Identification and Characterization of Metastasis-initiating cells

Abstract

Metastasis, the primary cause of cancer-related mortality, is a dynamic and complex process driven by a subset of cells known as metastasis-initiating cells (MICs). Accurate identification of MICs is therefore critical for metastasis diagnosis and therapeutic decision-making. However, current approaches rely either on mouse tracing experiments, which are difficult to translate to human systems, or on indirect strategies such as stemness, trajectory, pathway, and biomarker analyses that often yield inconsistent results. To address these limitations, we propose scMIC, a computational framework designed to explicitly and reliably identify MICs from single-cell data (available at https://github.com/swu13/scMIC). scMIC integrates an embedding-based representation, unbalanced optimal transport, and a top-k selection strategy to robustly capture metastasis-initiating potential. The framework was validated and applied across multiple cancer types, species, and multi-omics datasets. Our results demonstrate the reliability of scMIC for MIC identification, its potential clinical utility in metastasis prognosis, and its effectiveness in discovering metastasis-related gene programs and molecular biomarkers. Elucidating the mechanisms of metastasis initiation not only advances our understanding of metastatic progression but also enables the development of therapeutic strategies that target the more aggressive MIC population rather than non-MICs, thereby avoiding unintended increases in metastatic risk. Collectively, scMIC provides a powerful tool for cancer metastasis research and drug discovery.