(Genetic Engineering & Biotechnology News) — An international research team headed by scientists at the University of California, San Diego (UCSD), has developed a novel diagnostic approach that harnesses machine learning models to identify who has cancer, and often which type, by analyzing patterns of microbial DNA—bacterial and viral—in a single blood sample.
The researchers trained and tested their machine learning models on the distinct microbial signatures identified in more than 10,000 tumor samples from patients with 33 tumor types. They then showed that the models were able to identify whether an individual did, or did not have one of three types of cancer, and differentiate between the three, using only the microbial patterns in their blood.
The researchers suggest that this newly discovered cancer-associated blood microbiome may have applications beyond cancer diagnostics.
“This new understanding of the way microbial populations shift with cancer could open a completely new therapeutic avenue,” suggested Sandrine Miller-Montgomery, PhD, professor of practice in the Jacobs School of Engineering and executive director of the Center for Microbiome Innovation at UCSD, who is co-author of the team’s published paper in Nature. (…)
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