![\"AI](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2025\/12\/ai-and-cancer_500x500.jpg\")
\nArtificial intelligence (AI) is playing a growing role in biomedical discovery.<\/p>\n
\n 10 December 2025
\n <\/p>\n
\nArtificial intelligence (AI) is playing a growing role in biomedical discovery.<\/p>\n
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.<\/p>\n
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.<\/p>\n
Lead researcher Dr Andres Cifuentes-Bernal says his team used AI tools to identify groups of genes working in concert to push cancer forward.<\/p>\n
\u201cThe 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,\u201d Dr Cifuentes-Bernal says.<\/p>\n
\u201cTraditional 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.\u201d<\/p>\n
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.<\/p>\n
\u201cCancer is not static,\u201d he says. \u201cIt 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.\u201d<\/p>\n
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.<\/p>\n
Many of these are not mutated but still influence other genes, contributing to tumour progression.<\/p>\n
The method successfully recognised a significant number of known cancer drivers listed in the Cancer Gene Census \u2013 a respected international reference \u2013 confirming the accuracy of the approach.<\/p>\n
It also revealed novel candidates, including several genes involved in cell signalling, immune response and metastasis.<\/p>\n
Assoc Prof Le says the technique identifies cooperative networks rather than isolated genes.<\/p>\n
\u201cThese networks highlight how genes collaborate to collectively push cancer into more aggressive states,\u201d he says.<\/p>\n
The researchers are hopeful their method could help pinpoint new therapeutic targets, especially for patients whose tumours lack common high-profile mutations.<\/p>\n
\u201cUnderstanding these dynamics gives us a richer view of how tumours evolve,\u201d Dr Cifuentes-Bernal says.<\/p>\n
\u201cIt moves us beyond thinking about single-cell mutations and towards a better understanding of the broader biological systems at play.\u201d<\/p>\n
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.<\/p>\n
\u2018Identifying cooperative genes causing cancer progression with dynamic causal inference\u2019 is published in Royal Society Open Science. DOI: 10.1098\/rsos.250442<\/p>\n
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Media contact: Candy Gibson M: +61 434 605 142 E: Candy.Gibson@unisa.edu.au<\/a> \u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
10 December 2025
Artificial intelligence (AI) is playing a growing role in biomedical discovery. University of South…\n","protected":false},"author":2,"featured_media":338323,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[10],"tags":[256,254,64,63,1617,188222,137,188223],"class_list":{"0":"post-338322","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-health","8":"tag-ai","9":"tag-artificial-intelligence","10":"tag-au","11":"tag-australia","12":"tag-cancer","13":"tag-cancer-genes","14":"tag-health","15":"tag-mutated-genes"},"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/338322","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/comments?post=338322"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/posts\/338322\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media\/338323"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/media?parent=338322"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/categories?post=338322"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/au\/wp-json\/wp\/v2\/tags?post=338322"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Researcher contacts: Dr Andres Cifuentes-Bernal\u00a0 E: Andres.CifuentesBernal@unisa.edu.au<\/a>
Associate Professor Thuc Le E: Thuc.Le@unisa.edu.au<\/a><\/p>\n