Dataset of DNA methylation profiles of 189 pediatric central nervous system, soft tissue, and bone tumors.

Alterations in DNA methylation profiles belong to important mechanisms in cancer development, and their assessment can be utilized for rapid and precise diagnostics. Therefore, establishing datasets of methylation profiles can improve and deepen our understanding of the role of epigenetic changes in cancer development as well as improve our diagnostic capabilities. In this dataset, we generated NGS data for 189 samples of pediatric CNS, soft tissue, and bone tumors. The sequencing libraries were prepared using methyl capture bisulfite sequencing, an effective compromise between whole-genome bisulfite sequencing and array-based methods with a more limited scope of target regions. The larger part of the cohort was processed with the Agilent SureSelectXT Human Methyl-Seq kit (149 samples) and the rest with the Illumina TruSeq Methyl Capture EPIC Library Prep Kit (40 samples). The data presented in this article may help other researchers further elucidate the importance of methylation in diagnosing pediatric CNS tumors, soft tissue, and bone tumors.

Personalized dendritic cell vaccine in multimodal individualized combination therapy improves survival in high-risk pediatric cancer patients.

A lot of hope for high-risk cancers is being pinned on immunotherapy but the evidence in children is lacking due to the rarity and limited efficacy of single-agent approaches. Here, we aim to assess the effectiveness of multimodal therapy comprising a personalized dendritic cell (DC) vaccine in children with relapsed and/or high-risk solid tumors using the N-of-1 approach in real-world scenario. A total of 160 evaluable events occurred in 48 patients during the 4-year follow-up. Overall survival of the cohort was 7.03 years. Disease control after vaccination was achieved in 53.8% patients. Comparative survival analysis showed the beneficial effect of DC vaccine beyond 2 years from initial diagnosis (HR = 0.53, P = .048) or in patients with disease control (HR = 0.16, P = .00053). A trend for synergistic effect with metronomic cyclophosphamide and/or vinblastine was indicated (HR = 0.60 P = .225). A strong synergistic effect was found for immune check-point inhibitors (ICIs) after priming with the DC vaccine (HR = 0.40, P = .0047). In conclusion, the personalized DC vaccine was an effective component in the multimodal individualized treatment. Personalized DC vaccine was effective in less burdened or more indolent diseases with a favorable safety profile and synergized with metronomic and/or immunomodulating agents.

A computational workflow for analysis of missense mutations in precision oncology.

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individual's tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutation's effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving a balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/ .Scientific contributionThis work presents a novel bioinformatics pipeline that integrates multiple computational tools to predict the effects of missense mutations on proteins of oncological interest. The pipeline uniquely combines fast protein modelling, stability prediction, and evolutionary analysis with virtual drug screening, while offering actionable insights for precision oncology. This comprehensive approach surpasses existing tools by automating the interpretation of mutations and suggesting potential treatments, thereby striving to bridge the gap between sequencing data and clinical application.

Precision immuno-oncology approach for four malignant tumors in siblings with constitutional mismatch repair deficiency syndrome.

Constitutional mismatch repair deficiency (CMMRD) is a rare syndrome characterized by an increased incidence of cancer. It is caused by biallelic germline mutations in one of the four mismatch repair genes (MMR) genes: MLH1, MSH2, MSH6, or PMS2. Accurate diagnosis accompanied by a proper molecular genetic examination plays a crucial role in cancer management and also has implications for other family members. In this report, we share the impact of the diagnosis and challenges during the clinical management of two brothers with CMMRD from a non-consanguineous family harbouring compound heterozygous variants in the PMS2 gene. Both brothers presented with different phenotypic manifestations and cancer spectrum. Treatment involving immune checkpoint inhibitors significantly contributed to prolonged survival in both patients affected by lethal gliomas. The uniform hypermutation also allowed immune-directed treatment using nivolumab for the B-cell lymphoma, thereby limiting the intensive chemotherapy exposure in this young patient who remains at risk for subsequent malignancies.

Positive matrix factorization of seasonally resolved organic aerosol at three different central European background sites based on nuclear magnetic resonance Aerosolomics data.

Concentration data derived from 1H NMR analysis of the water-soluble organic compounds from fine aerosol (PM2.5) at three Central European background stations, Kosetice, Frýdlant (both in the Czech Republic), and Melpitz (Germany), were used for detailed source apportionment analysis. Two winter and two summer episodes (year 2021) with higher organic concentrations and similar wind directions were selected for NMR analyses. The concentration profiles of 61 water-soluble organic compounds were determined by NMR Aerosolomics and a principal component analysis (PCA) was performed on this dataset. Based on the PCA results, 23 compounds were selected for positive matrix factorization (PMF) analysis in order to identify dominant aerosol sources at rural background sites in Central Europe. Both the PCA and the subsequent PMF analyses clearly distinguished the characteristics of winter and summer aerosol particles. In summer, four factors were identified from PMF and were associated with biogenic aerosol (61-78 %), background aerosol (9-15 %), industrial biomass combustion (7-13 %), and residential heating (5-13 %). In winter, only 3 factors were identified - industrial biomass combustion (33-49 %), residential heating (37-45 %) and a background aerosol (8-30 %). The main difference was observed in the winter season with a stronger contribution of emissions from industrial biomass burning at the Czech stations Kosetice and Frýdlant (47-49 %) compared to the Melpitz station (33 %). However, in general, there were negligible differences in identified sources between stations in the given seasons, indicating a certain homogeneity in PM2.5 composition within Central Europe at least during the sampling periods.