MYCN Niche Score Predicts Liver Cancer Risk & Recurrence | News Medical
- Researchers have developed a new scoring system to predict the risk of liver cancer recurrence, offering a potential breakthrough in early detection and improved patient outcomes.
- Hepatocellular carcinoma (HCC), the most common type of liver cancer, is responsible for over 800,000 deaths globally each year.
- The research team focused on the MYCN gene, already known to contribute to liver cancer development in damaged livers.
New Score Predicts Risk of Liver Cancer Recurrence
Researchers have developed a new scoring system to predict the risk of liver cancer recurrence, offering a potential breakthrough in 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 a key protein, MYCN, as a driver of liver tumorigenesis, particularly in the most aggressive forms of the disease.
Hepatocellular carcinoma (HCC), the most common type of liver cancer, is responsible for over 800,000 deaths globally each year. A significant challenge in managing HCC is its high recurrence rate, affecting 70% to 80% of patients even after successful initial treatment. This new research focuses on identifying at-risk individuals before tumors develop, a critical step towards more effective intervention.
MYCN and the Tumor Microenvironment
The research team focused on the MYCN gene, already known to contribute to liver cancer development in damaged livers. To understand its role, they used a mouse model where the MYCN gene was intentionally overexpressed in liver cells. They found that overexpression of MYCN, combined with always-active AKT, led to the development of liver tumors in 72% of mice within 50 days. These tumors closely resembled human hepatocellular carcinoma.
Recognizing the importance of the surrounding environment in cancer development, the researchers employed spatial transcriptomics – a technique that maps gene activity within a tissue, pinpointing exactly where specific genes are turned on. In a mouse model of metabolic dysfunction-associated liver cancer, they tracked gene expression over time as tumors formed, focusing on areas with increased MYCN levels. This revealed a distinct cluster of 167 genes, termed the “MYCN niche,” that were differentially expressed in tumor-free liver sections showing early signs of MYCN activation.
A Machine-Learning Model for Risk Prediction
Building on this spatial transcriptomics data, the researchers developed a machine-learning model capable of assessing gene expression patterns and generating a “MYCN niche score.” This score indicates the likelihood of a tumor developing based on the characteristics of the gene expression. The model demonstrated 93% accuracy in identifying MYCN niches.
To validate the model’s clinical relevance, the MYCN niche score was calculated using datasets from human hepatocellular carcinoma patients. The results showed a strong correlation between higher scores and increased risk of tumor recurrence, as well as poorer clinical outcomes. Importantly, the score was most predictive when derived from non-tumor tissue, suggesting it can identify precancerous microenvironments.
“We have developed a clinically actionable strategy to identify high-risk patients by profiling gene expression in non-tumor liver tissue. 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,”
Xian-Yang Qin, RIKEN Center for Integrative Medical Sciences
Implications for Early Detection and Treatment
This research introduces a novel approach to liver cancer risk assessment, moving beyond traditional methods like the TNM and BCLC systems, which have limitations in predicting recurrence. The TIMES score, developed in a related study and mentioned in web search results, also demonstrates improved accuracy in predicting HCC recurrence, highlighting the growing importance of spatial analysis in cancer diagnostics.
The MYCN niche score represents a proof-of-concept spatial biomarker that can predict prognosis based on microenvironments that promote tumor formation. The researchers emphasize the potential for this score to inform treatment decisions for early-stage HCC, allowing for more targeted interventions in high-risk patients.
Looking ahead, the team plans to further investigate the biological mechanisms underlying the machine-learning-derived spatial feature scores. Understanding how cancer-permissive environments are established and maintained could lead to the development of new therapies aimed at preventing tumor development in the first place.
This research, published in in FASEB J., offers a promising step forward in the fight against liver cancer, potentially improving outcomes for patients at risk of this devastating disease.