A Deep Learning Approach for Tracking Colorectal Cancer-Derived Extracellular Vesicles in Colon and Lung Models.

According to the International Agency for Research on Cancer and the World Health Organization, colorectal cancer (CRC) is the third most common cancer in the world and the main cause of gastrointestinal cancer-related deaths. Despite advances in therapeutic regimens, the incidence of metastatic CRC is increasing due to the development of resistance to conventional treatments. Metastases, particularly in the liver and lungs, represent the leading cause of death and poor prognosis in CRC patients. Recent evidence demonstrates that extracellular vesicles (EVs) are involved in communication between cancer cells and the surrounding environment. Understanding the potential mechanisms underlying EV-driven metastasis and tumor progression could facilitate the development of innovative strategies for early diagnosis and effective treatment of CRC metastasis. In this work, we developed a deep learning-based approach to track CRC-derived EVs in colon and lung models, enabling precise quantification of their uptake and trafficking . Moreover, we observed their tropism toward heterologous healthy cells in biologically relevant 3D models of colon and lung tissues, indicating the inherent role of CRC-EVs in metastatic niche formation and tumor initiation, raising their potential as innovative diagnostic and prognostic biomarkers as well as therapeutic targets in CRC.