Open Access

REVIEW

Preclinical Models of Colorectal Cancer Liver Metastasis: Therapeutic Evaluation and Translational Implications

Ye Ri Han^1,*, Sang Bong Lee^2,3,4,*

1 Department of Chemistry, Duksung Women’s University, Seoul, Republic of Korea
2 Department of Life Science, Dongguk University, Dongguk-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, Republic of Korea
3 Department of Biomedical Sciences, Chonnam National University Medical School 264, Hwasun-eup, Hwasun-gun, Jeollanam-do, Republic of Korea
4 SimVista Inc., A-13, 194-25 Osongsaengmueong1-ro, Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, Republic of Korea

* Corresponding Authors: Ye Ri Han. Email: ; Sang Bong Lee. Email:

(This article belongs to the Special Issue: Advances in Liver Cancer: Novel Therapeutics and Biomarkers for HCC and CCA)

Oncology Research 2026, 34(7), 8 https://doi.org/10.32604/or.2026.079556

Received 23 January 2026; Accepted 30 March 2026; Issue published 16 June 2026

Abstract

Colorectal cancer liver metastasis (CRLM) remains a leading cause of cancer-related mortality, with clinical outcomes limited by biological heterogeneity and inconsistent therapeutic responses. Despite advances in systemic chemotherapy, targeted agents, immunotherapy, and liver-directed interventions, the translation of preclinical efficacy into clinical benefit remains suboptimal, highlighting the need for predictive experimental models. However, therapeutic efficacy in CRLM is highly model-dependent, and discrepancies between preclinical findings and clinical outcomes often arise from differences in biological fidelity across experimental platforms. This review critically examines preclinical platforms used to study CRLM, with emphasis on orthotopic and metastatic models that recapitulate hepatic colonization, tumor–microenvironment interactions, and immune regulation. We evaluate methodological innovations that enhance anatomical fidelity and reproducibility, including tissue adhesive–based implantation and biomaterial-assisted strategies. Importantly, we analyze how different models influence therapeutic assessment across systemic, immune-based, metabolic, and liver-directed treatments, and discuss their ability to predict clinical responses. By integrating insights from experimental studies with key clinical evidence, we delineate the strengths and limitations of current platforms and propose principles for rational model selection to improve translational success in CRLM research.

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Keywords

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