A new software tool seems to find genealogical connections between continental ancestry and cancer from tumor DNA and RNA. Developed by researchers at Cold Spring Harbor Laboratory (CSHL), the software is designed to uncover links between cancer and race and ethnicity.A paper describing their research cancer research.
“Epidemiologically, we know there is a relationship between ancestry and/or race and cancer,” said Alexander Krasnitz, senior author at Cold Spring Harbor Laboratory. For example, women of African descent tend to have a higher incidence of triple-negative breast cancer than other populations. “Cancer can look different in patients with different ancestry,” he added. So he and his team look for mathematical ways to mine the vast amounts of cancer research data already amassed in databases and hospital repositories for ancestral genetic clues to cancer types. I was.
This approach is not the first attempt to link cancer types with ancestry. “But cancer skews the data and skews the genome, so what’s been done before isn’t always optimal for ancestry inference,” said Pascal Belleau, the CSHL’s lead author. said. “It creates a lot of difficulties, and that’s what this tool is trying to overcome: develop a tool that finds ancestry from a data sample.”
For their tool, known as Robust Ancestry Inference Using Data Synthesis (RAIDS), the team relied on cancer-derived data to determine whether the individual had cancer and how the cancer affected their genome. We identified the patient’s ancestry, even if it might have given . To do so, they studied different types of molecular data, including whole genomes, whole exomes, targeted gene panels, and RNA sequences, from cancer patients in his four different databases. Other genetic data used in this tool include information from the 1000 genome data set, which includes genomes from African, East Asian, European, American, and South Asian ancestry. Molecular data from both datasets were grouped into comparable profiles to infer continental-level global ancestry.
At its core, the profiled patient’s ancestral background is replaced with one of an arbitrary number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, specifically allowing us to assess inference performance for specific molecular profiles and for each continent-level ancestor separately.
“This ability extends to all ancestry, including those that are not statistically well represented in existing cancer data,” the authors write.
Testing against four different types of pancreatic, ovarian, breast and hematological cancers and three molecular profiling modalities, the team was able to infer the continental ancestry of the patients. The team found that the software matched his hybrid profile to the continental population with over 95% accuracy.
“This study demonstrates that the vast amount of existing cancer-derived molecular data may be suitable for ancestry-directed studies of disease, without the need for cancer-free genomics or patient self-reported ancestry matching.” ,” the authors said. Additionally, the team believes the tool can be applied to any molecular data for which ancestry is difficult to infer.
Krasnitz and Belleau recently participated in a colorectal cancer study in collaboration with Northwell Health and SUNY Downstate Medical Center. This study will allow us to look at how colorectal cancer alters genes in different ways, depending on the specific race or ethnicity. They hope to further refine the software so that it can infer the ancestry of individual sequences, not just whole genomes.
“The ability to identify more localized ancestry that is predisposed to various cancers and other progressive diseases could help identify specific parts of the genome responsible and target them for treatment. “There is a lot of potential,” says Belleau.
