No abstract

Background The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. Results Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. Conclusions This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

Children show a higher incidence of leukemia compared to young adolescents, yet their cells have less age-related (oncogenic) somatic mutations. Newborns with Down syndrome have an even higher risk of developing leukemia, which is thought to be driven by mutations that accumulate during fetal development. to characterize mutation accumulation in individual stem and progenitor cells of Down syndrome and karyotypically normal fetuses, we clonally expanded single cells and performed whole-genome sequencing. We found a higher mutation rate in haematopoietic stem and progenitor cells during fetal development compared to the post-infant rate. In fetal trisomy 21 cells the number of somatic mutations is even further increased, which was already apparent during the first cell divisions of embryogenesis before gastrulation. the number and types of mutations in fetal trisomy 21 haematopoietic stem and progenitor cells were similar to those in Down syndrome-associated myeloid preleukemia and could be attributed to mutational processes that were active during normal fetal haematopoiesis. Finally, we found that the contribution of early embryonic cells to human fetal tissues can vary considerably between individuals. The increased mutation rates found in this study, may contribute to the increased risk of leukemia early during life and the higher incidence of leukemia in Down syndrome.

Summary Genetic instability is a major concern for successful application of stem cells in regenerative medicine. However, the mutational consequences of the most applied stem cell therapy in humans, hematopoietic stem cell transplantation (HSCT), remain unknown. Here we characterized the mutation burden of hematopoietic stem and progenitor cells (HSPCs) of human HSCT recipients and their donors using whole-genome sequencing. We demonstrate that the majority of transplanted HSPCs did not display altered mutation accumulation. However, in some HSCT recipients, we identified multiple HSPCs with an increased mutation burden after transplantation. This increase could be attributed to a unique mutational signature caused by the antiviral drug ganciclovir. Using a machine learning approach, we detected this signature in cancer genomes of individuals who received HSCT or solid organ transplantation earlier in life. Antiviral treatment with nucleoside analogs can cause enhanced mutagenicity in transplant recipients, which may ultimately contribute to therapy-related carcinogenesis.

Acquisition of oncogenic mutations with age is believed to be rate limiting for carcinogenesis.However, the incidence of leukemia in children is higher than in young adults. Here we compare somatic mutations across pediatric acute myeloid leukemia (pAML) patient-matched leukemic blasts and hematopoietic stem and progenitor cells (HSPCs), as well as HSPCs from age-matched healthy donors. HSPCs in the leukemic bone marrow have limited genetic relatedness and share few somatic mutations with the cell-of-origin of the malignant blasts, suggesting polyclonal hematopoiesis in pAML patients. Compared to normal HSPCs, a subset of pAML cases harbored more somatic mutations and a distinct composition of mutational process signatures. We hypothesize these cases might have arisen from a more committed progenitor. This subset had better outcomes than pAML cases with mutation burden comparable to age-matched healthy HSPCs. Our study provides insights into the etiology and patient stratification of pAML. Significance statementGenome-wide analysis of pediatric acute myeloid leukemia and patient-matched hematopoietic stem and progenitor cells provides new insights into the etiology of the disease and show the clinical potential of these analyses for patient stratification.

Rhabdomyosarcomas (RMS) are mesenchyme-derived tumors and the most common childhood soft tissue sarcomas. Treatment is intense, with a nevertheless poor prognosis for high-risk patients. Discovery of new therapies would benefit from additional preclinical models. Here, we describe the generation of a collection of 19 pediatric RMS tumor organoid (tumoroid) models (success rate of 41%) comprising all major subtypes. For aggressive tumors, tumoroid models can often be established within 4-8 weeks, indicating the feasibility of personalized drug screening. Molecular, genetic, and histological characterization show that the models closely resemble the original tumors, with genetic stability over extended culture periods of up to 6 months. Importantly, drug screening reflects established sensitivities and the models can be modified by CRISPR/Cas9 with TP53 knockout in an embryonal RMS model resulting in replicative stress drug sensitivity. Tumors of mesenchymal origin can therefore be used to generate organoid models, relevant for a variety of preclinical and clinical research questions.

BackgroundThe collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data.ResultsHere, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights.ConclusionsThis novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns.

From conception to death, human cells accumulate somatic mutations in their genomes. These mutations can contribute to the development of cancer and non-malignant diseases and have also been associated with aging. Rapid technological developments in sequencing approaches in the last few years and their application to normal tissues have greatly advanced our knowledge about the accumulation of these mutations during healthy aging. Whole genome sequencing studies have revealed that there are significant differences in mutation burden and patterns across tissues, but also that the mutation rates within tissues are surprisingly constant during adult life. In contrast, recent lineage-tracing studies based on whole-genome sequencing have shown that the rate of mutation accumulation is strongly increased early in life before birth. These early mutations, which can be shared by many cells in the body, may have a large impact on development and the origin of somatic diseases. For example, cancer driver mutations can arise early in life, decades before the detection of the malignancy. Here, we review the recent insights in mutation accumulation and mutagenic processes in normal tissues. We compare mutagenesis early and later in life and discuss how mutation rates and patterns evolve during aging. Additionally, we outline the potential impact of these mutations on development, aging and disease.