Researchers led by Xian-Yang Qin at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan have developed a score that predicts the risk of liver cancer. Published in the scientific journal Proceedings of the National Academy of Sciences, the study establishes that the protein MYCN drives liver tumorigenesis, specifically of the type of tumors found in the deadliest subtype of liver cancer. The study characterizes the microenvironment of genes that permit overexpression of MYCN, and describes a machine-learning algorithm that uses this data to predict how likely a tumor-free liver is to develop tumors.
Liver cancer, or hepatocellular carcinoma, is the cause of more than 800,000 deaths worldwide every year. The mortality rate is very high because the cancer often remains undetected until the late stages and because the recurrence rate is between 70% and 80%. In hopes of discovering a much-needed method that accurately predicts at-risk livers before tumors develop, Qin and his team have been studying a protein called MYCN.
The MYCN gene is recognized as a contributor to liver cancer that develops from damaged livers, but exactly how has remained unclear. The researchers reasoned that if its overexpression directly leads to liver tumorigenesis, it would be an ideal candidate as a biomarker and for further study. To test their theory, the team first used a hydrodynamic tail vein injection-based transposon system to insert MYCN (the transposon) into the mouse liver genome. Now they had a mouse liver that overexpressed MYCN.
The team found that when they used the system to overexpress MYCN with always-active AKT, 72% of the mice developed liver tumors within 50 days. A variety of tests showed that these tumors had all the characteristics of human hepatocellular carcinoma. Tumors did not develop when overexpressing one or the other of these genes by themselves.
Understanding how early microenvironmental cues trigger liver tumorigenesis is critical for developing ways to counter it. To characterize the microenvironment, the researchers turned to spatial transcriptomics. This technique shows which genes are turned on in a tissue and exactly where in the tissue that activity is happening. In a mouse model of metabolic dysfunction-associated liver cancer, the researchers used this method to look at gene expression over time and by location as liver tumors developed, focusing on where MYCN was increasing. They discovered a cluster of 167 genes that were differentially expressed in tumor-free sections of liver that had increased levels of MYCN. They named this cluster the "MYCN niche."
Based on the mouse spatial transcriptomics data, the researchers next developed a machine-learning model that can take the characteristics of a given gene-expression pattern and output a score that indicates whether or not it corresponds to a MYCN niche. The model can do this with 93% accuracy.
The MYCN niche score was then calculated for human hepatocellular carcinoma datasets. Patients with higher MYCN niche scores showed a greater risk of tumor recurrence and poorer clinical outcomes. This relationship was stronger when the score was derived from non-tumor tissue than from tumor tissue. The score thus represents a proof-of-concept spatial biomarker that predicts prognosis based on microenvironments that promote tumor formation.
"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."
"In the future, we aim to further dissect the biological mechanisms captured by machine learning-derived spatial feature scores and determine how cancer-permissive environments are established and maintained."
