New Blood Test Developed by UCSC Researchers Significantly Improve Cancer Detection

Assistant Professor, Daniel Kim. Photo by Carolyn Lagattuta

Cancer is a brutal disease causing millions of deaths annually in the United States and impacting many more patients and loved ones. When detected early, cancer treatment can begin earlier and potentially save lives.

Liquid biopsies offer a non-invasive method for early cancer diagnosis through DNA or RNA sequencing from a simple blood draw. Assistant Professor of Biomolecular Engineering, Daniel Kim, and his team are pioneering a novel approach to this technology by focusing on RNA "dark matter," a largely unexplored area of the genome. Research findings from Kim’s lab which were recently published in Nature Biomedical Engineering journal, reveal that this genetic material is present in the blood of cancer patients and can be used to diagnose specific types of cancer like lung, pancreatic, esophageal, and more.

Kim's lab has developed an RNA liquid biopsy platform, COMPLETE-seq, that can detect both protein-coding RNA and RNA dark matter in the blood. Most of the human genome generates noncoding RNA that does not code for proteins, a significant portion of which are derived from repetitive elements. These RNAs can move from the originating cell into the bloodstream. Although usually scarce in a healthy individual's blood, these repetitive RNAs are secreted out of cancer cells in the early stages of the disease, making them potent biomarkers that can be used to detect early-stage disease.

COMPLETE-seq offers a comprehensive analysis of a patient's blood sample, identifying both well-documented RNAs and often overlooked noncoding repetitive elements. Machine learning models trained to classify cancer perform better when repetitive cell-free RNAs are introduced as additional features, offering higher sensitivity in detecting cancer.

Current liquid biopsy tests lack sensitivity for early-stage cancer, missing up to 75% of stage I cancers due to the small tumor size. However, Kim’s research shows that incorporating repetitive RNA into their biopsy platform enhances the biological signal and improves the performance of machine-learning models identifying cancer. For example, using COMPLETE-seq improved performance to 91% sensitivity for identifying colorectal cancer.

Kim's vision is to develop an RNA liquid biopsy test for multi-cancer early detection. This platform could not only diagnose cancer at the earliest stages but also guide precise treatment strategies when the cancer is more treatable. Furthermore, this test could help identify a recurrence of cancer and diagnose other diseases that alter the repetitive RNA landscape, such as Alzheimer’s disease.

The research conducted also employed nanopore sequencing to read the cell-free RNAs in the blood using a handheld device, a technique believed to have been pioneered by Kim’s lab. This innovation holds promise for conducting cancer screening in remote or resource-poor settings where larger, more costly sequencers are not readily available. In collaboration with clinicians and other companies, the Kim lab plans to investigate diverse cancer types with additional samples across progressive stages of cancer.

This article was written with the assistance of Jasper.Ai.