Clinical outcome of biomarker-guided therapies in adult patients with tumors of the nervous system.

Background: The clinical utility of molecular profiling and targeted therapies for neuro-oncology patients outside of clinical trials is not established. We aimed at investigating feasibility and clinical utility of molecular profiling and targeted therapy in adult patients with advanced tumors in the nervous system within a prospective observational study. Methods: molecular tumor board (MTB)@ZPM (NCT03503149) is a prospective observational precision medicine study for patients with advanced tumors. After inclusion of patients, we performed comprehensive molecular profiling, formulated ranked biomarker-guided therapy recommendations based on consensus by the MTB, and collected prospective clinical outcome data. Results: Here, we present initial data of 661 adult patients with tumors of the nervous system enrolled by December 31, 2021. Of these, 408 patients were presented at the MTB. Molecular-instructed therapy recommendations could be made in 380/408 (93.1%) cases and were prioritized by evidence levels. Therapies were initiated in 86/380 (22.6%) cases until data cutoff. We observed a progression-free survival ratio >1.3 in 31.3% of patients. Conclusions: Our study supports the clinical utility of biomarker-guided therapies for neuro-oncology patients and indicates clinical benefit in a subset of patients. Our data might inform future clinical trials, translational studies, and even clinical care.

A data management infrastructure for the integration of imaging and omics data in life sciences.

BACKGROUND: As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management. RESULTS: We propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities. CONCLUSIONS: We describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.

Absence of Non-Canonical, Inhibitory MYD88 Splice Variants in B Cell Lymphomas Correlates With Sustained NF-κB Signaling.

Gain-of-function mutations of the TLR adaptor and oncoprotein MyD88 drive B cell lymphomagenesis via sustained NF-κB activation. In myeloid cells, both short and sustained TLR activation and NF-κB activation lead to the induction of inhibitory MYD88 splice variants that restrain prolonged NF-κB activation. We therefore sought to investigate whether such a negative feedback loop exists in B cells. Analyzing MYD88 splice variants in normal B cells and different primary B cell malignancies, we observed that MYD88 splice variants in transformed B cells are dominated by the canonical, strongly NF-κB-activating isoform of MYD88 and contain at least three novel, so far uncharacterized signaling-competent splice isoforms. Sustained TLR stimulation in B cells unexpectedly reinforces splicing of NF-κB-promoting, canonical isoforms rather than the 'MyD88s', a negative regulatory isoform reported to be typically induced by TLRs in myeloid cells. This suggests that an essential negative feedback loop restricting TLR signaling in myeloid cells at the level of alternative splicing, is missing in B cells when they undergo proliferation, rendering B cells vulnerable to sustained NF-κB activation and eventual lymphomagenesis. Our results uncover MYD88 alternative splicing as an unappreciated promoter of B cell lymphomagenesis and provide a rationale why oncogenic MYD88 mutations are exclusively found in B cells.

Challenges of big data integration in the life sciences.

Big data has been reported to be revolutionizing many areas of life, including science. It summarizes data that is unprecedentedly large, rapidly generated, heterogeneous, and hard to accurately interpret. This availability has also brought new challenges: How to properly annotate data to make it searchable? What are the legal and ethical hurdles when sharing data? How to store data securely, preventing loss and corruption? The life sciences are not the only disciplines that must align themselves with big data requirements to keep up with the latest developments. The large hadron collider, for instance, generates research data at a pace beyond any current biomedical research center. There are three recent major coinciding events that explain the emergence of big data in the context of research: the technological revolution for data generation, the development of tools for data analysis, and a conceptual change towards open science and data. The true potential of big data lies in pattern discovery in large datasets, as well as the formulation of new models and hypotheses. Confirmation of the existence of the Higgs boson, for instance, is one of the most recent triumphs of big data analysis in physics. Digital representations of biological systems have become more comprehensive. This, in combination with advances in machine learning, creates exciting new research possibilities. In this paper, we review the state of big data in bioanalytical research and provide an overview of the guidelines for its proper usage.

Targeted manipulation of bZIP53 DNA-binding properties influences Arabidopsis metabolism and growth.

bZIP transcription factors regulate diverse processes in eukaryotic cells. Arabidopsis bZIP members of the C and S1 groups form heterodimers and synergistically control metabolic reprogramming during stress responses. However, their functional characterization is complicated due to an overlapping heterodimerization network and high redundancy. In this study, we develop a simple but powerful approach for generating dominant negative mutants of bZIP factors with high specificity. By applying in vitro DNA-binding, reporter gene and protoplast two-hybrid assays, and plant mutant analysis, we show that phosphorylation-mimicking substitution of conserved serines in the DNA-binding domain of bZIP monomeric subunits suffices for the disruption of the interaction of both bZIP homo- and heterodimers with cognate DNA. This results in the transcriptional inactivation of target genes. The dominant-negative effect is achieved by the unaltered function of the intrinsic nuclear localization signal and dimerization properties of the mutated bZIP protein. Our findings not only reveal an additional regulatory mechanism of bZIP10 intracellular localization, but also provide evidence of the involvement of bZIP53 in the diurnal adjustments of amino acid metabolism. Our data demonstrate the advantages and the suitability of this new approach for the artificial inactivation of bZIP transcription factors in plants, and it may also be of use for other organisms.

dRNA-seq transcriptional profiling of the FK506 biosynthetic gene cluster in Streptomyces tsukubaensis NRRL18488 and general analysis of the transcriptome.

FK506 (tacrolimus) is a valuable immunosuppressant produced by several Streptomyces strains. In the genome of the wild type producer Streptomyces tsukubaensis NRRL18488, FK506 biosynthesis is encoded by a gene cluster that spans 83.5 (kb). A whole transcriptome differential shotgun sequencing (dRNA-seq) of S. tsukubaensis was performed to analyze transcription at 2 different time points; before and during active FK506 production. In total, 8,914 transcription start sites were identified in either condition, which enabled precise determination of the 5'-UTR length of the corresponding transcripts as well as the identification of 2 consensus sequence motifs in the promoter regions. The transcription start sites of all gene operons within the FK506 cluster were identified, including 3 examples of leaderless RNA transcripts. These data provide detailed insight into the transcription of the FK506 biosynthetic gene cluster to support future regulatory studies, genetic manipulation, and industrial production.