The complex interplay between tumor cells and clusters with endothelial tissues during metastasis, in particular with regard to the exosomes in mediating intercellular communication, is still not well understood. Here, we develop a tumoral-transendothelial migration model to replicate the microenvironment of circulating tumor cells infiltrating blood vessels during metastasis. We propose an exosome-integrated approach combining a tumoral-transendothelial migration chip (TEMOC) with machine learning (ML) to enable the simulation and prediction of exosome-mediated invasion into the endothelial layer at both the single-cell and cluster levels. Leveraging a microfluidic trap array and the inherent self-organizing properties of cells, we conducted high-throughput studies on 121 specific tumor microenvironments on a chip. We uncovered the impact of exosomes derived from highly metastatic breast cancer on individual breast cancer cells and clusters: exosomes disrupt the adhesive matrix between endothelial cells and enhance tumor cell invasion. Additionally, highly metastatic cell-derived exosomes were found to stimulate the epithelial-mesenchymal transition (EMT) process in low-metastatic breast cancer cells (MCF-7), thereby promoting metastasis. An ML algorithm, K-nearest neighbor (KNN), was subsequently utilized to evaluate the correlation between multiple biomarkers on tumor cells and tumor invasion capability. The optimized biomarker combination strategy achieved a prediction accuracy of 93.5%. These findings contribute to a deeper understanding of the mechanisms by which exosomes derived from highly metastatic breast cancer cells induce metastasis. Furthermore, the combined use of the TEMOC and ML approach offers a platform for exploring the mechanisms of exosome-mediated tumor-vascular invasion and accelerating anti-metastatic therapeutic discovery.
Keywords: cancer cells; endothelial cells; exosomes; metastasis; organ-on-a-chip.