HKU's World-First 'Liver-in-Cube' Revolutionises Liver Cancer Treatment with Precision

Liver cancer is the sixth most common cancer worldwide and the third leading cause of cancer-related deaths. A recent report published in 'The Lancet' predicts that the number of new liver cancer cases will increase from 870,000 in 2022 to 1.52 million by 2050, nearly doubling the figure. If the current trend continues, this increase could result in 1.37 million deaths.

Traditional cancer therapies often overlook patient-specific and dynamic tumor immune environments, causing ineffective treatments, toxic side effects, and critical delays. The University of Hong Kong (HKU) has developed 'Liver-in-Cube', the world's first 3D-bioprinted platform that precisely reconstructs the liver cancer microenvironment in the lab using a patient's own cells and extracellular matrix. This platform offers a fast and reliable service to help patients identify the most effective personalised treatments and pave the way for a standardised, high-throughput solution for drug assessment and development.

The invention can accurately simulate tumour characteristics of individual patients, such as the cell numbers, tissue stiffness and immune microenvironments, enabling doctors to swiftly assess the efficacy and side effects of various drugs and emerging therapies, leading to more precise treatment decision. This technology has received the ‘Gold Medal’ at the International Exhibition of Inventions of Geneva, ‘Special Grand Prize’ at Prize of the China Invention Association, and ‘Best-Performing Start-Up Award’at Asia Summit on Global Health (ASGC) Conference.

The technology has received the ‘Gold Medal’ at the International Exhibition of Inventions of Geneva, ‘Special Grand Prize’ at Prize of the China Invention Association, and ‘Best-Performing Start-Up Award’at Asia Summit on Global Health (ASGC)Conference. Photo source: HKUMed

From ‘Trials to Tailored Treatment’: Seizing the Golden Treatment Window

Currently, there is a lack of personalised drug-screening platforms for liver cancer treatment in the market, which often makes patients undergo multiple treatment failures in the search for the most suitable drug. This not only imposes a heavy financial burden but may also delay the timing in killing the tumour cells effectively. The 'Liver-in-Cube' directly addresses this pain point by accelerating the evaluation of the efficacy and side effects of various drugs for patients, allowing doctors to identify the optimal treatment method for patients and reducing the risk of cancer recurrence due to treatment delays. The ‘Liver-in-Cube' has now started patient recruitment at local hospitals to expedite clinical trials and advance its transition into clinical application.

The 'Liver-in-Cube' developed by HKU, Photo source: HKUMed

Dual Value in Clinical and Research Excellence

Professor Man Kwan, Chair Professor in the Department of Surgery, School of Clinical Medicine, HKUMed, who led the research project, stated that ‘Liver-in-Cube' can speed up the identification of the most effective drugs for patients, significantly enhancing treatment efficiency and reducing side effects, with the potential to greatly improve patient survival rates. In pharmaceutical development, the technology serves as a substitute for animal models, accelerating preclinical efficacy and safety testing for new drugs with more accurate data to increase the success rate of further clinical trials. This invention can shorten the new drug development cycle with reducing costs. In the field of basic research, the model assists researchers in exploring immune regulation mechanisms and discovering new therapeutic targets and strategies. The application prospects of the ‘Liver-in-Cube' are extensive, and its technological framework can be expanded to other liver diseases and cancers, promoting the widespread adoption of precision medicine. Currently, the technology is undergoing clinical trials in local public and private hospitals. The team aims to leverage the scientific data to facilitate the commercialisation of the technology and make it available in both local and overseas markets, thus benefitting a wide range of patients.

The University of Hong Kong (HKU) has developed‘Liver-in-Cube’,the world’s frst 3D-bioprinted platform that precisely reconstructs the liver cancer microenvironment in the lab using a patient’s own cells and extracellular matrix. Photo source: HKUMed

Five core technologies of ‘Liver-in-Cube':

• 1. Novel Technology for Cell and Matrix Protein Separation: Simultaneously extracts hepatocytes, tumor cells, immune cells, and matrix proteins from the tissue of the liver cancer patient, accurately simulating individual tumor characteristics.

• 2. Advanced 3D Bioprinting: Creates a biomimetic model incorporating normal tissue, tumor tissue, and vascular structures, surpassing traditional 3D cell culture and organoid products in reproducing a patient's tumor architecture.

• 3. AI-assisted patient parameterisation and printing: Utilising AI models trained on clinical biobanks, we correlate pathological features, tissue stiffness, and tumor immune microenvironment subtypes to determine the optimal geometric structure and bio-ink composition for each patient.

• 4. Customised patient-specific tumor characterisation system: By precisely measuring patient-specific parameters such as tumor stiffness and immune profiles, we reconstruct a highly biomimetic, individualized tumor microenvironment for each patient.

• 5. Innovative Tumor Model with Microvascular System: Enables continuous drug testing and evaluation of various therapies, enhancing the assessment of treatment effects in a laboratory setting.

Led by Professor Man Kwan from the Department of Surgery, HKUMed, the research
team focuses in the study of postoperative recurrence and immune microenvironment
of liver cancer. Over the decades, the team has become internationally recognised as
the leading force in the fields of liver oncology, transplant oncology, and transplant
immunology.

A research team from the Department of Pharmacology and Pharmacy at the LKS Faculty of Medicine of the University of Hong Kong (HKUMed) has developed an innovative AI-based cardiovascular risk prediction tool, called CardiOmicScore. With a single blood test, the system can accurately forecast the future risk of six major cardiovascular diseases (CVDs): coronary artery disease, stroke, heart failure, atrial fibrillation, peripheral artery disease and venous thromboembolism. It can also provide early warning signals up to 15 years before clinical onset. The findings were published in Nature Communications [link to the publication].

HKUMed develops a cardiovascular risk prediction tool that can accurately predict the future risk of six major cardiovascular diseases with a single blood test. The system can provide early warning signals up to 15 years before clinical onset. The research is led by Professor Zhang Qingpeng (left).

AI-based multiomics integration reflects the body’s real-time health status

CVDs remain the leading cause of death worldwide, accounting approximately 19.8 million fatalities in 2022 alone. In routine health assessments, physicians typically evaluate cardiovascular risk based on age, blood pressure, smoking and other conventional clinical indicators. However, these measures often fail to capture subtle and early biological changes before the disease becomes clinically apparent, leading to many patients missing the optimal window for preventive intervention. Although polygenic risk scores have become popular in recent years, genetic predisposition is largely fixed at birth and does not change over time. Consequently, polygenic risk scores cannot reflect the immediate impact on health conditions resulting from lifestyle or environmental changes. This creates an urgent need for tools that can capture a person’s current biological state and provide accurate, early warnings for CVDs.

To address this problem, the HKUMed research team applied deep learning techniques to integrate multiomics data, including genomics, metabolomics and proteomics, to develop the CardiOmicScore tool. The study was based on large-scale population data from the UK Biobank, analysing 2,920 circulating proteins and 168 metabolites measured from blood samples. These molecular signals act as ‘real-time recorders’ of the body, sensitively reflecting subtle changes in the immune system, metabolism, and vascular health.

Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy at HKUMed, explained, ‘Genes determine where we start—they define our baseline health risk. However, proteins and metabolites reflect our current physical health. Our AI tool is designed to decode these complex molecular signals, enabling doctors and patients to identify risks much earlier, which can potentially change the trajectory of disease through timely lifestyle modifications and early prevention.’

Accurate prediction of six major cardiovascular diseases with 15-year advance warning in high-risk groups

The results showed that CardiOmicScore transforms complex multiomics measurements into personalised risk scores with substantially improved predictive performance compared with conventional polygenic risk scores. When combined with clinical information such as age and gender, the model significantly enhanced the risk prediction accuracy of six common CVDs and can even flag elevated risk up to 15 years before symptoms appear.

This study marks a shift in precision medicine from a static, gene-centric paradigm towards a more dynamic, multiomics-based approach. In the future, a small-volume blood sample may be sufficient to generate a comprehensive cardiovascular risk profile for multiple diseases.

Professor Zhang added, ‘We aim to leverage technology to identify and prevent diseases before they develop. By shifting health management from reactive treatment to proactive prediction and intervention, we aim to create a lasting impact for both public health and individual patient care.’

About the research team

The study was led by Professor Zhang Qingpeng, Associate Professor in the Department of Pharmacology and Pharmacy, HKUMed, and the HKU Musketeers Foundation Institute of Data Science (IDS). The first author is Luo Yan from the HKU IDS.