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Becker, J.

Publications and source records attributed to Becker, J..

At least 19 recordsLinked to original sources

Artesunate Exploits Glutathione-Dependent Redox Vulnerability in Metastatic Lung Adenocarcinoma

Metastatic dissemination remains the principal cause of mortality in lung adenocarcinoma. To identify metabolic vulnerabilities associated with metastasis, we used cell lines with distinct metastatic abilities derived from the KrasG12D;p53KO mouse model. Building on evidence of mitochondrial dysfunction in metastasis-derived cells, we performed an in vivo screen of a focused metabolic drug library using a multiplexed DNA-barcoding platform. This approach identified artesunate as a selective inhibitor of metastatic seeding, which was validated in vivo to reduce metastatic burden while leaving primary tumor growth unaffected. Mechanistically, artesunate induced oxidative stress, characterized by reactive oxygen species and lipid peroxidation. Transcriptomic and mechanistic profiling revealed altered redox homeostasis in metastasis-derived cells. Inhibition of glutathione synthesis specifically sensitized these cells to artesunate, resulting in extensive cell death, whereas primary tumor-derived cells remained resistant. These findings identify glutathione-dependent redox homeostasis as a metabolic vulnerability in metastatic cells that can be specifically targeted using artesunate.

cancer biology↗

Spatial Compartmentalization of TCR Repertoires Between Primary Melanomas and Sentinel Lymph Nodes Reveals Distinct Clonal Architectures and Shared Antigen Recognition

Primary tumors and their sentinel lymph nodes are functionally linked sites of anti-tumor immunity, yet how T cell receptor (TCR) repertoires are organized across these compartments remains incompletely understood. We thus performed TCR{beta} sequencing on paired primary melanoma tumors and sentinel lymph nodes from 24 treatment-naive patients to quantify TCR repertoire diversity and clonal architecture as markers of antigen-driven selection. Primary tumors exhibited markedly reduced TCR diversity and pronounced clonal dominance compared with matched lymph nodes, consistent with selective expansion of tumor-reactive T cells. Tumor-associated clonotypes displayed significantly longer CDR3 sequences driven by increased non-templated nucleotide insertions, a feature associated with neoantigen recognition, and showed biased TRBV and TRBJ gene usage indicative of CD8+ T cell enrichment. Annotation against known melanoma differentiation and cancer testis antigens identified only a small fraction of clonotypes with characterized specificities, and fewer than 10% of these were shared between tumors and lymph nodes, suggesting that most expanded tumor clonotypes recognize patient-specific antigens. To identify shared immune features beyond individual clonotypes, we applied ClustIRR to build a joint graph linking clonotypes across repertoires by CDR3 sequence similarity and detected Communities on the Joint graph (CJs) to uncover recurrent TCR sequence motifs. Differential occupancy analysis identified distinct CJs segregating tumors and lymph nodes, including a limited number of recurrent CJs targeting MART-1 epitopes, while the majority were patient-specific. Together, these data define spatially structured TCR repertoire architectures in human melanoma and establish a scalable framework for interrogating tumor-immune interactions. eLife DigestMelanoma is a skin cancer harboring many UV-associated mutations, making it recognizable to the immune system. T cells attack cancer using specialized molecular structures called T cell receptors (TCRs). They are frequently referred to as specific keys that only fit into the respective locks, i.e. HLA molecules presenting the target antigen. Surgeons often remove both the tumor and the tumor draining lymph node (aka sentinel lymph node). The tumor is where T cells directly fight cancer, while the sentinel lymph node is where new anti-tumor T cells are first activated. How these two sites coordinate immune surveillance is not completely understood. Here, Kitanovski and colleagues sequenced TCRs from paired tumors and sentinel lymph nodes obtained from melanoma patients. These analyses demonstrated that primary tumors have dramatically reduced TCR diversity, dominated by a few massively expanded T cell clones, whereas sentinel lymph nodes contain broad, balanced repertoires. This reveals intense local selection within the tumor-a small set of tumor-specific T cells expand repeatedly while the lymph node preserves diverse immune reserves. Examining TCR structure revealed a second key difference. Tumor-infiltrating T cells possess on average slightly longer CDR3 loops-the region contacting antigens-especially among dominant clones. These longer loops arise from random nucleotide insertions. Prior work shows that self-antigen-specific T cells have shorter CDR3s due to thymic constraints, whereas T cells recognizing tumor-specific mutation-derived neoantigens escape these restrictions and have longer CDR3s. Thus, tumor TCRs appear tailored for recognizing patient-specific neoantigens. The authors could only use TCRs matching known databases for shared melanoma antigens like MART-1 to identify TCR-specific epitopes; most tumor-expanded TCRs could not be annotated because patient-specific neoantigens are not in public databases. This limitation itself is informative- most expanded tumor TCRs likely target private neoantigens. Grouping TCRs by sequence similarity into "communities" revealed striking patterns: sentinel lymph nodes showed conserved community composition across patients, consistent with responses to common viruses, while tumor repertoires were highly individualized, reflecting each tumors unique mutations. These findings establish how anti-melanoma immunity is spatially organized: sentinel lymph nodes contain diverse T cell landscapes, while in primary tumors melanoma-infiltrating T cells appear to have undergone intense selection for neoantigen recognition. This provides a foundation for using TCR features as biomarkers and designing personalized T cell therapies.

cancer biology↗

An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset

MicroRNAs are short noncoding RNAs that regulate gene expression and are commonly profiled by small RNA sequencing (miRNA-seq). Despite the widespread use of miRNA-seq, datasets are often analyzed with RNA-seq method such as DESeq2 or edgeR, which do not take into account the specific characteristics of miRNA-seq data. Here, we present a benchmark study of normalization and differential expression approaches using a realistic ground-truth dataset. By mixing mouse RNA of two organs, we generated expression trends while capturing biological and technical variability. Using monotonicity across the dataset and expected fold changes from the mixture design, we assessed normalization and differential expression methods. Normalization benchmarking showed that within-sample scaling, particularly Read Per Million (RPM), best preserved the expected monotonic trends, outperforming cross-sample methods such as TMM, rlog, and VST. These approaches sometimes recovered apparent monotonicity among abundant miRNAs, but inspection of individual profiles suggested likely over-correction. Regarding differential expression, edgeR consistently ranked among the best-performing methods across several metrics, including log2 fold-change estimation, with performance comparable to miRNA-seq-specific tools such as miRglmm and NBSR. DESeq2, edgeR-v4, and limma-based approaches tended to systematically underestimate log2 fold changes. Applying a common RPM-based normalization substantially improved the performance of cross-sample methods, highlighting the strong influence of normalization on differential expression analysis. Overall, our findings support within-sample scaling methods such as RPM for normalization, and edgeR, miRglmm, or NBSR for differential expression. The dataset has been made publicly available, providing a valuable resource for objective method comparison and future miRNA-seq software development.

bioinformatics↗

Railway Catenary Sparking as a Source of Toxic Copper Ultrafine Particles: Evidence from Realistic In Vitro Inhalation Exposure

Railway catenary sparking generates airborne ultrafine particles (UFPs) that may pose health risks due to their metallic composition and ability to penetrate deep into the alveolar region of the lungs. Copper, widely used in wires and pantographs, is a major component of these emissions, making copper-rich particles common in railway environments such as subways. However, exposure levels and health impacts remain poorly characterized, and localized hotspots may represent an underrecognized risk in densely populated areas. This study investigated the toxicity of copper UFPs under realistic dosimetry and deposition conditions. Copper UFPs were generated using a spark discharge generator and applied to two in vitro lung models: a 3D co-culture of Calu-3 epithelial cells, THP-1-derived macrophages, and EA.hy926 endothelial cells, and a monoculture of A549 alveolar epithelial cells. Cells were exposed at the air-liquid interface (ALI) using an automated platform to mimic inhalation exposure and UFPs deposition. Copper deposition ranged from 6.5 to 41 ng/cm2, within occupationally relevant levels. A549 cells showed cytotoxic responses consistent with previous studies, whereas the 3D co-culture model revealed broader adverse effects, including inflammation, impaired epithelial barrier integrity, oxidative stress, and early DNA damage. Inflammatory activation also differed between models: A549 cells mainly exhibited transcriptional responses, while the 3D model showed significant secretion of IL-6 and IL-8, associated with interferon signaling. These findings highlight the potential health risks of copper UFPs from railway systems and emphasize the need for improved characterization of UFP exposure in environmental and occupational railway settings.

pharmacology and toxicology↗

Expectation Consecutively boosts Neural Processing of Expected and Unexpected Visual Information

This electroencephalography (EEG) study provides evidence that expectations dynamically shape visually evoked brain responses through a biphasic temporal mechanism: an earlier enhancement of expected image components, followed by a later prioritization of unexpected components. Across six sessions each, six participants were presented with a deterministic sequence of four images with intermixed composite trials that contained both the expected and the unexpected image. To quantify expected and unexpected identity information over time, we applied Linear Discriminant Analysis. The observed temporal dissociation suggests that the brain initially integrates sensory input with prior expectations in a Bayesian manner before shifting to emphasize surprising information, thereby supporting both efficient recognition and adaptive model updating. Furthermore, expectations affected superordinate image processing (animal vs. non-animal), showing the same pattern as identity processing, with earlier enhancement of expected information followed by a prioritization of surprising information.

neuroscience↗

Genetically Programmed Shape-morphing of Engineered Living Materials

Engineered living materials (ELMs) promise genetically programmable functions by coupling biological regulation to synthetic material responses. Here, we introduce genetically encoded, reversible shape-morphing in a peptide-crosslinked polyethylene glycol (PEG) hydrogel whose network density is modulated by opposing enzymatic pairs that induce crosslinking or hydrolysis. This molecular programmability alternates the hydrogel between deswelling and swelling/disintegration and produces 2 - 5-fold changes in mechanical properties. By fabricating a bilayer hydrogel with an inert layer, these molecular modulations are translated into a reversible and directional motion with angular bending motions exceeding 80{degrees}. Further, by embedding genetically engineered bacteria or interfacing mammalian cells, producing the relevant enzymatic cues, the reversible shape-morphing of these ELMs is programmed at the genetic level. We further demonstrate genetically programmed, autonomous reversible bending in a bilayer hydrogel controlled by out-of-equilibrium counteracting biochemical reactions with dynamically changing respective reaction rates. This work establishes a concept where coordinated polymer/peptide material engineering and synthetic biology yield autonomous shape-morphing ELMs, opening avenues toward biohybrid soft robotics, adaptive microfluidic systems, and dynamic biomedical interfaces.

synthetic biology↗

A translational transcriptomic signature of vaccine reactogenicity for the evaluation of novel formulations

Accurately predicting vaccine reactogenicity at the preclinical stage remains a major challenge in vaccine development, as conventional animal studies and in vitro assays capture general inflammation but fail to quantify local or systemic reactogenicity relevant to humans. Using transcriptomic data from the BioVacSafe consortium encompassing seven vaccines and immunostimulants in mice and five licensed vaccines in humans, we developed a cross-compartment and cross-species predictive model of vaccine reactogenicity. Reactogenicity classes were defined in mouse muscle based on the magnitude of transcriptomic responses and literature evidence. A penalized ordinal regression model was trained to predict both discrete classes and continuous scores of reactogenicity. Transcriptomic profiles from mouse muscle were highly predictive of reactogenicity, with key genes enriched in inflammatory and tissue repair pathways such as IL6/JAK/STAT3 signalling. The model retained strong performance when transferred to mouse blood and revealed shared transcriptional programs between compartments, suggesting coordinated innate responses. When applied to human blood, the classifier correctly ranked licensed vaccines by reactogenicity, identifying Fluad (MF59-adjuvanted) as the most reactogenic, in agreement with elevated C-reactive protein and ReactoScore values, while Engerix-B, Varilrix, and Stamaril were classified as low-reactogenicity formulations. These results align with clinical safety data and demonstrate that early transcriptomic signatures in mice can predict human reactogenicity profiles. Our study presents a pan-vaccine, cross-species transcriptomic signature that bridges preclinical and clinical data, offering a foundation for translational biomarkers and mechanism-informed assessment of vaccine tolerability.

bioinformatics↗

The nucleotide analog bemnifosbuvir inhibits hepatitis E virus replication in preclinical models

BackgroundHepatitis E virus infections remain a global health concern. Immunocompromised patients are at an increased risk to develop chronic HEV infection and thereby severe liver disease. Current off-label regimens are suboptimal with treatment failure being reported. Therefore, there is an urgent need for an effective anti-HEV treatment. ObjectiveIn this study, we aimed to identify potent inhibitors of HEV replication. DesignWe developed a rapid, image-based screening platform based on a full-length HEV fluorescence reporter virus and screened a nucleotide/nucleoside analog library. The identified lead candidate was validated in authentic hepatocyte culture systems, as well as in a gerbil infection model. ResultsBemnifosbuvir (BEM), previously characterized as a nucleotide analog with activity against other RNA viruses, efficiently suppressed HEV replication in vitro and in vivo in a dose-dependent manner, with minimal cytotoxicity at effective concentrations. Combining BEM with ribavirin, the off-label drug given to chronic HEV patients, resulted in an additive antiviral effect against HEV. We found that HEV-3 remains susceptible to inhibition by BEM over an extended treatment period, reducing concerns about the rapid development of viral resistance. Importantly, BEM significantly reduced HEV viral loads and liver inflammation in a gerbil infection model. ConclusionsGiven BEMs favorable safety profile in preclinical and clinical settings, our results suggest investigating its efficacy in patients with chronic HEV infection.

microbiology↗

Pervasive Intraspecific Genetic Local Adaptation Within the Natural Gut Microbiome

Gut microbial communities are shaped by the physicochemical gradients and compartmentalization along the gastrointestinal tract. Whether spatially variable selection is strong enough to promote lasting genetic divergence within microbial species subject to migration and clonal expansion remains unclear. We performed a metagenomic population-genetic analysis of roe deer microbiota across metabolically and structurally distinct gut compartments. Remarkably, 10 of 20 prevalent bacterial species exhibited clear signatures of genetic local adaptation, despite the homogenizing effects of frequent migration. Nutrient availability and environmental stability emerged as primary drivers of selection, with horizontal gene transfer as a key mechanism enabling local adaptation. Our findings demonstrate that a comprehensive understanding of ecological and evolutionary dynamics in the gut necessitates considering fine-scale genetic variation within species.

evolutionary biology↗

Extended-spectrum beta-lactamase (ESBL)-producing E. coli in livestock and free-roaming wildlife: a combined phenotyping whole-genome sequencing One Health approach

Antimicrobial resistance (AMR) in Enterobacterales, particularly extended-spectrum beta-lactamase-producing Escherichia coli (ESBL-Ec), poses a significant public health concern. Widespread antimicrobial use exerts selection pressure, driving the persistence and spread of resistant bacteria in diverse environments. While ESBL-Ec is well-documented in clinical and agricultural settings, its presence in reservoirs remains poorly understood. This study assessed the prevalence, AMR profiles, and genetic diversity of ESBL-Ec in Swiss dairy cattle and wildlife. Between 2021 and 2023, 775 samples were analysed, including rectal swab samples from dairy cows (n=475), and fecal or cloacal swab samples and wildlife (n=300). ESBL-Ec was detected in 0.6% of dairy cattle and 3.7% of wildlife. Whole-genome sequencing of 54 ESBL-Ec isolates identified 28 sequence types with no overlap between livestock and wildlife, suggesting distinct evolutionary trajectories. Phenotypic testing revealed resistance beyond beta-lactams, notably against aminoglycosides, tetracyclines, and sulfonamides, whereas all isolates remained susceptible to tigecycline and meropenem. Multidrug resistance was prevalent (89%), with blaCTX-TEM1 (in dairy cattle), blaCTX-M55 and blaTEM-176 (in red foxes) widely distributed across Switzerland. Clonal plasmids (IncFIB, IncX1) were detected across hosts and some individuals harbored multiple ESBL-Ec strains. Despite their low prevalence, spatial clustering indicated local persistence in both livestock and wildlife, highlighting potential transmission. The observed presence of clinically relevant ESBL-Ec in wildlife highlights the need for a One Health approach to tackle the dangers of AMR. Our findings contribute to AMR surveillance by providing baseline data on ESBL-Ec reservoirs and informing the design of strategies to mitigate its environmental spread.

molecular biology↗

Community-level plant functional strategies explain ecosystem carbon storage across a tropical elevational gradient

1. Plant functional traits play an important role in shaping plant ecological responses to environmental conditions and influencing ecosystem functioning. However, how whole-plant functional strategies manifest at the community level to influence aboveground and belowground carbon storage across environmental gradients remains poorly understood. 2. We measured aboveground and belowground carbon stocks and the variation in whole-plant (above- and belowground) functional strategies at the community level in twelve ecosystem types across a broad savanna-forest-alpine elevational gradient of climate and land use on Mt. Kilimanjaro, Tanzania. Using Structural Equation Models, we disentangled the direct and land use mediated influences of climate on carbon storage from indirect influences mediated by variation in plant functional strategies. 3. We found strong coordination between above- and belowground functional traits at the whole community level, which corresponded with functional strategies related to two major trade-offs: a slow-conservation to fast-resource acquisition axis represented by a spectrum from high leaf dry matter content to high fine root nitrogen concentration; and a size-related woody to grassy community axis represented by a spectrum spanning high canopy height to high specific root length. The slow-fast and woody-grassy strategy axes were primarily driven by precipitation and land-use intensity, respectively. 4. Both functional strategies mediated the effects of climate on carbon storage. The slow-fast strategy axis was strongly and positively associated with aboveground carbon stocks. Meanwhile, the woody-grassy strategy axis was negatively associated with both aboveground carbon stocks and soil organic carbon stocks. 5. Synthesis. We demonstrated that major plant functional strategies manifest at the community level along elevational gradients. These strategies also explain variation in carbon storage, although aboveground storage is mostly driven by trait effects, and belowground storage by direct effects of climate. Together, these results underscore the importance of incorporating functional community data into future analysis of climate change impacts on carbon storage, which would enhance our ability to predict potential shifts in ecosystem functioning.

ecology↗

Antimicrobial Resistance in Wildlife across Switzerland and the Principality of Liechtenstein: Associations with Environmental Factors and Taxonomic Variation

Antimicrobial resistance (AMR) presents a significant challenge to global public health. Addressing this challenge requires a comprehensive One Health approach that integrates efforts in human, veterinary, and environmental domains. Whereas AMR research in medical and veterinary domains is extensive, AMR research in wildlife has received less attention. However, AMR prevalence in the environment, and specifically in wildlife, ultimately affects human life. In a large study conducted in Switzerland and the Principality of Liechtenstein, we collected 410 rectal swab samples from 37 different wildlife species to quantify the prevalence of Escherichia coli (E. coli) and its resistance to 16 antimicrobial drugs, determined through phenotypical antimicrobial susceptibility testing. The study yielded an 81.5% E. coli isolation rate with a considerable 10.8% of isolates resistant to one or more antimicrobial drugs. Notably, 4.5% of the isolates demonstrated multidrug resistance (MDR). Furthermore, we found that E. coli in omnivores exhibited the highest levels of AMR, significantly higher than in carnivores and herbivores. In addition to these dietary associations, we found that the percentage of forested areas surrounding the sampling locations was inversely related to AMR rates, suggesting that environmental conditions play a role in mitigating AMR. This study provides a large-scale quantification of AMR in a broad range of wildlife species and also identifies diverse patterns of AMR in E. coli, highlighting variations associated with both host species and environmental conditions. Our findings emphasize the role of wildlife as potential indicators of environmental contamination with antibiotics and as reservoirs of resistant bacteria.

microbiology↗

The circadian gene Dec2 promotes pancreatic cancer dormancy by regulating tumor cell antigen presentation to facilitate immune evasion

The mechanisms that regulate cancer dormancy remain poorly understood. Using a mouse model of resectable pancreatic adenocarcinoma (PDAC), we identified Dec2 as a gene that was upregulated in metastatic dormant tumor cells. Deletion of Dec2 from tumor cells substantially increased mouse survival after resection due to an immune-mediated mechanism as the survival benefit was abrogated in immunodeficient conditions. Dec2 promoted immune evasion by repressing multiple components of the MHC-I dependent antigen presentation pathway in tumor cells. Dec2 is a regulator of circadian rhythms, and we found several components of the antigen presentation pathway oscillated in a circadian manner that was lost upon deletion of Dec2. Moreover, T-cell mediated tumor cell killing varied depending on the time of day. We suggest that lowered MHC-I presentation of antigens during rest phase is a natural effect of the circadian clock, which is exploited by Dec2-overexpressing pancreatic tumors to evade the immune system.

cancer biology↗

Bacterial Engineered Living Materials modulate Mechanosignaling in Mammalian Cells

Engineered living materials (ELMs) are gaining momentum for biomedical applications as self-replenishing drug depots, smart wound dressings, or as wearable sensors. Current studies on ELM-host interaction are mainly limited to the exchange of biochemical cues between ELMs and surrounding cells and tissues. Here we show that the genetically programmed mechanical properties of ELMs modulate mechanosignaling pathways in mammalian cells cultivated onto the living materials. To this aim, we genetically modulated curli fiber production in E. coli and analyzed the impact on the mechanical properties of the resulting ELMs. The living materials were used as matrix for the cultivation of mammalian cells engineered with a fluorescent reporter to indicate the activation of the mechano-responsive Hippo signaling pathway. We demonstrate that different genetically programmed ELM compositions translated into differential regulation of mechanosignaling in mammalian cells. These findings provide the perspective of using ELMs as extracellular matrix with genetically programmable mechanics for mammalian cells while also highlighting the need to consider the mechanical properties of therapeutic ELMs when assessing interaction with surrounding tissues. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/620857v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@1a1ef9borg.highwire.dtl.DTLVardef@79c49forg.highwire.dtl.DTLVardef@5bb04dorg.highwire.dtl.DTLVardef@193a808_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOToC figureC_FLOATNO C_FIG

bioengineering↗

Data-driven inference of Boolean networks from transcriptomes to predict cellular differentiation and reprogramming

Boolean networks provide robust explainable and predictive models of cellular dynamics, especially for cellular differentiation and fate decision processes. Yet, the construction of such models is extremely challenging, as it requires integrating prior knowledge with experimental observation of transcriptome, potentially relating thousands of genes. We present a general methodology, implemented in the software tool BoNesis, for the qualitative modeling of gene regulation behind the observed state changes from transcriptome data and prior knowledge of the gene regulatory network. BoNesis allows computing ensembles of Boolean networks, where each of them is able to reproduce the modeled differentiation process. We illustrate the scalability and versatility of BoNesis with two applications: the modeling of hematopoiesis from single-cell RNA-Seq data, and modeling the differentiation of bone marrow stromal cells into adipocytes and osteoblasts from bulk RNA-seq time series data. For this later case, we took advantage of ensemble modeling to predict combinations of reprogramming factors for trans-differentiation that are robust to model uncertainties due to variations in experimental replicates and choice of binarization method. Moreover, we performed an in silico assessment of the fidelity and efficiency of the reprogramming, and conducted preliminary experimental validation.

systems biology↗

Breaking the cycle: How targeting CD44v6/MET signaling disrupts colorectal cancer cell plasticity

The concept of cancer stemness is undergoing rapid evolution, facilitated by technological advances allowing the tracking and elimination of stem cells in vivo. Following their identification in solid tumors, cancer stem cells were placed at the center of tumor initiation, growth and metastasis. However, increasing evidence suggests that stemness is a cellular state that can be acquired by differentiated cells in a process called plasticity. Here we show that CD44v6 may act as a molecular switch controlling plasticity of colorectal cancer cells. CD44v6/MET signaling controls the reappearance of Lgr5+ cells after ablation, in vitro and in vivo as demonstrated in tumor organoids derived from Lgr5DTR/eGFP mice using a CD44v6 blocking peptide. CD44v6 also affects the YAP/TAZ signaling pathway, involved in the very first steps of plasticity. In view of the essential role of plasticity for the establishment of metastases, blocking CD44v6 signaling may represent a pivotal and promising therapy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/614761v1_ufig1.gif" ALT="Figure 1"> View larger version (17K): org.highwire.dtl.DTLVardef@180b990org.highwire.dtl.DTLVardef@16c19corg.highwire.dtl.DTLVardef@113e5org.highwire.dtl.DTLVardef@c130d3_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Active repression of cell fate plasticity by PROX1 safeguards hepatocyte identity and prevents liver tumourigenesis

Cell fate plasticity enables development, yet unlocked plasticity is a cancer hallmark. Regulating cell identity requires gene activation and repression. While master regulators induce lineage-specific genes to restrict plasticity, it remains unclear whether unwanted plasticity is actively suppressed by lineage-specific repressors. Here, we computationally predict so-called safeguard repressors for 18 cell types that block phenotypic plasticity lifelong. We validated hepatocyte-specific candidates using reprogramming, revealing that Prospero homeobox protein 1 (PROX1) enhanced hepatocyte identity by direct repression of alternate fate master regulators. In mice, Prox1 was required for efficient hepatocyte regeneration after injury and acted as a tumour suppressor in multiple liver cancer models. In line with patient data, Prox1 depletion caused hepatocyte fate loss in vivo, and promoted transition of hepatocellular carcinoma to cholangiocarcinoma, conversely, overexpression promoted cholangiocarcinoma to hepatocellular carcinoma transdifferentiation. Our findings provide mechanistic evidence for PROX1 as a hepatocyte-specific safeguard and support a model where individual cell type-specific repressors actively suppress plasticity throughout life to safeguard lineage choice and prevent disease.

cancer biology↗

Cellular Dynamics Upon Immune Checkpoint Inhibition

Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assembled a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancers to map treatment-associated remodeling of the tumor immune microenvironment (TIME). Using a hierarchical reference-guided deep-phenotyping framework, we defined 77 immune and stromal subtypes and resolved four conserved TIME subtypes. Approximately 40% of tumors shifted between states during therapy, and the transition was more predictive of outcome than the baseline state. Across 1,988 bulk transcriptomic tumors, favorable transitions toward inflamed or B-cell-enriched subtype tracked with improved response and survival, while persistence in or shifts towards myeloid dominance indicated resistance. We derived a transition score that predicted outcomes for baseline tumors across independent cohorts. These findings establish immunotype transitions as a central determinant of ICI, offering new avenues for response prediction and rational immunotherapy design. HighlightsO_LIA pan-cancer meta-analysis maps treatment-associated remodeling of the tumor immune microenvironment. C_LIO_LIFour conserved TIME states emerge across cancer subtypes. C_LIO_LITIME transition patterns during treatment are associated with clinical outcome. C_LIO_LIBaseline immune programs derived transition scores predict treatment response and survival. C_LI

cancer biology↗