa core funding) and Adessium Foundation.

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.

With many rare tumor types, acquiring the correct diagnosis is a challenging but crucial process in pediatric oncology. Here, we present M&M, a pan-cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumor types. The RNA-seq based algorithm can classify 52 different tumor types (precision ∼ 99%, recall ∼ 80%), plus the underlying 96 tumor subtypes (precision ∼ 96%, recall ∼ 70%). For low-confidence classifications, a comparable precision is achieved when including the three highest-scoring labels. M&M’s pan-cancer setup allows for easy clinical implementation, requiring only one classifier for all incoming diagnostic samples, including samples from different tumor stages and treatment statuses. Simultaneously, its performance is comparable to existing tumor- and tissue-specific classifiers. The introduction of an extensive pan-cancer classifier in diagnostics has the potential to increase diagnostic accuracy for many pediatric cancer cases, thereby contributing towards optimal patient survival and quality of life.

Whole genome sequencing (WGS) is increasingly accessible in clinical practice, enabling pharmacogenomics (PGx) integration, including in pediatric oncology. However, the lack of validated software to accurately annotate clinically actionable PGx variants from WGS limits routine implementation. We therefore aimed to identify and validate a PGx annotation tool suitable for clinical use in pediatric oncology. We evaluated several tools for technical performance and clinical integration. The Pharmacogenomics Clinical Annotation Tool (PharmCAT) was selected for its alignment with expert‐reviewed PGx guidelines, inclusion of relevant gene‐drug pairs and prescribing recommendations. PharmCAT was validated using an in silico dataset by introducing known diplotypes into the Genome in a Bottle (GIAB) reference sample, complemented by four clinically confirmed diplotypes from three patients. Diplotype and phenotype outputs from WGS were compared against the GIAB and patient reference data. We tested 71 diplotypes across seven genes ( TPMT , NUDT15 , CYP3A5 , CYP2C9 , CYP2C19 , DPYD , UGT1A1 ), using ≥ 95% sensitivity and specificity as validation criteria. CYP2D6 was excluded from this validation due to genotyping limitations from the input data used by PharmCAT. The tool was integrated into our WGS analysis pipeline using containerization for consistent, reproducible execution. Diplotype and phenotype results from PharmCAT fully matched the in silico GIAB set and patient samples, achieving 100% sensitivity and specificity. These findings confirm PharmCAT as a reliable tool for preemptive PGx annotation, supporting implementation in pediatric oncology. Its clinical integration supports individualized dosing, reducing adverse effects and improving efficacy. Further validation of additional gene‐drug pairs will broaden its clinical utility.