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Predictive models could identify oncogenic genetic events

Researchers have shown that some cancer-causing genetic events are underpinned by regulatory gene activity. The study, published in Nature, highlights the use of predictive models to identify genetic events that drive oncogenesis. The models integrate information on regulatory gene activity and chromatin structure. The predictive models could be used to determine cancer prognosis and tailor treatment approaches.

“Switches can potentially turn ‘on’ a particular gene”

Cancer is the second most common cause of death worldwide. Cancers arise when normal cells accumulate genetic mutations that drive uncontrolled growth. Structural variants (SV) in cancer genomes can include deletions, insertions, duplications, inversions and translocations. The impact of SVs on the 3D genome structure and gene expression varies. They range from having a major impact and causing phenotypic changes to having very little or no impact on gene expression. The role of SVs and changes to the 3D genome structure in gene regulation is not fully elucidated.

Researchers at the Salk Institute investigated the specific mechanisms underpinning oncogene activation. They found that the oncogenic activity (of a specific subsets of genes) can be predicted using a model that integrates information on enhancer activity and chromatin structure.

Jesse Dixon, senior author and Assistant Professor in the Gene Expression Laboratory at the Salk Institute said, “A gene is like a light and what regulates it are like the light switches. We are seeing that, because of structural variants in cancer genomes, there are a lot of switches that can potentially turn ‘on’ a particular gene.”

Experimental approach

The researchers analysed SVs in cancer patient samples and cell lines. Numerous loci that pointed to changes in the genomic structure were identified. However, downstream gene expression was still variable.  

The team then used CRISPR-Cas9 genome editing to insert SVs at certain locations in the genome. They found that the difference in gene expression levels could be explained by enhancer activity and the 3D genome structure of the partner region. Enhancers are regulatory elements in the genome. The activity of enhancer regions requires interaction with various partner proteins and chromatin remodelling (figure 1). This allows activation of the promoter and, subsequently, gene transcription. 

Figure 1: Enhancer activity. Enhancer regulatory elements can be far away from promoter regions. Activators and transcription factors interact with enhancers. This brings the region closer to the promoter as the DNA folds back on itself. This leads to activation of the promoter region and gene transcription is initiated. Source: Addgene.

The researchers showed that models of enhancer activity and chromatin modelling could be used to predict the activation of oncogenes like MYC.

Predictive models are the future

The study highlights the potential use of predictive models to understand gene expression in cancer. Analysis of SVs in cancer patient samples could help determine whether the particular genetic event is driving oncogenesis.   

Assistant Professor Dixon said, “If we can better understand why a person has cancer, and what particular genetic mutations are driving it, we can better assess risk and pursue new treatments.”

More on these topics

Genetics / Oncogenes / Prediction / Regulation