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. By this approach, we identified in 6 children (2.6%) pathogenic germline variants in 5 of the noncancer-related genes on the American College of Medical Genetics and Genomics (ACMG) SF v3.0 list, of which the majority were related to cardiovascular diseases ( RYR2 , MYBPC3 , KCNQ1 ). Interestingly, only the patient harboring the KCNQ1 variant showed at the time point of the analysis signs of the related Long QT syndrome. Moreover, we report 3 variants of unknown significance which, although not classified as pathogenic, have been reported in the literature to occur in individuals with the respective disease. While the frequency of patients with SFs is low, the impact of such findings on the patients' life is enormous, with regard to the potential prevention of life-threatening diseases. Hence, we are convinced that such actionable SF should be routinely analyzed.

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. We detected a CHEK2 germline variant in 32/418 (7.7%) pediatric cancer patients and 46.8% of them had leukemia. Functional analysis of the pathogenic variants revealed that 5 pathogenic variants impaired CHK2 protein function. 6/32 patients carried one of these clearly damaging CHEK2 variants and two of them harbored a matching family history of cancer. In conclusion, we detected germline CHEK2 variants in 7.7% of all pediatric cancer patients, of which a minority but still relevant fraction of approximately 20% had a profound impact on protein expression or its phosphorylation after irradiation-induced DNA damage. Accordingly, we conclude that CHEK2 variants increase the risk for not only adult-onset but also pediatric cancer.

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. We identified in 11/160 patients a pathogenic germline variant predisposing to cancer and a further eleven patients carried a prioritized VUS with a strong association to the cancerogenesis of the patient. Through clinical screening, 51 patients (31.3%) were identified as suspicious for an underlying cancer predisposition syndrome (CPS), but only in ten of those patients a pathogenic variant could be identified. In contrast, one patient with a classical CPS and ten patients with prioritized VUS were classified as unremarkable in the clinical work-up. Taken together, a monogenetic causative variant was detected in 13.8% of our patients using WES. Nevertheless, the still unclarified clinical suspicious cases emphasize the need to consider other genetic mechanisms including new target genes, structural variants, or polygenic interactions not previously associated with cancer predisposition.

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. The tested methods comprise standard FBA; a variant of MOMA, which was specifically designed to predict fitness effects of non-essential gene knockouts; and two alternative implementations of FBA with macro-molecular crowding, which account approximately for enzyme kinetics. The number of interactions uniquely predicted by one method is typically larger than its overlap with any alternative method. Only 20% of negative and 10% of positive interactions jointly predicted by all methods are confirmed by the experimental data; almost all unique predictions appear to be false. More than two thirds of epistatic interactions are undetectable by any of the tested methods. The low prediction accuracies indicate that the physiology of yeast double metabolic gene knockouts is dominated by processes not captured by current constraint-based analysis methods.

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. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for Escherichia coli and Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes in these fast-growing microbes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same metabolic enzyme-encoding gene with increasingly reduced enzymatic function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts of metabolic genes will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.