Precision medicine is not always so precise when applied to diverse populations, according to researchers from the Translational Genomics Research Institute (TGen) and the Keck School of Medicine of the University of Southern California (USC).
The research team found that precision medicine using a tumor-only approach as a means of guiding therapeutic intervention is more precise for those of European decent, and less precise for those whose ancestry is from Latin America, Africa, and Asia. Their findings were published last week in BMC Medical Genomics.
"The field of precision medicine isn't taking into account population differences. The approaches being used are imprecise when you look at very specific populations," said Dr. John Carpten, director of the Institute of Translational Genomics at the Keck School of Medicine of USC, and one of the study's lead authors.
According to the researchers, of the tens of thousands of individuals worldwide who have undergone whole-genome sequencing, most are of European decent, biasing existing databases used to exclude false-positive variants.
"It is very difficult to identify a somatic, or potentially cancer-causing, variant when you don't have a germline, or normal, sample," explained Dr. Rebecca Halperin, a TGen assistant research professor and the study's other lead author. "It's even worse, depending on your ancestry. You get more false positives, those genetic variants that don't cause cancer, from populations with non-European ancestry."
To assist researchers in sorting out false positives, TGen and USC researchers devised a computational tool called LumosVar, which is essentially a tool to light up the genome's potentially cancer-causing genetic mutations. The team has made LumosVar available as an open-source tool for any researcher to use in searching for potentially cancer-causing mutations.
"Simply sequencing more individuals from various populations is not enough. We really need access to the germlines. But when those aren't available, we need better tools and this is where we have put our focus," said David W. Craig, vice chair of the Keck School of Medicine of USC's department of translational genomics and the study's senior author.
"We have clearly demonstrated that LumosVar has improved positive predictive value in calling somatic variants compared to database filtering, which is the most commonly used approach with unmatched tumor samples," the study says. "When analyzing archival samples in a research setting, we believe LumosVar would be of great utility," the researchers added.