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Updated: Dec 30, 2025
A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
Published on: February 2, 2024
Novel patient-derived preclinical models of liver cancer
Erin Bresnahan1, Pierluigi Ramadori2, Mathias Heikenwalder2
1Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, New York, USA; Liver Cancer Program, Division of Liver Diseases, Department of Medicine, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA; The Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
Abstract:
Preclinical models of cancer based on the use of human cancer cell lines and mouse models have enabled discoveries that have been successfully translated into patients. And yet the majority of clinical trials fail, emphasising the urgent need to improve preclinical research to better interrogate the potential efficacy of each therapy and the patient population most likely to benefit. This is particularly important for liver malignancies, which lack highly efficient treatments and account for hundreds of thousands of deaths around the globe. Given the intricate network of genetic and environmental factors that contribute to liver cancer development and progression, the identification of new druggable targets will mainly depend on establishing preclinical models that mirror the complexity of features observed in patients. The development of new 3D cell culture systems, originating from cells/tissues isolated from patients, might create new opportunities for the generation of more specific and personalised therapies. However, these systems are unable to recapitulate the tumour microenvironment and interactions with the immune system, both proven to be critical influences on therapeutic outcomes. Patient-derived xenografts, in particular with humanised mouse models, more faithfully mimic the physiology of human liver cancer but are costly and time-consuming, which can be prohibitive for personalising therapies in the setting of an aggressive malignancy. In this review, we discuss the latest advances in the development of more accurate preclinical models to better understand liver cancer biology and identify paradigm-changing therapies, stressing the importance of a bi-directional communicative flow between clinicians and researchers to establish reliable model systems and determine how best to apply them to expanding our current knowledge.
Insights
Improving preclinical cancer models is crucial for liver malignancies. New 3D cultures and patient-derived xenografts offer promise but have limitations, necessitating better research collaboration for personalized therapies.
Area of Science:
- Oncology
- Translational Research
- Biomedical Engineering
Background:
- Current preclinical cancer models, including cell lines and mouse models, have limitations in predicting clinical trial success.
- Liver malignancies lack effective treatments, highlighting the need for improved preclinical research to identify optimal therapies and patient populations.
- Existing models struggle to fully replicate the complex genetic and environmental factors, tumor microenvironment, and immune interactions crucial for liver cancer progression and treatment response.
Purpose of the Study:
- To review advancements in preclinical models for liver cancer.
- To discuss the potential and limitations of novel 3D cell culture systems and patient-derived xenografts (PDXs).
- To emphasize the need for better models that accurately reflect human liver cancer complexity for therapeutic development.
Main Methods:
- Review of current literature on preclinical cancer models.
- Discussion of 3D cell culture systems derived from patient cells/tissues.
- Analysis of patient-derived xenografts and humanized mouse models.
Main Results:
- 3D cell cultures offer personalized therapy potential but lack tumor microenvironment recapitulation.
- Patient-derived xenografts, especially in humanized models, better mimic human liver cancer but are resource-intensive.
- No single model perfectly replicates liver cancer complexity, necessitating a multi-model approach.
Conclusions:
- Developing more accurate preclinical models is essential for understanding liver cancer biology and discovering new therapies.
- A collaborative approach between clinicians and researchers is vital for creating reliable models and optimizing their clinical application.
- Enhanced preclinical models are key to improving the efficacy of liver cancer treatments and personalizing patient care.
