We focus on translational research into the molecular mechanisms of cancer progression, tumour heterogeneity, disease recurrence and treatment response, with particular emphasis on functional genomics, transcriptomics, systems biology and experimental cancer models. Our research aims to identify biologically and clinically relevant mechanisms that may support the development of novel biomarkers, molecular risk factors and therapeutic vulnerabilities for precision oncology.
One of our major research areas is the characterisation of tumour heterogeneity and clonal evolution at single-cell resolution. In prostate cancer, we investigate molecular profiles of tumour cells associated with clinical course, recurrence risk and disease progression using single-cell and single-nucleus transcriptomic and genomic approaches. We have also developed a dedicated nuclei-isolation workflow for radical prostatectomy specimens enabling high-resolution single-nucleus RNA sequencing of clinical material.
A key area of our expertise is transcriptomics and integrative molecular data analysis. We apply bulk RNA-seq, sn/scRNA-seq, differential expression analysis, GSEA, WGCNA, pathway activity analysis, co-expression networks, dimensionality reduction, clustering and integration of molecular, clinical and phenotypic data. These approaches identify biologically distinct patient subgroups and develop prognostic and predictive biomarkers. Representative examples include transcriptomic profiles of ESR1 and MMP3 associated with biochemical recurrence in prostate cancer and WNT-related predictive signatures in endometrial cancer.
Another major strength is systems biology and functional genomics of cancer signalling networks, particularly Notch, WWOX, WNT, Hedgehog, ErbB and TGF-β signalling, as well as biological processes related to EMT, cellular plasticity, invasion and tumour progression. Our long-standing research experience includes studies of Notch signalling in cancer progression and recurrence across several tumour types and functional characterisation of WWOX within broader regulatory and signalling networks.
Computational research is complemented by wet-lab expertise and translational experimental models. Our capabilities include established cancer cell lines, primary 2D cultures, 3D models and patient-derived organoids, RNA and nuclei isolation, gene-expression analysis, cell-based functional assays and core molecular biology techniques. We also have experience with CAGE-seq and transcriptional-regulation studies, enabling investigation of promoter activity and mechanisms controlling gene expression.
An important direction of development is integrating multi-omics with predictive modelling, machine learning, and AI/XAI approaches. Our analytical expertise includes multivariable modelling, survival analysis, feature selection, patient stratification and model validation. We have a particular interest in Explainable Artificial Intelligence, which can identify the molecular and clinical features driving predictions and help translate complex computational models into biologically interpretable and clinically meaningful signatures.
We develop our research in close collaboration with clinical units, creating a strong translational link between patient-derived material, molecular characterisation and real clinical outcomes. Particularly well-established collaborations cover prostate cancer, neuro-oncology and spinal tumours, as well as gynaecological malignancies. Our experience also includes methodological involvement in prospective and randomised clinical studies, including trials in glioblastoma and metastatic spinal disease.
Within international collaborations, we are particularly interested in partnerships that provide complementary expertise in CRISPR/CRISPRi screening, high-throughput drug screening, spatial transcriptomics and spatial multi-omics, epigenomics, proteomics, and advanced in vivo, orthotopic, and PDX models. Such collaborations would allow us to move beyond descriptive molecular profiling towards the functional identification of cancer dependencies, therapeutic vulnerabilities and clinically actionable biomarkers.