Biology subjects

Michor, F.

Publications and source records attributed to Michor, F..

18 recordsLinked to original sources

bioRxiv2026

MAPK signaling links BRD2 chromatin occupancy to PI3K/AKT inhibitor sensitivity

Bromodomain and extra-terminal (BET) proteins, BRD2, BRD3, BRD4, and BRDT, couple histone acetylation to transcription by recruiting elongation and transcription factor complexes to chromatin. Although BET proteins are promising therapeutic targets, the functions of individual family members remain incompletely understood. We identify BRD2 as a co-targetable vulnerability with PI3K/AKT inhibition in breast cancer. Pan-BET inhibition and BRD2 knockout synergized with PI3K pathway inhibitors in breast cancer cell lines, patient-derived organoids, and in vivo models. BRD2 knockout impaired proliferation of triple-negative breast cancer cells and upregulated signaling and stress-response pathways, including the DNA damage response. Mechanistically, BRD2 is phosphorylated at Ser37 by the mitogen- and stress-activated kinases MSK and RSK, and Ser37 phosphorylation is required for chromatin binding and reader function. Finally, mechanistic digital twin modeling identified BETi-PI3Ki regimens that maintained efficacy while reducing drug exposure. Together, these findings identify BRD2 as a phosphorylation-dependent, co-targetable vulnerability in PI3K-inhibited breast cancer.

cancer biology↗

bioRxiv2026

A mechanistic digital twin model for epigenetic therapy optimization in triple-negative breast cancer

Epigenetic therapies offer a promising approach to cancer treatment by modulating chromatin states that govern tumor cell identity, plasticity and therapeutic response. However, predicting and optimizing the effects of such interventions remains challenging. Here, we developed a digital twin framework that integrates mechanistic models of chromatin regulation, in vitro cell-state and treatment response data, and pharmacokinetics to simulate tumor progression and therapeutic response. We applied this framework to triple-negative breast cancer (TNBC), an aggressive disease in which chromatin dysregulation contributes to tumor progression, and investigated combination treatment with an EZH2 inhibitor promiting chromatin opening and an AKT inhibitor, which together enhance expression of GATA3 and BMF. Parameterized and validated using in vitro treatment response data, the model enables in silico clinical trials of alternative combination regimens and treatment schedules. These simulations identify regimens that achieve comparable therapeutic effects to reference schedules while substantially reducing cumulative drug exposure. We further demonstrated the digitan twin's ability of identifying personalized therapeutic strategies by incorporating patient-specific treatment-response data. Our work establishes a mechanistic digital twin framework for predicting tumor responses to chromatin-modifying therapies and provides a quantitative approach for optimizing treatment combinations and schedules across diverse cancer contexts.

cancer biology↗

bioRxiv2026

Move BeTween modAlities (MBTA) employs flow matching to predict single cell data modalities

Integrating diverse molecular modalities to obtain a comprehensive view of cellular identity remains a major challenge in single-cell biology. A fundamental but underappreciated obstacle is structural mismatch -- the phenomenon in which the neighborhood structure of a cell differs depending on which molecular modality is used to define it. Existing approaches typically embed modalities into a shared latent space, which actively erases the structural differences between modalities that make multimodal measurements scientifically valuable. Here we introduce Move BeTween modAlities (MBTA), the first framework explicitly designed to address structural mismatch. Rather than forcing modalities into a shared representation, MBTA maintains modality-specific latent spaces and connects them via flow matching, preserving the structural integrity of each modality while enabling accurate cross-modal translation. Across extensive benchmarks on multi-modal single-cell datasets, MBTA consistently outperformed existing methods, with the largest gains observed in datasets with pronounced structural mismatch. Applied to joint genomic and transcriptomic profiles of breast cancer patients, MBTA identified transcriptomic lineage relationships corroborated by genomic variation and outperformed state-of-the-art transcriptomics-based copy number inference methods. Extending this framework to mouse embryonic development, we reconstructed temporal trajectories jointly defined by gene expression and seven complementary epigenetic modalities. MBTA can connect any number of molecular readouts without erasing their individual character, serving as the computational foundation for assembling multi-layered portraits of cells.

bioinformatics↗

bioRxiv2026

H3K27M-driven hypertranscription leads to a new targetable dependency in diffuse midline gliomas

Diffuse midline gliomas (DMGs) are driven by the H3K27M oncohistone--a challenging therapeutic target. However, conventional therapeutic modalities are never curative. Against this backdrop, we address an important unresolved question--are there H3K27M-induced oncogenic vulnerabilities that can be exploited for therapeutic benefit. We show that H3K27M induces hypertranscription, thus identifying hypertranscription as a new molecular feature of H3K27M-driven DMGs. We demonstrate this finding in genetic mouse models, human DMG cells, and primary tumor specimens. We further demonstrate that H3K27M-induced hypertranscription perturbs replication, heightens basal replication stress, and enhances sensitivity to ATR inhibition. In exploring therapeutic implications of these findings, we document brain penetrance, target engagement, and therapeutic efficacy of a clinical-stage ATR inhibitor (alnodesertib) in vitro and in intracranial DMG xenografts. We further demonstrate synergistic activity of alnodesertib with radiotherapy--the current standard of care for DMGs. These findings provide the mechanistic underpinning and preclinical rationale for including alnodesertib as monotherapy and in combination with radiation in clinical trials for children with H3K27M DMGs. The broad implications of our studies highlight ATR inhibition as a therapy for aggressive human cancers displaying hypertranscription.

cancer biology↗

bioRxiv2026

Multi-region biopsies and patient-derived neurosphere cultures reveal spatial divergence in glioblastoma.

Intratumor heterogeneity (ITH) is one of the main reasons for the lack of effective targeted therapies for glioblastoma (GBM). Imaging-guided surgical navigation allows for tumor-wide sampling to account for variation across distant regions of the tumor, but typical drug screening is performed on cell lines derived from a single biopsy and does not account for GBM heterogeneity. Here we profiled matching MRI-guided multi-region primary tumor biopsies from 6 GBM cases (n=40 biopsies) and corresponding neurosphere cultures (n=30) derived from these spatially distinct tumor samples. We found that in vitro cultures derived from distinct regions of the same tumor display divergent phenotypes, proliferative capacity and ability to accumulate 5-aminolevulinic acid, used to visualize cancer cells during surgery. The differential drug response of the multi-region neurospheres remains linked to the gene expression of the original tumor biopsies. Thus, studies with multiregion-derived neurospheres are essential to faithfully model GBM ITH for therapeutic testing. KEY POINTSO_LIMulti-region biopsy-derived neurospheres represent distinct spatial locations in the GBM tumor. C_LIO_LICultures derived from different regions of the tumor retain phenotypic diversity. C_LIO_LIParental biopsy phenotype predicts drug response better than to in vitro phenotype. C_LI IMPORTANCE OF THE STUDYCell lines developed from spatially distinct regions of glioblastoma capture its intratumor heterogeneity. We show that while the transcriptional output of these cell lines is not connected to their spatial origin, their drug response can be linked to it. Thus, spatial heterogeneity reflected in our neurosphere collection provides a new paradigm for drug screening in these highly heterogenous and difficult to treat tumors.

cancer biology↗

bioRxiv2025

Mechanistic modeling and machine learning identifies optimum radiotherapy schedules to prevent treatment-induced metastasis

Lung cancer patients often experience increased metastasis formation after radiotherapy. However, it is incompletely understood whether radiation affects the migratory behavior of tumor cells and how altered radiotherapy schedules might mitigate this risk. To address these questions, we performed live-cell microscopy experiments to profile changes in cell migration during radiation across 12 cancer cell lines and developed a predictive computational modeling platform describing tumor volume and dissemination during radiotherapy. Using this platform, we identified optimal fractionation schedules and then performed extensive in silico clinical trials, establishing that our optimized schedules substantially reduce metastatic seeding relative to the standard of care schedule. Training transformer models on the in silico clinical trial data enabled us to recover mechanistic parameters with high accuracy, demonstrating that the features determining optimal radiotherapy can be inferred from longitudinal tumor data. Our integrative predictive approach enables the rational design of optimum clinical trials across indications.

bioinformatics↗

bioRxiv2025

Punctuated mutagenesis promotes multi-stepevolutionary adaptation in human cancers

The rate of acquisition of genomic changes in cancer has been the topic of much discussion, with several recent investigations finding evidence of punctuated evolution instead of gradual accumulation of such changes. Despite forays into the description and quantification of these punctuated events, the effects of such changes on subsequent cancer evolution remain incompletely understood. Here we investigate how non-gradual mutagenesis affects the ability of tumor cells to acquire and retain fitness-enhancing adaptations. We find that punctuated mutagenesis significantly facilitates adaptation in scenarios where adaptation requires crossing a fitness valley, i.e. when multiple mutations are required which individually are maladaptive but jointly confer a fitness advantage. By increasing the probability that multiple mutations occur in close succession, punctuation increases the chance that mutants in a fitness valley mutate further to reach a fitness peak before going extinct. Analyzing data from The Cancer Genome Atlas, we find that tumors with signatures of APOBEC mutagenesis, which has been shown to proceed in episodic bursts, exhibit patterns consistent with higher rates of crossing fitness valleys. Lastly, we characterize how the interplay between this enhanced ability to cross fitness valleys and adaptation-limiting effects of clonal interference affects overall adaptability in complex fitness landscapes.

cancer biology↗

bioRxiv2025

PROFET Predicts Continuous Gene Expression Dynamics from scRNA-seq Data to Elucidate Heterogeneity of Cancer Treatment Responses

Single-cell RNA sequencing profiles cellular heterogeneity but captures only static snapshots, limiting inference of gene expression dynamics. We developed PROFET (Particle-based Reconstruction Of generative Force-matched Expression Trajectories), a framework that reconstructs continuous, nonlinear single-cell trajectories from sparsely sampled scRNA-seq time series. PROFET combines a particle-based gradient-flow algorithm with simulation-free force matching to accurately infer cellular dynamics. Across mouse and human in vitro datasets and an in vivo axolotl regeneration dataset, PROFET achieved 2.6-12.5X lower prediction error than ten state-of-the-art trajectory inference methods. Applying PROFET to newly generated scRNA-seq data from a palbociclib-treated MCF7 cell line and three published breast cancer patient datasets, we reconstructed treatment-response trajectories and identified a resistant cell subpopulation exhibiting large phenotypic shifts and enrichment of the surface markers UNC5B, TLR3, PCDH19, PROCR, SLITRK6, and SEMA6B. PROFET provides a biologically grounded framework for reconstructing cell-state dynamics from static single-cell data across development, regeneration, and therapeutic response.

bioinformatics↗

bioRxiv2025

BESTDR: Bayesian quantification of mechanism-specific drug response in cell culture

Understanding drug responses at the cellular level is essential for elucidating mechanisms of action and advancing preclinical drug development. Traditional dose-response models rely on simplified metrics, limiting their ability to quantify parameters like cell division, death, and transition rates between cell states. To address these limitations, we developed Bayesian Estimation of STochastic processes for Dose-Response (BESTDR), a novel framework modeling cell growth and treatment response dynamics to estimate concentration-response relationships using longitudinal cell count data. BESTDR quantifies rates in multi-state systems across multiple cell lines using hierarchical modeling to support high-throughput screening. We validated BESTDR with synthetic and experimental datasets, demonstrating its robustness and accuracy in estimating drug response. By integrating mechanistic modeling of cytotoxic, cytostatic and other effects, BESTDR enhances dose-response studies, facilitating robust drug comparisons and mechanism-specific analyses. BESTDR offers a versatile tool for early-stage preclinical research, paving the way for drug discovery and informed experimental design.

systems biology↗

bioRxiv2024

Functional consequences of a p53-MDM2-p21 incoherent feedforward loop

Genetically identical tumor cells can respond heterogeneously to therapy, with a subpopulation of cells often entering a temporarily arrested treatment-tolerant state before repopulating the tumor. To investigate how heterogeneity in the cell cycle arrest protein p21 arises, we imaged the dynamics of p21 transcription and protein expression along with those of p53, its transcriptional regulator, in single MCF-7 cells using live cell fluorescence microscopy. Using this approach, we found that the rate of p21 transcription depends on the change in p53 rather than its absolute level. Through combined theoretical and experimental modeling, we determined that p21 transcription is governed by an incoherent feedforward loop mediated by MDM2. This network architecture facilitates rapid induction of p21 expression and variability in p21 transcription. Abrogating the feedforward loop overcomes rapid S-phase p21 degradation, with cells transitioning into a quiescent state that transcriptionally resembles a treatment-tolerant persister state. The potential implications of our findings warrant consideration in the design of therapeutic strategies based on activating p53.

systems biology↗

bioRxiv2023

Reconstruction of single cell lineage trajectories and identification of diversity in fates during the epithelial-to-mesenchymal transition

Exploring the complexity of the epithelial-to-mesenchymal transition (EMT) unveils a diversity of potential cell fates; however, the exact timing and intricate mechanisms by which early cell states diverge into distinct EMT trajectories remain unclear. Studying these EMT trajectories through single cell RNA sequencing is challenging due to the necessity of sacrificing cells for each measurement. In this study, we employed optimal-transport (OT) analysis to reconstruct the past trajectories of different cell fates during TGF-beta-induced EMT in the MCF10A cell line. Our analysis revealed three distinct trajectories leading to low EMT, partial EMT, and high EMT states. Cells along partial EMT trajectory showed substantial variations in the EMT signature and exhibited pronounced stemness. Throughout this EMT trajectory, we observed a consistent downregulation of the EED and EZH2 genes. This finding was validated by recent inhibitor screens of EMT regulators and CRISPR screen studies. Moreover, we applied our analysis of early-phase differential gene expression to gene sets associated with stemness and proliferation, pinpointing ITGB4, LAMA3, and LAMB3 as genes differentially expressed in the initial stages of the partial versus high EMT trajectories. We also found that CENPF, CKS1B, and MKI67 showed significant upregulation in the high EMT trajectory. While the first group of genes aligns with findings from previous studies, our work uniquely pinpoints the precise timing of these upregulations. Finally, the latter group of genes represents newly identified regulators, shedding light on potential targets for modulating EMT trajectories. Significance StatementIn our study, we investigated cellular trajectories during EMT using a time-series scRNAseq dataset. OT analysis was used to infer cell-to-cell connections from scRNAseq data, allowing us to predict cell linkages and overcome limitations of sequencing such as the need to sacrifice cells for each measurement. This approach allowed us to identify diverse EMT responses under uniform treatment, a significant advancement over previous studies limited by the static nature of scRNAseq data. Our analysis identified a broad set of genes involved in the EMT process, uncovering novel insights such as the upregulation of cell cycle genes in cells predisposed to a high EMT state and the enhancement of cell adhesion marker genes in cells veering towards a partial EMT state. This work enriches our understanding of the dynamic processes of EMT, showcasing the varied cellular fates within the same experimental setup.

systems biology↗

bioRxiv2022

Discovery and characterization of LNCSOX17 as an essential regulator in human endoderm formation

Long non-coding RNAs (lncRNAs) have emerged as fundamental regulators in various biological processes, including embryonic development and cellular differentiation. Despite much progress over the past decade, the genome-wide annotation of lncRNAs remains incomplete and many known non-coding loci are still poorly characterized. Here, we report the discovery of a previously not annotated lncRNA that is transcribed upstream of the SOX17 gene and located within the same topologically associating domain. We termed it LNCSOX17 and show that it is induced following SOX17 activation but its expression is more tightly restricted to early definitive endoderm. Loss of LNCSOX17 affects crucial functions independent of SOX17 and leads to an aberrant endodermal transcriptome, signaling pathway deregulation and epithelial to mesenchymal transition defects. Consequently, cells lacking the lncRNA cannot further differentiate into more mature endodermal cell types. We identified and characterized LNCSOX17 as an essential new actor in early human endoderm, thereby further expanding the list of functionally important non-coding regulators.

genomics↗

bioRxiv2022

A paracrine circuit of IL-1b/IL-1R1 between myeloid and tumor cells drives glioblastoma progression

Monocytes and monocyte-derived macrophages (MDM) from blood circulation infiltrate and promote glioblastoma growth. Here we discover that glioma cells induce the expression of potent pro-inflammatory cytokine IL-1{beta} in MDM, which engages IL-1R1 in glioma cells, activates NF-{kappa}B pathway, and subsequently leads to the induction of monocyte chemoattractant proteins (MCPs). Thus, a feedforward paracrine circuit of IL-1{beta}/IL-1R1 between the tumors and MDM creates an interdependence driving glioblastoma progression. Locally antagonizing IL-1{beta}/IL-1R1 leads to reduced MDM infiltration, diminished tumor growth, reduced exhausted CD8+ T cells, and thereby extends the survival of tumor-bearing mice. In contrast to IL-1{beta}, IL-1a exhibits anti-tumor effects. Genetic deletion of Il1a is associated with decreased recruitment of lymphoid cells and loss of interferon (IFN) signaling in various immune populations and subsets of malignant cells. IL-1{beta} antagonism of IL-1{beta} should be considered as an effective anti-glioblastoma therapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=196 HEIGHT=200 SRC="FIGDIR/small/486888v1_ufig1.gif" ALT="Figure 1"> View larger version (66K): org.highwire.dtl.DTLVardef@1c99e57org.highwire.dtl.DTLVardef@1ba9d24org.highwire.dtl.DTLVardef@a2c418org.highwire.dtl.DTLVardef@1f8091c_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

Single-cell genetic heterogeneity linked to immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most aggressive brain tumor with a median survival of ~15 months. Targeted approaches have not been successful in this tumor type due to the large extent of intratumor heterogeneity. Mosaic amplification of oncogenes suggests that multiple genetically distinct clones are present in each tumor. To uncover the relationships between genetically diverse subpopulations of GBM cells and their native tumor microenvironment, we employed highly multiplexed spatial protein profiling, coupled with single-cell spatial mapping of fluorescence in situ hybridization (FISH) for EGFR, CDK4, and PDGFRA. Single-cell FISH analysis of a total of 35,843 single nuclei (~2,100 per tumor) revealed that tumors in which amplifications of EGFR and CDK4 more frequently co-occur in the same cell exhibit higher infiltration of CD163+ immunosuppressive macrophages. Our results suggest that high throughput assessment of genomic alterations at the single cell level could provide a measure for predicting the immune state of GBM.

cancer biology↗

bioRxiv2021

Barcoded bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits. Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

genetics↗

bioRxiv2020

Reconstructing the lineage histories and differentiation trajectories of individual cancer cells in JAK2-mutant myeloproliferative neoplasms

Some cancers originate from a single mutation event in a single cell. For example, blood cancers known as myeloproliferative neoplasms (MPN) are thought to originate through the acquisition of a driver mutation (most commonly JAK2-V617F) in a hematopoietic stem cell (HSC). However, when the mutation first occurs in individual patients and how it impacts the behavior of HSCs in their native context is not known. Here we quantified the impact of the JAK2-V617F mutation on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual MPN patients. We reconstructed the lineage history of individual HSCs obtained from MPN patients using the patterns of spontaneous somatic mutations accrued in their genomes over time. Strikingly, we found that the JAK2-V617F mutation occurred in a single HSC several decades before MPN diagnosis -- at age 9{+/-}2 years in a 34-year-old patient, and at age 19{+/-}3 years in a 63-year-old patient. For each patient, we inferred the number of mutated HSCs over time and computed their fitness. The population of JAK2-mutated HSCs grew exponentially by 63{+/-}15% and 44{+/-}13% every year in the two patients, respectively. To contrast the differentiation trajectories of the JAK2-mutated HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single hematopoietic stem and progenitor cells (HSPCs). We found that the fraction of JAK2-mutant HSPCs varied significantly across different myeloid cell types within the same patient. The erythroid progenitor cells were often entirely JAK2-mutant, even when the peripheral blood JAK2-V617F allele burden was low. The novel biological insights uncovered by this work have implications for the prevention and treatment of MPN, as well as the accurate assessment of disease burden in patients. The technology platforms and computational frameworks developed here are broadly applicable to other types of hematological malignancies and cancers.

cancer biology↗

bioRxiv2020

Mutational dynamics and transmission properties of SARS-CoV-2 superspreading events in Austria

Superspreading events shape the COVID-19 pandemic. Here we provide a national-scale analysis of SARS-CoV-2 outbreaks in Austria, a country that played a major role for virus transmission across Europe and beyond. Capitalizing on a national epidemiological surveillance system, we performed deep whole-genome sequencing of virus isolates from 576 samples to cover major Austrian SARS-CoV-2 clusters. Our data chart a map of early viral spreading in Europe, including the path from low-frequency mutations to fixation. Detailed epidemiological surveys enabled us to calculate the effective SARS-CoV-2 population bottlenecks during transmission and unveil time-resolved intra-patient viral quasispecies dynamics. This study demonstrates the power of integrating deep viral genome sequencing and epidemiological data to better understand how SARS-CoV-2 spreads through populations. Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

microbiology↗

bioRxiv2017

A computational model of MGUS progression to Multiple Myeloma identifies optimum screening strategies and their effects on mortality

Recent advances uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as from Monoclonal Gammopathy of Undetermined Significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework. We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals, to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence and baseline MM survival. We measured MM-specific mortality and MM prevalence following MGUS detection from simulations and mathematical precition modeling. We showed that our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available progression risk-point estimate of 61% risk, starting screening at age 55 and follow-up screening every 6yrs reduced total MM prevalence by 19%. The same reduction could be achieved with starting age 65 and follow-up every 2yrs. A 40% progression risk reduction per MGUS patient per year would reduce MM-specific mortality by 40%. Generally, age of screening onset and frequency impact disease prevalence, progression risk reduction impacts both prevalence and disease-specific mortality, and screeenign would generally be favorable in high-risk individuals. Screening efforts should focus on specifically identified groups of high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk MGUS individuals would require improved preventions.

epidemiology↗