Ependymal tumors across age groups are currently classified and graded solely by histopathology. It is, however, commonly accepted that this classification scheme has limited clinical utility based on its lack of reproducibility in predicting patients’ outcome. We aimed at establishing a uniform molecular classification using DNA methylation profiling. Nine molecular subgroups were identified in a large cohort of 500 tumors, 3 in each anatomical compartment of the CNS, spine, posterior fossa, supratentorial. Two supratentorial subgroups are characterized by prototypic fusion genes involving RELA and YAP1, respectively. Regarding clinical associations, the molecular classification proposed herein outperforms the current histopathological classification and thus might serve as a basis for the next World Health Organization classification of CNS tumors.

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

Gene expression-based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.

No abstract

Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression-based classification and established prognostic markers.Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 Â 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n ¼ 634) by Kaplan-Meier estimates and Cox regression analyses.Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients On the basis of these findings, we propose to integrate the classifier into a revised risk stratification system for low-risk/intermediate-risk patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS, 0.19 AE 0.08; 5-year OS, 0.59 AE 0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS, 0.87 AE 0.05; 5-year OS, 1.0), who may benefit from treatment de-escalation.Conclusions: Combination of gene expression-based classification and established prognostic markers improves risk estimation of patients with low-risk/intermediate-risk neuroblastoma. We propose to implement our revised treatment stratification system in a prospective clinical trial.

Relapsed precursor T-cell acute lymphoblastic leukemia is characterized by resistance against chemotherapy and is frequently fatal. We aimed at understanding the molecular mechanisms resulting in relapse of T-cell acute lymphoblastic leukemia and analyzed 13 patients at first diagnosis, remission and relapse by whole exome sequencing, targeted ultra-deep sequencing, multiplex ligation dependent probe amplification and DNA methylation array. Compared to primary T-cell acute lymphoblastic leukemia, in relapse the number of single nucleotide variants and small insertions and deletions approximately doubled from 11.5 to 26. Targeted ultra-deep sequencing sensitively detected subclones that were selected for in relapse. The mutational pattern defined two types of relapses. While both are characterized by selection of subclones and acquisition of novel mutations, 'type 1' relapse derives from the primary leukemia whereas 'type 2' relapse originates from a common pre-leukemic ancestor. Relapse-specific changes included activation of the nucleotidase NT5C2 resulting in resistance to chemotherapy and mutations of epigenetic modulators, exemplified by SUZ12, WHSC1 and SMARCA4. While mutations present in primary leukemia and in relapse were enriched for known drivers of leukemia, relapse-specific changes revealed an association with general cancer-promoting mechanisms. This study thus identifies mechanisms that drive progression of pediatric T-cell acute lymphoblastic leukemia to relapse and may explain the characteristic treatment resistance of this condition. ALL-BFM 86/90, 1989-1998 INS 98 protocol based on ALL-BFM 95 40 , 1998-2003 and ALL Intercontinental (IC) -BFM 200315, 2003-2005 analyzed at the time of primary diagnosis, during remission and at relapse. Pediatric T-cell lymphoblastic leukemia evolves into relapse by clonal selection, acquisition of mutations and promoter hypomethylation ABSTRACT © F e r r a t a S t o r t i F o u n d a t i o n Methods Patients' clinical characteristicsPatients were treated according to ALL-BFM 2000 or related frontline protocols 14 IC 15 ). One patient was aged 18 at diagnosis, all others were children or adolescents. The 13 patients (Table 1) were recruited between 1993 and 2007 from the ALL-REZ BFM 2002 trials (patients T-ALL-H-A61, -E114, -F110, -KI17, -MD40, -T92, -T128) or from Schneider Children's Medical Center of Israel, Petah Tikva, Israel (patients T-ALL-H-S00169, -S00207, -S00285, -S00438, -S00456, -S00472) and selected on the basis of sufficient material being available from the time points of first diagnosis, remission and relapse. Minimal residual disease (MRD) response was assessed as described previously 2,16 (Online Supplementary Table S3).This study was approved by the institutional review boards of the Charité Universitätsmedizin Berlin and the Medical Faculty Heidelberg. Informed consent was obtained in accordance with the Declaration of Helsinki. Exome capture, target capture and Illumina sequencingThe Agilent SureSelect Target Enrichment Kit (Agilent...

Children with Down syndrome (DS) are prone to development of high-risk B-cell precursor ALL (DS-ALL), which differs genetically from most sporadic pediatric ALLs. Increased expression of cytokine receptor-like factor 2 (CRLF2), the receptor to thymic stromal lymphopoietin (TSLP), characterizes about half of DS-ALLs and also a subgroup of sporadic "Philadelphia-like" ALLs. To understand the pathogenesis of relapsed DS-ALL, we performed integrative genomic analysis of 25 matched diagnosis-remission and -relapse DSALLs. We found that the CRLF2 rearrangements are early events during DS-ALL evolution and generally stable between diagnoses and relapse. Secondary activating signaling events in the JAK-STAT/ RAS pathway were ubiquitous but highly redundant between diagnosis and relapse, suggesting that signaling is essential but that no specific mutations are "relapse driving." We further found that activated JAK2 may be naturally suppressed in 25% of CRLF2 pos DSALLs by loss-of-function aberrations in USP9X, a deubiquitinase previously shown to stabilize the activated phosphorylated JAK2. Interrogation of large ALL genomic databases extended our findings up to 25% of CRLF2 pos , Philadelphia-like ALLs. Pharmacological or genetic inhibition of USP9X, as well as treatment with low-dose ruxolitinib, enhanced the survival of pre-B ALL cells overexpressing mutated JAK2. Thus, somehow counterintuitive, we found that suppression of JAK-STAT "hypersignaling" may be beneficial to leukemic B-cell precursors. This finding and the reduction of JAK mutated clones at relapse suggest that the therapeutic effect of JAK specific inhibitors may be limited. Rather, combined signaling inhibitors or direct targeting of the TSLP receptor may be a useful therapeutic strategy for DS-ALL.C hildren with Down syndrome (DS) are at a markedly increased risk for B-cell precursor acute lymphoblastic leukemia (BCP-ALL) (1). The poor survival of DS-ALL compared with ALL in children without DS ("sporadic" ALL) is related to increased treatment toxicity and to increased incidence of relapse (2). Thus, better therapy is needed for these patients.Previous studies by our group and others revealed differences between the genetics of DS-ALLs and of sporadic ALLs (3-6). The typical cytogenetic subgroups, ETV6-RUNX1 and hyperdiploid ALLs, are less common in DS-ALLs. Acquired somatic activation of the thymic stromal lymphopoietin (TSLP) pathway are present at diagnosis in about half of DS-ALLs. Aberrant expression of cytokine receptor-like factor 2 (CRLF2) in these leukemias is caused by chromosomal rearrangements consisting either of a microdeletion on chromosome X, juxtaposing the promoter of P2RY8 with the coding region of CRLF2, or by a translocation of CRLF2 into the Ig heavy chain (IgH) locus. CRLF2 heterodimerizes with IL7 receptor-α (IL7R) to form the receptor to TSLP (reviewed in ref. 7). TSLP receptors signal by activation of the JAK-STAT pathway. Interestingly, in the majority of DS-ALLs, SignificanceChildren with Down syndrome are at increased risk ...