Researchers led by Xian-Yang Qin at the RIKEN Center for Integrative Medical Sciences (IMS) in Japan have developed a score that predicts liver cancer risk. Published in the scientific journal Proceedings of the National Academy of Sciencesthe study demonstrates that MYCN protein drives liver tumorigenesis, especially the type of tumors found in the deadliest subtype of liver cancer. The study characterizes the microenvironment of genes that enable MYCN overexpression 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 each 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 the hope of discovering a much-needed method that accurately predicts livers at risk before tumors develop, Qin and his team are studying a protein called MYCN.
THE MYCN The gene is recognized as a contributing factor in liver cancer developing from damaged livers, but exactly how has remained unclear. The researchers argued that if its overexpression directly leads to liver tumorigenesis, it would be an ideal candidate as a biomarker for further study. To test their theory, the team first used a hydrodynamic transposon system based on tail vein injection to introduce MYCN (the transposon) in the mouse liver genome. Now they had an overexpressing mouse liver MYCN.
The team found that when they used the system to overexpress MYCN with always-on AKT72% of 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 they overexpressed one or the other of these genes alone.
Understanding how early microenvironmental cues trigger liver tumorigenesis is critical to developing ways to combat it. To characterize the microenvironment, researchers turned to spatial transcriptomics. This technique shows which genes are activated in a tissue and exactly where in the tissue this activity occurs. In a mouse model of liver cancer associated with metabolic dysfunction, the researchers used this method to examine gene expression over time and by location as liver tumors developed, focusing on where MYCN increased. They discovered a cluster of 167 genes that were differentially expressed in tumor-free liver sections that had elevated levels of MYCN. They named this cluster the “MYCN locus”.
Based on the mouse spatial transcriptome data, the researchers then developed a machine learning model that can take the characteristics of a given gene expression pattern and provide a score indicating whether or not it corresponds to a MYCN locus. The model can do this with 93% accuracy.
The MYCN locus score was then calculated for the human hepatocellular carcinoma datasets. Patients with higher MYCN scores showed a higher risk of tumor recurrence and poorer clinical outcomes. This relationship was stronger when the score was from non-tumor tissue than from tumor tissue. The score thus represents a proof-of-concept spatial biomarker that predicts prognosis based on tumorigenic microenvironments.
We have developed a clinically actionable strategy to identify high-risk patients by determining gene expression in non-cancerous liver tissue. By integrating spatial transcriptomics with machine learning, we have generated a specialized MYCN score that predicts risk of recurrence and detects precancerous microenvironments predisposing to de novo liver tumorigenesis.
In the future, we aim to further analyze the biological mechanisms captured by machine learning-derived spatial feature scores and determine how cancer-permissive environments are created and maintained.”
Xian-Yang Qin, RIKEN Center for Integrative Medical Sciences
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Journal Reference:
DOI: 10.1073/pnas.2521923123
