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 athttp://bioconductor.org/packages/MutationalPatterns.

Cytokine stimulation drives nanoscale reorganization of surface FcγRI to augment antibody-dependent cellular functions.

Childhood cancer survivors are confronted with various chronic health conditions like therapy-related malignancies. However, it is unclear how exposure to chemotherapy contributes to the mutation burden and clonal composition of healthy tissues early in life. Here, we studied mutation accumulation in hematopoietic stem and progenitor cells (HSPC) before and after cancer treatment of 24 children. Of these children, 19 developed therapy-related myeloid neoplasms (t-MN). Posttreatment HSPCs had an average mutation burden increase comparable to what treatment-naïve cells accumulate during 16 years of life, with excesses up to 80 years. In most children, these additional mutations were induced by clock-like processes, which are also active during healthy aging. Other patients harbored mutations that could be directly attributed to treatments like platinum-based drugs and thiopurines. Using phylogenetic inference, we demonstrate that most t-MN in children originate after the start of treatment and that leukemic clones become dominant during or directly after chemotherapy exposure. Significance: Our study shows that chemotherapy increases the mutation burden of normal blood cells in cancer survivors. Only few drugs damage the DNA directly, whereas in most patients, chemotherapy-induced mutations are caused by processes similar to those present during normal aging.

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 (HSPC), 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 patients with pAML. Compared with normal HSPCs, a subset of pAML cases harbored more somatic mutations and a distinct composition of mutational process signatures. We hypothesize that these cases might have arisen from a more committed progenitor. This subset had better outcomes than pAML cases with mutation burden comparable with age-matched healthy HSPCs. Our study provides insights into the etiology and patient stratification of pAML. Significance: Genome-wide analysis of pAML and patient-matched HSPCs provides new insights into the etiology of the disease and shows the clinical potential of these analyses for patient stratification. This article is highlighted in the In This Issue feature, p. 403

Thymus and activation-regulated chemokine (TARC) is produced by different cell types and is highly expressed in the thymus. It plays an important role in T cell development, trafficking and activation of mature T cells after binding to its receptor C-C chemokine receptor type 4 (CCR4) and consecutive signal transducer and activator of transcription 6 (STAT6) activation. Importantly, TARC is also produced by malignant Hodgkin and Reed–Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL). In cHL, HRS cells survive and proliferate due to the micro-environment consisting primarily of type 2 T helper (Th2) cells. TARC-mediated signaling initiates a positive feedback loop that is crucial for the interaction between HRS and T cells. The clinical applicability of TARC is diverse. It is useful as diagnostic biomarker in both children and adults with cHL and in other Th2-driven diseases. In adult cHL patients, TARC is also a biomarker for treatment response and prognosis. Finally, blocking TARC signaling and thus inhibiting pathological Th2 cell recruitment could be a therapeutic strategy in cHL. In this review, we summarize the biological functions of TARC and focus on its role in cHL pathogenesis and as a biomarker for cHL and other diseases. We conclude by giving an outlook on putative therapeutic applications of antagonists and inhibitors of TARC-mediated signaling.

Detection of somatic mutations in single cells has been severely hampered by technical limitations of whole genome amplification. Novel technologies including primary template-directed amplification (PTA) significantly improved the accuracy of single-cell whole genome sequencing (WGS), but still generate hundreds of artefacts per amplification reaction. We developed a comprehensive bioinformatic workflow, called the PTA Analysis Toolkit (PTATO), to accurately detect single base substitutions, small insertions and deletions (indels) and structural variants in PTA-based WGS data. PTATO includes a machine learning approach to distinguish PTA-artefacts from true mutations with high sensitivity (up to 90% for base substitution and 95% for indels), outperforming existing bioinformatic approaches. Using PTATO, we demonstrate that many hematopoietic stem and progenitor cells of patients with Fanconi anemia, which cannot be analyzed using regular WGS technologies, have normal somatic single base substitution burdens, but increased numbers of deletions. Our results show that PTATO enables studying somatic mutagenesis in the genomes of single cells with unprecedented sensitivity and accuracy.