Corinna Ernst

Mutational dynamics between primary and relapse neuroblastomas

Neuroblastoma is a malignancy of the developing sympathetic nervous system that is often lethal when relapse occurs. We here used whole-exome sequencing, mRNA expression profiling, array CGH and DNA methylation analysis to characterize 16 paired samples at diagnosis and relapse from individuals with neuroblastoma. The mutational burden significantly increased in relapsing tumors, accompanied by altered mutational signatures and reduced subclonal heterogeneity. Global allele frequencies at relapse indicated clonal mutation selection during disease progression. Promoter methylation patterns were consistent over disease course and were patient specific. Recurrent alterations at relapse included mutations in the putative CHD5 neuroblastoma tumor suppressor, chromosome 9p losses, DOCK8 mutations, inactivating mutations in PTPN14 and a relapse-specific activity pattern for the PTPN14 target YAP. Recurrent new mutations in HRAS, KRAS and genes mediating cell-cell interaction in 13 of 16 relapse tumors indicate disturbances in signaling pathways mediating mesenchymal transition. Our data shed light on genetic alteration frequency, identity and evolution in neuroblastoma.

Computational pan-genomics: status, promises and challenges

Abstract Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic data sets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this article, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Association Between Loss-of-Function Mutations Within the FANCM Gene and Early-Onset Familial Breast Cancer

Importance Germline mutations in established moderately or highly penetrant risk genes for breast cancer (BC) and/or ovarian cancer (OC), including BRCA1 and BRCA2, explain fewer than half of all familial BC and/or OC cases. Based on the genotyping of 2 loss-of-function (LoF) variants c.5101C>T (p.GIn1701Ter [rs147021911]) and c.5791C>T (p.Arg1931Ter [rs144567652]), the FANCM gene has been suggested as a novel BC predisposition gene, while the analysis of the entire coding region of the FANCM gene in familial index cases and geographically matched controls is pending. Objectives To assess the mutational spectrum within the FANCM gene, and to determine a potential association of LoF germline mutations within the FANCM gene with BC and/or OC risk. Design, Setting, and Participants For the purpose of identification and characterization of novel BC and/or OC predisposition genes, a total of 2047 well-characterized familial BC index cases, 628 OC cases, and 2187 geographically matched controls were screened for LoF mutations within the FANCM gene by next-generation sequencing. All patients previously tested negative for pathogenic BRCA1 and BRCA2 mutations. All data collection occurred between June 1, 2013, and April 30, 2016. Data analysis was performed from May 1, 2016, to July 1, 2016. Main Outcomes and Measures FANCM LoF mutation frequencies in patients with BC and/or OC were compared with the FANCM LoF mutation frequencies in geographically matched controls by univariate logistic regression. Positive associations were stratified by age at onset and cancer family history. Results In this case-control study, 2047 well-characterized familial female BC index cases, 628 OC cases, and 2187 geographically matched controls were screened for truncating FANCM alterations. Heterozygous LoF mutations within the FANCM gene were significantly associated with familial BC risk, with an overall odds ratio (OR) of 2.05 (95% CI, 0.94-4.54; P = .049) and a mutation frequency of 1.03% in index cases. In familial patients whose BC onset was before age 51 years, an elevated OR of 2.44 (95% CI, 1.08-5.59; P = .02) was observed. A more pronounced association was identified for patients with a triple-negative BC tumor phenotype (OR, 3.75; 95% CI, 1.00-12.85; P = .02). No significant association was detected for unselected OC cases (OR, 1.74; 95% CI, 0.57-5.08; P = .27). Conclusions and Relevance Based on the significant associations of heterozygous LoF mutations with early-onset or triple-negative BC, FANCM should be included in diagnostic gene panel testing for individual risk assessment. Larger studies are required to determine age-dependent disease risks for BC and to assess a potential role of FANCM mutations in OC pathogenesis.

Gene panel testing of 5589 BRCA1/2-negative index patients with breast cancer in a routine diagnostic setting: results of the German Consortium for Hereditary Breast and Ovarian Cancer

The prevalence of germ line mutations in non‐BRCA1/2 genes associated with hereditary breast cancer (BC) is low, and the role of some of these genes in BC predisposition and pathogenesis is conflicting. In this study, 5589 consecutive BC index patients negative for pathogenic BRCA1/2 mutations and 2189 female controls were screened for germ line mutations in eight cancer predisposition genes (ATM, CDH1, CHEK2, NBN, PALB2, RAD51C, RAD51D, and TP53). All patients met the inclusion criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for germ line testing. The highest mutation prevalence was observed in the CHEK2 gene (2.5%), followed by ATM (1.5%) and PALB2 (1.2%). The mutation prevalence in each of the remaining genes was 0.3% or lower. Using Exome Aggregation Consortium control data, we confirm significant associations of heterozygous germ line mutations with BC for ATM (OR: 3.63, 95%CI: 2.67–4.94), CDH1 (OR: 17.04, 95%CI: 3.54–82), CHEK2 (OR: 2.93, 95%CI: 2.29–3.75), PALB2 (OR: 9.53, 95%CI: 6.25–14.51), and TP53 (OR: 7.30, 95%CI: 1.22–43.68). NBN germ line mutations were not significantly associated with BC risk (OR:1.39, 95%CI: 0.73–2.64). Due to their low mutation prevalence, the RAD51C and RAD51D genes require further investigation. Compared with control datasets, predicted damaging rare missense variants were significantly more prevalent in CHEK2 and TP53 in BC index patients. Compared with the overall sample, only TP53 mutation carriers show a significantly younger age at first BC diagnosis. We demonstrate a significant association of deleterious variants in the CHEK2, PALB2, and TP53 genes with bilateral BC. Both, ATM and CHEK2, were negatively associated with triple‐negative breast cancer (TNBC) and estrogen receptor (ER)‐negative tumor phenotypes. A particularly high CHEK2 mutation prevalence (5.2%) was observed in patients with human epidermal growth factor receptor 2 (HER2)‐positive tumors.

BRIP1 loss-of-function mutations confer high risk for familial ovarian cancer, but not familial breast cancer

BackgroundGermline mutations in the BRIP1 gene have been described as conferring a moderate risk for ovarian cancer (OC), while the role of BRIP1 in breast cancer (BC) pathogenesis remains controversial.MethodsTo assess the role of deleterious BRIP1 germline mutations in BC/OC predisposition, 6341 well-characterized index patients with BC, 706 index patients with OC, and 2189 geographically matched female controls were screened for loss-of-function (LoF) mutations and potentially damaging missense variants. All index patients met the inclusion criteria of the German Consortium for Hereditary Breast and Ovarian Cancer for germline testing and tested negative for pathogenic BRCA1/2 variants.ResultsBRIP1 LoF mutations confer a high OC risk in familial index patients (odds ratio (OR) = 20.97, 95% confidence interval (CI) = 12.02–36.57, P < 0.0001) and in the subgroup of index patients with late-onset OC (OR = 29.91, 95% CI = 14.99–59.66, P < 0.0001). No significant association of BRIP1 LoF mutations with familial BC was observed (OR = 1.81 95% CI = 1.00–3.30, P = 0.0623). In the subgroup of familial BC index patients without a family history of OC there was also no apparent association (OR = 1.42, 95% CI = 0.70–2.90, P = 0.3030). In 1027 familial BC index patients with a family history of OC, the BRIP1 mutation prevalence was significantly higher than that observed in controls (OR = 3.59, 95% CI = 1.43–9.01; P = 0.0168). Based on the negative association between BRIP1 LoF mutations and familial BC in the absence of an OC family history, we conclude that the elevated mutation prevalence in the latter cohort was driven by the occurrence of OC in these families. Compared with controls, predicted damaging rare missense variants were significantly more prevalent in OC (P = 0.0014) but not in BC (P = 0.0693) patients.ConclusionsTo avoid ambiguous results, studies aimed at assessing the impact of candidate predisposition gene mutations on BC risk might differentiate between BC index patients with an OC family history and those without. In familial cases, we suggest that BRIP1 is a high-risk gene for late-onset OC but not a BC predisposition gene, though minor effects cannot be excluded.

Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics

BackgroundThe use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer.MethodsWe tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches.ResultsPolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer.ConclusionWe show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.

Diagnosis of Li-Fraumeni Syndrome: Differentiating TP53 germline mutations from clonal hematopoiesis: Results of the observational AGO-TR1 trial

The Li‐Fraumeni cancer predisposition syndrome (LFS1) presents with a variety of tumor types and the TP53 gene is covered by most diagnostic cancer gene panels. We demonstrate that deleterious TP53 variants identified in blood‐derived DNA of 523 patients with ovarian cancer (AGO‐TR1 trial) were not causal for the patients’ ovarian cancer in three out of six TP53‐positive cases. In three out of six patients, deleterious TP53 mutations were identified with low variant fractions in blood‐derived DNA but not in the tumor of the patient seeking advice. The analysis of the TP53 and PPM1D genes, both intimately involved in chemotherapy‐induced and/or age‐related clonal hematopoiesis (CH), in 523 patients and 1,053 age‐matched female control individuals revealed that CH represents a frequent event following chemotherapy, affecting 26 of the 523 patients enrolled (5.0%). Considering that TP53 mutations may arise from chemotherapy‐induced CH, our findings help to avoid false‐positive genetic diagnoses of LFS1.