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Zichner, T.

Publications and source records attributed to Zichner, T..

4 recordsLinked to original sources

Preclinical efficacy of a systemically-administered, second-generation STING agonist that promotes antitumour immunity in combination with radiotherapy

As potent triggers of innate immunity, STING agonists hold promise as active immunotherapeutic agents for cancer treatment. Second-generation STING agonists, suitable for systemic delivery, are being investigated in preclinical research and have entered clinical trials. Here, the novel synthetic STING agonist, BI-1703880 (STINGa), which was designed for intravenous delivery, was investigated for anti-tumour and immunological effects. We show that STINGa activates the STING pathway and results in a transient and dose-dependent upregulation and secretion of interferons and proinflammatory cytokines in vitro and in vivo. We show that intravenous administration of repeated dosing with low-dose STINGa is well tolerated. We report that radiotherapy (RT) and STING agonism synergizes to generate innate immune cell and CD8+ T cell responses that control tumour growth. Anti-tumour activity induced by combined RT / STINGa was reduced in mice lacking a functional immune system. RT / STINGa combination treatment also initiated development of protective immune memory. RT / STINGa upregulated PD-L1, PD-1 and CTLA-4 in the tumour microenvironment. Our findings show that combining RT / STINGa with immune checkpoint inhibitors further increases therapeutic benefit. Our data confirm STING as a therapeutic target in cancer and support the clinical development of BI-1703880 STING agonist, thereby suggesting radiotherapy as a potential combination for enhancing anti-tumour efficacy.

cancer biology↗

Kokiri: Random Forest-Based Cohort Comparison and Characterization

AO_SCPLOWBSTRACTC_SCPLOWWe propose an interactive visual analytics approach to characterizing and comparing patient subgroups (i.e., cohorts). Despite having the same disease and similar demographic characteristics, patients respond differently to therapy. One reason for this is the vast number of variables in the genome that influence a patients outcome. Nevertheless, most existing tools do not offer effective means of identifying the attributes that differ most, or look at them in isolation and thus ignore combinatorial effects. To fill this gap, we present Kokiri, a visual analytics approach that aims to separate cohorts based on user-selected data, ranks attributes by their importance in distinguishing between cohorts, and visualizes cohort overlaps and separability. With our approach, users can additionally characterize the homogeneity and outliers of a cohort. To demonstrate the applicability of our approach, we integrated Kokiri into the Coral cohort analysis tool to compare and characterize lung cancer patient cohorts.

bioinformatics↗

Coral: a web-based visual analysis tool for creating and characterizing cohorts

SummaryA main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. Coral visualizes the evolution of cohorts and also provides intuitive access to prevalence information. Furthermore, findings can be stored, shared, and reproduced via the integrated session management. Coral is pre-loaded with data from over 128,000 samples from the AACR Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. Availability and ImplementationCoral is publicly available at https://coral.caleydoapp.org. The source code is released at https://github.com/Caleydo/coral. Contactthomas.zichner@boehringer-ingelheim.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples

SummaryOrdino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter, and explore genes, cell lines, and tissue samples based on pre-loaded data, including The Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE), and manually uploaded information. Interactive tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items complement the exploration. Findings can be stored, shared, and reproduced via the integrated session management.\n\nAvailability and ImplementationOrdino is publicly available at https://ordino.caleydoapp.org. The source code is released at https://github.com/Caleydo/ordino under the Mozilla Public License 2.0.\n\nContactmarc.streit@jku.at

bioinformatics↗