Emergence of new ALK mutations at relapse of neuroblastoma.

PURPOSE: In neuroblastoma, the ALK receptor tyrosine kinase is activated by point mutations. We investigated the potential role of ALK mutations in neuroblastoma clonal evolution. METHODS: We analyzed ALK mutations in 54 paired diagnosis-relapse neuroblastoma samples using Sanger sequencing. When an ALK mutation was observed in one paired sample, a minor mutated component in the other sample was searched for by more than 100,000× deep sequencing of the relevant hotspot, with a sensitivity of 0.17%. RESULTS: All nine ALK-mutated cases at diagnosis demonstrated the same mutation at relapse, in one case in only one of several relapse nodules. In five additional cases, the mutation seemed to be relapse specific, four of which were investigated by deep sequencing. In two cases, no mutation evidence was observed at diagnosis. In one case, the mutation was present at a subclonal level (0.798%) at diagnosis, whereas in another case, two different mutations resulting in identical amino acid changes were detected, one only at diagnosis and the other only at relapse. Further evidence of clonal evolution of ALK-mutated cells was provided by establishment of a fully ALK-mutated cell line from a primary sample with an ALK-mutated cell population at subclonal level (6.6%). CONCLUSION: In neuroblastoma, subclonal ALK mutations can be present at diagnosis with subsequent clonal expansion at relapse. Given the potential of ALK-targeted therapy, the significant spatiotemporal variation of ALK mutations is of utmost importance, highlighting the potential of deep sequencing for detection of subclonal mutations with a sensitivity 100-fold that of Sanger sequencing and the importance of serial samplings for therapeutic decisions.

Learning smoothing models of copy number profiles using breakpoint annotations.

BACKGROUND: Many models have been proposed to detect copy number alterations in chromosomal copy number profiles, but it is usually not obvious to decide which is most effective for a given data set. Furthermore, most methods have a smoothing parameter that determines the number of breakpoints and must be chosen using various heuristics. RESULTS: We present three contributions for copy number profile smoothing model selection. First, we propose to select the model and degree of smoothness that maximizes agreement with visual breakpoint region annotations. Second, we develop cross-validation procedures to estimate the error of the trained models. Third, we apply these methods to compare 17 smoothing models on a new database of 575 annotated neuroblastoma copy number profiles, which we make available as a public benchmark for testing new algorithms. CONCLUSIONS: Whereas previous studies have been qualitative or limited to simulated data, our annotation-guided approach is quantitative and suggests which algorithms are fastest and most accurate in practice on real data. In the neuroblastoma data, the equivalent pelt.n and cghseg.k methods were the best breakpoint detectors, and exhibited reasonable computation times.

Characterization of rearrangements involving the ALK gene reveals a novel truncated form associated with tumor aggressiveness in neuroblastoma.

Activating mutations of the ALK gene have been identified in sporadic and familial cases of neuroblastoma (NB), a cancer of the peripheral nervous system, and are thought to be the primary mechanism of oncogenic activation of this receptor in this pediatric neoplasm. To address the possibility that ALK activation may occur through genomic rearrangements as detected in other cancers, we first took advantage of high-resolution array-comparative genomic hybridization to search for ALK rearrangements in NB samples. Using complementary experiments by capture/paired-end sequencing and FISH experiments, various types of rearrangements were fully characterized, including partial gains or amplifications, in several NB cell lines and primary tumors. In the CLB-Bar cell line, we described a genomic rearrangement associated with an amplification of the ALK locus, leading to the expression of a 170 kDa protein lacking part of the extracellular domain encoded by exons 4 to 11, named ALK(Δ4-11). Analysis of genomic DNA from the tumor at diagnosis and relapse revealed that the ALK gene was amplified at diagnosis but that the rearranged ALK allele was observed at the relapse stage only, suggesting that it may be implicated in tumor aggressiveness. Consistently, oncogenic and tumorigenic properties of the ALK(Δ4-11) variant were shown after stable expression in NIH3T3 cells. Moreover, we documented an increased constitutive kinase activity of this variant, as well as an impaired maturation and retention into intracellular compartments. These results indicate that genomic rearrangements constitute an alternative mechanism to ALK point mutations resulting in receptor activation.

Control-FREEC: a tool for assessing copy number and allelic content using next-generation sequencing data.

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.