Sarcomas are malignant soft tissue and bone tumours affecting adults, adolescents and children. They represent a morphologically heterogeneous class of tumours and some entities lack defining histopathological features. Therefore, the diagnosis of sarcomas is burdened with a high inter-observer variability and misclassification rate. Here, we demonstrate classification of soft tissue and bone tumours using a machine learning classifier algorithm based on array-generated DNA methylation data. This sarcoma classifier is trained using a dataset of 1077 methylation profiles from comprehensively pre-characterized cases comprising 62 tumour methylation classes constituting a broad range of soft tissue and bone sarcoma subtypes across the entire age spectrum. The performance is validated in a cohort of 428 sarcomatous tumours, of which 322 cases were classified by the sarcoma classifier. Our results demonstrate the potential of the DNA methylation-based sarcoma classification for research and future diagnostic applications.

In 2012, an international consensus paper reported that medulloblastoma comprises four molecular subgroups (WNT, SHH, Group 3, and Group 4), each associated with distinct genomic features and clinical behavior. Independently, multiple recent reports have defined further intra-subgroup heterogeneity in the form of biologically and clinically relevant subtypes. However, owing to differences in patient cohorts and analytical methods, estimates of subtype number and definition have been inconsistent, especially within Group 3 and Group 4. Herein, we aimed to reconcile the definition of Group 3/Group 4 MB subtypes through the analysis of a series of 1501 medulloblastomas with DNA-methylation profiling data, including 852 with matched transcriptome data. Using multiple complementary bioinformatic approaches, we compared the concordance of subtype calls between published cohorts and analytical methods, including assessments of class-definition confidence and reproducibility. While the lowest complexity solutions continued to support the original consensus subgroups of Group 3 and Group 4, our analysis most strongly supported a definition comprising eight robust Group 3/Group 4 subtypes (types I–VIII). Subtype II was consistently identified across all component studies, while all others were supported by multiple class-definition methods. Regardless of analytical technique, increasing cohort size did not further increase the number of identified Group 3/Group 4 subtypes. Summarizing the molecular and clinico-pathological features of these eight subtypes indicated enrichment of specific driver gene alterations and cytogenetic events amongst subtypes, and identified highly disparate survival outcomes, further supporting their biological and clinical relevance. Collectively, this study provides continued support for consensus Groups 3 and 4 while enabling robust derivation of, and categorical accounting for, the extensive intertumoral heterogeneity within Groups 3 and 4, revealed by recent high-resolution subclassification approaches. Furthermore, these findings provide a basis for application of emerging methods (e.g., proteomics/single-cell approaches) which may additionally inform medulloblastoma subclassification. Outputs from this study will help shape definition of the next generation of medulloblastoma clinical protocols and facilitate the application of enhanced molecularly guided risk stratification to improve outcomes and quality of life for patients and their families.

The large diversity of central nervous system (CNS) tumor types in children and adolescents results in disparate patient outcomes and renders accurate diagnosis challenging. In this study, we prospectively integrated DNA methylation profiling and targeted gene panel sequencing with blinded neuropathological reference diagnostics for a population-based cohort of more than 1,200 newly diagnosed pediatric patients with CNS tumors, to assess their utility in routine neuropathology. We show that the multi-omic integration increased diagnostic accuracy in a substantial proportion of patients through annotation to a refining DNA methylation class (50%), detection of diagnostic or therapeutically relevant genetic alterations (47%) or identification of cancer predisposition syndromes (10%). Discrepant results by neuropathological WHO-based and DNA methylation-based classification (30%) were enriched in histological high-grade gliomas, implicating relevance for current clinical patient management in 5% of all patients. Follow-up (median 2.5 years) suggests improved survival for patients with histological high-grade gliomas displaying lower-grade molecular profiles. These results provide preliminary evidence of the utility of integrating multi-omics in neuropathology for pediatric neuro-oncology.

Patients after solid organ transplantation (SOT) carry a substantially increased risk to develop malignant lymphomas. This is in part due to the immunosuppression required to maintain the function of the organ graft. Depending on the transplanted organ, up to 15% of pediatric transplant recipients acquire posttransplant lymphoproliferative disease (PTLD), and eventually 20% of those succumb to the disease. Early diagnosis of PTLD is often hampered by the unspecific symptoms and the difficult differential diagnosis, which includes atypical infections as well as graft rejection. Treatment of PTLD is limited by the high vulnerability towards antineoplastic chemotherapy in transplanted children. However, new treatment strategies and especially the introduction of the monoclonal anti-CD20 antibody rituximab have dramatically improved outcomes of PTLD. This review discusses risk factors for the development of PTLD in children, summarizes current approaches to therapy, and gives an outlook on developing new treatment modalities like targeted therapy with virus-specific T cells. Finally, monitoring strategies are evaluated.

Human adenovirus (HAdV) infection after hematopoietic stem cell transplantation (HSCT) is associated with significant morbidity and mortality in children. The optimal surveillance and treatment strategies are under discussion. Here, we present data from 238 consecutive pediatric allogeneic HSCT recipients who underwent transplantation in a single center who were included in a prospective, weekly HAdV DNAemia monitoring program by quantitative PCR. HAdV loads >1000 copies/mL were detected in 15.5% of all patients. Despite a low mortality directly attributed to HAdV infection (2 patients, 0.84%), blood HAdV loads >10,000 copies/mL (6.7% of all patients) were significant and independent risk factors for poor survival. We searched for patient, virus, and treatment-related risk factors of HAdV DNAemia and disease. Detection of HAdV in blood before day 50 post transplantation was a major independent risk factor for the development of blood HAdV loads >10,000 copies/mL. HAdV typing revealed A31, C1, and C2 as the predominant pathogens among several other HAdV strains with type C species detected in most patients with severe HAdV disease. Stool HAdV loads were prospectively monitored in 111 patients and correlated with but did not significantly precede detection in blood. Treatment with cidofovir led to stable or reduced viral load in 70% of patients with blood HAdV loads >1000 copies/mL. Thus, early occurrence of HAdV-DNA in blood of pediatric HSCT recipients predisposes for development of high viral loads. Control of HAdV infections was attempted by preemptive cidofovir treatment of patients with high blood HAdV loads or with symptomatic organ infections and correlated with low HAdV-attributed mortality.