Integrated Translational & Functional Genomics Pipeline

We offer potential partners access to patient-derived biological material through our clinical collaborators and downstream analyses within an integrated translational functional genomics framework that combines transcriptomics, single-cell genomics, systems biology, multi-omics integration, AI/XAI, wet-lab validation, and clinical research methodology.

Clinical and translational research: We provide access to clinically annotated material and expertise in prostate cancer, endometrial cancer and brain tumours/neuro-oncology, supported by collaboration with clinicians and access to real-world and public multi-omics data. Our prostate cancer work includes a nuclei-isolation workflow for radical prostatectomy specimens enabling snRNA-seq (PMID: 42297938). We also collaborate with neurosurgery/neuro-oncology teams and are developing endometrial cancer studies focused on transcript isoforms and variants linked to progression and treatment response.

Transcriptomics and single-cell genomics: We have expertise in bulk RNA-seq and sn/scRNA-seq, including sample preparation, nuclei isolation, quality control and downstream analysis. We identify cellular subpopulations, assess tumour heterogeneity, perform differential expression, trajectory and pseudotime analyses, and characterise molecular cell states. Our current work addresses clonal heterogeneity and evolution in prostate cancer using single-cell transcriptomic and genomic approaches.

Systems biology and functional interpretation: We analyse pathway and regulatory networks, with particular expertise in Notch, WNT, Hedgehog, ErbB and TGF-β signalling and EMT. Our toolbox includes GSEA, WGCNA, pathway activity analysis, co-expression networks, dimensionality reduction and nonlinear/fuzzy-logic approaches. Representative studies include Notch-related prostate cancer aggressiveness (PMID: 36613607), Notch pathway bioinformatics (PMID: 33673145), female reproductive tract cancers (PMID: 33384996) and a WNT effector signature in endometrial cancer (PMID: 33273537).

Applied functional genomics: WWOX is one of our key functional genomics models within broader signalling and metabolic networks. We combine in vitro and in silico approaches to study gene regulation, differentiation, phenotype plasticity and metabolic reprogramming. Representative studies include WWOX in neuronal differentiation (PMID: 31543760) and _WWOX_-associated TGFα-EGFR signalling (PMID: 35290621).

Multi-omics, AI/XAI and biomarker discovery: We integrate transcriptomic, genomic, clinical, phenotypic and experimental data using statistical modelling, machine learning, neural-network, nonlinear and network-based approaches. We develop prognostic, predictive, recurrence-associated and aggressiveness-related biomarkers and are particularly interested in Explainable AI to identify molecular and clinical variables driving model predictions. In prostate cancer, ESR1/MMP3 transcriptomic profiles stratified biochemical recurrence risk beyond clinical features (PMID: 37176106).

Predictive modelling and biostatistics: Our expertise includes survival analysis, multivariable modelling, patient stratification, feature selection, machine learning, statistical validation, sample-size calculation, study design and Statistical Analysis Plans. We also developed the Evaluate Cutpoints R Shiny approach for survival-based optimisation of continuous biomarkers (PMID: 31319941).

Wet-lab and functional validation: Our capabilities include established cancer cell lines, primary 2D cultures, patient-derived organoids, cell-based functional assays, RNA isolation, gene-expression analysis, nuclei isolation from clinical material, single-cell RNA-seq sample preparation, CAGE-seq and core molecular biology techniques. We also collaborate with external sequencing providers to generate high-quality sequencing libraries and data.

Clinical study methodology: We support investigator-initiated and multicentre studies through endpoint definition, power and sample-size calculations, randomisation, SAP development, data-quality oversight, GCP compliance and statistical analysis. Current work includes prospective and randomised studies in metastatic spinal disease, cancer imaging, small-cell lung cancer and glioblastoma.

Collaboration sought: We may complement partners handling genome-wide or targeted CRISPR/CRISPRi screening, high-throughput drug and combination screening, spatial transcriptomics/spatial multi-omics, advanced in vivo/orthotopic/PDX models, epigenomics, proteomics, targeted drug development and large longitudinal or Real-World Data cohorts.

Together, these capabilities enable a translational workflow that links patient-derived samples and molecular discovery to functional dependencies, therapeutic vulnerabilities, experimental validation, and clinically actionable, AI/XAI-interpretable biomarkers.