Vast repositories of biological samples and data collections can hold unexpected treasures. For example, tumor samples and cancer-related molecular profiles can be used to advance genetic ancestry-oriented cancer research. It doesn’t even matter if the ancient research materials lack ancestral documentation or if the available data is entirely related to cancer. That is, inferences can be made even in the absence of matching cancer-free data.
These findings were reported by scientists at the Cold Spring Harbor Laboratory (CSHL) who were led by Associate Professor Alexander Krasnitz, PhD. He is the lead author of a new article (“Inference of genetic ancestry from cancer-derived molecular data via genomic and transcriptomic platforms”), which recently appeared on cancer research.
“[We] examined the feasibility and accuracy of computational inference of genetic ancestry based solely on cancer-derived data,” the paper’s authors wrote. These data come from procedures such as whole-exome sequencing, transcriptome sequencing, and gene-specific panels, very often in the absence of matching cancer-free genomic data.
“The inference procedure was shown to be accurate and robust over a wide range of sequencing depths,” the authors continued. “Testing the approach in four representative cancer types and in three molecular profiling modalities showed that the continental-level ancestry of patients can be inferred with high precision, as quantified by their agreement with the gold standard of deriving ancestry. from the comparison of cancer-free molecular data. .”
Krasnitz and the paper’s lead author, CSHL postdoctoral fellow Pascal Belleau, have been working to reveal genealogical connections between cancer and race or ethnicity. They have developed new software that accurately infers continental ancestry from tumor DNA and RNA. Their work can also help doctors develop new strategies for early detection of cancer and personalized treatments.
“Why do people of different races and ethnicities get sick at different rates with different types of cancer?” Krasnitz asked. “They have different habits, living conditions, exposures, all kinds of social and environmental factors. But there may also be a genetic component.”
Krasnitz’s team trained their software tools using hybrid DNA profiling. They created these profiles from unrelated cancerous and cancer-free genomes of known backgrounds. They then tested the performance of the software against pancreatic, ovarian, breast, and blood cancer samples from patients of known ancestry. The team found that the software matched their hybrid profiles to continental populations with more than 95% accuracy.
“We have a good blueprint to build on,” Krasnitz said. “But very few individuals come from only one ancestry. We are all mixed up to some degree. So now we are working to dig deeper, analyze tumor samples of unknown ancestry, reveal ancestral admixtures, and achieve greater regional specificity.” How specific? For now, think West Africa instead of East Africa.
Krasnitz and Belleau recently joined a colorectal cancer study in collaboration with Northwell Health and SUNY Downstate Medical Center. The study allows them to explore how colorectal cancer mutates genes in different ways based on specific races or ethnicities. They hope to further refine their software to infer ancestry not just from entire genomes but from each individual sequence in them.
“If we can identify more localized ancestors that are susceptible to different types of cancer or other aggressive diseases, it could help us identify the specific part of the genome responsible and treat it,” Belleau said.
Right now, a simple DNA swab can tell you where you came from and what diseases you may inherit. In the future, it might give you the means to defeat them as well.