Investigators identified more than 100 proteins linked to inherited cancer risk and dozens of existing drugs that could be repurposed for cancer prevention.
Researchers at Vanderbilt University Medical Center (VUMC) and the University of Calgary have developed a new analytical framework that integrates genomic, proteomic, and electronic health record (EHR) data to uncover proteins linked to cancer risk and to identify existing drugs that may be repurposed for cancer prevention. The approach, described in a study published Dec. 2 in the American Journal of Human Genetics, represents a step toward translating large-scale genetic discoveries into actionable prevention strategies across multiple cancer types.
For clinical laboratory directors, the new framework offers a glimpse of how combined genomic, proteomic, and EHR datasets could soon reshape biomarker discovery and test development.
Genome-wide association studies (GWAS) have already identified hundreds of genetic variants associated with increased cancer risk, particularly for breast, colorectal, and prostate cancers, as well as dozens of variants linked to lung, pancreatic, and ovarian cancers.
However, most of these studies have focused on genetic variation and gene expression rather than the downstream proteins that ultimately drive biological function and are more directly targetable by drugs.
Xingyi Guo, PhD, associate professor of medicine in the Division of Epidemiology at VUMC and a co–senior author of the study said, “Previous research, including our work, has identified hundreds of putative cancer susceptibility genes that could be regulated by these risk variants; however, most dysregulated gene expression has not been thoroughly investigated at the protein level.” (Photo credit: VUMC)
Integrating GWAS and Proteomics to Identify Druggable Cancer Risk Proteins
To bridge that gap, the investigators combined large GWAS datasets for six major cancers—breast, colorectal, lung, ovarian, pancreatic, and prostate—with population-scale proteomics data drawn from more than 75,000 participants. The data came from multiple large cohorts, including the Atherosclerosis Risk in Communities (ARIC) study, deCODE genetics, and the UK Biobank Pharma Proteomics Project. The goal was to identify proteins whose circulating levels are associated with inherited cancer risk.
“To deepen the understanding of causal mechanisms and enhance drug discovery efforts, it is imperative to explore data from transcriptomic to proteomic studies,” said Zhijun Yin, PhD, MS, associate professor of biomedical informatics at VUMC and co–senior author, along with Quan Long, PhD, associate professor of biochemistry and molecular biology at the University of Calgary.
Using this integrated approach, the research team identified 365 proteins associated with cancer risk across the six cancer types studied. Through additional analyses to prioritize the most robust findings, they narrowed this list to 101 risk proteins. Notably, 74 of these proteins had not been previously reported as being linked to cancer susceptibility, highlighting the potential of proteomics to reveal novel biology that may be missed by gene-level analyses alone.
The researchers then evaluated whether these risk proteins could be therapeutically targeted. By systematically annotating the proteins using multiple pharmaceutical and drug-development databases, they assessed whether any were already the targets of approved drugs or agents in clinical testing. This step was designed to identify opportunities for drug repurposing—using existing medications for new preventive indications.
“Traditional drug discovery faces challenges of escalating costs, lengthy timelines, and high failure rates. Drug repurposing is a promising strategy to identify new applications for existing drugs with well-documented characteristics,” Guo said.
Among the 101 prioritized proteins, the investigators identified 36 that were considered druggable and potentially targetable by 404 drugs that are already approved or undergoing clinical trials. Of these, 19 proteins were targeted by drugs currently approved or in development for cancer treatment, suggesting a possible extension of oncology therapeutics into the prevention setting.
EHR-Based Analyses Suggest Reduced Cancer Risk with Certain Approved Drugs
To explore real-world relevance, the team leveraged more than 3.5 million de-identified EHRs from VUMC. Using this data, they conducted simulated clinical trials to examine associations between drug exposure and cancer risk. Several approved medications showed signals consistent with reduced cancer risk. One example highlighted in the study was acetazolamide, a diuretic, which was associated with a reduced risk of colorectal cancer in the EHR-based analyses.
“Our findings offer additional insights into therapeutic drugs targeting risk proteins for cancer prevention and intervention,” Yin said. “It is essential to evaluate the effects of the reported candidate drugs through both in vitro and in vivo assays in future research.”
EHRs are rich in diagnostic data, so there is a clear connection between the researchers’ drug discovery efforts and the information that clinical laboratory test results can provide.
—Janette Wider
