Biology subjects

Siveke, J.

Publications and source records attributed to Siveke, J..

7 recordsLinked to original sources

bioRxiv2025

Redirecting resistance evolution in BRAFV600 melanoma by inhibition of the peroxiredoxin-thioredoxin system

Drug-tolerant persister cells (DTPs) exhibit remarkable cell state heterogeneity and phenotypic evolvability. However, the central question of how DTPs epigenetically coordinate their metabolic flexibility to adapt early to therapeutic stress remains unanswered. We have recently shown that the histone demethylase KDM5B, which is intrinsically expressed in differentiated melanoma DTPs, reprograms the metabolic cell landscape. However, the exact mechanism by which KDM5B affects underlying metabolic enzymes, and whether this reveals new druggable vulnerabilities, remain unknown. By transcriptional and epigenetic profiling of BRAFV600 melanoma cells following KDM5B gene silencing, we discovered a direct molecular axis between the epigenetic regulator KDM5B and the PRDX/TXN ROS detoxification system. This metabolic axis is regulated independently of KDM5Bs demethylase activity. Furthermore, RNAi approaches and the pharmacological inhibition of the PRDX/TXN system led to ROS-induced cell death in differentiated melanoma DTPs and a delay of resistance development to MAPK inhibition. This process was independent of lipid-ROS-driven ferroptosis. Additionally, single-cell transcriptome analyses from pre-clinical melanoma models under continuous MAPK inhibitory treatment demonstrated altered cellular differentiation dynamics, with a reduction in the early evolution into the mesenchymal DTP state under concomitant PRDX inhibition. Interestingly, the degree of melanoma cell state differentiation at the onset of treatment was a major determinant for the transition towards the neural crest-like DTP state. Our study identified a high degree of epigenetic-metabolic connectivity and flexibility within the melanoma DTP pool and urges caution with single redox pathway-targeted strategies for tumor elimination in the future. Prospectively, our results point towards a new resistance targeting strategy for BRAFV600 melanoma patients based on pharmacological re-direction of the evolution of melanoma cell states already at therapy onset. HighlightsO_LITranscriptional and epigenetic profiling identified KDM5B as a regulator of the PRDX/TXN-ROS detoxification system C_LIO_LIPRDX inhibition increases the vulnerability of KDM5Bhigh DTPs to ROS, independent of ferroptosis C_LIO_LIPRDX inhibition delays resistance to MAPK inhibition in BRAFV600 melanoma cells C_LIO_LILongitudinal single-cell transcriptome analysis reveals that PRDX inhibition re-directs early DTP evolution C_LIO_LIPRDX/TXN gene expression is predictive for melanoma patient survival C_LI

cancer biology↗

bioRxiv2024

Key Regulatory Elements of the TGFbeta-LRRC15 Axis Predict Disease Progression and Immunotherapy Resistance Across Cancer Types

Transforming growth factor-beta (TGF{beta}) has dual roles in cancer, initially suppressing tumors but later promoting metastasis and immune evasion. Efforts to inhibit TGF{beta} have been largely unsuccessful due to significant toxicity and indiscriminate immunosuppression. Leucine-rich repeat-containing protein 15 (LRRC15) is a TGF{beta}-regulated antigen expressed by mesenchymal-derived cancer cells and cancer-associated fibroblasts (CAFs). In preclinical studies, ablation of TGF{beta}-driven LRRC15+ CAFs increased tumor infiltration of CD8+ T cells. However, the underlying pathobiological mechanisms prompting TGF{beta}s upregulation of LRRC15 expression are unclear. Using an integrated approach combining functional compound screening with single-cell RNA sequencing, we reveal key genomic features regulating TGF{beta}s ability to increase LRRC15 expression on cancer cells. Construction of gene regulatory networks converged our analyses on four key genes--MMP2, SPARC, TGF{beta}R2, and WNT5B--central to TGF{beta}-induced LRRC15 pathobiology. Validation of these genes in cell models and their use in predicting immunotherapy responses highlight their potential in refining immunotherapy strategies and personalizing co-treatment options.

pharmacology and toxicology↗

bioRxiv2024

Shift in cellular crosstalk reveals the immunosuppressive environment in cholangiocarcinoma patients.

Background and aimsCholangiocarcinoma (CCA) is a deadly disease, and this cancer entity is characterized by an abundant stroma. The tumor microenvironment (TME) plays an important role in aggressive behavior and poor response to therapeutics; however, underlying pathways are unknown. MethodsTo fill this gap, we used multiplexed immunohistochemistry, high-dimensional phenotyping, and transcriptomics to analyze human CCA samples and identify cell cluster crosstalk in patients with a poor prognosis. ResultsOur findings confirmed the presence of Tregs and the lack of effector memory cells in the tumor. New findings are the spatiality of the effector memory cells being more present in the peripheral tissue, for some reason these immune cells fail to reach the tumor niche. We revealed cancer crosstalk with macrophages and stromal cells and identified responsible genes in the poor prognosis group. Amongst the responsible ligand pairs are GAS6-AXL belonging to the TAM family. We then identified VCAN-TLR2 to be present and influencing the ECM in a way to supports immune exhaustion. Last, EGFR-TGF-{beta} is expressed in macrophages and this finding is important in Tregs induction and blocking cytotoxic T cell function. ConclusionsThe multiple mechanisms leading to the exclusion of relevant immune cells needed for an anti-cancer response and mechanisms leading to active immune suppression are part of complex cell-cell crosstalk. This study provides a deeper insight into the immune exhausted phenotype in CCA.

cancer biology↗

bioRxiv2024

Development of a LRRC15-Targeted Radio-Immunotheranostic Approach to Deplete Pro-tumorigenic Mechanisms and Immunotherapy Resistance

Leucine-rich repeat containing 15 (LRRC15) has emerged as an attractive biomarker and target for cancer therapy. We have developed a humanized monoclonal antibody (mAb), DUNP19, that specifically binds to a phylogenetically conserved LRRC15 epitope and is internalized by target-expressing cancer and stromal cells. In xenograft mouse models, Lutetium-177 labeled DUNP19 ([177Lu]-DUNP19) enables non-invasive imaging and precise radiotherapy to LRRC15-expressing cancer cells and murine cancer-associated fibroblasts (CAFs), halting tumor progression and prolonging survival with minimal toxicity. Transcriptomic analyses of [177Lu]-DUNP19-treated tumors reveal a loss of pro-tumorigenic mechanisms, including a transforming growth factor beta (TGF{beta})-driven and LRRC15+ signature associated with immunotherapy resistance. Together, these results demonstrate that radio-theranostic targeting of LRRC15 with DUNP19 is a compelling precision medicine platform for image-guided diagnosis, eradication, and reprogramming of LRRC15+ tumor tissue that drives immuno-resistance and aggressive disease. SIGNIFICANCEWe introduce a pioneering LRRC15-guided radio-theranostic approach integrating clinical imaging and radioimmunotherapy. Our strategy utilizes a mAb, DUNP19, to target LRRC15-expressing cancer cells and fibroblasts, demonstrating significant tumor reduction, prolonged survival, and reversal of TGF{beta}-driven treatment resistance. This approach offers a promising strategy for improving outcomes in aggressive cancers.

pharmacology and toxicology↗

bioRxiv2022

Peyers Patch B cells undergo cell death via neutrophil-released toxic DNA following sterile tissue injury

Lymphocyte contraction (LC) in central immune organs is a concomitant of sterile tissue injury, for example after stroke. Intestinal Peyers patches (PP) harbor large numbers of B cells, but how sterile tissue injury leads to LC in PP has not been explored. We observed rapid and macroscopically evident shrinkage of PP after stroke and myocardial infarction. Light-sheet fluorescence microscopy and flow cytometry revealed a strong reduction in the number of PP-resident B cells. Mechanistically, tissue injury triggered the activation of neutrophils that released B cell-toxic neutrophil extracellular traps (NETs) decorated with citrullinated histone-H3. Antibody-mediated or genetically induced neutrophil-loss, NETs-degradation or blockade of their generation completely reversed B cell loss and preserved the tissue architecture of PP. We also found NET-like elements in human post-stroke plasma. Hence, we propose that targeting NET-generation or -function counteracts post-injury B cell contraction in PP and thereby maintains immune homeostasis at mucosal barriers. In briefHigh numbers of B cells reside in the intestinal Peyers patches. Tuz et al. revealed that in response to sterile tissue injury, activated neutrophils release histone-decorated DNA into the circulation which induces B cell death. The loss of B cells results in the shrinkage of Peyers patches and reduced amounts of secretory IgA. HighlightsO_LIStroke and myocardial infarction induce the melting of Peyers patch C_LIO_LILight-sheet microscopy and cytometry revealed B cell loss in Peyers patch C_LIO_LIPost-injury activated neutrophils release NETs and trigger B cell death C_LIO_LIInhibition of NETs rescues B cell loss and degeneration of Peyers patch C_LI

immunology↗

bioRxiv2019

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

PurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.\n\nMethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm (XGBoost) was trained on 70% of the patients (N=28) and tested on 30% (N=17) to predict KRT81+ vs. KRT81-tumor subtypes. The average sensitivity, specificity and ROC-AUC value were calculated. Chemotherapy response was assessed stratified by subtype. Radiomic feature importance was ranked.\n\nResultsThe mean{+/-}STDEV sensitivity, specificity and ROC-AUC were 0.90{+/-}0.07, 0.92{+/-}0.11, and 0.93{+/-}0.07, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81-patients (7.0 vs. 22.6 months, HR 1.44, log-rank-test P=<0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 0.85, P=0.037) compared to KRT81-patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 0.88, P=0.027).\n\nConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for overall patient survival and response to chemotherapy.

bioinformatics↗

bioRxiv2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

PurposeTo develop a supervised machine learning algorithm capable of predicting above vs. below-median overall survival from medical imaging-derived radiomic features in a cohort of patients with pancreatic ductal adenocarcinoma (PDAC).\n\nMaterials and Methods102 patients with histopathologically proven PDAC were retrospectively assessed as the training cohort and 30 prospectively enrolled patients served as the external validation cohort. Tumors were segmented in pre-operative diffusion weighted-(DW)-MRI derived ADC maps and radiomic features were extracted. A Random Forest machine learning algorithm was fit to the training cohort and tested in the external validation cohort. The histopathological subtype of the tumor samples was assessed by immunohistochemistry in 21/30 patients of the external validation cohort. Individual radiomic feature importance was evaluated.\n\nResultsThe machine learning algorithm achieved a sensitivity of 87% and a specificity of 80% (ROC-AUC 90%) for the prediction of above- vs. below-median survival on the unseen data of the external validation cohort. Heterogeneity-related features were highly ranked by the model. Of the 21 patients for whom the histopathological subtype was determined, 8/9 patients predicted by the model to experience below-median overall survival exhibited the quasi-mesenchymal subtype, while 11/12 patients predicted to experience above-median survival exhibited a non-quasi-mesenchymal subtype (Fishers exact test P<0.001).\n\nConclusionThe application of machine-learning to the radiomic analysis of DW-MRI-derived ADC maps allowed the prediction of overall survival with high diagnostic accuracy in a prospectively collected cohort. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging workflows in pre-operative subtyping and risk assessment in PDAC.

bioinformatics↗