ARResT/Interrogate: an interactive immunoprofiler for IG/TR NGS data.

Abstract Motivation The study of immunoglobulins and T cell receptors using next-generation sequencing has finally allowed exploring immune repertoires and responses in their immense variability and complexity. Unsurprisingly, their analysis and interpretation is a highly convoluted task. Results We thus implemented ARResT/Interrogate, a web-based, interactive application. It can organize and filter large amounts of immunogenetic data by numerous criteria, calculate several relevant statistics, and present results in the form of multiple interconnected visualizations. Availability and Implementation ARResT/Interrogate is implemented primarily in R, and is freely available at http://bat.infspire.org/arrest/interrogate/ Supplementary information Supplementary data are available at Bioinformatics online

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data and allows for the deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data need to be managed, preserved and shared. The heterogeneity of such data calls for highly flexible models to represent the data from fabrication workflows, measurements and simulations. We propose the use of a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we create an ontology that serves as the blueprint of the data model. The Python framework Django is used to enable seamless integration into the virtual materials intelligence platform VIMI. The Django framework relies on the Object Graph Mapper neomodel to create a mapping between database classes and Python objects. The model can store the whole bandwidth of the data from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research

Synergizing ontologies and graph databases for highly flexible materials-to-device workflow representations

The escalating adoption of high-throughput methods in applied materials science dramatically increases the amount of generated data, demandingthe deployment and use of sophisticated data-driven methods. To exploit the full potential of these accelerated approaches, the generated data needs to be managed, preserved, and shared. Its heterogeneity calls for highly flexible data models to represent data from fabrication workflows, measurements, and simulations. We propose employing a native graph database to store the data instead of relying on rigid relational data models. To develop a flexible and extendable data model, we have created an EMMO-based ontology, where EMMO stands for European Materials Modelling Ontology. The Python framework Django is used to allow for the intuitive integration into the virtual materials intelligence platform, VIMI. The Django framework relies on the Object-Graph-Mapper (OGM) neomodel to create a mapping between database classes and python objects. The model can store the whole bandwidth of data, from fabrication to simulation data. Implementing the database into a platform will encourage researchers to share data while profiting from rich and highly curated data to accelerate their research

GLA/DRST real-world outcome analysis of CAR T-cell therapies for large B-cell lymphoma in Germany

CD19-directed chimeric antigen receptor (CAR) T cells have evolved as a new standard-of-care (SOC) treatment in patients with relapsed/refractory (r/r) large B-cell lymphoma (LBCL). Here, we report the first German real-world data on SOC CAR T-cell therapies with the aim to explore risk factors associated with outcomes. Patients who received SOC axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel) for LBCL and were registered with the German Registry for Stem Cell Transplantation (DRST) were eligible. The main outcomes analyzed were toxicities, response, overall survival (OS), and progression-free survival (PFS). We report 356 patients who received axi-cel (n = 173) or tisa-cel (n = 183) between November 2018 and April 2021 at 21 German centers. Whereas the axi-cel and tisa-cel cohorts were comparable for age, sex, lactate dehydrogenase (LDH), international prognostic index (IPI), and pretreatment, the tisa-cel group comprised significantly more patients with poor performance status, ineligibility for ZUMA-1, and the need for bridging, respectively. With a median follow-up of 11 months, Kaplan-Meier estimates of OS, PFS, and nonrelapse mortality (NRM) 12 months after dosing were 52%, 30%, and 6%, respectively. While NRM was largely driven by infections subsequent to prolonged neutropenia and/or severe neurotoxicity and significantly higher with axi-cel, significant risk factors for PFS on the multivariate analysis included bridging failure, elevated LDH, age, and tisa-cel use. In conclusion, this study suggests that important outcome determinants of CD19-directed CAR T-cell treatment of LBCL in the real-world setting are bridging success, CAR-T product selection, LDH, and the absence of prolonged neutropenia and/or severe neurotoxicity. These findings may have implications for designing risk-adapted CAR T-cell therapy strategies

GLA/DRST real-world outcome analysis of CAR-T cell therapies for large B-cell lymphoma in Germany

CD19-directed chimeric antigen receptor (CAR) T cells have evolved as a new standard-of-care (SOC) treatment in patients with relapsed/refractory (r/r) large B-cell lymphoma (LBCL). Here, we report the first German real-world data on SOC CAR T-cell therapies with the aim to explore risk factors associated with outcomes. Patients who received SOC axicabtagene ciloleucel (axi-cel) or tisagenlecleucel (tisa-cel) for LBCL and were registered with the German Registry for Stem Cell Transplantation (DRST) were eligible. The main outcomes analyzed were toxicities, response, overall survival (OS), and progression-free survival (PFS). We report 356 patients who received axi-cel (n = 173) or tisa-cel (n = 183) between November 2018 and April 2021 at 21 German centers. Whereas the axi-cel and tisa-cel cohorts were comparable for age, sex, lactate dehydrogenase (LDH), international prognostic index (IPI), and pretreatment, the tisa-cel group comprised significantly more patients with poor performance status, ineligibility for ZUMA-1, and the need for bridging, respectively. With a median follow-up of 11 months, Kaplan-Meier estimates of OS, PFS, and nonrelapse mortality (NRM) 12 months after dosing were 52%, 30%, and 6%, respectively. While NRM was largely driven by infections subsequent to prolonged neutropenia and/or severe neurotoxicity and significantly higher with axi-cel, significant risk factors for PFS on the multivariate analysis included bridging failure, elevated LDH, age, and tisa-cel use. In conclusion, this study suggests that important outcome determinants of CD19-directed CAR T-cell treatment of LBCL in the real-world setting are bridging success, CAR-T product selection, LDH, and the absence of prolonged neutropenia and/or severe neurotoxicity. These findings may have implications for designing risk-adapted CAR T-cell therapy strategies