SUMMARY Survival rates for the childhood cancer neuroblastoma have not substantively improved despite dramatic escalation in chemotherapy intensity. Like most human cancers, this embryonal malignancy can be inherited, but the genetic etiology of familial and sporadically occurring neuroblastoma was largely unknown. Here we show that germline mutations in the anaplastic lymphoma kinase gene (ALK) explain the majority of hereditary neuroblastomas, and that activating mutations can also be somatically acquired. We first identified a significant linkage signal at the short arm of chromosome 2 (maximum nonparametric LOD=4.23 at rs1344063) using a whole-genome scan in neuroblastoma pedigrees. Resequencing of regional candidate genes identified three separate missense mutations in the tyrosine kinase domain of ALK (G1128A, R1192P and R1275Q) that segregated with the disease in eight separate families. Examination of 491 sporadically occurring human neuroblastoma samples showed that the ALK locus was gained in 22.8%, and highly amplified in an additional 3.3%, and that these aberrations were highly associated with death from disease (P=0.0003). Resequencing of 194 high-risk neuroblastoma samples showed somatically acquired mutations within the tyrosine kinase domain in 12.4%. Nine of the ten mutations map to critical regions of the kinase domain and were predicted to be oncogenic drivers with high probability. Mutations resulted in constitutive phosphorylation consistent with activation, and targeted knockdown of ALK mRNA resulted in profound growth inhibition of 4 of 4 cell lines harboring mutant or amplified ALK, as well as 2 of 6 wild type for ALK. Our results demonstrate that heritable mutations of ALK are the major cause of familial neuroblastoma, and that germline or acquired activation of this cell surface kinase is a tractable therapeutic target for this lethal pediatric malignancy.

Summary Medulloblastoma is an aggressively-growing tumour, arising in the cerebellum or medulla/brain stem. It is the most common malignant brain tumour in children, and displays tremendous biological and clinical heterogeneity1. Despite recent treatment advances, approximately 40% of children experience tumour recurrence, and 30% will die from their disease. Those who survive often have a significantly reduced quality of life. Four tumour subgroups with distinct clinical, biological and genetic profiles are currently discriminated2,3. WNT tumours, displaying activated wingless pathway signalling, carry a favourable prognosis under current treatment regimens4. SHH tumours show hedgehog pathway activation, and have an intermediate prognosis2. Group 3 & 4 tumours are molecularly less well-characterised, and also present the greatest clinical challenges2,3,5. The full repertoire of genetic events driving this distinction, however, remains unclear. Here we describe an integrative deep-sequencing analysis of 125 tumour-normal pairs. Tetraploidy was identified as a frequent early event in Group 3 & 4 tumours, and a positive correlation between patient age and mutation rate was observed. Several recurrent mutations were identified, both in known medulloblastoma-related genes (CTNNB1, PTCH1, MLL2, SMARCA4) and in genes not previously linked to this tumour (DDX3X, CTDNEP1, KDM6A, TBR1), often in subgroup-specific patterns. RNA-sequencing confirmed these alterations, and revealed the expression of the first medulloblastoma fusion genes. Chromatin modifiers were frequently altered across all subgroups. These findings enhance our understanding of the genomic complexity and heterogeneity underlying medulloblastoma, and provide several potential targets for new therapeutics, especially for Group 3 & 4 patients.

BackgroundGene expression profiling is being widely applied in cancer research to identify biomarkers for clinical endpoint prediction. Since RNA-seq provides a powerful tool for transcriptome-based applications beyond the limitations of microarrays, we sought to systematically evaluate the performance of RNA-seq-based and microarray-based classifiers in this MAQC-III/SEQC study for clinical endpoint prediction using neuroblastoma as a model.ResultsWe generate gene expression profiles from 498 primary neuroblastomas using both RNA-seq and 44 k microarrays. Characterization of the neuroblastoma transcriptome by RNA-seq reveals that more than 48,000 genes and 200,000 transcripts are being expressed in this malignancy. We also find that RNA-seq provides much more detailed information on specific transcript expression patterns in clinico-genetic neuroblastoma subgroups than microarrays. To systematically compare the power of RNA-seq and microarray-based models in predicting clinical endpoints, we divide the cohort randomly into training and validation sets and develop 360 predictive models on six clinical endpoints of varying predictability. Evaluation of factors potentially affecting model performances reveals that prediction accuracies are most strongly influenced by the nature of the clinical endpoint, whereas technological platforms (RNA-seq vs. microarrays), RNA-seq data analysis pipelines, and feature levels (gene vs. transcript vs. exon-junction level) do not significantly affect performances of the models.ConclusionsWe demonstrate that RNA-seq outperforms microarrays in determining the transcriptomic characteristics of cancer, while RNA-seq and microarray-based models perform similarly in clinical endpoint prediction. Our findings may be valuable to guide future studies on the development of gene expression-based predictive models and their implementation in clinical practice.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0694-1) contains supplementary material, which is available to authorized users.

No abstract