Exosome-derived Extrahepatic Metastasis Detection By Liquid Biopsy In Colorectal Cancer Liver Metastases

Colorectal cancer is the third most common malignancy worldwide, and prognosis largely depends on how effectively metastatic disease is managed. The liver is the most frequent and prognostically important site of metastasis, and patients responding well to chemotherapy may become candidates for curative hepatic resection. However, the presence of extrahepatic metastasis (EHM) critically influences treatment eligibility and survival. Although clinical scores such as the Fong and Beppu systems include EHM as a determinant, its detection by imaging remains limited, especially for small or occult lesions. Accurate identification of EHM is also essential when considering liver transplantation for unresectable colorectal liver metastases (CRLM), where EHM remains an exclusion criterion. The EXELION Study aims to develop a non-invasive diagnostic model using serum exosomal microRNAs (miRNAs) to detect both hepatic and extrahepatic metastases in patients with CRLM. By integrating circulating miRNA profiling with machine learning-based analysis, this study seeks to supplement imaging diagnostics, improve treatment stratification, and enhance clinical decision-making for metastatic colorectal cancer.

Despite improvements in diagnostic imaging-such as CT, MRI, and PET-CT-the sensitivity of EHM detection remains limited, particularly for small or occult lesions in the lung, peritoneum, or lymph nodes. As a result, patients may be inappropriately excluded from curative surgery or exposed to non-beneficial interventions. Thus, there is a pressing need for novel, non-invasive biomarkers capable of detecting EHM with higher accuracy than imaging alone. MicroRNAs (miRNAs), especially those encapsulated within exosomes, have emerged as stable and reproducible biomarkers reflecting tumor dynamics. Recent studies have shown that circulating miRNA signatures are associated with liver metastasis, therapeutic response, and recurrence risk in CRC. However, their utility for detecting extrahepatic metastasis has not yet been validated in clinical cohorts. In this research effort, the investigators will leverage small RNA sequencing and machine learning to develop a predictive model for the presence of hepatic and extrahepatic metastases in patients with CRLM. The research plan will consist of three phases: 1. A discovery phase, identifying candidate miRNAs associated with the presence of EHM using next-generation sequencing (NGS) or microarray-based profiling of serum exosomal miRNAs. 2. A model development phase, establishing a quantitative reverse transcription PCR (RT-qPCR) assay and training a predictive algorithm. 3. A validation phase, independently testing the predictive accuracy of the model in an external cohort. This diagnostic framework is provisionally termed "EXELION" (Exosome-derived Extrahepatic Metastasis Detection by LIquid Biopsy in Colorectal Cancer Liver Metastases). At the end of this study, the EXELION assay is expected to serve as a non-invasive tool to assist clinical decision-making by accurately predicting the presence of extrahepatic metastasis in patients with CRLM.