Neuroblastic tumours may occur in a predisposition context. Two main genes are involved: PHOX2B, observed in familial cases and frequently associated with other neurocristopathies (Ondine's and Hirschsprung's disease); and ALK, mostly in familial tumours. We have assessed the frequency of mutations of these two genes in patients with a presumable higher risk of predisposition. We sequenced both genes in 26 perinatal cases (prebirth and <1 month of age, among which 10 were multifocal), 16 multifocal postnatal (>1 month) cases, 3 pairs of affected relatives and 8 patients with multiple malignancies. The whole coding sequences of the two genes were analysed in tumour and/or constitutional DNAs. We found three ALK germline mutations, all in a context of multifocal tumours. Two mutations (T1151R and R1192P) were inherited and shared by several unaffected patients, thus illustrating an incomplete penetrance. Younger age at tumour onset did not seem to offer a relevant selection criterion for ALK analyses. Conversely, multifocal tumours might be the most to benefit from the genetic screening. Finally, no PHOX2B germline mutation was found in this series. In conclusion, ALK deleterious mutations are rare events in patients with a high probability of predisposition. Other predisposing genes remain to be discovered.

Recently, activating mutations of the full length ALK receptor, with two hot spots at positions F1174 and R1275, have been characterized in sporadic cases of neuroblastoma. Here, we report similar basal patterns of ALK phosphorylation between the neuroblastoma IMR-32 cell line, which expresses only the wild-type receptor (ALKWT), and the SH-SY5Y cell line, which exhibits a heterozygous ALK F1174L mutation and expresses both ALKWT and ALKF1174L receptors. We demonstrate that this lack of detectable increased phosphorylation in SH-SY5Y cells is a result of intracellular retention and proteasomal degradation of the mutated receptor. As a consequence, in SH-SY5Y cells, plasma membrane appears strongly enriched for ALKWT whereas both ALKWT and ALKF1174L were present in intracellular compartments. We further explored ALK receptor trafficking by investigating the effect of agonist and antagonist mAb (monoclonal antibodies) on ALK internalization and down-regulation, either in SH-SY5Y cells or in cells expressing only ALKWT. We observe that treatment with agonist mAbs resulted in ALK internalization and lysosomal targeting for receptor degradation. In contrast, antagonist mAb induced ALK internalization and recycling to the plasma membrane. Importantly, we correlate this differential trafficking of ALK in response to mAb with the recruitment of the ubiquitin ligase Cbl and ALK ubiquitylation only after agonist stimulation. This study provides novel insights into the mechanisms regulating ALK trafficking and degradation, showing that various ALK receptor pools are regulated by proteasome or lysosome pathways according to their intracellular localization.

Summary: More and more cancer studies use next-generation sequencing (NGS) data to detect various types of genomic variation. However, even when researchers have such data at hand, single-nucleotide polymorphism arrays have been considered necessary to assess copy number alterations and especially loss of heterozygosity (LOH). Here, we present the tool Control-FREEC that enables automatic calculation of copy number and allelic content profiles from NGS data, and consequently predicts regions of genomic alteration such as gains, losses and LOH. Taking as input aligned reads, Control-FREEC constructs copy number and B-allele frequency profiles. The profiles are then normalized, segmented and analyzed in order to assign genotype status (copy number and allelic content) to each genomic region. When a matched normal sample is provided, Control-FREEC discriminates somatic from germline events. Control-FREEC is able to analyze overdiploid tumor samples and samples contaminated by normal cells. Low mappability regions can be excluded from the analysis using provided mappability tracks.

Availability: C++ source code is available at: http://bioinfo.curie.fr/projects/freec/

Contact: freec@curie.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND

More accurate prognostic assessment of patients with neuroblastoma is required to improve the choice of risk-related therapy. The aim of this study is to develop and validate a gene expression signature for improved outcome prediction.

METHODS

Fifty-nine genes were carefully selected based on an innovative data-mining strategy and profiled in the largest neuroblastoma patient series (n=579) to date using RT-qPCR starting from only 20 ng of RNA. A multigene expression signature was built using 30 training samples, tested on 313 test samples and subsequently validated in a blind study on an independent set of 236 additional tumours.

FINDINGS

The signature accurately classifies patients with respect to overall and progression-free survival (p<0·0001). The signature has a performance, sensitivity, and specificity of 85·4% (95%CI: 77·7–93·2), 84·4% (95%CI: 66·5–94·1), and 86·5% (95%CI: 81·1–90·6), respectively to predict patient outcome. Multivariate analysis indicates that the signature is a significant independent predictor after controlling for currently used riskfactors. Patients with high molecular risk have a higher risk to die from disease and for relapse/progression than patients with low molecular risk (odds ratio of 19·32 (95%CI: 6·50–57·43) and 3·96 (95%CI: 1·97–7·97) for OS and PFS, respectively). Patients with increased risk for adverse outcome can also be identified within the current treatment groups demonstrating the potential of this signature for improved clinical management. These results were confirmed in the validation study in which the signature was also independently statistically significant in a model adjusted for MYCN status, age, INSS stage, ploidy, INPC grade of differentiation, and MKI. The high patient/gene ratio (579/59) underlies the observed statistical power and robustness.

INTERPRETATION

A 59-gene expression signature predicts outcome of neuroblastoma patients with high accuracy. The signature is an independent risk predictor, identifying patients with increased risk in the current clinical risk groups. The applied method and signature is suitable for routine lab testing and ready for evaluation in prospective studies.

FUNDING

The Belgian Foundation Against Cancer, found of public interest (project SCIE2006-25), the Children Cancer Fund Ghent, the Belgian Society of Paediatric Haematology and Oncology, the Belgian Kid’s Fund and the Fondation Nuovo-Soldati (JV), the Fund for Scientific Research Flanders (KDP, JH), the Fund for Scientific Research Flanders (grant number: G•0198•08), the Institute for the Promotion of Innovation by Science and Technology in Flanders, Strategisch basisonderzoek (IWT-SBO 60848), the Fondation Fournier Majoie pour l’Innovation, the Instituto Carlos III,RD 06/0020/0102 Spain, the Italian Neuroblastoma Foundation, the European Community under the FP6 (project: STREP: EET-pipeline, number: 037260), and the Belgian program of Interuniversity Poles of Attraction, initiated by the Belgian State, Prime Minister's Office, Science Policy Programming.

Summary: We present a tool for control-free copy number alteration (CNA) detection using deep-sequencing data, particularly useful for cancer studies. The tool deals with two frequent problems in the analysis of cancer deep-sequencing data: absence of control sample and possible polyploidy of cancer cells. FREEC (control-FREE Copy number caller) automatically normalizes and segments copy number profiles (CNPs) and calls CNAs. If ploidy is known, FREEC assigns absolute copy number to each predicted CNA. To normalize raw CNPs, the user can provide a control dataset if available; otherwise GC content is used. We demonstrate that for Illumina single-end, mate-pair or paired-end sequencing, GC-contentr normalization provides smooth profiles that can be further segmented and analyzed in order to predict CNAs.

Availability: Source code and sample data are available at http://bioinfo-out.curie.fr/projects/freec/.

Contact: freec@curie.fr

Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: We present SVDetect, a program designed to identify genomic structural variations from paired-end and mate-pair next-generation sequencing data produced by the Illumina GA and ABI SOLiD platforms. Applying both sliding-window and clustering strategies, we use anomalously mapped read pairs provided by current short read aligners to localize genomic rearrangements and classify them according to their type, e.g. large insertions–deletions, inversions, duplications and balanced or unbalanced inter-chromosomal translocations. SVDetect outputs predicted structural variants in various file formats for appropriate graphical visualization.

Availability: Source code and sample data are available at http://svdetect.sourceforge.net/

Contact: svdetect@curie.fr

Supplementary information: Supplementary data are available at Bioinformatics online.