For over 150 years, histopathology—the microscopic study of tissue—has relied on morphological assessment of tumors and the surrounding tumor microenvironment to guide cancer diagnosis, grading, and staging. While many recurring visual patterns observed in standard stained tissue slides correlate strongly with patient prognosis and therapeutic response, this analysis remains largely descriptive and dependent on subjective expert interpretation. This limits the consistency of these findings and the insights they can offer into the underlying biology of the disease. Since these visual signatures are not integrated with molecular data, it is also difficult to trace them to specific genetic or signaling mechanisms.
This ASPIRE award seeks to redefine the spatial hallmarks of cancer by building a precise and scalable framework that integrates computationally defined (but human-interpretable) morphological features with single-cell and molecular data. The research team will focus on glioblastoma and melanoma—two aggressive and heterogeneous cancers with some overlapping structural patterns. By applying multivariate modeling to clinical and biological data from well-documented patient cohorts, they will identify spatial hallmarks associated with survival and treatment outcomes. Ultimately, the project aims to pair histopathology with modern molecular profiling and computational approaches to uncover mechanisms of tumor immunity, inform therapeutic development, and lay the groundwork for a new class of diagnostics rooted in both spatial and molecular precision.