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Analysis of the genomes, exomes and transcriptomes of 1,699 childhood cancers identifies 142 driver genes. Genomic landscape of childhood cancers The genetic alterations that give rise to childhood cancer are less well studied than those that give rise to adult cancers. Two papers in this issue report some of the first pan-cancer analyses of childhood cancers. Stefan Pfister and colleagues studied germline and somatic genomes from 914 young cancer patients, including children, adolescents and young adults. The tumour samples comprised 24 distinct molecular cancer types, including the most frequent and clinically relevant childhood cancers. The team characterized somatic mutation frequencies, genomic alterations, including structural variations and copy-number analysis, and mutational signatures. They found signatures associated with deficiencies of double-stranded break repair across all cancer types. Additionally, 7.6% of patients carried a likely pathogenic germline variant in a candidate cancer predisposition gene. Jinghui Zhang and colleagues analysed the genomes, exomes and transcriptomes of 1,699 paediatric leukaemias and solid tumours. They identified 142 driver genes in paediatric cancers, over half of which were specific to a single histotype. They also characterized copy number alterations and structural variation and identified 11 mutational signatures. Together, these papers provide a comprehensive resource for genomic alterations across common paediatric tumours, and highlight differences compared with the genomic alterations seen in adult cancers. Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes that are dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult 1 , 2 , 3 , 4 but not paediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues 5 . Here we present a pan-cancer study of somatic alterations, including single nucleotide variants, small insertions or deletions, structural variations, copy number alterations, gene fusions and internal tandem duplications in 1,699 paediatric leukaemias and solid tumours across six histotypes, with whole-genome, whole-exome and transcriptome sequencing data processed under a uniform analytical framework. We report 142 driver genes in paediatric cancers, of which only 45% match those found in adult pan-cancer studies; copy number alterations and structural variants constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukaemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for paediatric cancers and emphasize the need for paediatric cancer-specific development of precision therapies.

Charles Mullighan, Stephen Hunger, Jinghui Zhang and colleagues report a genomic analysis of 264 pediatric and young adult T-lineage acute lymphoblastic leukemia (T-ALL) samples. They identify 106 candidate driver genes, 53 of which have not been described previously in pediatric T-ALL, as well as associations between mutations and disease stage or subtype. Genetic alterations that activate NOTCH1 signaling and T cell transcription factors, coupled with inactivation of the INK4/ARF tumor suppressors, are hallmarks of T-lineage acute lymphoblastic leukemia (T-ALL), but detailed genome-wide sequencing of large T-ALL cohorts has not been carried out. Using integrated genomic analysis of 264 T-ALL cases, we identified 106 putative driver genes, half of which had not previously been described in childhood T-ALL (for example, CCND3 , CTCF , MYB , SMARCA4 , ZFP36L2 and MYCN ). We describe new mechanisms of coding and noncoding alteration and identify ten recurrently altered pathways, with associations between mutated genes and pathways, and stage or subtype of T-ALL. For example, NRAS / FLT3 mutations were associated with immature T-ALL, JAK3 / STAT5B mutations in HOXA1 deregulated ALL, PTPN2 mutations in TLX1 deregulated T-ALL, and PIK3R1 / PTEN mutations in TAL1 deregulated ALL, which suggests that different signaling pathways have distinct roles according to maturational stage. This genomic landscape provides a logical framework for the development of faithful genetic models and new therapeutic approaches.

Acute erythroid leukemia (AEL) is a high-risk leukemia of poorly understood genetic basis, with controversy regarding diagnosis in the spectrum of myelodysplasia and myeloid leukemia. We compared genomic features of 159 childhood and adult AEL cases with non-AEL myeloid disorders and defined five age-related subgroups with distinct transcriptional profiles: adult, TP53 mutated; NPM1 mutated; KMT2A mutated/rearranged; adult, DDX41 mutated; and pediatric, NUP98 rearranged. Genomic features influenced outcome, with NPM1 mutations and HOXB9 overexpression being associated with a favorable prognosis and TP53 , FLT3 or RB1 alterations associated with poor survival. Targetable signaling mutations were present in 45% of cases and included recurrent mutations of ALK and NTRK1 , the latter of which drives erythroid leukemogenesis sensitive to TRK inhibition. This genomic landscape of AEL provides the framework for accurate diagnosis and risk stratification of this disease, and the rationale for testing targeted therapies in this high-risk leukemia. Analysis of genomic and clinical features of acute erythroid leukemia in comparison to other myeloid disorders supports its distinct classification, defines subgroups and suggests therapeutic vulnerabilities.

Methylation profiling has radically transformed our understanding of tumors previously called central nervous system primitive neuro-ectodermal tumors (CNS-PNET). While this marks a momentous step toward defining key differences, reclassification has thrown treatment into disarray. To shed light on response to therapy and guide clinical decision-making, we report outcomes and molecular features of children with CNS-PNETs from two multi-center risk-adapted studies (SJMB03 for patients ≥ 3 years; SJYC07 for patients < 3 years) complemented by a non-protocol institutional cohort. Seventy patients who had a histological diagnosis of CNS-PNET or CNS embryonal tumor from one of the new categories that has supplanted CNS-PNET were included. This cohort was molecularly characterized by DNA methylation profiling (n = 70), whole-exome sequencing (n = 53), RNA sequencing (n = 20), and germline sequencing (n = 28). Clinical characteristics were detailed, and treatment was divided into craniospinal irradiation (CSI)-containing (SJMB03 and SJMB03-like) and CSI-sparing therapy (SJYC07 and SJYC07-like). When the cohort was analyzed in its entirety, no differences were observed in the 5-year survival rates even when CSI-containing therapy was compared to CSI-sparing therapy. However, when analyzed by DNA methylation molecular grouping, significant survival differences were observed, and treatment particulars provided suggestions of therapeutic response. Patients with CNS neuroblastoma with FOXR2 activation (CNS-NB-FOXR2) had a 5-year event-free survival (EFS)/overall survival (OS) of 66.7% ± 19.2%/83.3% ± 15.2%, and CIC rearranged sarcoma (CNS-SARC-CIC) had a 5-year EFS/OS both of 57.1% ± 18.7% with most receiving regimens that contained radiation (focal or CSI) and multidrug chemotherapy. Patients with high-grade neuroepithelial tumor with BCOR alteration (HGNET-BCOR) had abysmal responses to upfront chemotherapy-only regimens (5-year EFS = 0%), but survival extended with salvage radiation after progression [5-year OS = 53.6% ± 20.1%]. Patients with embryonal tumor with multilayered rosettes (ETMR) or high-grade glioma/glioblastoma multiforme (HGG/GBM) did not respond favorably to any modality (5-year EFS/OS = 10.7 ± 5.8%/17.9 ± 7.2%, and 10% ± 9.0%/10% ± 9.0%, respectively). As an accompaniment, we have assembled this data onto an interactive website to allow users to probe and query the cases. By reporting on a carefully matched clinical and molecular cohort, we provide the needed insight for future clinical management.

Analysis of molecular aberrations across multiple cancer types, known as pan-cancer analysis, identifies commonalities and differences in key biological processes dysregulated in cancer cells from diverse lineages. Pan-cancer analyses have been performed for adult1–4 but not pediatric cancers, which commonly occur in developing mesodermic rather than adult epithelial tissues5. Here we present a pan-cancer study of somatic alterations, including single nucleotide variants (SNVs), small insertion/deletions (indels), structural variations (SVs), copy number alterations (CNAs), gene fusions and internal tandem duplications (ITDs), in 1,699 pediatric leukemia and solid tumours across six histotypes, with whole-genome (WGS), whole-exome (WES) and transcriptome (RNA-seq) sequencing data processed under a uniform analytical framework (Online Methods and Extended Data Fig. 1). We report 142 driver genes in pediatric cancers, of which only 45% matched those found in adult pan-cancer studies and CNAs and SVs constituted the majority (62%) of events. Eleven genome-wide mutational signatures were identified, including one attributed to ultraviolet-light exposure in eight aneuploid leukemias. Transcription of the mutant allele was detectable for 34% of protein-coding mutations, and 20% exhibited allele-specific expression. These data provide a comprehensive genomic architecture for pediatric cancers and emphasize the need for pediatric cancer-specific development of precision therapies.

Effective data sharing is key to accelerating research that will improve the precision of diagnoses, efficacy of treatments and long-term survival of pediatric cancer and other childhood catastrophic diseases. We present St. Jude Cloud (https://www.stjude.cloud), a cloud-based data sharing ecosystem developed via collaboration between St. Jude Children’s Research Hospital, DNAnexus, and Microsoft, for accessing, analyzing and visualizing genomic data from >10,000 pediatric cancer patients, long-term survivors of pediatric cancer and >800 pediatric sickle cell patients. Harmonized genomic data totaling 1.25 petabyes on St. Jude Cloud include 12,104 whole genomes, 7,697 whole exomes and 2,202 transcriptomes, which are freely available to researchers worldwide. The resource is expanding rapidly with regular data uploads from St. Jude’s prospective clinical genomics programs, providing public access as soon as possible rather than holding data back until publication. Three interconnected apps within the St. Jude Cloud ecosystem—Genomics Platform, Pediatric Cancer Knowledgebase (PeCan) and Visualization Community—provide a unique experience for simultaneously performing advanced data analysis in the cloud and enhancing the pediatric cancer knowledgebase. We demonstrate the value of the St. Jude Cloud ecosystem through use cases that classify 48 pediatric cancer subtypes by gene expression profiling and map mutational signatures across 35 subtypes of pediatric cancer.