Biology subjects

Radlwimmer, B.

Publications and source records attributed to Radlwimmer, B..

4 recordsLinked to original sources

bioRxiv2026

Identifying Treatment Related Signatures In Glioblastoma Using KaleidoCell

Understanding how transcriptional heterogeneity is organized across tumors, patients, and treatment conditions remains a central challenge in cancer biology. Here, we present kaleidoCell, a GPU-accelerated Python framework for consensus non-negative matrix factorization that identifies reproducible meta-programs across independent samples. When benchmarked against its principal counterpart, the geneNMF R package, kaleidoCell achieves a twofold speed improvement on large datasets. In addition, it includes an integrated analysis module that generates a comprehensive HTML report containing key results and visualizations--including marker genes corresponding to the meta-programs, gene set enrichment analysis, UMAP projections and violin plots--without requiring additional user code. Using glioblastoma as a case study, we applied kaleidoCell to two published datasets. In a panobinostat-treated cohort, kaleidoCell resolves the cellular landscape of the tumor microenvironment and delineates how HDAC inhibition reshapes malignant cell states at single-cell resolution. We extend prior descriptions of the metallothionein-associated stress program in treatment response and identify co induction of IER3 as a candidate component of the associated survival signalling. In addition, we uncover novel transcriptional signatures associated with HDAC inhibition. Beyond confirming suppression of a neural progenitor cell-/oligodendrocyte progenitor cell-like program which is consistent with prior reports, kaleidoCell identifies loss of an astrocyte-like identity program as a previously unrecognized candidate mechanism of panobinostat action in glioblastoma. Together, these results establish kaleidoCell as a fast, user-friendly framework that enables robust discovery of biologically meaningful transcriptional programs in large, heterogeneous single-cell datasets.

bioinformatics↗

bioRxiv2023

BCAT1 regulates glioblastoma cell plasticity and contributes to immunosuppression

Glioblastoma is the most common malignant brain tumor in adults. Cellular plasticity and the poorly differentiated features result in a fast relapse of the tumors following treatment. Moreover, the immunosuppressive microenvironment proved to be a major obstacle to immunotherapeutic approaches. Branched-chain amino acid transaminase 1 (BCAT1) is a metabolic enzyme that converts branched-chain amino acids into branched-chain keto acids, depleting cellular -ketoglutarate and producing glutamate. BCAT1 is expressed in and drives the growth of glioblastoma and other cancers. Here we show that low-BCAT1 expression correlates with differentiated glioblastoma subtypes and its knockout (KO) results in a differentiated phenotype in human and mouse glioblastoma cells. Consistent with these observations, Bcat1-KO mouse glioblastoma cells were highly susceptible to serum-induced differentiation in vitro. The transition to a differentiated cell state was linked to the increased activity of TET demethylases and the hypomethylation and activation of neuronal differentiation genes. Orthotopic tumor injection into immunocompetent mice demonstrated that the brain microenvironment is sufficient to induce differentiation of Bcat1-KO tumors in vivo. In addition, the knockout of Bcat1 attenuated immunosuppression, allowing for an extensive infiltration of CD8+ cytotoxic T-cells and complete abrogation of tumor growth. Additional analysis in immunodeficient hosts revealed that both Bcat1-KO-induced differentiation and immunomodulation contribute to the long-term suppression of tumor growth. In summary, our study demonstrates that BCAT1 promotes glioblastoma growth by blocking tumor cell differentiation and sustaining an immunosuppressive microenvironment. These findings suggest novel modes limiting glioblastoma phenotypic plasticity and therapeutic failure through targeting BCAT1. Importance of the studyHigh expression of BCAT1 occurs in many tumor entities and is related to aggressiveness, proliferation and invasion of tumor cells. In this study, we show that its expression is crucial for the continuous growth of glioblastoma cells by preventing their differentiation. Furthermore, we show that the expression of BCAT1 modulates the tumor immune microenvironment, suppressing the CD8 T-cell response. BCAT1 knockout causes glioblastoma cell differentiation and a persistent CD8 T-cell response, which is sufficient to abrogate tumor growth and prolong survival in in vivo immunocompetent and immunodeficient models, respectively. Our findings consolidate BCAT1 as a major player in glioblastoma and highlight its importance as a potential future target of research in this and other tumor entities. Key PointsO_LIBCAT1 expression maintains poorly differentiated features of glioblastoma cells and provides resistance to differentiation. C_LIO_LIExpression of BCAT1 in glioblastoma cells contributes to the immunosuppressive features of the tumor. C_LI

cancer biology↗

bioRxiv2021

Methylation-mediated retuning on the enhancer-to-silencer activity scale of networked regulatory elements guides driver-gene misregulation

Cancers arise when particular disease-driving genes adopt abnormal functions, but analyses of coding and regulatory sequences leave many of these abnormalities unexplained. We developed a strategy to explore alternations in the regulatory effects of silencers and enhancers in cancer tumors. Applying the method to 177 gene regulatory domains in human glioblastomas, we produced a driver-gene wide dataset of gene-associated, functional regulatory elements. Many genes were controlled by cis-regulatory networks composed of multiple regulatory elements, each of them providing a defined positive or negative input to the overall regulatory output of the network. Surprisingly, DNA methylation induces enhancers and silencers to acquire new activity setpoints within wide ranges of potential regulatory effects, varying between strong transcriptional enhancing to strong silencing. Extensive analysis of methylation-expression associations reveals the organization of domain-wide cis-regulatory networks, and highlighted key regulatory sites which provide pivotal contributions to the network outputs. Consideration of these effects through mathematical models of gene expression variations signified prime molecular events underlying cancer-genes misregulation in hitherto unexplained tumors. Of the observed gene-malfunctioning events, gene misregulation due to epigenetic retuning of networked enhancers and silencers dominated driver-genes mutagenesis, compared with other types of mutation including coding or regulatory sequence alterations. Elucidation of this gene-transformation mechanism may open the way for methodological disclosing of the driving forces behind cancers and other diseases.

genomics↗

bioRxiv2020

A novel neural stem cell-derived immunocompetent mouse model of glioblastoma for preclinical studies

Glioblastomas are the most lethal tumors affecting the central nervous system in adults. Simple and inexpensive syngeneic in vivo models that closely mirror human glioblastoma, including interactions between tumor and immune cells, are urgently needed for deciphering glioma biology and developing more effective treatments. Here, we generated glioblastoma cell lines by repeated in-vivo passaging of cells isolated from a neural stem cell-specific Pten/p53 double-knockout genetic mouse brain tumor model. Transcriptome and genome analyses of the cell lines revealed molecular heterogeneity comparable to that observed in human glioblastoma. Upon orthotopic transplantation into syngeneic hosts they formed high-grade gliomas that faithfully recapitulated the histopathological features, invasiveness and myeloid cell infiltration characteristic of human glioblastoma. These features make our cell lines unique and useful tools to study multiple aspects of glioblastoma pathomechanism and test novel treatments, especially immunotherapies in syngeneic preclinical models.

cancer biology↗