Researchers with The Cancer Genome Atlas (TCGA) Research Network have completed the largest, most diverse tumor genetic analysis ever conducted, revealing a new approach to classifying cancers. They say the work, published in Cell, not only revamps traditional ideas of how cancers are diagnosed and treated, but could also have a profound impact on the future landscape of drug development.
Since 2006, much of the research has identified cancer as not a single disease, but many types and subtypes and has defined these disease types based on the tissue breast, lung, colon, etc. in which it originated. In this scenario, treatments were tailored to which tissue was affected, but questions have always existed because some treatments work, and fail for others, even when a single tissue type is tested.
In their work, the TCGA researchers analyzed more than 3,500 tumors across 12 different tissue types to see how they compared to one another -- the largest dataset of tumor genomics ever assembled, explained Katherine Hoadley, a research assistant professor in genetics at the University of North Carolina, and lead author of the study. They found that cancers are more likely to be genetically similar based on the type of cell in which the cancer originated, compared to the type of tissue in which it originated.
One striking example of the genetic differences within a single tissue type is breast cancer. The breast gives rise to multiple types of breast cancer, including luminal A, luminal B, HER2-enriched and basal-like, which was previously known. In this analysis, the basal-like breast cancers looked more like ovarian cancer and cancers of a squamous-cell type origin, a type of cell that composes the lower-layer of a tissue, rather than other cancers that arise in the breast.
Study senior author Charles Perou said the research further solidifies that basal-like breast cancer is an entirely unique disease and is completely distinct from other types of breast cancer. In addition, bladder cancers were also quite diverse and might represent at least three different disease types that also showed differences in patient survival.
We found that one in 10 cancers analyzed in this study would be classified differently using this new approach, Perou said. That means that 10 percent of the patients might be better off getting a different therapy thats huge.
The full study is available here.
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