Ancestry-Informed Genomics Advances Precision Cancer Prognosis

Outcome disparities among patient populations also persist, and the genomic drivers behind these differences are not fully characterized. Integrating ancestry-informed genomics with environmental context may refine risk stratification for laboratories and oncologists. Researchers now report that combining genetic ancestry with tumor sequencing data improves prediction of survival across several cancers.

At the University of Texas Health Science Center, Houston (UTHealth Houston) investigators developed a mutation-based scoring system to estimate mortality risk and then tested whether adding ancestry information enhanced performance. The analysis drew on genetic sequencing data from more than 30,000 patients treated at two large U.S. cancer centers, Dana Farber (Boston, MA, USA) and MD Anderson (Houston, TX, USA). Five tumor types were included: breast, colorectal, glioma, pancreatic, and lung cancers.

Nearly 1,900 tumor mutations were examined while adjusting for socioeconomic status and air pollution to limit confounding. The mutation-derived score predicted survival particularly well in breast cancer and glioma. Incorporating ancestry information further improved prediction, with the largest gains seen in pancreatic cancer. The team identified dozens of mutations that varied significantly by ancestry, about half of which are targetable with existing therapies. Examples included enrichment of CDK6 alterations in African American breast cancer and loss of SMAD2 in American colorectal cancer patients with admixed ancestry.

The authors note that ancestry can be inferred from routine tumor sequencing and environmental exposures can be estimated from residence, meaning added testing or cost may not be necessary; the primary barrier is integrating these elements into clinical workflows. The work was presented at the annual conference of the European Society of Human Genetics (ESHG), and the researchers plan to extend analyses to additional cancers and factors such as smoking, while seeking replication in other cohorts.

“It was very encouraging to see consistent ancestry-related signals replicated between our two different biobanks despite the geographic and population differences between them,” said Yixuan He, assistant professor of epidemiology at the University of Texas Health Science Center, Houston. "By identifying specific genetic markers linked to ancestry, we can pinpoint targetable mutations to help doctors use treatments with better survival outcomes. In validating these signals across different populations, we can ensure that a particular treatment is adapted to and effective across a diverse range of patients."

Related Links
UT Health Houston
ESHG