Flower flies (Diptera: Syrphidae) are one of the most species‐rich dipteran families and provide important ecosystem services such as pollination, biological control of pests, recycling of organic matter and redistributions of essential nutrients. Flower fly adults generally feed on pollen and nectar, but their larval feeding habits are strikingly diverse. In the present study, high‐throughput sequencing was used to capture and enrich phylogenetically and evolutionary informative exonic regions. With the help of the baitfisher software, we developed a new bait kit (SYRPHIDAE1.0) to target 1945 CDS regions belonging to 1312 orthologous genes. This new bait kit was successfully used to exon capture the targeted loci in 121 flower fly species across the different subfamilies of Syrphidae. We analysed different amino acid and nucleotide data sets (1302 loci and 154 loci) with maximum likelihood and multispecies coalescent models. Our analyses yielded highly supported similar topologies, although the degree of the SRH (global stationarity, reversibility and homogeneity) conditions varied greatly between amino acid and nucleotide data sets. The sisterhood of subfamilies Pipizinae and Syrphinae is supported in all our analyses, confirming a common origin of taxa feeding on soft‐bodied arthropods. Based on our results, we define Syrphini stat.rev. to include the genera Toxomerus and Paragus . Our divergence estimate analyses with beast inferred the origin of the Syrphidae in the Lower Cretaceous (125.5–98.5 Ma) and the diversification of predatory flower flies around the K–Pg boundary (70.61–54.4 Ma), coinciding with the rise and diversification of their prey.

The wasp family Chrysididae (cuckoo wasps, gold wasps) comprises exclusively parasitoid and kleptoparasitic species, many of which feature a stunning iridescent coloration and phenotypic adaptations to their parasitic life style. Previous attempts to infer phylogenetic relationships among the family’s major lineages (subfamilies, tribes, genera) based on Sanger sequence data were insufficient to statistically resolve the monophyly and the phylogenetic position of the subfamily Amiseginae and the phylogenetic relationships among the tribes Allocoeliini, Chrysidini, Elampini, and Parnopini (Chrysidinae). Here, we present a phylogeny inferred from nucleotide sequence data of 492 nuclear single-copy genes (230,915 aligned amino acid sites) from 94 species of Chrysidoidea (representing Bethylidae, Chrysididae, Dryinidae, Plumariidae) and 45 outgroup species by combining RNA-seq and DNA target enrichment data. We find support for Amiseginae being more closely related to Cleptinae than to Chrysidinae. Furthermore, we find strong support for Allocoeliini being the sister lineage of all remaining Chrysidinae, whereas Elampini represent the sister lineage of Chrysidini and Parnopini. Our study corroborates results from a recent phylogenomic investigation, which revealed Chrysidoidea as likely paraphyletic.

(1) Background: Next-generation sequencing (NGS) of patients with advanced tumors is becoming an established method in Molecular Tumor Boards. However, somatic variant detection, interpretation, and report generation, require in-depth knowledge of both bioinformatics and oncology. (2) Methods: MIRACUM-Pipe combines many individual tools into a seamless workflow for comprehensive analyses and annotation of NGS data including quality control, alignment, variant calling, copy number variation estimation, evaluation of complex biomarkers, and RNA fusion detection. (3) Results: MIRACUM-Pipe offers an easy-to-use, one-prompt standardized solution to analyze NGS data, including quality control, variant calling, copy number estimation, annotation, visualization, and report generation. (4) Conclusions: MIRACUM-Pipe, a versatile pipeline for NGS, can be customized according to bioinformatics and clinical needs and to support clinical decision-making with visual processing and interactive reporting.

Background: Understanding the similarities of patients with cancer is essential to advancing personalized medicine, improving patient outcomes, and developing more effective and individualized treatments. It enables researchers to discover important patterns, biomarkers, and treatment strategies that can have a significant impact on cancer research and oncology. In addition, the identification of previously successfully treated patients supports oncologists in making treatment decisions for a new patient who is clinically or molecularly similar to the previous patient. Objective: The planned review aims to systematically summarize, map, and describe existing evidence to understand how patient similarity is defined and used in cancer research and clinical care. Methods: To systematically identify relevant studies and to ensure reproducibility and transparency of the review process, a comprehensive literature search will be conducted in several bibliographic databases, including Web of Science, PubMed, LIVIVIVO, and MEDLINE, covering the period from 1998 to February 2024. After the initial duplicate deletion phase, a study selection phase will be applied using Rayyan, which consists of 3 distinct steps: title and abstract screening, disagreement resolution, and full-text screening. To ensure the integrity and quality of the selection process, each of these steps is preceded by a pilot testing phase. This methodological process will culminate in the presentation of the final research results in a structured form according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flowchart. The protocol has been registered in the Journal of Medical Internet Research. Results: This protocol outlines the methodologies used in conducting the scoping review. A search of the specified electronic databases and after removing duplicates resulted in 1183 unique records. As of March 2024, the review process has moved to the full-text evaluation phase. At this stage, data extraction will be conducted using a pretested chart template. Conclusions: The scoping review protocol, centered on these main concepts, aims to systematically map the available evidence on patient similarity among patients with cancer. By defining the types of data sources, approaches, and methods used in the field, and aligning these with the research questions, the review will provide a foundation for future research and clinical application in personalized cancer care. This protocol will guide the literature search, data extraction, and synthesis of findings to achieve the review’s objectives. International Registered Report Identifier (IRRID): DERR1-10.2196/58705

Soft tissue sarcomas (STS) are rare tumors of mesenchymal origin with high mortality. After curative resection, about one third of patients suffer from distant metastases. Tumor follow-up only covers a portion of recurrences and is associated with high cost and radiation burden. For metastasized STS, only limited inferences can be drawn from imaging data regarding therapy response. To date there are no established and evidence-based diagnostic biomarkers for STS due to their rarity and diversity. In a proof-of-concept study, circulating tumor DNA (ctDNA) was quantified in (n = 25) plasma samples obtained from (n = 3) patients with complex karyotype STS collected over three years. Genotyping of tumor tissue was performed by exome sequencing. Patient-individual mini-panels for targeted next-generation sequencing were designed encompassing up to 30 mutated regions of interest. Circulating free DNA (cfDNA) was purified from plasma and ctDNA quantified therein. ctDNA values were correlated with clinical parameters. ctDNA concentrations correlated with the tumor burden. In case of full remission, no ctDNA was detectable. Patients with a recurrence at a later stage showed low levels of ctDNA during clinical remission, indicating minimal residual disease. In active disease (primary tumor or metastatic disease), ctDNA was highly elevated. We observed direct response to treatment, with a ctDNA decline after tumor resections, radiotherapy, and chemotherapy. Quantification of ctDNA allows for the early detection of recurrence or metastases and can be used to monitor treatment response in STS. Therapeutic decisions can be made earlier, such as the continuation of a targeted adjuvant therapy or the implementation of extended imaging to detect recurrences. In metastatic disease, therapy can be adjusted promptly in case of no response. These advantages may lead to a survival benefit for patients in the future.

Background: Complex and expanding datasets in clinical oncology applications require flexible and interactive visualization of patient data to provide physicians and other medical professionals with maximum amount of information. In particular, interdisciplinary tumor conferences profit from customized tools to integrate, link, and visualize relevant data from all professions involved. Objective: Our objective was to identify and present currently available data visualization tools for tumor boards and related areas. We wanted to provide an overview of not only the digital tools currently used in tumor board settings but also of the data they include, their respective visualization solutions, and their integration into hospital processes. Methods: This scoping review was based on the scoping study framework by Arksey and O’Malley and attempted to answer the following research question: “What are the key features of data visualization solutions used in molecular and organ tumor boards, and how are these elements integrated and used within the clinical setting?” The following electronic databases were searched for articles: PubMed, Web of Science, and Scopus. Articles were deemed eligible if published in English in the last 10 years. Eligible articles were first deduplicated, followed by screening of titles and abstracts. Full-text screening was then conducted to decide on article selection. All included articles were analyzed using a data extraction template. The template included a variety of meta-information, as well as specific fields aiming to answer the research question. Results: The review process started with 2049 articles, of which 1014 (49.49%) were included in the title and abstract screening. A total of 5.47% (112/2049) of the publications were eligible for full-text screening, leading to 2.93% (60/2049) of the publications being eligible for final inclusion. They covered 49 distinct visualization tools and applications. We discovered a variety of innovative visualization solutions, most often driven by the complexity of omics data, represented in 96% (47/49) of the tools. Tables remained the most used tool for the visualization of data types described in the articles. Approximately one-third of the identified tools (16/49, 33%) were systematically evaluated in some form. For most discovered tools (37/49, 76%), there was no documentation of implementation into the clinical routine. A significant number of applications (21/49, 43%) were available through open-source access. Conclusions: There is a wide range of projects providing visualization solutions for tumor boards and clinical oncology applications. Among the few tools that have made their way into clinical routine settings, there are both commercial and academic solutions. While tables for a variety of data types remain the dominant visualization strategy, the complexity of omics data appears to be the driving force behind many visualization innovations in the domain of tumor boards. International Registered Report Identifier (IRRID): RR2-10.2196/53627