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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. Study Highlights What is the current knowledge on the topic? Pharmacogenomics (PGx) uses genetic information to guide drug therapy, improving efficacy and reducing side effects. In pediatric oncology, PGx is mostly reactive, applied after adverse reactions occur, with only limited proactive use such as TPMT and NUDT15 testing for thiopurines. Despite its potential, broader pharmacogenomics implementation is limited by lack of pediatric‐specific evidence and logistical challenges but also missing validated software tools that can accurately predict diplotypes and phenotypes from whole genome sequencing (WGS) data. What question did this study address? Which pharmacogenomic annotation tool is most suitable for clinical use in pediatric oncology, to reliably translate genomic information into actionable genotype and phenotype predictions from WGS data? What does this study add to our knowledge? We identified and analytically validated PharmCAT as a reliable PGx annotation tool for pediatric oncology. PharmCAT accurately interprets WGS data for key pharmacogenes, providing diplotype and phenotype predictions aligned with clinical guidelines. Complex loci, like CYP2D6, require complementary tools, but overall, PharmCAT enables comprehensive, preemptive PGx analysis using existing genomic data without additional sampling. How might this change clinical pharmacology or translational science? Integrating PharmCAT into routine WGS workflows can support safer, more personalized therapy by preemptively identifying actionable variants. This WGS‐based PGx approach is feasible for implementation in pediatric oncology care and can be extended to additional pharmacogenes and other patient populations. Selection and validation of a whole genome sequencing pharmacogenomics annotation tool.

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.

Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.

Purpose The interpretation of genetic variants after genome-wide analysis is complex in heterogeneous disorders such as intellectual disability (ID). We investigate whether algorithms can be used to detect if a facial gestalt is present for three novel ID syndromes and if these techniques can help interpret variants of uncertain significance. Methods Facial features were extracted from photos of ID patients harboring a pathogenic variant in three novel ID genes ( PACS1 , PPM1D , and PHIP ) using algorithms that model human facial dysmorphism, and facial recognition. The resulting features were combined into a hybrid model to compare the three cohorts against a background ID population. Results We validated our model using images from 71 individuals with Koolen–de Vries syndrome, and then show that facial gestalts are present for individuals with a pathogenic variant in PACS1 ( p  = 8 × 10 −4 ), PPM1D ( p  = 4.65 × 10 −2 ), and PHIP ( p  = 6.3 × 10 −3 ). Moreover, two individuals with a de novo missense variant of uncertain significance in PHIP have significant similarity to the expected facial phenotype of PHIP patients ( p  < 1.52 × 10 −2 ). Conclusion Our results show that analysis of facial photos can be used to detect previously unknown facial gestalts for novel ID syndromes, which will facilitate both clinical and molecular diagnosis of rare and novel syndromes.

Background Gene fusions are important cancer drivers in pediatric cancer and their accurate detection is essential for diagnosis and treatment. Clinical decision-making requires high confidence and precision of detection. Recent developments show RNA sequencing (RNA-seq) is promising for genome-wide detection of fusion products but hindered by many false positives that require extensive manual curation and impede discovery of pathogenic fusions. Methods We developed Fusion-sq to overcome existing disadvantages of detecting gene fusions. Fusion-sq integrates and “fuses” evidence from RNA-seq and whole genome sequencing (WGS) using intron–exon gene structure to identify tumor-specific protein coding gene fusions. Fusion-sq was then applied to the data generated from a pediatric pan-cancer cohort of 128 patients by WGS and RNA sequencing. Results In a pediatric pan-cancer cohort of 128 patients, we identified 155 high confidence tumor-specific gene fusions and their underlying structural variants (SVs). This includes all clinically relevant fusions known to be present in this cohort (30 patients). Fusion-sq distinguishes healthy-occurring from tumor-specific fusions and resolves fusions in amplified regions and copy number unstable genomes. A high gene fusion burden is associated with copy number instability. We identified 27 potentially pathogenic fusions involving oncogenes or tumor-suppressor genes characterized by underlying SVs, in some cases leading to expression changes indicative of activating or disruptive effects. Conclusions Our results indicate how clinically relevant and potentially pathogenic gene fusions can be identified and their functional effects investigated by combining WGS and RNA-seq. Integrating RNA fusion predictions with underlying SVs advances fusion detection beyond extensive manual filtering. Taken together, we developed a method for identifying candidate gene fusions that is suitable for precision oncology applications. Our method provides multi-omics evidence for assessing the pathogenicity of tumor-specific gene fusions for future clinical decision making.

Copy number variants (CNVs) have recently been recognized as a common form of genomic variation in humans. Hundreds of CNVs can be detected in any individual genome using genomic microarrays or whole genome sequencing technology, but their phenotypic consequences are still poorly understood. Rare CNVs have been reported as a frequent cause of neurological disorders such as mental retardation (MR), schizophrenia and autism, prompting widespread implementation of CNV screening in diagnostics. In previous studies we have shown that, in contrast to benign CNVs, MR-associated CNVs are significantly enriched in genes whose mouse orthologues, when disrupted, result in a nervous system phenotype. In this study we developed and validated a novel computational method for differentiating between benign and MR-associated CNVs using structural and functional genomic features to annotate each CNV. In total 13 genomic features were included in the final version of a Naïve Bayesian Tree classifier, with LINE density and mouse knock-out phenotypes contributing most to the classifier's accuracy. After demonstrating that our method (called GECCO) perfectly classifies CNVs causing known MR-associated syndromes, we show that it achieves high accuracy (94%) and negative predictive value (99%) on a blinded test set of more than 1,200 CNVs from a large cohort of individuals with MR. These results indicate that this classification method will be of value for objectively prioritizing CNVs in clinical research and diagnostics.

Whole-genome sequencing is used to identify genetic alterations in patients with severe intellectual disability for whom all other tests, including array and exome sequencing, returned negative results; de novo single-nucleotide and copy number variations affecting the coding region seem to be a major cause of this disorder. Gene variation in intellectual disability Intellectual disability has been shown to be linked to genetic variation but the majority of cases remain undiagnosed. This paper demonstrates the use of whole-genome sequencing to identify genetic alterations in patients with severe intellectual disability for whom all other tests, including array and exome sequencing, had returned negative results. Whole-genome sequencing of 50 patients with severe intellectual disability — and with no family history of the condition — resulted in a conclusive genetic diagnosis in 21 patients. The results suggest that de novo copy number variations and single-nucleotide variations affecting the coding region are a major cause of severe intellectual disability. Severe intellectual disability (ID) occurs in 0.5% of newborns and is thought to be largely genetic in origin 1 , 2 . The extensive genetic heterogeneity of this disorder requires a genome-wide detection of all types of genetic variation. Microarray studies and, more recently, exome sequencing have demonstrated the importance of de novo copy number variations (CNVs) and single-nucleotide variations (SNVs) in ID, but the majority of cases remain undiagnosed 3 , 4 , 5 , 6 . Here we applied whole-genome sequencing to 50 patients with severe ID and their unaffected parents. All patients included had not received a molecular diagnosis after extensive genetic prescreening, including microarray-based CNV studies and exome sequencing. Notwithstanding this prescreening, 84 de novo SNVs affecting the coding region were identified, which showed a statistically significant enrichment of loss-of-function mutations as well as an enrichment for genes previously implicated in ID-related disorders. In addition, we identified eight de novo CNVs, including single-exon and intra-exonic deletions, as well as interchromosomal duplications. These CNVs affected known ID genes more frequently than expected. On the basis of diagnostic interpretation of all de novo variants, a conclusive genetic diagnosis was reached in 20 patients. Together with one compound heterozygous CNV causing disease in a recessive mode, this results in a diagnostic yield of 42% in this extensively studied cohort, and 62% as a cumulative estimate in an unselected cohort. These results suggest that de novo SNVs and CNVs affecting the coding region are a major cause of severe ID. Genome sequencing can be applied as a single genetic test to reliably identify and characterize the comprehensive spectrum of genetic variation, providing a genetic diagnosis in the majority of patients with severe ID.

In 2013, the American College of Medical Genetics (ACMG) examined the issue of incidental findings in whole exome and whole genome sequencing, and introduced recommendations to search for, evaluate and report medically actionable variants in a set of 56 genes. At a debate held during the 2014 European Society for Human Genetics Conference (ESHG) in Milan, Italy, the first author of that paper presented this view in a debate session that did not end with a conclusive vote from the mainly European audience for or against reporting back actionable incidental findings. In this meeting report, we elaborate on the discussions held during a special meeting hosted at the ESHG in 2013 from posing the question 'How to reach a (European) consensus on reporting incidental findings and unclassified variants in diagnostic next generation sequencing'. We ask whether an European consensus exists on the reporting of incidental findings in genome diagnostics, and present a series of key issues that require discussion at both a national and European level in order to develop recommendations for handling incidental findings and unclassified variants in line with the legal and cultural particularities of individual European member states.

Rare copy number variants (CNVs) are frequently associated with common neurological disorders such as mental retardation (MR; learning disability), autism, and schizophrenia. CNV screening in clinical practice is limited because pathological CNVs cannot be distinguished routinely from benign CNVs, and because genes underlying patients' phenotypes remain largely unknown. Here, we present a novel, statistically robust approach that forges links between 148 MR-associated CNVs and phenotypes from approximately 5,000 mouse gene knockout experiments. These CNVs were found to be significantly enriched in two classes of genes, those whose mouse orthologues, when disrupted, result in either abnormal axon or dopaminergic neuron morphologies. Additional enrichments highlighted correspondences between relevant mouse phenotypes and secondary presentations such as brain abnormality, cleft palate, and seizures. The strength of these phenotype enrichments (>100% increases) greatly exceeded molecular annotations (<30% increases) and allowed the identification of 78 genes that may contribute to MR and associated phenotypes. This study is the first to demonstrate how the power of mouse knockout data can be systematically exploited to better understand genetically heterogeneous neurological disorders.

Genetic test results can have considerable importance for patients, their parents and more remote family members. Clinical therapy and surveillance, reproductive decisions and genetic diagnostics in family members, including prenatal diagnosis, are based on these results. The genetic test report should therefore provide a clear, concise, accurate, fully interpretative and authoritative answer to the clinical question. The need for harmonizing reporting practice of genetic tests has been recognised by the External Quality Assessment (EQA), providers and laboratories. The ESHG Genetic Services Quality Committee has produced reporting guidelines for the genetic disciplines (biochemical, cytogenetic and molecular genetic). These guidelines give assistance on report content, including the interpretation of results. Selected examples of genetic test reports for all three disciplines are provided in an annexe.