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Researchers at Stanford Medicine have identified a new method of classifying breast cancer tumors based on DNA variations, which could improve treatment strategies and recurrence screening. The study categorizes breast cancers into three main groups by analyzing structural DNA variations, including oncogene amplifications and the presence of small DNA circles known as extrachromosomal DNA (ecDNA).

Christina Curtis, PhD, the RZ Cao Professor and a professor of oncology, genetics, and biomedical data science at Stanford Medicine, is the senior author of the research published in Nature. She explained that "breast tumors develop key structural variants that set the tumor on its course very early in its development." This classification system aims to identify patients who may benefit from aggressive early intervention versus those who can defer certain treatments.

Curtis's team analyzed nearly 2,000 breast cancer samples across various stages. They discovered that high-risk hormone-receptor positive subgroups overlapped with HER-2 positive subgroups due to similar chromosomal instability patterns. Meanwhile, triple-negative tumors exhibited global genomic instability.

The study suggests potential therapeutic options based on these findings. For instance, drugs targeting impaired DNA repair pathways might help patients with estrogen-receptor positive breast cancers showing DNA repair deficiencies. Other treatments could focus on targeting focal amplifications or ecDNA-driven processes.

Curtis emphasized that understanding these genetic alterations offers opportunities for earlier interventions: "Despite the complexity of their genomes, there are constraints and only so many evolutionary paths for a tumor to follow."

The research was supported by grants from the National Institutes of Health and the Breast Cancer Research Foundation.