Consolandi, C. | bioRxiv Science

bioRxiv2025

Red Blood Cell-derived Extracellular Vesicles enable Cisplatin and Cetuximab Synergistic Therapy against Triple-Negative Breast Cancer

BackgroundTriple-negative breast cancer is an aggressive breast cancer subtype characterized by the absence of human epidermal growth factor receptor 2, estrogen and progesterone receptors, limiting targeted therapy options. Cisplatin, a chemotherapeutic agent, induces DNA damage and exhibits some efficacy against triple-negative breast cancer, but its effectiveness is often reduced by chemoresistance and systemic toxicity. A very promising strategy to augment cisplatin treatment can be based on combining it with the biologic Cetuximab, an epidermal growth factor receptor inhibitor, which boosts cisplatin efficacy by inducing ferroptosis. ResultsTo optimize this strategy in a biocompatible and precise manner, we developed a nanoplatform based on red blood cell-derived extracellular vesicles for the combined delivery of Cetuximab and cisplatin, enabling immune evasion, and the possibility of autologous personalization and GMP-compliant production. Owing to their DNA-free lumen and lack of EGFR, RBC-EVs preserve cisplatin activity and prevent interference with cetuximab. This formulation enhances cisplatins cytotoxicity by up to 50%, as shown in vitro and in patient-derived organoids. It effectively reduces chemoresistance by downregulating hypoxia-related genes and promoting ferroptosis, additionally, it improves cisplatins cytotoxic effects while reducing hemotoxicity compared to the administration of free cisplatin. ConclusionsThese findings highlight the potential of red blood cell-derived extracellular vesicles as a biocompatible delivery system enabling combined therapy and offering a promising strategy to overcome current limitations in TNBC treatment.

cell biology↗

bioRxiv2021

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. First, metabolic regulation controls metabolic fluxes (i.e., the rate of individual metabolic reactions) through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. A second regulatory layer sets the maximal theoretical level for each enzyme-controlled reaction by controlling the expression level of the catalyzing enzyme. In isolation, high-throughput data, such as metabolomics and transcriptomics data do not allow for accurate characterization of the hierarchical regulation of metabolism outlined above. Hence, they must be integrated in order to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we proposes INTEGRATE, a computational pipeline that integrates metabolomics (intracellular and optionally extracellular) and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomic data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients. Author summaryThe study of metabolism and its regulation finds increasing application in various fields, including biotransformations, wellness, and health. Metabolism can be studied using post-genomic technologies, notably transcriptomics and metabolomics, that provide snapshots of transcripts and metabolites in specific physio-pathological conditions. In the health field, the transcriptome and, more recently, the metabolome have been broadly profiled at the pre-clinical and clinical levels. The informative power of single omic technologies is inadequate since metabolism regulation involves a complex interplay of regulatory steps. While gene expression regulates metabolism by setting the upper level of metabolic enzymes, the interaction of metabolites with metabolic enzymes directly auto-regulates metabolism. Therefore there is a need for methods that integrate multiple data sources. We present INTEGRATE, a computational pipeline that captures dynamic features from the static snapshots provided by transcriptomic and metabolomic data. Through integration in a steady-state metabolic model, the pipeline predicts which reactions are controlled purely by metabolic control rather than by gene expression or a combination of the two. This knowledge is crucial in a clinical setting to develop personalized therapies in patients of multifactorial diseases, such as cancer. Besides cancer, INTEGRATE can be applied to different fields in which metabolism plays a driving role.

systems biology↗