Biology subjects

Keller, U.

Publications and source records attributed to Keller, U..

7 recordsLinked to original sources

bioRxiv2026

Multiscale biological interactions define clinical trajectories in acute myeloid leukemia

Cancer is characterized by complex interactions across genetic, cellular, and microenvironmental scales. However, a quantitative understanding of how these interactions shape clinical trajectories remains limited. Here, we present a multi-scale single-cell dataset from 184 treatment-naive acute myeloid leukemia (AML) patients spanning all major genetic subtypes, together with an analytical framework to dissect interactions across biological scales. We show that distinct clinical outcomes are encoded by specific cross-scale, cross-compartment interactions present at diagnosis: response to induction therapy is governed by interactions between genetic alterations and leukemic differentiation state; relapse following chemotherapy is associated with non-genetic programs linked to metabolism; and relapse after allogeneic stem cell transplantation is driven by interactions between the immune microenvironment and residual healthy hematopoiesis. Together, our study provides a framework to resolve intra- and inter-patient heterogeneity in cancer and supports a model in which clinical trajectories in AML emerge from defined interactions across biological scales.

cancer biology↗

bioRxiv2025

Optical recordings of synaptic vesicle fusion reveal diffusional dispersion

The primary unit of exocytosis, the synaptic vesicle, is replete with proteins which enable vesicle fusion. For maintaining synaptic transmission the vesicle proteins have to be rapidly cleared from the active zone. The immediate fate of the cargo post-fusion and the precise mechanism of its resorting into a readily retrievable pool (RRetP) for endocytosis remains unclear. To address this, we developed a purely presynaptic preparation, the xenapse, presynaptic boutons formed en face directly onto a glass coverslip enabling optical single vesicle recording by total internal reflection microscopy (TIRFM). Electron microscopy showed xenapses contain a few hundred synaptic vesicles (SVs), of which [~]40 SVs are primed (readily releasable pool, RRP), as revealed by single vesicle recordings using the pH-sensitive fluorescent protein pHluorin. Single fusion events synchronous with action potentials could be localized with [~]20 nm precision and rapid post-fusion diffusional dispersion of vesicular proteins observed with diffusion constants in the order of 0.1 {micro}m2/s. Unroofing of xenapses revealed numerous Clathrin-coated structures, enriched for vesicle cargo and numerically equivalent to about twice the measured RRP size. In this way SV proteins are rapidly cleared from the release site into this RRetP of pre-assembled pits to allow for rapid re-docking and re-priming at vacated release sites in synapses tuned for high-fidelity fast signaling.

neuroscience↗

bioRxiv2022

Tetraspanner-based nanodomains modulate BAR domain-induced membrane curvature

Topography is a critical feature driving formation and dynamics of protein and lipid domains within biological membranes. The yeast plasma membrane (PM) has provided a powerful model system to study lateral domain formation, including characteristic BAR domain-induced PM furrows. Currently, it is not clear how the components involved in the establishment of these furrows cooperate to precisely regulate local PM topography. Here we report opposing functions for the Sur7 and Nce102 families of tetraspanner proteins in modulating membrane curvature and domain topography. Using STED nanoscopy and freeze-fracture EM we found that Sur7 tetraspanners form multimeric strands at the upper edges of PM furrows, which counteract the forces exerted by BAR domain proteins and prevent membrane tubulation. In contrast, Nce102 tetraspanners are located basal to the Sur7 proteins and promote BAR domain-induced curvature. The segregation of the two tetraspanner-based nanodomains is further supported by differential distribution of ergosterol to the upper edge of furrows and PIP2 lipids at the furrow base. These findings suggest a general role of tetraspanner proteins in sculpting local membrane domains.

cell biology↗

bioRxiv2021

NOXA expression drives synthetic lethality to RUNX1 inhibition in pancreatic cancer

Evasion from drug-induced apoptosis is a crucial mechanism of cancer treatment resistance. The pro-apoptotic protein NOXA marks an aggressive pancreatic ductal adenocarcinoma (PDAC) subtype. To identify drugs that unleash the death-inducing potential of NOXA, we performed an unbiased drug screening experiment. In NOXA-deficient isogenic cellular models we identified an inhibitor of the transcription factor heterodimer CBF{beta}/RUNX1. By genetic gain and loss of function experiments we validated that the mode of action depends on RUNX1 and NOXA. Of note, RUNX1 expression is significantly higher in PDACs compared to normal pancreas. We show that pharmacological RUNX1 inhibition significantly blocks tumor growth in vivo and in primary patient-derived PDAC organoids. Through genome wide analysis, we detected that RUNX1-loss reshapes the epigenetic landscape, which gains H3K27ac enrichment at the NOXA promoter. Our study demonstrates a previously unknown mechanism of NOXA-dependent cell death, which can be triggered pharmaceutically. Therefore, our data show a novel way to target a therapy resistant PDAC, an unmet clinical need. SignificanceRecent evidence demonstrated the existence of molecular subtypes in pancreatic ductal adenocarcinoma (PDAC), which resist all current therapies. The paucity of therapeutic options, including a complete lack of targeted therapies, underscore the urgent and unmet medical need for the identification of targets and novel treatment strategies for PDAC. Our study unravels a function of the transcription factor RUNX1 in apoptosis regulation in PDAC. We show that pharmacological RUNX1 inhibition in PDAC is feasible and leads to NOXA-dependent apoptosis. The development of targeted therapies that influence the transcriptional landscape of PDAC might have great benefits for patients who are resistant to conventional therapies. RUNX1 Inhibition as a new therapeutic intervention offers an attractive strategy for future therapies.

cancer biology↗

bioRxiv2020

Targeting the ubiquitin-proteasome system in a pancreatic cancer subtype with hyperactive MYC

Purpose The myelocytomatosis oncogene (MYC) is an important driver in a subtype of pancreatic ductal adenocarcinoma (PDAC). However, MYC remains a challenging therapeutic target, therefore identifying druggable synthetic lethal interactions in MYC-active PDAC may lead to novel precise therapies.Methods Cluster analysis using direct MYC target genes was used to identify PDAC with active MYC. We profiled the transcriptome of established human cell lines, murine primary PDAC cell lines and also accessed public available repositories for transcriptomic profiling. Networks active in MYC hyperactive subtypes were analyzed by gene set enrichment analysis. An unbiased pharmacological drug screen with FDA-approved anti-cancer drugs was conducted to define MYC-associated vulnerabilities, which were validated by analysis of drug response repositories and genetic gain- and loss-of-function experiments.Results In an unbiased pharmacological drug screen with FDA-approved anti-cancer drugs we detected that the proteasome inhibitor bortezomib triggers a MYC-associated vulnerability. By integrating publicly available data sets we found the unfolded protein response as a signature connected to MYC. Furthermore, the increased sensitivity of MYC hyperactive PDACs to bortezomib was validated in genetically modified PDAC cells.Conclusions In sum, we provide evidence that perturbing the ubiquitin proteasome system might be an option to target MYC hyperactive PDAC cells and our data provide the rationale to further develop precise targeting of the ubiquitin-proteasome system as a subtype-specific therapeutic approach.Competing Interest StatementThe authors have declared no competing interest.Abbrevations4-OHT4-hydoxytamoxifenATF4activating transcription factor 4BETbromodomain and extra terminal motifCNVcopy number variationCTD2cancer target discovery and development networkdepmapdependency mapDoRothEAdiscriminant regulon expression analysisGSEAgene set enrichment analysisICGCinternational cancer gene consortiumMYCmyelocytomatosis oncogenePDACpancreatic ductal adenocarcinomaPERKproteinkinase RNA-activated-like ER kinaseSUMOsmall-ubiquitin-like modifierTCGAthe cancer genome atlasUPRunfolded protein responseUPSubiquitin proteasome systemView Full Text

cancer biology↗

bioRxiv2019

Towards Quantitative Imaging Biomarkers of Tumor Dissemination: a Multi-scale Parametric Modeling of Multiple Myeloma

The advent of medical imaging and automatic image analysis is bringing the full quantitative assessment of lesions and tumor burden at every clinical examination within reach. This opens avenues for the development and testing of functional disease models, as well as their use in the clinical practice for personalized medicine. In this paper, we introduce a Bayesian statistical framework, based on mixed-effects models, to quantitatively test and learn functional disease models at different scales, on population longitudinal data. We also derive an effective mathematical model for the crossover between initially detected lesions and tumor dissemination, based on the Iwata-Kawasaki-Shigesada model. We finally propose to leverage this descriptive disease progression model into model-aware biomarkers for personalized risk-assessment, taking all available examinations and relevant covariates into account. As a use case, we study Multiple Myeloma, a disseminated plasma cell cancer, in which proper diagnostics is essential, to differentiate frequent precursor state without end-organ damage from the rapidly developing disease requiring therapy. After learning the best biological models for local lesion growth and global tumor burden evolution on clinical data, and computing corresponding population priors, we use individual model parameters as biomarkers, and can study them systematically for correlation with external covariates, such as sex or location of the lesion. On our cohort of 63 patients with smoldering Multiple Myeloma, we show that they perform substantially better than other radiological criteria, to predict progression into symptomatic Multiple Myeloma. Our study paves the way for modeling disease progression patterns for Multiple Myeloma, but also for other metastatic and disseminated tumor growth processes, and for analyzing large longitudinal image data sets acquired in oncological imaging. It shows the unprecedented potential of model-based biomarkers for better and more personalized treatment decisions and deserves being validated on larger cohorts to establish its role in clinical decision making.

bioinformatics↗

bioRxiv2018

MDM4 is an essential disease driver targeted by 1q gain in Burkitt lymphoma

Oncogenic MYC activation promotes cellular proliferation in Burkitt lymphoma (BL), but also induces cell cycle arrest and apoptosis mediated by TP53, a tumor suppressor gene that is mutated in 40% of BL cases. To identify therapeutic targets in BL, we investigated molecular dependencies in BL cell lines using RNAi-based, loss-of-function screening. By integrating genotypic and RNAi data, we identified a number of genotype-specific dependencies including the dependence of TCF3/ID3 mutant cell lines on TCF3 and of MYD88 mutant cell lines on TLR signaling. TP53 wild-type (TP53wt) BL were dependent on MDM4, a negative regulator of TP53. In BL cell lines, MDM4 knockdown induced cell cycle arrest and decreased tumor growth in a xenograft model in a p53-dependent manner, while small molecule inhibition of the MDM4-p53 interaction restored p53 activity resulting in cell cycle arrest. Consistent with the pathogenic effect of MDM4 upregulation in BL, we found that TP53wt BL samples were enriched for gain of chromosome 1q which includes the MDM4 locus. 1q gain was also enriched across non-BL cancer cell lines (n=789) without TP53 mutation (23% in TP53wt and 12% in TP53mut, p<0.001). In a set of 216 cell lines representing 19 cancer entities from the Achilles project, MDM4 was the strongest genetic dependency in TP53wt cell lines (p<0.001).\n\nOur findings show that in TP53wt BL, MDM4-mediated inhibition of p53 is a mechanism to evade cell cycle arrest. The data highlight the critical role of p53 as a tumor suppressor in BL, and identifies MDM4 as a key functional target of 1q gain in a wide range of cancers, which is therapeutically targetable.

cancer biology↗