High-grade B-cell lymphoma with 11q-aberration (HGBCL-11q) is a rare pediatric non-Hodgkin lymphoma. This study assessed outcome in 90 children with HGBCL-11q. With survival rates ≥95%, patients with HGBCL-11q and no predisposition are candidates for deescalated therapy in future prospective trials.

While survival has improved for Burkitt lymphoma patients, potential differences in outcome between pediatric and adult patients remain unclear. In both age groups, survival remains poor at relapse. Therefore, we conducted a comparative study in a large pediatric cohort, including 191 cases and 97 samples from adults. While TP53 and CCND3 mutation frequencies are not age related, samples from pediatric patients showed a higher frequency of mutations in ID3 , DDX3X, ARID1A and SMARCA4 , while several genes such as BCL2 and YY1AP1 are almost exclusively mutated in adult patients. An unbiased analysis reveals a transition of the mutational profile between 25 and 40 years of age. Survival analysis in the pediatric cohort confirms that TP53 mutations are significantly associated with higher incidence of relapse (25 ± 4% versus 6 ± 2%, p-value 0.0002). This identifies a promising molecular marker for relapse incidence in pediatric BL which will be used in future clinical trials.

Copy number variants (CNVs) are known to play an important role in the development and progression of several diseases. While a majority of variants, e.g. single nucleotide variants or small structural variants, can be detected by the help of next-generation sequencing (NGS) experiments, the detection of CNVs in NGS data is challenging. Usually, additional experiments have to be performed. We developed a novel algorithm for CNV calling in matched whole-exome sequencing (WES) data. Similar to the analysis of SNP array data, we focus on the evaluation of polymorphisms. Analyzing germline samples, an individual set of heteroyzgous polymorphisms is determined for every patient. Subsequently, the matching tumor samples are analyzed for coverage and frequency of these polymorphisms. Different from other algorithms, we account for the subclonal composition of tumors and consider the expected ratio of cells featuring a CNV. A sliding window is evaluated to identify regions of significant difference between tumor and germline. A “merge and filter”-step generates the final output. To test our algorithm, we consider matched WES data from patients with Non-Hodgkin lymphoma, including patients with and without relapse. Validation of our results is performed by the help of SNP arrays. Information on validated hotspot-mutations, located in regions with CNVs, is evaluated. Results indicate that our new algorithm provides a valid approach to detect CNVs in matched WES data, where common tools suffer from low sensitivity and a high number of false positive calls.