New Score Predicts Risk of Liver Cancer Recurrence
Researchers have developed a new scoring system to predict the risk of developing liver cancer, offering a potential tool for early detection and improved patient outcomes. The study, led by Xian-Yang Qin at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan, identifies the protein MYCN as a key driver of liver tumorigenesis, particularly in the most aggressive forms of the disease.
Hepatocellular carcinoma, the most common type of liver cancer, is responsible for over 800,000 deaths globally each year. A significant challenge in managing this cancer is its tendency to recur, with rates ranging from 70% to 80%. Early identification of individuals at high risk is therefore crucial for effective intervention and resource allocation.
Uncovering the Role of MYCN
The MYCN gene has long been recognized as a contributor to liver cancer development, especially in damaged livers. However, the precise mechanisms by which it promotes tumorigenesis remained unclear. Qin and his team hypothesized that increased expression of MYCN directly contributes to cancer formation, making it a promising biomarker for risk assessment.
To investigate this, researchers utilized a mouse model where the MYCN gene was intentionally overexpressed in liver cells. They found that combining MYCN overexpression with activation of the AKT pathway led to tumor development in 72% of the mice within 50 days. Importantly, overexpression of either gene alone did not induce tumor formation, highlighting the synergistic effect of these two factors.
Spatial Transcriptomics Reveals the ‘MYCN Niche’
To understand the microenvironmental factors influencing MYCN-driven tumorigenesis, the researchers employed spatial transcriptomics. This advanced technique maps gene activity within a tissue, revealing not only which genes are active but also precisely where they are expressed. By analyzing gene expression patterns in a mouse model of liver cancer, they identified a cluster of 167 genes that were specifically upregulated in areas with increased MYCN levels. This cluster was termed the “MYCN niche.”
This discovery suggests that a specific microenvironment, characterized by the expression of these 167 genes, promotes MYCN-driven tumor development. The researchers then developed a machine-learning model capable of identifying this MYCN niche based on gene expression patterns with 93% accuracy.
Predicting Risk in Human Patients
The MYCN niche score was then applied to datasets from human hepatocellular carcinoma patients. The results showed a strong correlation between higher scores and an increased risk of tumor recurrence, as well as poorer clinical outcomes. Notably, the score was more predictive when derived from non-tumor tissue compared to tumor tissue itself, suggesting it can identify precancerous microenvironments before tumors even form.
“We have developed a clinically actionable strategy to identify high-risk patients by profiling gene expression in non-tumor liver tissue,” explains Qin. “By integrating spatial transcriptomics with machine learning, we have established a MYCN niche score that predicts recurrence risk and detects precancerous microenvironments predisposed to de novo liver tumorigenesis.”
A New Tool for Early Detection and Intervention
This research introduces a potentially valuable tool for identifying individuals at high risk of developing liver cancer. The MYCN niche score, derived from gene expression analysis of non-tumor liver tissue, could help clinicians prioritize surveillance and consider early interventions for those most likely to benefit.
The researchers emphasize that further investigation is needed to fully understand the biological mechanisms underlying the MYCN niche and to determine how these cancer-promoting environments are established and maintained. However, this study represents a significant step forward in the fight against liver cancer, offering a new avenue for early detection and improved patient care.
Another study, published in , detailed the development and validation of a REcurrent Liver cAncer Prediction ScorE (RELAPSE) following liver transplantation. This score, developed using data from nearly 5,000 patients undergoing liver transplantation for HCC, identified alpha-fetoprotein, neutrophil-lymphocyte ratio and tumor diameter as key predictors of recurrence. This highlights the ongoing effort to refine risk stratification tools for HCC patients.
a recently released tool – the TIMES score – predicts the risk of hepatocellular carcinoma recurrence post-resection, outperforming traditional methods like the TNM and BCLC systems. The TIMES score is based on the spatial expression patterns of five biomarkers: SPON2, ZFP36L2, ZFP36, VIM, and HLA-DRB1. It achieved an accuracy of 82.2% and specificity of 85.7% in validating studies.
Reference: Qin XY, Xu Y, Mishra H, et al. Oncogenic function and transcriptional dynamics of MYCN in liver tumorigenesis. Proc Natl Acad Sci U S A. ;123(8):e2521923123. Doi: 10.1073/pnas.2521923123
