New gene-mapping method unlocks hidden drivers of cancer

(Press-News.org) University of South Australia scientists have developed a powerful new way to uncover the genetic interactions that fuel cancer progression, paving the way for earlier and more precise treatments.

The AI-driven method, published today in Royal Society Open Science, reveals that tumour progression is driven by cooperating groups of genes, rather than mutated genes acting alone.

Lead researcher Dr Andres Cifuentes-Bernal says his team used AI tools to identify groups of genes working in concert to push cancer forward.

“The system assesses how genes influence each other over time, providing a clearer picture of the underlying biological approaches that enable tumours to grow, spread and resist treatment,” Dr Cifuentes-Bernal says.

“Traditional genome-wide cancer studies typically focus on mutations that appear frequently across patients. While this approach has uncovered many well-known cancer drivers, it overlooks subtle or rare genetic changes. Crucially, it also misses the complex interplay between genes that allow malignant cells to gain momentum.”

Co-author, UniSA Associate Professor Thuc Le, says the new framework highlights the growing role of artificial intelligence in biomedical discovery, addressing a long-standing gap in cancer biology.

“Cancer is not static,” he says. “It develops through a cascade of dynamic changes. Many genes act together to disrupt normal cell behaviour, but existing methods can struggle to detect that. Our approach is designed to capture that complexity.”

Using large breast cancer datasets to test their method, the researchers showed that the AI-driven system not only detects well-known cancer genes but also uncovers previously hidden ones.

Many of these are not mutated but still influence other genes, contributing to tumour progression.

The method successfully recognised a significant number of known cancer drivers listed in the Cancer Gene Census – a respected international reference – confirming the accuracy of the approach.

It also revealed novel candidates, including several genes involved in cell signalling, immune response and metastasis.

Assoc Prof Le says the technique identifies cooperative networks rather than isolated genes.

“These networks highlight how genes collaborate to collectively push cancer into more aggressive states,” he says.

The researchers are hopeful their method could help pinpoint new therapeutic targets, especially for patients whose tumours lack common high-profile mutations.

“Understanding these dynamics gives us a richer view of how tumours evolve,” Dr Cifuentes-Bernal says.

“It moves us beyond thinking about single-cell mutations and towards a better understanding of the broader biological systems at play.”

The researchers say the framework is adaptable and could be applied to other diseases where regulation changes over time, such as neurodegeneration, autoimmune disorders and chronic inflammatory conditions.

‘Identifying cooperative genes causing cancer progression with dynamic causal inference’ is published in Royal Society Open Science. DOI: 10.1098/rsos.250442

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