Spatial and Systems Pathology
Our research focuses on integrating spatial biology and computational pathology to decode tumor ecosystems and improve precision oncology.
Short Description
We combine advanced molecular profiling techniques with AI-driven image analysis to better understand how tumors evolve, interact with their microenvironment, and respond to therapy. By linking histopathology with high-dimensional molecular data, we aim to uncover clinically relevant patterns that are not visible through conventional approaches.
Our work spans spatial transcriptomics, digital pathology, and liquid biopsy technologies. A key focus is the integration of these modalities to build multi-scale models of cancer biology—from single cells to tissue architecture and systemic circulation. Ultimately, our goal is to translate these insights into clinically actionable biomarkers and predictive tools.
Selected Projects
- Spatial transcriptomics of tumor microenvironments
Mapping cellular interactions and niche structures in solid tumors. - AI-based histopathology analysis
Developing deep learning models to predict molecular phenotypes directly from H&E slides. - Circulating tumor cell (CTC) profiling
Characterizing tumor dissemination and therapy response using liquid biopsy approaches. - Multi-omics integration in cancer progression
Combining imaging, genomic, and transcriptomic data for holistic tumor profiling. - Translational biomarker development
Identifying clinically relevant markers for prognosis and treatment stratification.
