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Grossmann, L.

Publications and source records attributed to Grossmann, L..

2 recordsLinked to original sources

Lineage-dependence of the neuroblastoma surfaceome defines tumor cell state-dependent and independent immunotherapeutic targets

BackgroundNeuroblastoma is a heterogeneous disease with adrenergic (ADRN)- and therapy resistant mesenchymal (MES)-like cells driven by distinct transcription factor networks. Here, we investigate the expression of immunotherapeutic targets in each neuroblastoma subtype and propose pan-neuroblastoma and cell state specific targetable cell-surface proteins. MethodsWe characterized cell lines, patient-derived xenografts, and patient samples as ADRN-dominant or MES- dominant to define subtype-specific and pan-neuroblastoma gene sets. Targets were validated with ChIP- sequencing, immunoblotting, and flow cytometry in neuroblastoma cell lines and isogenic ADRN-to-MES transition cell line models. Finally, we evaluated the activity of MES-specific agents in vivo and in vitro. ResultsMost immunotherapeutic targets being developed for neuroblastoma showed significantly higher expression in the ADRN subtype with limited expression in MES-like tumor cells. In contrast, CD276 (B7-H3) and L1CAM maintained expression across both ADRN and MES states. We identified several receptor tyrosine kinases (RTKs) enriched in MES-dominant samples and showed that AXL targeting with ADCT-601 was potently cytotoxic in MES-dominant cell lines and showed specific anti-tumor activity in a MES cell line-derived xenograft. ConclusionsImmunotherapeutic strategies for neuroblastoma must address the potential of epigenetic downregulation of antigen density as a mechanism for immune evasion. We identified several RTKs as candidate MES-specific immunotherapeutic target proteins for the elimination of therapy-resistant cells. We hypothesize that the phenomena of immune escape will be less likely when targeting pan-neuroblastoma cell surface proteins such as B7-H3 and L1CAM, and/or dual targeting strategies that consider both the ADRN- and MES-cell states. Key PointsO_LICellular plasticity influences the abundance of immunotherapeutic targets. C_LIO_LISubtype-specific targets may be susceptible to epigenetically-mediated downregulation. C_LIO_LIImmunotherapeutic targets in development, B7-H3 and L1CAM, show "pan-subtype" expression. C_LI Importance of StudyNeuroblastoma is a lethal childhood malignancy that shows cellular plasticity in response to anti-cancer therapies. Several plasma membrane proteins are being developed as immunotherapeutic targets in this disease. Here we define which cell surface proteins are susceptible to epigenetically regulated downregulation during an adrenergic to mesenchymal cell state switch and propose immunotherapeutic strategies to anticipate and circumvent acquired immunotherapeutic resistance.

cancer biology↗

TaxMapper: An Analysis Tool, Reference Database and Workflow for Metatranscriptome Analysis of Eukaryotic Microorganisms

BackgroundHigh-throughput sequencing (HTS) technologies are increasingly applied to analyse complex microbial ecosystems by mRNA sequencing of whole communities, also known as metatranscriptome sequencing. This approach is at the moment largely limited to prokaryotic communities and communities of few eukaryotic species with sequenced genomes. For eukaryotes the analysis is hindered mainly by a low and fragmented coverage of the reference databases to infer the community composition, but also by lack of automated workflows for the task.\n\nResultsFrom the databases of the National Center for Biotechnology Information and Marine Microbial Eukaryote Transcriptome Sequencing Project, 142 references were selected in such a way that the taxa represent the main lineages within each of the seven supergroups of eukaryotes and possess predominantly complete transcriptomes or genomes. From these references, we created an annotated microeukaryotic reference database. We developed a tool called TaxMapper for a reliably mapping of sequencing reads against this database and filtering of unreliable assignments. For filtering, a classifier was trained and tested on sequences in the database, sequences of related taxa to those in the database and randomly generated sequences. Additionally, TaxMapper is part of a metatranscriptomic Snakemake workflow developed to perform quality assessment, functional and taxonomic annotation and (multivariate) statistical analysis including environmental data. The workflow is provided and described in detail to empower researchers to easily apply it for metatranscriptome analysis of any environmental sample.\n\nConclusionsTaxMapper shows superior performance compared to standard approaches, resulting in a higher number of true positive taxonomic assignments. Both the TaxMapper tool and the workflow are available as open-source code at Bitbucket under the MIT license: https://bitbucket.org/dbeisser/taxmapper and as a Bioconda package: https://bioconda.github.io/recipes/taxmapper/README.html.

bioinformatics↗