AI Uncovers Cancer Drivers in Non-Coding DNA

Researchers at the Garvan Institute have discovered putative drivers of cancer concealed in so-called "junk" DNA regions, creating opportunities for novel approaches to diagnosis and treatment using artificial intelligence.

According to a recent study from the Garvan Institute of Medical Research, non-coding DNA - the 98% of the human genome that lacks instructions for forming proteins - may hold the secret to a novel method of cancer diagnosis and treatment.

The results, which were published in the journal Nucleic Acids Research, show that mutations in previously unrecognized genomic regions may play a role in the initiation and development of at least 12 distinct cancers, including colorectal, breast, and prostate cancer.

The finding may result in early cancer detection and novel therapies that work for a variety of cancer forms.

Non-coding DNA was once called ‘junk DNA’ due to its apparent lack of function. Our research has found mutations in these DNA regions that could open an entirely new, universal approach to cancer treatment.”

Dr. Amanda Khoury, Research Officer and Study Co-Corresponding Author, Garvan Institute of Medical Research

Investigating DNA “Anchors” Disrupted in Cancer

The scientists concentrated on mutations that alter the binding sites of a protein known as CTCF, which aids in the folding of lengthy DNA strands into precise forms. According to their earlier research, these binding sites form 3D structures that regulate gene expression by drawing far-flung regions of DNA closer together.

Dr. Khoury said, “We had already identified a subset of CTCF binding sites that are ‘persistent’ – that is they act like anchors in the genome, present across different cell types. We hypothesized that if these anchors become faulty, it could disrupt the normal 3D organization of the genome and contribute to cancer.”

The researchers used genomic and epigenomic features to predict which CTCF sites are likely to be persistent anchors in a total of 12 cancer types using a new, highly advanced machine learning (AI) tool they called CTCF-INSITE. After evaluating over 3000 tumor samples from patients with the 12 cancer types that were accessible through the International Genome Consortium database, they discovered that the persistent anchors had a high mutational density.

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

This research confirmed that persistent CTCF binding sites are ‘mutational hotspots’ in cancers. We think these mutations give cancer cells a survival advantage, allowing them to proliferate and spread,” added Dr. Khoury.

Towards a Universal Cancer Treatment Approach

The findings could have broad implications for understanding and treating many types of cancer.

Most new cancer treatments have to be carefully targeted to specific mutations not always common amongst different tumor types, but because these CTCF anchors are mutated across multiple different cancer types, we’re opening up the possibility of developing approaches that could be effective for multiple cancers.”

Susan Clark, Professor, Lead Author and Head, Cancer Epigenetics Lab, Garvan Institute of Medical Research

Further large-scale CRISPR gene editing experiments are now being planned by researchers to look into how these anchor mutations affect the 3D genome and possibly encourage the growth of cancer.

Clark said, “Now that we’ve discovered what we believe to be critical anchors of the genome and shown they are important to maintaining homeostasis of the genome architecture, it makes sense that these non-coding DNA mutations would disrupt this homeostasis in the cancer cell – a hypothesis we will test when we edit them out. Observing the downstream impact, we hope to identify key genes or gene pathways that are affected by the mutations, which could serve as markers for early cancer detection or targets for new treatments.”

Finding these clues that were hidden in a vast amount of data is a powerful example of how artificial intelligence is boosting medical research. This is a whole new frontier in the study of cancer, and we’re excited to explore it further.”

Susan Clark, Professor, Lead Author and Head, Cancer Epigenetics Lab, Garvan Institute of Medical Research

Source:
Journal reference:

Chen, W., et al. (2024) Machine learning enables pan-cancer identification of mutational hotspots at persistent CTCF binding sites. Nucleic Acids Research. doi.org/10.1093/nar/gkae530

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