Cancers are not just masses of abnormal cells; they are complex ecosystems composed of various cell types, each playing significant roles in disease progression and patient outcomes. However, profiling these diverse cell types at the single-cell level across different cancer types and correlating them with clinical data remains an unachieved task. In our study, we aim to analyze over 22,000 tissue samples from 18 different cancer types using an advanced tissue imaging technique known as multiplex immunofluorescence. This technique offers a detailed spatial map of the cells and their interactions within tumors and their surroundings. Our research at the University of Helsinki will compare cell subsets across various cancers to determine how these profiles correlate with clinical features such as patient survival and treatment response. This project unites expertise in tissue imaging, AI-based data science, and translational pathology to deepen our understanding of cancer ecosystems.

2024

2028