Tumor Spatial Biology

Research Description

Our research focuses on the development and application of spatially resolved (single-cell) technologies to understand how tumors evolve, evade immune control, and respond to therapy.
Cancer is not only a genetic disease but an ecological system of malignant, stromal, and immune cells interacting in space. By mapping these interactions directly in human samples, we aim to define actionable vulnerabilities and biomarkers that can guide clinical decisions. Therefore, use highly multiplexed imaging and other spatial omics readouts, with a focus on antibody-based spatial proteomics technologies. These technologies allow us to measure dozens to hundreds of proteins, cell states, and signaling pathways in situ while preserving the native (three-dimensional) architecture of the tissue. We combine this with digital pathology, AI-assisted image analysis, and machine learning models that learn from millions of archived clinical samples linked to outcome data. In parallel, we integrate various other spatial and non-spatial readouts with relevant clinical metadata to enable us to move beyond descriptive atlases toward mechanistic insight and clinically relevant endpoints.
Our programme is inherently collaborative. We are establishing a strong bridge between IFOM in Milan and the Translational Spatial Profiling Center in Heidelberg, enabling bidirectional exchange of technology, data, and expertise. We will also engage with international groups for comparative analyses across cohorts, tumor types, and therapeutic settings.
The ultimate goal is translation. We work on clinically relevant material (routinely collected biopsies and resections) and aim to deliver spatial biomarkers that can stratify patients, predict response to targeted and immune therapies, and reveal druggable microenvironmental niches. By turning spatial context into clinically usable knowledge, we seek to accelerate precision oncology.