Research Projects

Tumour progression and therapeutic resistance are profoundly shaped by complex, dynamic interactions within the tumour microenvironment (TME). A critical yet underexplored aspect involves the reciprocal phenotypic plasticity between tumour-associated macrophages (TAMs) and cancer cells undergoing Epithelial-Mesenchymal Transition (EMT). While macrophage polarization was traditionally viewed as binary (M1/M2), recent studies reveal a continuous spectrum of states, influenced by cytokine signalling, epigenetic memory, and environmental cues. Furthermore, macrophage hysteresis, where prior exposures shape future responses, highlights this complexity. Similarly, cancer cells exhibit a spectrum of hybrid epithelial/mesenchymal states during EMT. The dynamic interdependencies and coupled regulation of these continuous plasticity remain poorly understood, hindering the development of effective, targeted cancer therapies. This project aims to address this by unravelling the dynamic mechanisms underlying the coupled, spectrum-like plasticity of macrophages and EMT in cancer using a comprehensive systems biology framework. This is achieved by developing a bidirectional validated dynamic computational model of interactions between macrophages and cancer cells relevant to their coupled plasticity and EMT (including intermediate/hybrid states) and identifying intracellular network motifs. We will further derive data-driven insights into macrophage hysteresis and plasticity by processing scRNA-seq data to generate pseudotime trajectories and hysteresis-associated gene signatures, crucial for model empirical validation. We will then predict therapeutic vulnerabilities and optimal combinatorial interventions via insilico perturbation analyses on this coupled model, identifying specific molecular targets and synergistic strategies to shift dynamics towards anti-tumorigenic states. A personalized modelling framework using a computational pipeline to integrate patient-specific scRNA-seq data for tailored model calibration will be developed, generating patient-specific coupled models and stratifying patients based on predicted responses to guide therapy. This research will provide a powerful tool for insilico experimentation on TME responses, offering a fundamentally new perspective on tumour evolution and therapeutic resistance by illuminating the crucial interdependency where macrophage plasticity dictates cancer cell EMT progression and vice versa. By enabling patient-specific predictions, the framework will guide clinical trial design, identify patient stratification biomarkers, and ultimately inform personalized cancer management, significantly advancing cancer systems biology.