Biology subjects

Rosenbauer, J.

Publications and source records attributed to Rosenbauer, J..

6 recordsLinked to original sources

bioRxiv2026

Tissue resident CD4+ memory T-cells mark response to immune checkpoint inhibition in high-grade glioma

Background: Immune checkpoint inhibitors (ICI) are efficacious in many solid tumors, but response in glioma is restricted to a small subgroup. The determinants of response and resistance to ICI remain poorly understood. Methods: Here we exploit a syngeneic hypermutated high-grade glioma model with dichotomous response to combined PD-1 and CTLA-4 inhibition to unravel determinants of tumor-infiltrating T-cells driving response. Tumor-infiltrating T-cells from ICI-responsive and non-responsive tumors were analyzed by single-cell RNA and T-cell receptor sequencing and tumor-reactive T-cell receptor clonotypes were functionally validated to characterize their transcriptional phenotypes. We verify our findings in IDH1 wildtype glioblastoma patients treated with neoadjuvant pembrolizumab. Results: ICI response was associated with intratumoral clonal expansion of tumor-reactive cytotoxic T-cells and increased infiltration of CXCR6+ CD4+ tissue resident memory T-cells (Trm). CD4 stem-like memory T-cells in responding tumors demonstrated elevated interferon responses, following trajectories toward clonally expanded Trm, versus trajectories toward exhaustion in non-responsive tumors. In responsive tumors, CD4+ Trm interacted with infiltrating CXCR3+ tumor-reactive and clonally expanded, yet transcriptionally versatile cytotoxic T-cells. Probing the post neoadjuvant ICI high-grade glioma patient tissue dataset, we confirmed increased CXCR6 expression in CD4+ T cells and the association of CD4+ Trm with prolonged overall survival. Conclusion: These findings identify CD4 tissue-resident memory T-cells as determinants of ICI response in IDH1 wildtype high-grade glioma and warrant their further investigation to improve immunotherapy outcomes.

cancer biology↗

bioRxiv2026

Inflammatory endothelial cells promote infiltration of antigen-licensed cytotoxic T cells in malignant gliomas after irradiation

Insufficient T cell infiltration into malignant gliomas fundamentally limits the efficacy of adoptive T cell therapy. Here we show that fractionated irradiation overcomes this barrier by reprogramming the tumor endothelium towards an immune-recruiting interface. Using complementary murine glioma models combined with adoptive T cell transfer, antigen-specific vaccination, and single-cell transcriptomic and T cell receptor profiling, we demonstrate that irradiation enhances the accumulation, clonal expansion, and effector differentiation of tumor-specific CD8+ T cells. Irradiated tumors showed increased T cell receptor clonality and local enrichment of proliferative effector CD8 T cells with enhanced cytotoxic, interferon-responsive, and oxidative metabolic programs. Mechanistically, irradiation triggers a conserved interferon-driven endothelial program marked by antigen presentation and upregulation of adhesion molecules, including ICAM-1 and VCAM-1. This radiation-induced endothelial activation program preferentially seen in inflammatory endothelial subsets was conserved in human glioblastoma and linked to T cell recruitment and maintenance of activated CD8 T cell states. Functionally, irradiation synergized with adoptive T cell transfer and antigen-specific vaccination to promote glioma-specific T cell accumulation and effector differentiation, improving tumor control and survival. Together, these findings identify radiation-induced endothelial activation as a key regulator of T cell trafficking across the brain tumor vasculature highlighting the vascular niche as a critical determinant of immunotherapy efficacy and a rational target for combination strategies in glioblastoma.

immunology↗

bioRxiv2025

HIV-1 Reprograms CD4 T Cell Responses by Impairing Antigen-specific Communication with Dendritic Cells

HIV-1 infection causes general dysfunction of adaptive immune cells that persists even under therapy but the underlying mechanisms remain elusive. Antigen-specific interactions of the main target cells of HIV, CD4 T cells, with dendritic cells (DCs) orchestrate global T cell responses and convey help to CD8 T cells. Here we report that HIV-1, by virtue of its pathogenesis factor Nef, impairs activation and transcriptionally reprograms CD4 T cells to dampen Th1 differentiation in response to antigen-specific stimulation by DCs. These alterations also disrupt functional communication to DCs to reduce DC activation and limit Th1 helper cytokine production. Mechanistically, Nef achieves this modulation of antigen-specific CD4 T cell function by reducing T cell surface levels of CD4. These results define modulation of CD4 T cell-DC communication as pathogenic principle by which HIV-1 disrupts adaptive immunity and emphasize the direct role of CD4 in immune cell communication. One Sentence SummaryHIV-1 suppresses Th1 polarization by disrupting the communication between CD4 T cells and Dendritic cells through cell surface CD4 downregulation by the viral pathogenesis factor Nef.

immunology↗

bioRxiv2022

Development of a scoring function for comparing simulated and experimental tumor spheroids

Enormous progress continues in the field of cancer biology, yet much remains to be unveiled regarding the mechanisms of cancer invasion. In particular, complex biophysical mechanisms enable a tumor to remodel the surrounding extracellular matrix (ECM), thus allowing cells to escape and invade alone or as multicellular collectives. Tumor spheroids cultured in collagen represent a simplified, reproducible 3D model system, which is sufficiently complex to recapitulate the evolving internal organization of cells and external interaction with the ECM that occur during invasion. Recent experimental approaches enable high resolution imaging and quantification of the internal structure of invading tumor spheroids. Concurrently, computational modeling enables simulations of complex multicellular aggregates based on first principles. The comparison between real and simulated spheroids represents a way to fully exploit both data sources, but remains a challenging task. We hypothesize that comparing any two spheroids requires first the extraction of basic features from the raw data, and second the definition of key metrics to match such features. Here, we present a novel data-agnostic method to compare spatial features of spheroids in 3D. To do so, we define and extract features from spheroid point cloud data, which we simulated using Cells in Silico (CiS), a high-performance framework for large-scale tissue modeling previously developed by our group. We then define metrics to compare features between individual spheroids, and combine all metrics into an overall deviation score. Finally, we use our features to compare experimental data on invading spheroids in increasing collagen densities. We propose that our approach represents the basis for defining improved metrics to compare large 3D data sets. Moving forward, this approach will enable informing in silico spheroids based on their in vitro counterparts, and vice versa, thus enabling both basic and applied researchers to close the loop between modeling and experiments in cancer research. Author summaryCells within a tumor use various methods to escape and thereby invade into healthy parts of the body. These methods are studied experimentally by examining tumor spheroids, spherical aggregates of hundreds to thousands of individual cells. Such spheroids can also be simulated, and the comparison of simulation and experiment is desirable. Here, we present an analysis strategy for the comparison of tumor spheroids, a widely used workhorse of cancer research. Using this strategy, we aim to improve the collaborative potential between experimentalists and theorists.

cancer biology↗

bioRxiv2021

Mechanical influences on in silico tumor evolution

Experimental insight and conceptual understanding of tumor growth are steadily growing and leading to new therapeutic interventions. Experiments and clinical studies are able to link single-cell properties to macroscopic tumor attributes. The development of cellular subpopulations in heterogeneous tumors can be understood as an evolutionary system with different cell types competing over both space and nutrients. However, to predict the growth trajectory and development of a tumor, fitness and trade-offs of cell properties in the context of the surroundings are required and often inaccessible. The optimum of the evolutionary trajectory provides a target for intervention, but can mostly not be identified. We propose that the optimal value of cellular properties is influenced by the tumor surrounding. Computational multiscale-modeling of tissue enables the observation of the trajectory of each cell while modeling the tumor surrounding. We model a 3D spheroid tumor and the fitness of individual cells and the evolutionary behavior of the tumor are quantified and linked to global parameters. Cell-cell adhesion and cell motility are two important mechanical properties for cell development and used as free parameters. Mechanical properties alone are able to drive the tumor towards low adhesion.We implement a dynamically changing nutrient surrounding representing the fluctuating blood-supply through blood vessel collapse and angiogenesis. We find that the evolutionary speed depends on the frequency of the fluctuations. We identify a frequency domain in which the evolutionary speed is significantly increased over a tumor with constant nutrient supply. The findings suggest that mechanically-induced fluctuations can accelerate tumor evolution. Author summaryLimited space and nutrients together with competing cell types drive an evolutionary process inside tumors. This process selects for the fittest cell types and optimizes the growing behavior for its local surroundings. An expanding tumor exerts mechanical forces on its cells and its surroundings, leading to a fluctuating nutrient supply through collapsing blood vessels. Here, we observe the influence of a dynamically changing surrounding on the evolutionary behavior of heterogeneous tumors in a high-resolution computational model. We find that the evolutionary speed depends on the frequency of the fluctuations and a fitness advantage of low-adhesion cells.

cancer biology↗

bioRxiv2020

Emerging Tumor Development by Simulating Single-cell Events

Despite decades of substantial research, cancer remains a ubiquitous scourge in the industrialized world. Effective treatments require a thorough understanding of macroscopic cancerous tumor growth out of individual cells. Clinical imaging methods, however, only detect late-stage macroscopic tumors, while many quantitative experiments focus on small clusters of cancerous cells in microscopic detail but struggle to grow full tumors in-vitro. Here, we introduce the critical scale-bridging link between both these scopes. We are able to simulate the growth of mm-sized tumors composed of 1.5 million m-resolved individual cells by employing highly parallelized code on a supercomputer. We observe the competition for resources and space, which can lead to hypoxic or necrotic tissue regions. Cellular mutations and tumor stem cells can lead to tissue heterogeneity and change tumor properties. We probe the effects of different chemotherapy and radiotherapy treatments and observe selective pressure. This improved theoretical understanding of cancer growth as emerging behavior from single-cells opens new avenues for various scientific fields, ranging from developing better early-stage cancer detection devices to testing treatment regimes in-silico for personalized medicine. Author summaryExperimental and microscopy techniques are rapidly advancing biology and the observability of tissue. The theoretical understanding of tissue either focuses on a few cells or continuous tissue. Here we introduce the scale-bridging theoretical link that is able to model single cells as well as tissue consisting of millions of those cells, harvesting the power of modern supercomputers. We close the gap between single-cells and tissue through access to the full time-resolved trajectories of each cell and the emerging behavior of the tissue. We apply our framework on a generalized model for tumor growth. Tumor heterogeneity, as well as tumor stem cells are introduced, and the changes of behavior in response to cancer treatments is observed and validated.

biophysics↗