Interindividual variability in drug response is a challenge in pediatric oncology, where the risk of treatment-related toxicity is exacerbated by narrow therapeutic windows and combination therapies. Pharmacogenetics (PGx) aims to reduce this variability by linking genetic variants to differences in drug pharmacokinetics, efficacy, and toxicity. Although evidence-based pharmacogenetic guidelines are available covering over 100 gene-drug pairs, systematic use of pre-emptive PGx screening is not yet embedded in routine pediatric oncology care. In a recent study, we demonstrated the relevance of pre-emptive PGx screening in pediatric oncology by showing that 16% of 1,151 patients were eligible for genotype-based drug dose or treatment modifications. Building on these findings, we implemented pre-emptive PGx screening into clinical practice of pediatric oncology in the Netherlands. Successful implementation required addressing challenges, including adaptation of existing infrastructure, interpretation of pharmacogenetic results for a pediatric population, integration into electronic health records, and education of clinical staff. We repurposed diagnostic germline whole genome sequencing (WGS) for individual genetic profiling and applied existing pharmacogenetic guidelines to guide drug dosing and treatment decisions. Here, we describe our implementation strategy for pre-emptive PGx screening in pediatric oncology and share insights to support other centers aiming to integrate PGx screening into routine care.

ABSTRACT 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.

Germline structural variants are a risk factor for pediatric extracranial solid tumors

PURPOSE:Treatment stratification in ALL includes diverse (cyto)genetic aberrations, requiring diverse tests to yield conclusive data. We optimized the diagnostic workflow to detect all relevant aberrations with a limited number of tests in a clinically relevant time frame. METHODS:In 467 consecutive patients with ALL (0-20 years), we compared RNA sequencing (RNAseq), fluorescence in situ hybridization (FISH), reverse transcriptase polymerase chain reaction (RT-PCR), karyotyping, single-nucleotide polymorphism (SNP) array, and multiplex ligation-dependent probe amplification (MLPA) for technical success, concordance of results, and turnaround time. RESULTS:To detect stratifying fusions (ETV6::RUNX1, BCR::ABL1, ABL-class, KMT2Ar, TCF3::HLF, IGH::MYC), RNAseq and FISH were conclusive for 97% and 96% of patients, respectively, with 99% concordance. RNAseq performed well in samples with a low leukemic cell percentage or low RNA quality. RT-PCR for six specific fusions was conclusive for >99% but false-negative for six patients with alternatively fused exons. RNAseq also detected gene fusions not yet used for stratification in 14% of B-cell precursor-ALL and 33% of T-ALL. For aneuploidies and intrachromosomal amplification of chromosome 21, SNP array gave a conclusive result in 99%, thereby outperforming karyotyping, which was conclusive for 64%. To identify deletions in eight stratifying genes/regions, SNP array was conclusive in 99% and MLPA in 95% of patients, with 98% concordance. The median turnaround times were 10 days for RNAseq, 9 days for FISH, 10 days for SNP array, and <7 days for MLPA and RT-PCR in this real-world prospective study. CONCLUSION:Combining RNAseq and SNP array outperformed current diagnostic tools to detect all stratifying genetic aberrations in ALL. The turnaround time is <15 days matching major treatment decision time points. Moreover, combining RNAseq and SNP array has the advantage of detecting new lesions for studies on prognosis and pathobiology.

In pediatric oncology, pharmacogenetic guidelines are underutilized and the potential impact of pre-emptive pharmacogenetic screening remains largely unexplored despite this field's need for individualized approaches. While comprehensive pharmacogenetic guidelines are not yet available for all anticancer drugs, evidence-based recommendations exist for a subset of supportive care drugs and anticancer drugs, including thiopurines, irinotecan, capecitabine, and 5-fluorouracil. In this study, we evaluate the potential impact of pre-emptive pharmacogenetic screening by retrospectively identifying opportunities for dose or treatment adjustments within a national pediatric oncology cohort. Our analysis focused on ten genes and 28 drugs relevant to pediatric oncology, which are included in the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group guidelines. In a cohort of 1,151 pediatric oncology subjects, we identified that 16% of individuals could have benefited from altered drug dosing or treatment. These include dose and treatment recommendations for allopurinol, nonsteroidal anti-inflammatory drugs, phenytoin, amitriptyline, proton pump inhibitors, voriconazole, tramadol, codeine, paroxetine, tacrolimus, rasburicase, and 6-mercaptopurine. As genetic data increasingly becomes available through molecular diagnostics in pediatric oncology, there is a unique opportunity to re-utilize this data for pre-emptive pharmacogenetic screening. Leveraging genetic profiles to guide clinicians in drug selection and dose optimization can improve patient outcomes by enhancing the safety and efficacy of treatments. We therefore recommend incorporating pharmacogenetic screening into clinical workflows to advance personalized medicine in pediatric oncology.

BACKGROUND: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. METHODS:Here, we present M&M, a pan-paediatric cancer ensemble-based machine learning algorithm tailored towards inclusion of rare tumour types. FINDINGS: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. INTERPRETATION: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. FUNDING:Financial support was provided by the Foundation Children Cancer Free (KiKa core funding) and Adessium Foundation.

PURPOSE:In high-grade osteosarcoma, prognostic factors at diagnosis are insufficient for stratifying patients into relevant subgroups. Recently, a transcriptomic study developed the G1/G2 gene expression signature, in which the G2 signature was associated with unfavorable survival. An orthogonal study identified MYC amplification as an unfavorable prognostic factor using targeted next-generation sequencing. The purpose of this study was to validate the independent prognostic value and to investigate the combined prognostic value of the G1/G2 signature with MYC amplification and/or MYC expression for survival prediction. MATERIALS AND METHODS:This study included pediatric and adolescent patients with high-grade osteosarcoma. RNA-seq was performed in 48 patients. Whole-exome sequencing was performed in 40 patients. Gene expression signature scores, MYC amplification (defined as >seven copies), and MYC expression levels were calculated. Multivariable Cox proportional hazards analysis was performed for event-free survival (EFS; primary end point) and overall survival (OS; secondary end point). RESULTS:In the full cohort, the 3-year EFS rate was 37%. In multivariable Cox regression analysis with metastatic disease stage (n = 21, 44%) as covariate, the G2 signature and MYC expression were independently associated with worse outcomes in terms of EFS (hazard ratio [HR], 3.32 [95% CI, 1.34 to 8.21] and HR, 3.38 [95% CI, 1.71 to 6.66], respectively) and OS (HR, 4.07 [95% CI, 1.19 to 13.9] and HR, 2.88 [95% CI, 1.22 to 6.76], respectively). MYC amplification was not associated with EFS or OS in univariable analysis (HR, 1.88 [95% CI, 0.74 to 4.77] and HR, 0.79 [95% CI, 0.21 to 3.05], respectively). CONCLUSION:The G2 gene expression signature and MYC expression were independently associated with unfavorable outcomes in a pediatric cohort of patients with high-grade osteosarcoma. The combined prognostic value warrants further prospective validation and could potentially serve as a stratification marker for future osteosarcoma treatment protocols.

Background and aims Treatment stratification in pediatric acute lymphoblastic leukemia (ALL) is, besides clinical parameters, guided by (cyto)genetic aberrations. These aberrations include a variety of gene fusions, aneuploidy groups, and copy number alterations (CNA), requiring multiple diagnostic assays to yield conclusive data. Over the past decade, the number and complexity of aberrations to be addressed for stratification increased whereas the turn-around-time decreased. We aimed to optimize the diagnostic workflow with a limited number of assays while allowing detection of all relevant genetic aberrations in a clinically relevant timeframe. Methods In a consecutive cohort of 467 newly diagnosed patients (0 to <19 years) immunophenotyped as ALL, we compared the diagnostic yield of RNA sequencing, 7 fluorescence in situ hybridization (FISH) probes, 7 RT-PCR targets, karyotyping, SNP array (CytoSNP-850K), and multiplex ligation-dependent probe amplification (MLPA; P335-ALL-IKZF1) for conclusiveness, concordance and turn-around-time. Results To detect stratifying fusions (ETV6::RUNX1, BCR::ABL1, ABL-class, KMT2Ar, TCF3::HLF, IGH::MYC), RNA sequencing and FISH were conclusive for 97% and 96% of patients, respectively, with 99% (433/437) concordance of test results. RT-PCR for 6 fusion transcripts was conclusive for >99%, but false-negative for 6 patients who had alternative exons in their fusion genes. RNA sequencing detected fusion genes even in samples with a leukemic cell percentage as low as 10% or RNA integrity score as low as 2. A TCF3::HLF fusion was not detected by RNA sequencing due to the insertion of intronic sequences in the chimeric transcript. A KMT2A::USP2 and a BCR::ABL1 fusion were not detected by FISH because of an inversion and probably insertion, respectively, not causing a break-apart FISH pattern. RNA sequencing detected subtype-defining fusions not (yet) used for stratification in 14% of BCP-ALL and 33% of T-ALL as well as rare fusions in 2% of BCP-ALL and 8% of T-ALL, including a targetable ETV6::NTRK3 fusion which were by definition not detected by targeted FISH or RT-PCR. Moreover, RNA sequencing showed added value for classification based on expression profile and detection of expressed mutations. The turn-around-time for RNA sequencing (7-15 days) and FISH (6-13 days) were both compatible with the demand for diagnostic reporting by day 15 of high-risk genetics and ABL-class fusions for the ALLTogether01 protocol and of BCR::ABL1 positivity for referral to the EsPhALL protocol. Because KMT2A rearrangement should be detected within 7 days to enter the Interfant-21 protocol, the shorter turn-around-time of FISH and RT-PCR were needed in infants (<1 year). For the detection of aneuploidy groups (high hyperdiploidy, low hypodiploidy and near haploidy) and intrachromosomal amplification of chromosome 21 (iAMP21), SNP array gave conclusive results for 99% of the patients, thereby outperforming karyotyping, which was conclusive for 64% and mistook 2 cases of masked hypodiploidy for high hyperdiploidy. Based on the amplification pattern on chromosome 21, 13 iAMP21 cases were identified by SNP array. Using a combination of karyotyping and RUNX1 metaphase FISH, 9/13 iAMP21 cases were identified with the remaining 4 cases lacking informative karyotypes. To identify deletions in eight genes/regions relevant for CNA risk stratification (IKZF1, CDKN2A/B, PAX5, EBF1, ETV6, RB1, BTG1 and PAR1), SNP array was conclusive in 99% and MLPA in 95% of patients with 98% concordant CNA risk calls. SNP array was more sensitive than MLPA in aneuploid samples and samples with low leukemic cell percentage. Beyond the currently required detection of MLPA-based deletions, SNP array detected deletions in (single) exons and genes not covered by the MLPA assay as well as aberrations in low mosaicism. Conclusions Our assay conclusiveness for ≥97% of patients and concordance of results with classic methods of 99% in 467 consecutive patients has resulted in the implementation of RNA sequencing and SNP array as the primary choice in the molecular diagnostics of newly diagnosed ALL in the Netherlands with addition of FISH and RT-PCR to detect KMT2A rearrangement in the infant population only. Performing RNA sequencing and SNP array for all patients has the advantage of detecting new lesions and expressed mutations to retrospectively study their role in prognosis and pathobiology.

1. Introduction. Cancer is one of the primary causes of death among children. Despite recent progress in treatment, the prognosis for patients with high-risk and relapse disease is still very poor, while survivors commonly suffer from adverse treatment-related effects. Identifying the genetic aberrations underlying the different types of pediatric cancer could help understand tumor biology, drive drug development, and improve prognosis overall. We hereby describe the NL-4C: the Dutch Comprehensive Childhood Cancer Commons, a research infrastructure initiative that aims to collect over 4,000 pediatric cancer genomes from the Dutch population to develop new insights into tumor biology. 2. Description. The NL-4C platform is hosted by the Prinses Máxima Center, the Dutch national institute for pediatric oncology that receives, on average 600 new cases every year. At the Máxima, molecular characterization of all consented patients is performed using next generation sequencing techniques including whole genome (WGS), whole exome (WES) and RNA sequencing. The latter two are standard of care, and used in our precision medicine program MaxPM. NL-4C will build up on this, and offer 3 core research infrastructure components: 1) a large collection of pediatric genomes with harmonized downstream computational analyses such as germline and somatic single-nucleotide variants and indels, copy number and larger structural variants. 2) A patient data registry with baseline and clinical annotations; all these data will be made available through, 3) a 3-tiered cloud-based portal tailored for general and scientific audiences to foster international collaboration. Importantly, NL-4C adheres to strict data protection and privacy regulations to guarantee that patient information is safely used in the cloud and patients’ rights are preserved. 3. Results. The NL-4C initiative has taken the first steps to deliver a working platform: on April 2024, the first 100 genomes were placed within the Google Cloud tenant of the Prinses Máxima Center. By September 2024, we anticipate that the number of genomes available in the cloud will surpass the 1,000 mark,we will have released the first version of computational pipelines and resulting data, and provide a minimal data portal.4. Discussion Through the collection of genomic and clinical data of Dutch pediatric cancer patients, we aim to join efforts of similar precision oncology programs worldwide, and develop a large federated, harmonized data resource to drive pediatric cancer research, help characterize tumor subtypes, identify actionable events, and improve treatment outcome overall. NL-4C is a scientific research infrastructure project funded by the NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek or Dutch Research Council, 2023-2028). Citation Format: Karina C. Borja Jiménez, Bastiaan B.J. Tops, Jayne H. Hehir-Kwa, Hinri H.D. Kerstens, Harm van Tinteren, Harriët F.A. Zoon, Patrick C.W. Kemmeren. NL-4C: The Dutch Comprehensive Childhood Commons, a resource to tackle pediatric cancer worldwide [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 B068.

Abstract With many documented tumor entities, acquiring the correct diagnosis is a challenging but crucial process in pediatric oncology. Notably, rare tumors present a unique challenge given their infrequency and relative unfamiliarity among pathologists. As a result, these tumor entities tend to be affected by higher misclassification rates. Here we present M&M, a pan-cancer ensemble-based machine-learning algorithm specifically tailored towards inclusion of rare pediatric tumor (sub)types. The RNA-seq based algorithm can classify 52 different tumor types, plus the underlying 96 tumor subtypes. Furthermore, M&M encompasses samples from all tumor stages, treatment statuses and from several non-neoplastic tissues. To facilitate infrequently occurring tumor (sub)type classifications, two different classifiers were created and integrated: a Minority classifier tailored towards correct classification of rare tumor (sub)types, and a Majority classifier with more predictive power for high frequency tumor entities. Each classifier was created using the same four steps of feature selection, feature reduction, down-sampling, and classification algorithm selection, using different focusing methods. Classification took place on the tumor subtype level, from which the tumor type could be extrapolated.M&M could correctly classify the tumor type for 94.5% of the samples within the reference cohort, and the underlying tumor subtype for 86.3%. When filtering on high-confidence classifications, M&M could reach a precision of ∼99% for ∼80% of the tumor type, and a precision of ∼96% for 70% of the tumor subtype classifications. For the low-confidence classifications, the correct tumor classification was often included in the three highest-scoring labels, leading to an overall accuracy of 98% within the top 3. For the tumor subtype classifications, this score was 95%. More than two-third of the samples from infrequently occuring tumor types (3-5 samples) received a high-confidence classification, accompanied by a precision of ∼94%. For classes covered within the classifier, M&M’s performance is comparable to existing class-restricted classifiers like the DKFZ methylation classifier for central nervous system tumors. An independent test cohort confirmed the robustness of M&M's performance.Machine-learning algorithms for both adult and childhood cancer are increasingly used in the clinic, contributing towards increased patient survival. However, many tumor entities are currently missing from existing classifiers. Developing and introducing an extensive agnostic pan-cancer classifier in diagnostics has the potential to increase the diagnostic accuracy for many pediatric cancer cases, thereby contributing towards optimal patient survival and quality of life. Citation Format: Fleur S.A. Wallis, John L. Baker-Hernandez, Marc van Tuil, Claudia van Hamersveld, Marco J. Koudijs, Eugène T.P. Verwiel, Alex Janse, Laura S. Hiemcke-Jiwa, Ronald R. de Krijger, Mariëtte E.G. Kranendonk, Marijn A. Vermeulen, Pieter Wesseling, Uta E. Flucke, Valérie de Haas, Maaike Luesink, Jayne Y. Hehir-Kwa, Bastiaan B.J. Tops, Patrick Kemmeren, Lennart A. Kester. M&M: An RNA-seq based pan-cancer classifier for pediatric tumors [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 B077.

With many documented tumor entities, acquiring the correct diagnosis is a challenging but crucial process in pediatric oncology. Notably, rare tumors present a unique challenge given their infrequency and relative unfamiliarity among pathologists. As a result, these tumor entities tend to be affected by higher misclassification rates. Here we present M&M, a pan-cancer ensemble-based machine-learning algorithm specifically tailored towards inclusion of rare pediatric tumor (sub)types. The RNA-seq based algorithm can classify 52 different tumor types, plus the underlying 96 tumor subtypes. Furthermore, M&M encompasses samples from all tumor stages, treatment statuses and from several non-neoplastic tissues. To facilitate infrequently occurring tumor (sub)type classifications, two different classifiers were created and integrated: a Minority classifier tailored towards correct classification of rare tumor (sub)types, and a Majority classifier with more predictive power for high frequency tumor entities. Each classifier was created using the same four steps of feature selection, feature reduction, down-sampling, and classification algorithm selection, using different focusing methods. Classification took place on the tumor subtype level, from which the tumor type could be extrapolated.M&M could correctly classify the tumor type for 94.5% of the samples within the reference cohort, and the underlying tumor subtype for 86.3%. When filtering on high-confidence classifications, M&M could reach a precision of ∼99% for ∼80% of the tumor type, and a precision of ∼96% for 70% of the tumor subtype classifications. For the low-confidence classifications, the correct tumor classification was often included in the three highest-scoring labels, leading to an overall accuracy of 98% within the top 3. For the tumor subtype classifications, this score was 95%. More than two-third of the samples from infrequently occuring tumor types (3-5 samples) received a high-confidence classification, accompanied by a precision of ∼94%. For classes covered within the classifier, M&M’s performance is comparable to existing class-restricted classifiers like the DKFZ methylation classifier for central nervous system tumors. An independent test cohort confirmed the robustness of M&M's performance.Machine-learning algorithms for both adult and childhood cancer are increasingly used in the clinic, contributing towards increased patient survival. However, many tumor entities are currently missing from existing classifiers. Developing and introducing an extensive agnostic pan-cancer classifier in diagnostics has the potential to increase the diagnostic accuracy for many pediatric cancer cases, thereby contributing towards optimal patient survival and quality of life. Citation Format: Fleur S.A. Wallis, John L. Baker-Hernandez, Marc van Tuil, Claudia van Hamersveld, Marco J. Koudijs, Eugène T.P. Verwiel, Alex Janse, Laura S. Hiemcke-Jiwa, Ronald R. de Krijger, Mariëtte E.G. Kranendonk, Marijn A. Vermeulen, Pieter Wesseling, Uta E. Flucke, Valérie de Haas, Maaike Luesink, Jayne Y. Hehir-Kwa, Bastiaan B.J. Tops, Patrick Kemmeren, Lennart A. Kester. M&M: An RNA-seq based pan-cancer classifier for pediatric tumors [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 B077.

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement We gratefully acknowledge that financial support was provided by the Foundation Children Cancer Free (KiKa core funding) and Adessium Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Biobank and Data Access Committee (BDAC) of the Princess Máxima Center for Pediatric Oncology gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available or will be available online on Github, Zenodo and ArrayExpress.

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.

Over the past 10 years, institutional and national molecular tumor boards have been implemented for relapsed or refractory pediatric cancer to prioritize targeted drugs for individualized treatment based on actionable oncogenic lesions, including the Dutch iTHER platform. Hematological malignancies form a minority in precision medicine studies. Here, we report on 56 iTHER leukemia/lymphoma patients for which we considered cell surface markers and oncogenic aberrations as actionable events, supplemented with ex vivo drug sensitivity for six patients. Prior to iTHER registration, 34% of the patients had received allogeneic hematopoietic cell transplantation (HCT) and 18% CAR-T therapy. For 51 patients (91%), a sample with sufficient tumor percentage (≥20%) required for comprehensive diagnostic testing was obtained. Up to 10 oncogenic actionable events were prioritized in 49/51 patients, and immunotherapy targets were identified in all profiled patients. Targeted treatment(s) based on the iTHER advice was given to 24 of 51 patients (47%), including immunotherapy in 17 patients, a targeted drug matching an oncogenic aberration in 12 patients, and a drug based on ex vivo drug sensitivity in one patient, resulting in objective responses and a bridge to HCT in the majority of the patients. In conclusion, comprehensive profiling of relapsed/refractory hematological malignancies showed multiple oncogenic and immunotherapy targets for a precision medicine approach, which requires multidisciplinary expertise to prioritize the best treatment options for this rare, heavily pretreated pediatric population.

EDITORIAL article Front. Genet., 04 January 2023Sec. Computational Genomics Volume 13 - 2022 | https://doi.org/10.3389/fgene.2022.1114542