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Abstract Background Pituitary neuroendocrine tumors (PitNET) are among the most common intracranial tumors. Despite a frequent benign course, aggressive behavior can occur. Tumor behavior is known to be under the influence of the tumor microenvironment (TME). However, the relationship between TME cells and aggressive tumor behavior has not been adequately explored in PitNET. Methods We performed differential expression analysis as well as gene expression program identification based on single-cell RNA sequencing to comparatively characterize the transcriptome of seven gonadotroph and three lactotroph PitNET and correlate it with clinical features using bulk RNA-seq data from an independent cohort of 134 PitNET. Tumor immune infiltration was quantified via immunostaining on tissue sections of gonadotroph and lactotroph PitNET. Results In lactotroph PitNET, we detect a highly proliferative gene profile with significantly increased expression levels in aggressively growing tumors within bulk RNA-seq data of an independent cohort of 134 PitNET samples. We also report high intratumoral heterogeneity in gonadotroph PitNET (GoPN) and lactotroph PitNET (LaPN) and identify signatures of epithelial, endocrine, and immunological gene networks in both subtypes. A comparison of their TME composition shows enrichment of SPP1+ macrophages and CD4+ T cells in GoPN, as well as enrichment of CD4/CD8 double-negative T cells (DN) and natural killer cells (NK) in LaPN. Also notable is the presence of proliferative lymphocytes, the occurrence of which positively correlates with more aggressive tumor behavior in the bulk RNA-seq cohort. However, increased CD8+ T and NK cell abundances correlate significantly with reduced aggressiveness indicating potential anti-tumoral effects. Conclusions Our study expands the knowledge of the differences in cellular composition of gonadotroph and lactotroph PitNET subtypes. It lays the foundation for further studies on the influence of lymphoid cells on the variable aggressive behavior of PitNET. Regarding the treatment of drug-resistant lactotroph PitNET, proliferative lymphocytes, CD8+ T, and NK cells could represent potentially valuable targets for developing new cancer immunotherapies.

Abstract Peripheral T-cell lymphoma, not otherwise specified (PTCL-NOS) is a heterogeneous group of malignancies with poor outcome. Here, we identify a subgroup, PTCL-NOS SMARCB1- , which is characterized by the lack of the SMARCB1 protein and occurs more frequently in young patients. Human and murine PTCL-NOS SMARCB1- show similar DNA methylation profiles, with hypermethylation of T-cell-related genes and hypomethylation of genes involved in myeloid development. Single-cell analyses of human and murine tumors revealed a rich and complex network of interactions between tumor cells and an immunosuppressive and exhausted tumor microenvironment (TME). In a drug screen, we identified histone deacetylase inhibitors (HDACi) as a class of drugs effective against PTCL-NOS Smarcb1- . In vivo treatment of mouse tumors with SAHA, a pan-HDACi, triggered remodeling of the TME, promoting replenishment of lymphoid compartments and reversal of the exhaustion phenotype. These results provide a rationale for further exploration of HDACi combination therapies targeting PTCL-NOS SMARCB1- within the TME.

BackgroundTechnological advances allow for recording and sharing health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable close management of individual health trajectories, monitoring of risk factors, and asynchronous feedback. However, most remote patient monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack a patient-centric design. ObjectiveThe objective of this study was to conceptualize and implement a user-friendly, reusable, interoperable, and secure RPM system incorporating asynchronous feedback mechanisms using a broadly available consumer wearable (Apple Watch). In addition, this study sought to evaluate factors influencing patient acceptance of such systems. MethodsThe RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was used as part of the activeDCM prospective randomized interventional study focusing on dilated cardiomyopathy. Each patient used the system for at least 12 months. The System Usability Scale was used to measure usability from a subjective patient perspective. In addition, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data classified into sensor-based health data (SHD) and patient-reported outcome measures (PROMs). Descriptive statistics using box plots and bootstrapped multiple linear regression with 95% CIs were used for evaluation analyzing the influence of age, sex, device experience, and intervention group membership. ResultsThe RPM system comprised 4 interoperable components: patient devices, a data server, a data viewer, and a notification service. The system was evaluated with 95 consecutive patients with dilated cardiomyopathy (28/95, 29% female; mean age 50, SD 12 y) who completed the activeDCM study protocol. The system’s app achieved a mean System Usability Scale score of 78 (SD 17), which was most influenced by device experience. In total, 87% (83/95) of the patients could integrate the use of the app well or very well into their daily routine, and 71% (67/95) saw a benefit of the RPM system for management of their health condition. On average, patients interacted with the wearable on 61% (SD 26%) of days enrolled in the study. SHD were available on average for 78% (SD 23%) of days, and PROM data were available on 64% (SD 27%) of weeks enrolled in the study. Wearable interaction frequency, SHD, and PROM completeness were most influenced by intervention group membership. ConclusionsOur results mark a first step toward integrating RPM systems based on a consumer wearable device for primary patient input into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable, and secure RPM system that can be integrated into patients’ daily routines.

Abstract Structured patient data play a key role in all types of clinical research. They are often collected in study databases for research purposes. In order to describe characteristics of a next-generation study database and assess the feasibility of its implementation a proof-of-concept study in a German university hospital was performed. Key characteristics identified include FAIR access to electronic case report forms (eCRF), regulatory compliant Electronic Data Capture (EDC), an EDC with electronic health record (EHR) integration, scalable EDC for medical documentation, patient generated data, and clinical decision support. In a local case study, we then successfully implemented a next-generation study database for 19 EDC systems (n = 2217 patients) that linked to i.s.h.med (Oracle Cerner) with the local EDC system called OpenEDC. Desiderata of next-generation study databases for patient data were identified from ongoing local clinical study projects in 11 clinical departments at Heidelberg University Hospital, Germany, a major tertiary referral hospital. We compiled and analyzed feature and functionality requests submitted to the OpenEDC team between May 2021 and July 2023. Next-generation study databases are technically and clinically feasible. Further research is needed to evaluate if our approach is feasible in a multi-center setting as well.

BackgroundThe Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher. ObjectiveOur study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required. MethodsWe have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability. ResultsThe use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers. ConclusionsThis paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

Abstract Background Clinical decision‐making for patients with myelodysplastic syndromes (MDS) is challenging, and both disease and treatment effects heavily impact health‐related quality of life (HRQoL) of these patients. Therefore, disease‐specific HRQoL measures can be critical to harness the patient voice in MDS research. Methods We report a prospective international validation study of the Quality of Life in Myelodysplasia Scale (QUALMS) with a main focus on providing information on the psychometric characteristics of its three subscales: physical burden (QUALMS‐P), emotional burden (QUALMS‐E), and benefit finding (QUALMS‐BF). The analysis is based on patients enrolled from three European countries and Israel, participating to the MDS‐RIGHT Project. The scale structure and psychometric properties of the QUALMS were assessed. Results Overall, 270 patients with a median age of 74 years were analyzed and the majority of them (60.3%) had a low MDS‐Comorbidity Index score. Results of the confirmatory factor analysis supported the underlying scale structure of the QUALMS, which, in addition to a total score, includes three subscales: QUALMS‐P, QUALMS‐E, and the QUALMS‐BF. The QUALMS‐P exhibited the highest Cronbach's alpha coefficients. Discriminant validity analysis indicated good results with the QUALMS‐P and QUALMS‐E distinguishing between patients with different performance status, comorbidity, anemia, and transfusion dependency status. No floor and ceiling effects were observed. Responsiveness to change analysis supported the validity of the measure. Patients with a hemoglobin (Hb) level of

Abstract Background Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently. Results Cogito “COmpare annotated Genomic Intervals TOol” provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation. Conclusions Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html , a comprehensive documentation with detailed descriptions and reproducible examples is included.

Survival outcomes in Burkitt lymphoma differ between adult and paediatric patients. Here, the authors show differences in mutational frequencies between age groups, and a transition between mutational profiles which occurs between 25 and 40 years.

Abstract Background Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools. Objective The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment. Methods We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal’s data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach. Results Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains. Conclusions Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.

Rhabdoid tumors (RT) are aggressive paediatric cancers with yet unknown cells of origin. Here, the authors establish genetically engineered mouse models of RT and, using single-cell RNA-seq and epigenomics, identify potential cells of origin for the SHH and MYC subtypes.

Marta Interlandi et al. develop InterCellar, a user-friendly tool for analyzing ligand−receptor interactions from scRNA-seq data, and establishing a link between these interactions and well-known molecular pathways.

Molecular screening tools have significantly eased the assessment of potential germline susceptibility factors that may underlie the development of pediatric malignancies. Most of the hitherto published studies utilize the comparative analyses of the respective patients' germline and tumor tissues for this purpose. Since this approach is not able to discriminate between de novo and inherited sequence variants, we performed whole exome trio analyses in a consecutive series of 131 children with various forms of hematologic malignancies and their parents. In total, we identified 458 de novo variants with a range from zero to 28 (median value = 3) per patient, although most of them (58%) had only up to three per exome. Overall, we identified bona fide cancer predisposing alterations in five of the investigated 131 (3.8%) patients. Three of them had de novo pathogenic lesions in the SOS1, PTPN11 and TP53 genes and two of them parentally inherited ones in the STK11 and PMS2 genes that are specific for a Peutz-Jeghers and a constitutional mismatch repair deficiency (CMMRD) syndrome, respectively. Notwithstanding that we did not identify a disease-specific alteration in the two cases with the highest number of de novo variants, one of them developed two almost synchronous malignancies: a myelodysplastic syndrome and successively within two months a cerebral astrocytoma. Moreover, we also found that the rate of de novo sequence variants in the offspring increased especially with the age of the father, but less so with that of the mother. We therefore conclude that trio analyses deliver an immediate overview about the inheritance pattern of the entire spectrum of sequence variants, which not only helps to securely identify the de novo or inherited nature of genuinely disease-related lesions, but also of all other less obvious variants that in one or the other way may eventually advance our understanding of the disease process.

Abstract With increasing numbers of patients recovering from COVID-19, there is increasing evidence for persistent symptoms and the need for follow-up studies. This retrospective study included patients without comorbidities, who recovered from COVID-19 and attended an outpatient clinic at a university hospital for follow-up care and potential convalescent plasma donation. Network analysis was applied to visualize symptom combinations and persistent symptoms. Comprehensive lab-testing was ascertained at each follow-up to analyze differences regarding patients with vs without persistent symptoms. 116 patients were included, age range was 18–69 years (median: 41) with follow-ups ranging from 22 to 102 days. The three most frequent persistent symptoms were Fatigue (54%), Dyspnea (29%) and Anosmia (25%). Lymphopenia was present in 13 of 112 (12%) cases. Five of 35 cases (14%) had Lymphopenia in the later follow-up range of 80–102 days. Serum IgA concentration was the only lab parameter with significant difference between patients with vs without persistent symptoms with reduced serum IgA concentrations in the patient cohort of persistent symptoms (p = 0.0219). Moreover, subgroup analyses showed that patients with lymphopenia experienced more frequently persistent symptoms. In conclusion, lymphopenia persisted in a noticeable percentage of recovered patients. Patients with persistent symptoms had significantly lower serum IgA levels. Furthermore, our data provides evidence that lymphopenia is associated with persistence of COVID-19 symptoms.

BackgroundIntensive care unit (ICU) readmissions are associated with mortality and poor outcomes. To improve discharge decisions, machine learning (ML) could help to identify patients at risk of ICU readmission. However, as many models are black boxes, dangerous properties may remain unnoticed. Widely used post hoc explanation methods also have inherent limitations. Few studies are evaluating inherently interpretable ML models for health care and involve clinicians in inspecting the trained model.MethodsAn inherently interpretable model for the prediction of 3 day ICU readmission was developed. We used explainable boosting machines that learn modular risk functions and which have already been shown to be suitable for the health care domain. We created a retrospective cohort of 15,589 ICU stays and 169 variables collected between 2006 and 2019 from the University Hospital Münster. A team of physicians inspected the model, checked the plausibility of each risk function, and removed problematic ones. We collected qualitative feedback during this process and analyzed the reasons for removing risk functions. The performance of the final explainable boosting machine was compared with a validated clinical score and three commonly used ML models. External validation was performed on the widely used Medical Information Mart for Intensive Care version IV database.ResultsThe developed explainable boosting machine used 67 features and showed an area under the precision-recall curve of 0.119 ± 0.020 and an area under the receiver operating characteristic curve of 0.680 ± 0.025. It performed on par with state-of-the-art gradient boosting machines (0.123 ± 0.016, 0.665 ± 0.036) and outperformed the Simplified Acute Physiology Score II (0.084 ± 0.025, 0.607 ± 0.019), logistic regression (0.092 ± 0.026, 0.587 ± 0.016), and recurrent neural networks (0.095 ± 0.008, 0.594 ± 0.027). External validation confirmed that explainable boosting machines (0.221 ± 0.023, 0.760 ± 0.010) performed similarly to gradient boosting machines (0.232 ± 0.029, 0.772 ± 0.018). Evaluation of the model inspection showed that explainable boosting machines can be useful to detect and remove problematic risk functions.ConclusionsWe developed an inherently interpretable ML model for 3 day ICU readmission prediction that reached the state-of-the-art performance of black box models. Our results suggest that for low- to medium-dimensional datasets that are common in health care, it is feasible to develop ML models that allow a high level of human control without sacrificing performance.

Patients with myeloid neoplasia are classified by the WHO classification systems. Besides clinical and hematological criteria, cytogenetic and molecular genetic alterations highly impact treatment stratification. In routine diagnostics, a combination of methods is used to decipher different types of genetic variants. Eight patients were comprehensively analyzed using karyotyping, fluorescence in situ hybridization, array-CGH and a custom NGS panel. Clonal evolution was reconstructed manually, integrating all mutational information on single nucleotide variants (SNVs), insertions and deletions (indels), structural variants and copy number variants (CNVs). To allow a correct integration, we differentiate between three scenarios: 1) CNV occurring prior to the SNV/indel, but in the same cells. 2) SNV/indel occurring prior to the CNV, but in the same cells. 3) SNV/indel and CNV existing in parallel, independent of each other. Applying this bioinformatics approach, we reconstructed clonal evolution for all patients. This generalizable approach offers the possibility to integrate various data to analyze identification of driver and passenger mutations as well as possible targets for personalized medicine approaches. Furthermore, this model can be used to identify markers to assess the minimal residual disease.

The microenvironment of cancer cells is receiving increasing attention as an important factor influencing the progression and prognosis of tumor diseases. In multiple myeloma (MM), a hematological cancer of plasma cells, mesenchymal stem cells (MSCs) represent an integral part of the bone marrow niche and tumor microenvironment. It has been described that MM cells alter MSCs in a way that MM-associated MSCs promote the proliferation and survival of MM cells. Yet, our understanding of the molecular mechanisms governing the interaction between MM cells and MSCs and whether this can be targeted for therapeutic interventions is limited. To identify potential molecular targets, we examined MSCs by RNA sequencing and Western blot analysis. We report that MSCs from MM patients with active disease (MM-Act-MSCs) show a distinct gene expression profile as compared with MSCs from patients with other (non-) malignant diseases (CTR-MSCs). Of note, we detected a significant enrichment of the PI3K–AKT–mTOR hallmark gene set in MM-Act-MSCs and further confirmed the increased levels of related proteins in these MSCs. Pictilisib, a pan-PI3K inhibitor, selectively reduced the proliferation of MM-Act-MSCs as compared with CTR-MSCs. Furthermore, pictilisib treatment impaired the MM-promoting function of MM-Act-MSCs. Our data thus provide a deeper insight into the molecular signature and function of MSCs associated with MM and show that targeting PI3K–AKT–mTOR signaling in MSCs may represent an additional therapeutic pathway in the treatment of MM patients.

Abstract Background The variety of medical documentation often leads to incompatible data elements that impede data integration between institutions. A common approach to standardize and distribute metadata definitions are ISO/IEC 11179 norm-compliant metadata repositories with top-down standardization. To the best of our knowledge, however, it is not yet common practice to reuse the content of publicly accessible metadata repositories for creation of case report forms or routine documentation. We suggest an alternative concept called pragmatic metadata repository, which enables a community-driven bottom-up approach for agreeing on data collection models. A pragmatic metadata repository collects real-world documentation and considers frequent metadata definitions as high quality with potential for reuse. Methods We implemented a pragmatic metadata repository proof of concept application and filled it with medical forms from the Portal of Medical Data Models. We applied this prototype in two use cases to demonstrate its capabilities for reusing metadata: first, integration into a study editor for the suggestion of data elements and, second, metadata synchronization between two institutions. Moreover, we evaluated the emergence of bottom-up standards in the prototype and two medical data managers assessed their quality for 24 medical concepts. Results The resulting prototype contained 466,569 unique metadata definitions. Integration into the study editor led to a reuse of 1836 items and item groups. During the metadata synchronization, semantic codes of 4608 data elements were transferred. Our evaluation revealed that for less complex medical concepts weak bottom-up standards could be established. However, more diverse disease-related concepts showed no convergence of data elements due to an enormous heterogeneity of metadata. The survey showed fair agreement (Kalpha = 0.50, 95% CI 0.43–0.56) for good item quality of bottom-up standards. Conclusions We demonstrated the feasibility of the pragmatic metadata repository concept for medical documentation. Applications of the prototype in two use cases suggest that it facilitates the reuse of data elements. Our evaluation showed that bottom-up standardization based on a large collection of real-world metadata can yield useful results. The proposed concept shall not replace existing top-down approaches, rather it complements them by showing what is commonly used in the community to guide other researchers.

Abstract Patients with HPV‐driven (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) have a significantly improved overall survival compared to patients with HPV‐negative (HPV−) OPSCC. Nevertheless, 13%–25% of patients with HPV+OPSCC develop local/distant recurrence (LDR) and have a course of disease similar to HPV−OPSCC. We hypothesize that HPV+OPSCCs of patients with LDR have a mutation frequency and pattern similar to HPV−OPSCCs, which is associated with severe outcome. We performed targeted next‐generation sequencing using a customized gene panel and compared data from 56 matched HPV+and HPV−OPSCC of patients with/without LDR regarding protein‐altering variants. Despite improved overall survival of patients with HPV+OPSCC, those who develop LDR show a strongly reduced survival rate that is similar or even worse compared to HPV−OPSCC patients. Overall, the number of mutations was similar in OPSCC of patients with and without LDR. In total and with respect to TP53, HPV−OPSCC had significantly more protein‐altering mutations than HPV+OPSCC. The number of mutations was similar in HPV−OPSCC of patients with and without LDR with the exception of FAT1, which was mutated more frequently in patients without LDR. In HPV+OPSCC, HRAS, PIK3R1, STK11 and TP63 were more frequently mutated in patients with LDR compared to patients without. HPV+OPSCC of patients with LDR have a similar mutation pattern as HPV−OPSCC, except TP53, which was mutated to a significantly lower extent. In conclusion, HPV−and HPV+OPSCC with LDR have similar mutation counts in the analyzed genes. We suspect that the number of mutations is not causal for disease progression, rather specific mutations could be important.

Abstract Background Transthyretin familial amyloid polyneuropathy (ATTR-FAP) is a rare autosomal dominant inherited disease affecting multiple organ systems. ATTR-FAP patients’ experiences have rarely been documented. The aim of this study was to collect patient reported outcomes across different countries to assess unmet needs and challenges. An anonymous survey was conducted at the 2nd European meeting on ATTR amyloidosis in Berlin in September 2019. Survey questions captured information on demographics, clinical characteristics, diagnostic experience, quality of life, disability and ATTR-FAP management. Results A total of 38 ATTR-FAP patients from 15 different countries participated in the survey. ATTR-FAP had a substantial impact on patients’ day-to-day life, including difficulties in standing, walking, and participation in community activities. It also had negative effects on the mental health of patients. The survey highlighted several unmet needs and challenges from a patients’ perspective, including (i) a need for increased awareness and a standardized diagnostic pathway, (ii) a need for better treatment access and supportive care and (iii) a need for better information about research and clinical trials. Conclusions This global patient survey provides valuable findings to address ATTR-FAP patients’ needs and challenges in order to further the goal of patient-centered care.

Abstract Background Next-Generation Sequencing (NGS) enables large-scale and cost-effective sequencing of genetic samples in order to detect genetic variants. After successful use in research-oriented projects, NGS is now entering clinical practice. Consequently, variant analysis is increasingly important to facilitate a better understanding of disease entities and prognoses. Furthermore, variant calling allows to adapt and optimize specific treatments of individual patients, and thus is an integral part of personalized medicine.However, the analysis of NGS data typically requires a number of complex bioinformatics processing steps. A flexible and reliable software that combines the variant analysis process with a simple, user-friendly interface is therefore highly desirable, but still lacking. Results With AMLVaran (AML Variant Analyzer), we present a web-based software, that covers the complete variant analysis workflow of targeted NGS samples. The software provides a generic pipeline that allows free choice of variant calling tools and a flexible language (SSDL) for filtering variant lists. AMLVaran’s interactive website presents comprehensive annotation data and includes curated information on relevant hotspot regions and driver mutations. A concise clinical report with rule-based diagnostic recommendations is generated.An AMLVaran configuration with eight variant calling tools and a complex scoring scheme, based on the somatic variant calling pipeline appreci8, was used to analyze three datasets from AML and MDS studies with 402 samples in total. Maximum sensitivity and positive predictive values were 1.0 and 0.96, respectively. The tool’s usability was found to be satisfactory by medical professionals. Conclusion Coverage analysis, reproducible variant filtering and software usability are important for clinical assessment of variants. AMLVaran performs reliable NGS variant analyses and generates reports fulfilling the requirements of a clinical setting. Due to its generic design, the software can easily be adapted for use with different targeted panels for other tumor entities, or even for whole-exome data. AMLVaran has been deployed to a public web server and is distributed with Docker scripts for local use.

Infection or chronic inflammation is a risk factor for childhood B-cell precursor acute lymphoblastic leukemia. Here, the authors show that the DNA editing enzyme AID is expressed in infected B cells but using genetic mouse models show that it does not contribute to leukemia pathogenesis.

The clinical course of COVID-19 is very heterogeneous: most infected individuals can be managed in an outpatient setting, but a substantial proportion of patients requires intensive care, resulting in a high rate of fatalities. We performed a biomarker study to assess the impact of prior infections with seasonal coronaviruses on COVID-19 severity. Sixty patients with confirmed COVID-19 infections were included (age 30–82; 52 males, 8 females): 19 inpatients with critical disease, 16 inpatients with severe or moderate disease, and 25 outpatients. Patients with critical disease had significantly lower levels of anti-HCoV OC43-NP (P = 0.016) and HCoV HKU1-NP (P = 0.023) antibodies at the first encounter compared to other COVID-19 patients. Our results indicate that prior infections with seasonal coronaviruses might protect against a severe course of disease.

BackgroundMedical research and machine learning for health care depend on high-quality data. Electronic data capture (EDC) systems have been widely adopted for metadata-driven digital data collection. However, many systems use proprietary and incompatible formats that inhibit clinical data exchange and metadata reuse. In addition, the configuration and financial requirements of typical EDC systems frequently prevent small-scale studies from benefiting from their inherent advantages. ObjectiveThe aim of this study is to develop and publish an open-source EDC system that addresses these issues. We aim to plan a system that is applicable to a wide range of research projects. MethodsWe conducted a literature-based requirements analysis to identify the academic and regulatory demands for digital data collection. After designing and implementing OpenEDC, we performed a usability evaluation to obtain feedback from users. ResultsWe identified 20 frequently stated requirements for EDC. According to the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) 25010 norm, we categorized the requirements into functional suitability, availability, compatibility, usability, and security. We developed OpenEDC based on the regulatory-compliant Clinical Data Interchange Standards Consortium Operational Data Model (CDISC ODM) standard. Mobile device support enables the collection of patient-reported outcomes. OpenEDC is publicly available and released under the MIT open-source license. ConclusionsAdopting an established standard without modifications supports metadata reuse and clinical data exchange, but it limits item layouts. OpenEDC is a stand-alone web app that can be used without a setup or configuration. This should foster compatibility between medical research and open science. OpenEDC is targeted at observational and translational research studies by clinicians.

BackgroundMedical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. ObjectiveThis study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. MethodsMedical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. ResultsIn total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. ConclusionsStandardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research.

Summary: Despite the high incidence of male infertility, only 30% of infertile men receive a causative diagnosis. To explore the regulatory mechanisms governing human germ cell function in normal and impaired spermatogenesis (crypto), we performed single-cell RNA sequencing (>30,000 cells). We find major alterations in the crypto spermatogonial compartment with increased numbers of the most undifferentiated spermatogonia (PIWIL4+). We also observe a transcriptional switch within the spermatogonial compartment driven by increased and prolonged expression of the transcription factor EGR4. Intriguingly, the EGR4-regulated chromatin-associated transcriptional repressor UTF1 is downregulated at transcriptional and protein levels. This is associated with changes in spermatogonial chromatin structure and fewer Adark spermatogonia, characterized by tightly compacted chromatin and serving as reserve stem cells. These findings suggest that crypto patients are disadvantaged, as fewer cells safeguard their germline’s genetic integrity. These identified spermatogonial regulators will be highly interesting targets to uncover genetic causes of male infertility.

Replication competent vesicular stomatitis virus (VSV) is the basis of a vaccine against Ebola and VSV strains are developed as oncolytic viruses. Both functions depend on the ability of VSV to induce adequate amounts of interferon-α/β. It is therefore important to understand how VSV triggers interferon responses. VSV activates innate immunity via retinoic acid-inducible gene I (RIG-I), a sensor for viral RNA. Our results show that VSV needs to replicate for a robust interferon response. Analysis of RIG-I-associated RNA identified a copy-back defective-interfering (DI) genome and full-length viral genomes as main trigger of RIG-I. VSV stocks depleted of DI genomes lost most of their interferon-stimulating activity. The remaining full-length genome and leader-N-read-through sequences, however, still triggered RIG-I. Awareness for DI genomes as trigger of innate immune responses will help to standardize DI genome content and to purposefully deplete or use DI genomes as natural adjuvants in VSV-based therapeutics.

Chronic nodular prurigo is characterized by recalcitrant itch. Patient perspectives on therapeutic goals, satisfaction with therapy and efficacy of therapeutic regimens for this condition are unknown. This questionnaire study examined these issues in 406 patients with chronic nodular prurigo from 15 European dermatological centres. Improvements in itch, skin lesions and sleep were the most important goals. Emollients, topical corticosteroids and antihistamines were the most frequently used treatments, while a minority of patients were prescribed potent medications, such as systemic immunosuppressants and gabapentinoids. Most patients were not satisfied with their previous therapy (56.8%), while 9.8% did not receive any therapy despite having active disease. A substantial number of respondents (28.7%) considered none of the therapeutic options effective. Although chronic nodular prurigo is a severe disease, most patients were not treated with potent systemic drugs, which may contribute to the high levels of dissatisfaction and disbelief in available therapies. Specific guidelines for chronic nodular prurigo and the development of novel therapies are necessary to improve care.

The murine esBAF complex plays a major role in the regulation of gene expression during stem cell development and differentiation. As one of its core subunits, Smarcb1 is indispensable for its function and its loss is connected to neurodevelopmental disorders and participates in the carcinogenesis of entities such as rhabdoid tumours. We explored how Smarcb1 regulates gene programs in murine embryonic stem cells (mESC) and in this way orchestrates differentiation. Our data underline the importance of Smarcb1 expression and function for the development of the nervous system along with basic cellular functions, such as cell adhesion and cell organisation. Using ChIP-seq, we were able to portray the consequences of Smarcb1 knockdown (kd) for the binding of esBAF and PRC2 as well as its influence on histone marks H3K27me3, H3K4me3 and H3K27ac. Their signals are changed in gene and enhancer regions of genes connected to nervous system development and offers a plausible explanation for changes in gene expression. Further, we describe a group of genes that are, despite increased BAF binding, suppressed after Smarcb1 kd by mechanisms independent of PRC2 function.

T-cell lymphomas are highly heterogeneous and their prognosis is poor under the currently available therapies. Enhancers of zeste homologue 1 and 2 (EZH1/2) are histone H3 lysine-27 trimethyltransferases (H3K27me3). Despite the rapid development of new drugs inhibiting EZH2 and/or EZH1, the molecular interplay of these proteins and the impact on disease progression and prognosis of patients with T-cell lymphomas remains insufficiently understood. In this study, EZH1/2 mutation status was evaluated in 33 monomorphic epitheliotropic intestinal T-cell lymphomas by next generation sequencing and EZH1/2 and H3K27me3 protein expression levels were detected by immunohistochemistry in 46 T-cell lymphomas. Correlations with clinicopathologic features were analyzed and survival curves generated. No EZH1 mutations and one (3%) EZH2 missense mutation were identified. In univariable analysis, high EZH1 expression was associated with an improved overall survival (OS) and progression-free survival (PFS) whereas high EZH2 and H3K27me3 expression were associated with poorer OS and PFS. Multivariable analysis revealed EZH1 (hazard ratio (HR) = 0.183; 95% confidence interval (CI): 0.044–0.767; p = 0.020;) and EZH2 (HR = 8.245; 95% CI: 1.898–35.826; p = 0.005) to be independent, divergent prognostic markers for OS. In conclusion, EZH1/2 protein expression had opposing effects on the prognosis of T-cell lymphoma patients.

In order to improve diagnosis and treatment, physicians require information about the social context and quality of life of their patients. The Center for Chronic Pruritus at the University Hospital Münster achieves this goal using the electronic patient-reported outcome system "Mobile Patient Survey", which assesses pruritus and quality of life measures. The aim of this study is to evaluate the consistency and reliability of such measures. A total of 42 patients, age range 19–82 years, participated in the study and were asked to assess the measures at baseline via a paper questionnaire, and to use the "Mobile Patient Survey" at baseline and after 1 h in order to test reliability. Statistical analysis was performed using coefficient rc for metric variables and weighted kappa κw for categorical variables. The internal consistency of all measures was unaffected. It was shown that 6 out of 7 measures can be assessed without loss of reliability. It is recommended that questionnaires for electronic usage are assessed for validity and reliability.

Myelodysplastic syndromes are a broad group of haematopoietic malignancies that often progress to acute myeloid leukaemia. Here, the authors show that linear and branched evolution occurs within myelodysplastic syndrome and these patterns can be impacted by treatment.

Abstract Background The development of Electronic Health Records (EHRs) in hospitals offers the ability to reuse data from patient care activities for clinical research. EHR4CR is a European public-private partnership aiming to develop a computerized platform that enables the re-use of data collected from EHRs over its network. However, the reproducibility of queries may depend on attributes of the local data. Our objective was 1/ to describe the different steps that were achieved in order to use the EHR4CR platform and 2/ to identify the specific issues that could impact the final performance of the platform. Methods We selected three institutional studies covering various medical domains. The studies included a total of 67 inclusion and exclusion criteria and ran in two University Hospitals. We described the steps required to use the EHR4CR platform for a feasibility study. We also defined metrics to assess each of the steps (including criteria complexity, normalization quality, and data completeness of EHRs). Results We identified 114 distinct medical concepts from a total of 67 eligibility criteria Among the 114 concepts: 23 (20.2%) corresponded to non-structured data (i.e. for which transformation is needed before analysis), 92 (81%) could be mapped to terminologies used in EHR4CR, and 86 (75%) could be mapped to local terminologies. We identified 51 computable criteria following the normalization process. The normalization was considered by experts to be satisfactory or higher for 64.2% (43/67) of the computable criteria. All of the computable criteria could be expressed using the EHR4CR platform. Conclusions We identified a set of issues that could affect the future results of the platform: (a) the normalization of free-text criteria, (b) the translation into computer-friendly criteria and (c) issues related to the execution of the query to clinical data warehouses. We developed and evaluated metrics to better describe the platforms and their result. These metrics could be used for future reports of Clinical Trial Recruitment Support Systems assessment studies, and provide experts and readers with tools to insure the quality of constructed dataset.

It is known that severe COVID-19 cases in small children are rare. If a childhood-related infection were protective against a severe course of COVID-19, it would be expected that adults with intensive and regular contact with small children also may have a mild course of COVID-19 more frequently. To test this hypothesis, a survey among 4010 recovered COVID-19 patients was conducted in Germany. 1186 complete answers were collected. 6.9% of these patients reported frequent and regular job-related contact with children below ten years of age, and 23.2% had their own small children, which was higher than expected. In the relatively small subgroup with intensive care treatment (n = 19), patients without contact with small children were overrepresented. These findings are not well explained by age, gender, or BMI distribution of those patients and should be validated in other settings.

Although routine health care and clinical trials usually require the documentation of similar information, data collection is performed independently from each other, resulting in redundant documentation efforts. Standardizing routine documentation can enable secondary use for medical research. Neuroinflammatory demyelinating diseases (NIDs) represent a heterogeneous group of diseases requiring further research to improve patient management. The aim of this work is to develop, implement and evaluate a minimal core dataset in routine health care with a focus on secondary use as case study for NIDs. Therefore, a draft minimal core dataset for NIDs was created by analyzing routine, clinical trial, registry, biobank documentation and existing data standards for NIDs. Data elements (DEs) were converted into the standard format Operational Data Model, semantically annotated and analyzed via frequency analysis. The analysis produced 1958 DEs based on 864 distinct medical concepts. After review and finalization by an interdisciplinary team of neurologists, epidemiologists and medical computer scientists, the minimal core dataset (NID CDEs) consists of 46 common DEs capturing disease-specific information for reuse in the discharge letter and other research settings. It covers the areas of diagnosis, laboratory results, disease progress, expanded disability status scale, therapy and magnetic resonance imaging findings. NID CDEs was implemented in two German university hospitals and a usability study in clinical routine was conducted (participants n = 16) showing a good usability (Mean SUS = 75). From May 2017 to February 2018, 755 patients were documented with the NID CDEs, which indicates the feasibility of developing a minimal core dataset for structured documentation based on previously used documentation standards and integrating the dataset into clinical routine. By sharing, translating and reusing the minimal dataset, a transnational harmonized documentation of patients with NIDs might be realized, supporting interoperability in medical research.

Parkinson's disease and Essential Tremor are two of the most common movement disorders and are still associated with high rates of misdiagnosis. Collected data by technology-based objective measures (TOMs) has the potential to provide new promising and highly accurate movement data for a better understanding of phenotypical characteristics and diagnostic support. A technology-based system called Smart Device System (SDS) is going to be implemented for multi-modal high-resolution acceleration measurement of patients with PD or ET within a clinical setting. The 2-year prospective observational study is conducted to identify new phenotypical biomarkers and train an Artificial Intelligence System. The SDS is going to be integrated and tested within a 20-min assessment including smartphone-based questionnaires, two smartwatches at both wrists and tablet-based Archimedean spirals drawing for deeper tremor-analyses. The electronic questionnaires will cover data on medication, family history and non-motor symptoms. In this paper, we describe the steps for this novel technology-utilizing examination, the principal steps for data analyses and the targeted performances of the system. Future work considers integration with Deep Brain Stimulation, dissemination into further sites and patient's home setting as well as integration with further data sources as neuroimaging and biobanks. Study Registration ID on ClinicalTrials.gov: NCT03638479.

Abstract Background Data capture is one of the most expensive phases during the conduct of a clinical trial and the increasing use of electronic health records (EHR) offers significant savings to clinical research. To facilitate these secondary uses of routinely collected patient data, it is beneficial to know what data elements are captured in clinical trials. Therefore our aim here is to determine the most commonly used data elements in clinical trials and their availability in hospital EHR systems. Methods Case report forms for 23 clinical trials in differing disease areas were analyzed. Through an iterative and consensus-based process of medical informatics professionals from academia and trial experts from the European pharmaceutical industry, data elements were compiled for all disease areas and with special focus on the reporting of adverse events. Afterwards, data elements were identified and statistics acquired from hospital sites providing data to the EHR4CR project. Results The analysis identified 133 unique data elements. Fifty elements were congruent with a published data inventory for patient recruitment and 83 new elements were identified for clinical trial execution, including adverse event reporting. Demographic and laboratory elements lead the list of available elements in hospitals EHR systems. For the reporting of serious adverse events only very few elements could be identified in the patient records. Conclusions Common data elements in clinical trials have been identified and their availability in hospital systems elucidated. Several elements, often those related to reimbursement, are frequently available whereas more specialized elements are ranked at the bottom of the data inventory list. Hospitals that want to obtain the benefits of reusing data for research from their EHR are now able to prioritize their efforts based on this common data element list.

Abstract Background The volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare. Methods Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository. Results A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal. Conclusion Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

MLL fusions are leukemogenic transcription factors that enhance transcriptional elongation through modification of chromatin and RNA Pol II. Global transcription rates and chromatin changes accompanying the transformation process induced by MLL-ENL were monitored by nascent RNA-seq and ChIP-seq, revealing 165 direct target genes separated into two distinct clades. ME5 genes bound MLL-ENL at the promoter, relied on DOT1L-mediated histone methylation, and coded preferentially for transcription factors, including many homeobox genes. A distinct ME3 group accumulated MLL-ENL beyond the termination site, was dependent on P-TEFb-mediated phosphorylation of RNA Pol II for transcription, and translated mainly into proteins involved in RNA biology and ribosome assembly. This dichotomy was reflected by a differential sensitivity toward small molecule inhibitors, suggesting the possibility of a combinatorial strategy for treatment of MLL-induced leukemia.

INTRODUCTION:A required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data. METHODS:The system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application's performance and functionality. RESULTS:The system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects. DISCUSSION:Medical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

BACKGROUND:There are various next-generation sequencing techniques, all of them striving to replace Sanger sequencing as the gold standard. However, false positive calls of single nucleotide variants and especially indels are a widely known problem of basically all sequencing platforms. METHODS:We considered three common next-generation sequencers-Roche 454, Ion Torrent PGM and Illumina NextSeq-and applied standard as well as optimized variant calling pipelines. Optimization was achieved by combining information of 23 diverse parameters characterizing the reported variants and generating individually calibrated generalized linear models. Models were calibrated using amplicon-based targeted sequencing data (19 genes, 28,775 bp) from seven to 12 myelodysplastic syndrome patients. Evaluation of the optimized pipelines and platforms was performed using sequencing data from three additional myelodysplastic syndrome patients. RESULTS:Using standard analysis methods, true mutations were missed and the obtained results contained many artifacts-no matter which platform was considered. Analysis of the parameters characterizing the true and false positive calls revealed significant platform- and variant specific differences. Application of optimized variant calling pipelines considerably improved results. 76% of all false positive single nucleotide variants and 97% of all false positive indels could be filtered out. Positive predictive values could be increased by factors of 1.07 to 1.27 in case of single nucleotide variant calling and by factors of 3.33 to 53.87 in case of indel calling. Application of the optimized variant calling pipelines leads to comparable results for all next-generation sequencing platforms analyzed. However, regarding clinical diagnostics it needs to be considered that even the optimized results still contained false positive as well as false negative calls.

Medical documentation is applied in various settings including patient care and clinical research. Since procedures of medical documentation are heterogeneous and developed further, secondary use of medical data is complicated. Development of medical forms, merging of data from different sources and meta-analyses of different data sets are currently a predominantly manual process and therefore difficult and cumbersome. Available applications to automate these processes are limited. In particular, tools to compare multiple documentation forms are missing. The objective of this work is to design, implement and evaluate the new system ODMSummary for comparison of multiple forms with a high number of semantically annotated data elements and a high level of usability.System requirements are the capability to summarize and compare a set of forms, enable to estimate the documentation effort, track changes in different versions of forms and find comparable items in different forms. Forms are provided in Operational Data Model format with semantic annotations from the Unified Medical Language System. 12 medical experts were invited to participate in a 3-phase evaluation of the tool regarding usability.ODMSummary (available at https://odmtoolbox.uni-muenster.de/summary/summary.html) provides a structured overview of multiple forms and their documentation fields. This comparison enables medical experts to assess multiple forms or whole datasets for secondary use. System usability was optimized based on expert feedback.The evaluation demonstrates that feedback from domain experts is needed to identify usability issues. In conclusion, this work shows that automatic comparison of multiple forms is feasible and the results are usable for medical experts.

Next generation sequencing technologies have provided insights into the molecular heterogeneity of various myeloid neoplasms, revealing previously unknown somatic genetic events. In our cohort of 1444 cases analyzed by next generation sequencing, somatic mutations in the gene BRCA1-BRCA2-containing complex 3 (BRCC3) were identified in 28 cases (1.9%). BRCC3 is a member of the JAMM/MPN+ family of zinc metalloproteases capable of cleaving Lys-63 linked polyubiquitin chains, and is implicated in DNA repair. The mutations were located throughout its coding region. The average variant allelic frequency of BRCC3 mutations was 30.1%, and by a serial sample analysis at two different time points a BRCC3 mutation was already identified in the initial stage of a myelodysplastic syndrome. BRCC3 mutations commonly occurred in nonsense (n=12), frameshift (n=4), and splice site (n=5) configurations. Due to the marginal male dominance (odds ratio; 2.00, 0.84–4.73) of BRCC3 mutations, the majority of mutations (n=23; 82%) were hemizygous. Phenotypically, BRCC3 mutations were frequently observed in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms and associated with -Y abnormality (odds ratio; 3.70, 1.25–11.0). Clinically, BRCC3 mutations were also related to higher age (P=0.01), although prognosis was not affected. Knockdown of Brcc3 gene expression in murine bone marrow lineage negative, Sca1 positive, c-kit positive cells resulted in 2-fold more colony formation and modest differentiation defect. Thus, BRCC3 likely plays a role as tumor-associated gene in myelodysplastic syndromes and myelodysplastic/myeloproliferative neoplasms.

The adapter protein metastasis suppressor 1 (MTSS1) is implicated as a tumor suppressor or tumor promoter, depending on the type of solid cancer. Here, we identified Mtss1 expression to be increased in AML subsets with favorable outcome, while suppressed in high risk AML patients. High expression of MTSS1 predicted better clinical outcome of patients with normal-karyotype AML. Mechanistically, MTSS1 expression was negatively regulated by FLT3-ITD signaling but enhanced by the AML1-ETO fusion protein. DNMT3B, a negative regulator of MTSS1, showed strong binding to the MTSS1 promoter in PML-RARA positive but not AML1-ETO positive cells, suggesting that AML1-ETO leads to derepression of MTSS1. Pharmacological treatment of AML cell lines carrying the FLT3-ITD mutation with the specific FLT3 inhibitor PKC-412 caused upregulation of MTSS1. Moreover, treatment of acute promyelocytic cells (APL) with all-trans retinoic acid (ATRA) increased MTSS1 mRNA levels. Taken together, our findings suggest that MTSS1 might have a context-dependent function and could act as a tumor suppressor, which is pharmacologically targetable in AML patients.

Design, execution and analysis of clinical studies involves several stakeholders with different professional backgrounds. Typically, principle investigators are familiar with standard office tools, data managers apply electronic data capture (EDC) systems and statisticians work with statistics software. Case report forms (CRFs) specify the data model of study subjects, evolve over time and consist of hundreds to thousands of data items per study. To avoid erroneous manual transformation work, a converting tool for different representations of study data models was designed. It can convert between office format, EDC and statistics format. In addition, it supports semantic annotations, which enable precise definitions for data items. A reference implementation is available as open source package ODMconverter at http://cran.r-project.org.

Medical forms are very heterogeneous: on a European scale there are thousands of data items in several hundred different systems. To enable data exchange for clinical care and research purposes there is a need to develop interoperable documentation systems with harmonized forms for data capture. A prerequisite in this harmonization process is comparison of forms. So far--to our knowledge--an automated method for comparison of medical forms is not available. A form contains a list of data items with corresponding medical concepts. An automatic comparison needs data types, item names and especially item with these unique concept codes from medical terminologies. The scope of the proposed method is a comparison of these items by comparing their concept codes (coded in UMLS). Each data item is represented by item name, concept code and value domain. Two items are called identical, if item name, concept code and value domain are the same. Two items are called matching, if only concept code and value domain are the same. Two items are called similar, if their concept codes are the same, but the value domains are different. Based on these definitions an open-source implementation for automated comparison of medical forms in ODM format with UMLS-based semantic annotations was developed. It is available as package compareODM from http://cran.r-project.org. To evaluate this method, it was applied to a set of 7 real medical forms with 285 data items from a large public ODM repository with forms for different medical purposes (research, quality management, routine care). Comparison results were visualized with grid images and dendrograms. Automated comparison of semantically annotated medical forms is feasible. Dendrograms allow a view on clustered similar forms. The approach is scalable for a large set of real medical forms.

Background Very severe aplastic anemia is characterized by a hypoplastic bone marrow due to destruction of CD34+ stem cells by autoreactive T cells. Investigation of the pathomechanism by patient-specific gene expression analysis of the attacked stem cells has previously been impractical because of the scarcity of these cells at diagnosis.Design and Methods Employing unbiased RNA amplification, patient-specific gene expression profiling was carried out for CD34+ cells from patients newly diagnosed with very severe aplastic anemia (n=13), refractory anemia (n=8) and healthy controls (n=10). These data were compared to profiles of myelodysplastic disease (n=55), including refractory anemia (n=18). To identify possible targets of autoimmune attack, presence of autoreactive antibodies was tested in pre-therapeutic sera of patients with very severe aplastic anemia (n=19).Results CD34+ gene expression profiling distinguished between healthy controls, children with aplastic or refractory anemia and clonal disease. Interferon stimulated genes such as the apoptosis inducing death ligand TRAIL were strongly up-regulated in CD34+ cells of patients with aplastic anemia, in particular in patients responding to immunosuppressive treatment. In contrast, mRNA expression of integrin GPVI and the integrin complexes GPIa/IIa, GPIIb/IIIa, GPIB/GPIX/GPV was significantly down-regulated and corresponding antibodies were detected in 7 of 11 profiled patients and in 11 of 19 aplastic anemia patients.Conclusions As a potential diagnostic tool, patient-specific gene expression profiling of CD34+ stem cells made it possible to make the difficult differential diagnosis of most patients with aplastic and refractory anemia. Profiling indicated a prognostic correlation of TRAIL expression and patient benefit from immunosuppressive therapy. Downregulation of integrin expression and concurrent presence of autoreactive anti-integrin-antibodies suggested a previously unrecognized pathological role of integrins in aplastic anemia.

Leukemias are exceptionally well studied at the molecular level and a wealth of high-throughput data has been published. But further utilization of these data by researchers is severely hampered by the lack of accessible integrative tools for viewing and analysis. We developed the Leukemia Gene Atlas (LGA) as a public platform designed to support research and analysis of diverse genomic data published in the field of leukemia. With respect to leukemia research, the LGA is a unique resource with comprehensive search and browse functions. It provides extensive analysis and visualization tools for various types of molecular data. Currently, its database contains data from more than 5,800 leukemia and hematopoiesis samples generated by microarray gene expression, DNA methylation, SNP and next generation sequencing analyses. The LGA allows easy retrieval of large published data sets and thus helps to avoid redundant investigations. It is accessible at www.leukemia-gene-atlas.org.