Prevalence of (Epi)genetic Predisposing Factors in a 5-Year Unselected National Wilms Tumor Cohort: A Comprehensive Clinical and Genomic Characterization.

PURPOSE: Wilms tumor (WT) is associated with (epi)genetic predisposing factors affecting a growing number of WT predisposing genes and loci, including those causing Beckwith-Wiedemann spectrum (BWSp) or WT1-related syndromes. To guide genetic counseling and testing, we need insight into the prevalence of WT predisposing (epi)genetic factors. PATIENTS AND METHODS: All children diagnosed with WT in the Netherlands between 2015 and 2020 were referred to a clinical geneticist. Phenotypic data, disease characteristics, and diagnostic test results were collected. If no genetic predisposition was identified by targeted diagnostic testing, germline (trio-)whole-exome sequencing and BWSp testing on normal kidney-derived DNA were offered. RESULTS: A total of 126 cases were analyzed of 128 identified patients. (Epi)genetic predisposing factors were present in 42 of 126 patients (33.3%) on the basis of a molecular diagnosis in blood-derived DNA (n = 26), normal kidney-derived DNA (n = 12), or solely a clinical diagnosis of BWSp (n = 4). Constitutional, heterozygous DIS3L2 variants were identified as a recurrent predisposing factor in five patients (4%), with a second somatic hit in 4 of 5 tumors. Twenty patients (16%) were diagnosed with BWSp while four additional patients without BWSp features harbored chromosome 11p15 methylation defects in normal kidney tissue. Remaining findings included WT1-related syndromes (n = 10), Fanconi anemia (n = 1), neurofibromatosis type 1 (n = 1), and a pathogenic REST variant (n = 1). In addition, (likely) pathogenic variants in adult-onset cancer predisposition genes (BRCA2, PMS2, CHEK2, and MUTYH) were identified in 5 of 56 (8.9%) patients with available whole-exome sequencing data. Several candidate WT predisposition genes were identified, which require further validation. CONCLUSION: (Epi)genetic WT predisposing factors, including mosaic aberrations and recurrent heterozygous DIS3L2 variants, were present in at least 33.3% of patients with WT. On the basis of these results, we encourage standard genetic testing after counseling by a clinical geneticist.

Clonal dynamics in pediatric B-cell precursor acute lymphoblastic leukemia with very early relapse.

INTRODUCTION: One-quarter of the relapses in children with B-cell precursor acute lymphoblastic leukemia (BCP-ALL) occur very early (within 18 months, before completion of treatment), and prognosis in these patients is worse compared to cases that relapse after treatment has ended. METHODS: In this study, we performed a genomic analysis of diagnosis-relapse pairs of 12 children who relapsed very early, followed by a deep-sequencing validation of all identified mutations. In addition, we included one case with a good initial treatment response and on-treatment relapse at the end of upfront therapy. RESULTS: We observed a dynamic clonal evolution in all cases, with relapse almost exclusively originating from a subclone at diagnosis. We identified several driver mutations that may have influenced the outgrowth of a minor clone at diagnosis to become the major clone at relapse. For example, a minimal residual disease (MRD)-based standard-risk patient with ETV6-RUNX1-positive leukemia developed a relapse from a TP53-mutated subclone after loss of the wildtype allele. Furthermore, two patients with TCF3-PBX1-positive leukemia that developed a very early relapse carried E1099K WHSC1 mutations at diagnosis, a hotspot mutation that was recurrently encountered in other very early TCF3-PBX1-positive leukemia relapses as well. In addition to alterations in known relapse drivers, we found two cases with truncating mutations in the cohesin gene RAD21. CONCLUSION: Comprehensive genomic characterization of diagnosis-relapse pairs shows that very early relapses in BCP-ALL frequently arise from minor subclones at diagnosis. A detailed understanding of the therapeutic pressure driving these events may aid the development of improved therapies.

Computational tools for plant small RNA detection and categorization.

Small RNAs (sRNAs) are important short-length molecules with regulatory functions essential for plant development and plasticity. High-throughput sequencing of total sRNA populations has revealed that the largest share of sRNA remains uncategorized. To better understand the role of sRNA-mediated cellular regulation, it is necessary to create accurate and comprehensive catalogues of sRNA and their sequence features, a task that currently relies on nontrivial bioinformatic approaches. Although a large number of computational tools have been developed to predict features of sRNA sequences, these tools are mostly dedicated to microRNAs and none integrates the functionalities necessary to describe units from all sRNA pathways thus far discovered in plants. Here, we review the different classes of sRNA found in plants and describe available bioinformatics tools that can help in their detection and categorization.

Epigenetic mapping of the Arabidopsis metabolome reveals mediators of the epigenotype-phenotype map.

Identifying the sources of natural variation underlying metabolic differences between plants will enable a better understanding of plant metabolism and provide insights into the regulatory networks that govern plant growth and morphology. So far, however, the contribution of epigenetic variation to metabolic diversity has been largely ignored. In the present study, we utilized a panel of Arabidopsis thaliana epigenetic recombinant inbred lines (epiRILs) to assess the impact of epigenetic variation on the metabolic composition. Thirty epigenetic QTL (QTLepi) were detected, which partly overlap with QTLepi linked to growth and morphology. In an effort to identify causal candidate genes in the QTLepi regions and their putative trans-targets, we performed in silico small RNA and qPCR analyses. Differentially expressed genes were further studied by phenotypic and metabolic analyses of knockout mutants. Three genes were detected that recapitulated the detected QTLepi effects, providing evidence for epigenetic regulation in cis and in trans These results indicate that epigenetic mechanisms impact metabolic diversity, possibly via small RNAs, and thus aid in further disentangling the complex epigenotype-phenotype map.

Small RNAs Reflect Grandparental Environments in Apomictic Dandelion.

Plants can show long-term effects of environmental stresses and in some cases a stress "memory" has been reported to persist across generations, potentially mediated by epigenetic mechanisms. However, few documented cases exist of transgenerational effects that persist for multiple generations and it remains unclear if or how epigenetic mechanisms are involved. Here, we show that the composition of small regulatory RNAs in apomictic dandelion lineages reveals a footprint of drought stress and salicylic acid treatment experienced two generations ago. Overall proportions of 21 and 24 nt RNA pools were shifted due to grandparental treatments. While individual genes did not show strong up- or downregulation of associated sRNAs, the subset of genes that showed the strongest shifts in sRNA abundance was significantly enriched for several GO terms including stress-specific functions. This suggests that a stress-induced signal was transmitted across multiple unexposed generations leading to persistent changes in epigenetic gene regulation.

Features of the Arabidopsis recombination landscape resulting from the combined loss of sequence variation and DNA methylation.

The rate of meiotic crossing over (CO) varies considerably along chromosomes, leading to marked distortions between physical and genetic distances. The causes underlying this variation are being unraveled, and DNA sequence and chromatin states have emerged as key factors. However, the extent to which the suppression of COs within the repeat-rich pericentromeric regions of plant and mammalian chromosomes results from their high level of DNA polymorphisms and from their heterochromatic state, notably their dense DNA methylation, remains unknown. Here, we test the combined effect of removing sequence polymorphisms and repeat-associated DNA methylation on the meiotic recombination landscape of an Arabidopsis mapping population. To do so, we use genome-wide DNA methylation data from a large panel of isogenic epigenetic recombinant inbred lines (epiRILs) to derive a recombination map based on 126 meiotically stable, differentially methylated regions covering 81.9% of the genome. We demonstrate that the suppression of COs within pericentromeric regions of chromosomes persists in this experimental setting. Moreover, suppression is reinforced within 3-Mb regions flanking pericentromeric boundaries, and this effect appears to be compensated by increased recombination activity in chromosome arms. A direct comparison with 17 classical Arabidopsis crosses shows that these recombination changes place the epiRILs at the boundary of the range of natural variation but are not severe enough to transgress that boundary significantly. This level of robustness is remarkable, considering that this population represents an extreme with key recombination barriers having been forced to a minimum.

Modelling proteolytic enzymes with Support Vector Machines.

The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.