The CHK2 kinase is recurrently mutated and functionally impaired in the germline of pediatric cancer patients.

Predisposing CHEK2 germline variants are associated with various adult-type malignancies, whereas their impact on cancer susceptibility in childhood cancer is unclear. To understand the frequency of germline variants in the CHEK2 gene and their impact on pediatric malignancies, we used whole-exome sequencing to search for CHEK2 variants in the germlines of 418 children diagnosed with cancer in our clinics. Moreover, we performed functional analysis of the pathogenic CHEK2 variants to analyze the effect of the alterations on CHK2 protein function.

Noncancer-related Secondary Findings in a Cohort of 231 Children With Cancer and Their Parents.

Application of next-generation sequencing may lead to the detection of secondary findings (SF) not related to the initially analyzed disease but to other severe medically actionable diseases. However, the analysis of SFs is not yet routinely performed. We mined whole-exome sequencing data of 231 pediatric cancer patients and their parents who had been treated in our center for the presence of SFs.

Comprehensive germline-genomic and clinical profiling in 160 unselected children and adolescents with cancer.

In childhood cancer, the frequency of cancer-associated germline variants and their inheritance patterns are not thoroughly investigated. Moreover, the identification of children carrying a genetic predisposition by clinical means remains challenging. In this single-center study, we performed trio whole-exome sequencing and comprehensive clinical evaluation of a prospectively enrolled cohort of 160 children with cancer and their parents.

Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast.

Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in vivo epistasis, as FBA predicted only a minority of experimentally observed genetic interactions between non-essential metabolic genes in yeast. Here, we perform a detailed comparison of yeast experimental epistasis data to predictions generated with different constraint-based metabolic modeling algorithms.

Alleles of a gene differ in pleiotropy, often mediated through currency metabolite production, in E. coli and yeast metabolic simulations.

A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts.