Potentially cancer-causing mutations have been identified, hidden in 'junk DNA'

Thank you. Listen to this article using the player above. ✖

Non-coding DNA – the 98% of our genome that does not contain instructions for making proteins – could hold the key to a new approach to diagnosing and treating cancers, according to a new study from the Garvan Institute of Medical Research. The findings, published in the journal Nucleic Acid Researchreveal mutations in previously overlooked regions of the genome that may contribute to the formation and progression of at least 12 different cancers, including prostate, breast and colorectal.

This discovery could lead to early diagnosis and new effective treatments for many types of cancer.

“Non-coding DNA was once called ‘junk DNA’ because of its apparent lack of function,” says Dr Amanda Khoury, a research fellow at Garvan and co-corresponding author of the study. “Our research has uncovered mutations in these regions of DNA that could pave the way for an entirely new and universal approach to treating cancer.”

Investigation into disrupted DNA 'anchors' in cancer

The researchers focused on mutations affecting the binding sites of a protein called CTCF, which helps fold long strands of DNA into specific shapes. In their previous work, they found that these binding sites bring distant parts of DNA together, forming 3D structures that control which genes are turned on or off.

“We had previously identified a subset of CTCF binding sites that are ‘persistent’, meaning they act as anchors in the genome, present in different cell types,” says Dr. Khoury. “We hypothesized that if these anchors became defective, it could disrupt the normal 3D organization of the genome and contribute to cancer.”

To test this theory, the researchers developed a sophisticated new machine learning (AI) tool called CTCF-INSITE, which uses genomic and epigenomic features to predict which CTCF sites are likely to be persistent anchors in a total of 12 cancer types. They then evaluated more than 3,000 tumor samples from patients diagnosed with the 12 cancer types, available in the International Genome Consortium database, and found that persistent anchors were rich in mutations.

“Using our machine learning tool, we identified persistent CTCF binding sites in 12 different cancer types,” said Dr. Wenhan Chen, first author of the study. “Remarkably, we found that every cancer sample had at least one mutation in a persistent CTCF binding site.”

“This study confirmed that persistent CTCF binding sites are ‘mutational hotspots’ in cancers. We believe that these mutations confer a survival advantage to cancer cells, allowing them to proliferate and spread,” adds Dr. Khoury.

Towards a universal approach to cancer treatment

These findings could have broad implications for understanding and treating many types of cancer. “Most new cancer treatments need to be carefully targeted to specific mutations that are not always common across different tumour types, but because these CTCF anchors are mutated in many different cancer types, we open up the possibility of developing approaches that could be effective across multiple cancers,” says Professor Susan Clark, Director of the Cancer Epigenetics Laboratory at Garvan and lead author of the study.

The researchers are now planning further large-scale experiments using CRISPR gene editing to study how these anchor mutations disrupt the 3D genome and potentially promote cancer growth.

“Now that we have discovered what we believe to be essential anchors of the genome and shown that they are important for maintaining the homeostasis of genome architecture, it makes sense that these non-coding DNA mutations would disrupt that homeostasis in the cancer cell – a hypothesis we will test by knocking them out,” says Professor Clark. “By looking at the downstream impact, we hope to identify key genes or genetic pathways affected by the mutations, which could serve as markers for early cancer detection or targets for new treatments.”

“Discovering these clues hidden in a huge amount of data is a powerful example of how artificial intelligence is driving medical research,” she says. “This is a whole new frontier in the study of cancer, and we’re excited to explore it further.”

Reference: Chen W, Zeng YC, Achinger-Kawecka J, et al. Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites. Nucleic Acids Res. 2024:gkae530. doi: 10.1093/nar/gkae530

This article was republished from the following sources. Note: The content and length of the document may have been edited. For more information, please contact the cited source. Our press release publication policy is available here.