Biology subjects

Berking, C.

Publications and source records attributed to Berking, C..

6 recordsLinked to original sources

bioRxiv2025

Network-based analysis reveals microRNA regulation of oncogenic pathways in SOX10-depleted uveal melanoma

SOX10 is essential for melanocyte development and maintenance and plays a critical role in uveal melanoma (UM) initiation and progression. While SOX10s transcriptional regulation of protein-coding genes is well characterized, its role on microRNA (miRNA) regulatory landscape in UM remains unexplored. Here, we employed network-based modeling to systematically characterize miRNA regulatory functions following SOX10 depletion in UM. First, we profiled mRNA and miRNA expression levels in SOX10 wild-type and knockdown UM cells. Then, we integrated the transcriptomic data, a UM network, and a Bayesian model to quantify miRNAs regulatory activities and identify key miRNAs. Subsequently, we employed pathway enrichment analysis combined with literature mining to elucidate the functional roles of identified miRNAs through their target genes and associated signaling pathways in UM. We identified 17 miRNAs that show significant changes in regulatory activities following SOX10 knockdown in UM cells. These miRNAs regulate the expression of genes involved in cancer hallmark pathways, including cell cycle progression, mTORC1 signaling, and fatty acid metabolism. Notably, miR-34a, miR-25, miR-186, and miR-211 have tumor-suppressive potential by targeting genes involved in UM progression and metastasis. Our results suggested that SOX10 depletion in UM can activate tumor-suppressive mechanisms through regulating miRNAs.

cancer biology↗

bioRxiv2025

A computational SOX10 network-based selection strategy to identify new drug targets in uveal melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), effective treatment options for metastatic UM are limited. The transcription factor SOX10 is crucial for CM initiation and survival, making it an interesting candidate for new targeted therapies, but its relevance in UM was unclear. We found that SOX10 was widely expressed in UM and essential for proliferation, cell cycle progression, and survival. The effects were partially mediated by SOX10-related genes including MITF, highlighting high addiction of UM to the SOX10-MITF axis. Additionally, SOX10 knockdown induced massive transcriptomic changes. Due to a lack of specific inhibitors of SOX10 and MITF, a computational approach was used to identify druggable targets by curating a UM-specific protein interaction network to search for candidates downregulated upon SOX10 inhibition. Thereby, the E2F transcription factor family was identified and their potential as druggable target candidates in UM was confirmed using the pan-E2F inhibitor HLM006474, resulting in cell cycle arrest and apoptosis. Taken together, SOX10 is crucial for UM survival and SOX10-associated proteins may serve as promising targets for developing new therapeutic strategies in UM.

cancer biology↗

bioRxiv2025

Multiomic profiling of a unique in-transit melanoma cohort identifies melanoma differentiation as predictor of tumor progression and therapy response

Melanoma patients with in-transit metastasis (ITM), a stage of disease where melanoma has metastasized to sites in between the primary lesion and draining lymph node, vary significantly in their clinical outcomes, but the biology driving differential outcomes in ITM is poorly understood. To elucidate the mechanisms of differential outcomes, we utilized multimodal molecular profiling (WES, RNA-seq, highly multiplexed immunofluorescence, spatial transcriptomics) in 1) evolutionary analysis of longitudinal tumor samples and 2) identifying prognostic tumor intrinsic and microenvironmental features in a unique cohort of patients with unresectable ITM. Among other findings, we observed a persistent dedifferentiated AXL/NGFR clonal lineage pre-existing and following immune checkpoint blockade in in-transit and distant metastases. Concordantly, we found that low pigmentation and high T cell exhaustion signatures were independently associated with distant progression. Our findings highlight tumor cell state and immune dysfunction as key predictors and potential biomarkers of metastatic risk in ITM. STATEMENT OF SIGNIFICANCEWhat drives distant progression in melanoma is unclear. Analyzing tumor and immune features in a rare in-transit melanoma patient cohort, we identify biological signals highlighting how immune and tumor states observable in pre-distant metastasis melanomas shape long-term outcomes, and nominate potential prognostic biomarkers.

cancer biology↗

bioRxiv2024

SOX10 and microRNAs: decoding their interplay in regulating melanoma plasticity

Recent studies show that the dysregulation of the transcription factor SOX10 is essential for development and progression of melanoma. MicroRNAs (miRNAs) can regulate the expression of transcription factors at the post-transcriptional level. The interactions between SOX10 and its targeting miRNAs form network motifs such as feedforward and feedback loops. Such motifs can result in non-linear dynamics in gene expression levels, therefore playing a crucial role in regulating tumor proliferation and metastasis as well as tumors responses to therapies. Here, we reviewed and discussed the intricate interplay between SOX10 and miRNAs in melanoma biology including melanogenesis, phenotype switch, and therapy resistance. Additionally, we investigated the gene regulatory interactions in melanoma, identifying crucial network motifs that involve both SOX10 and miRNAs. We also analyzed the expression levels of the components within these motifs. From a control theory perspective, we explained how these dynamics are linked to the phenotypic plasticity of melanoma cells. In summary, we underscored the importance of employing a data-driven network biology approach to elucidate the complex regulatory mechanisms and identify driver network motifs within the melanoma network. This methodology facilitates a deeper understanding of the regulation of SOX10 by miRNAs in melanoma. The insight gained could potentially contribute to the development of miRNA-based treatments for SOX10, thereby enhancing the clinical management of this malignancy.

systems biology↗

bioRxiv2024

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

BackgroundUveal melanoma (UM) is a highly malignant intraocular tumor with a poor prognosis and response to therapy, including immune checkpoint inhibitors (ICIs), after the onset of liver metastasis. The metastatic microenvironment contains high levels of tumor-associated macrophages (TAMs) that correlate positively with a worse patient prognosis. We hypothesized that one could increase the efficacy of ICIs in UM metastases by immunomodulating UM-associated macrophages. MethodsTo identify potential targets for the immunomodulation, we created a network-based representation of the biology of TAMs and employed (bulk and single-cell) differential gene expression analysis to obtain a regulatory core of UM macrophages-associated genes. We utilized selected targets for pharmacophore-based virtual screening against a library of FDA-approved chemical compounds, followed by refined flexible docking analysis. Finally, we ranked the interactions and selected one novel drug-target combination for in vitro validation. ResultsBased on the generated TAM-specific interaction network (3863 nodes, 9073 edges), we derived a UM macrophages-associated regulatory core (74 nodes, 286 edges). From the regulatory core genes, we selected eight potential targets for pharmacophore-based virtual screening (YBX1, GSTP1, NLRP3, ISG15, MYC, PTGS2, NFKB1, CASP1). Of 266 drug-target interactions screened, we identified the interaction between the antibiotic Clindamycin and Caspase-1 as a priority for experimental validation. Our in vitro validation experiments showed that Clindamycin specifically interferes with activated Caspase-1 and inhibits the secretion of IL-1{beta}, IL-18, and lactate dehydrogenase (LDH) in macrophages after stimulation. Our results suggest that repurposed Clindamycin could reduce pyroptosis in TAMs, a pro-inflammatory form of programmed immune cell death favouring tumor progression. ConclusionWe were able to predict a novel Clindamycin-Caspase-1 interaction that effectively blocks Caspase-1-mediated inflammasome activity and pyroptosis in vitro in macrophages. This interaction is a promising clinical immunomodulator of the tumor microenvironment for improving ICI responsivenss. This work demonstrates the power of combining network-based transcriptomic analysis with pharmacophore-guided screening for de novo drug-target repurposing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/576201v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@310a9aorg.highwire.dtl.DTLVardef@1af0dcaorg.highwire.dtl.DTLVardef@1b272aborg.highwire.dtl.DTLVardef@86572b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Integration of transcriptomics data into agent-based models of solid tumor metastasis

Most of the recent progress in our understanding of cancer relies in the systematic profiling of patient samples with high throughput techniques like transcriptomics. This approach has helped in finding gene signatures and networks underlying cancer aggressiveness and therapy resistance. However, -omics data alone is not sufficient to generate insights into the spatiotemporal aspects of tumor progression. Here, multi-level computational models are promising approaches, which would benefit from the possibility to integrate in their characterization the data and knowledge generated by the high throughput profiling of patient samples. We present a computational workflow to integrate transcriptomics data from tumor patients into hybrid, multi-scale models of cancer. In the method, we employ transcriptomics analysis to select key differentially regulated pathways in therapy responders and non-responders and link them to agent-based model parameters. We next utilize global and local sensitivity together with systematic model simulations to assess the relevance of variations in the selected parameters in triggering cancer progression and therapy resistance. We illustrate the methodology with a de novo generated agent-based model accounting for the interplay between tumor and immune cells in melanoma micrometastasis. Application of the workflow identifies three different scenarios of therapy resistance.

systems biology↗