Stanford Researchers Develop Novel RNA Blood Test for Cancer Detection and Treatment Resistance Monitoring
Key Insights
Stanford Medicine researchers have developed a revolutionary blood test that analyzes cell-free RNA (search) molecules to detect cancers at various stages, including early-stage lung cancer with 73% accuracy.
The test can monitor cancer treatment resistance that doesn't involve genetic mutations, potentially allowing earlier intervention before disease progression becomes apparent on scans or through symptoms.
The novel method focuses on analyzing approximately 5,000 rare abundance genes not typically expressed in healthy blood, increasing cancer detection capability by over 50-fold.
Stanford Medicine researchers have developed a groundbreaking blood test that analyzes RNA molecules in the bloodstream to detect cancers, monitor treatment resistance, and assess tissue injury from non-cancerous conditions. The research, published in Nature on April 16, 2025, represents more than six years of development and offers a new approach to liquid biopsy technology.
Revolutionary RNA Analysis Method
The test analyzes cell-free RNA (search), tiny molecules that no longer inhabit cells and float freely in blood as byproducts of natural cell death from tissues and organs throughout the body, including cancerous tumors. Unlike existing DNA-based tests that focus on genetic mutations, this approach targets messenger RNA (search), which comprises less than 5% of the cell-free RNA pool but serves as a crucial signal about gene expression.
"Just as archeologists can learn about ancient societies by studying the garbage they left behind, we can learn a lot about what is going on in the cells of a patient's body based on the degraded RNA molecules that are cleared through the blood," said co-lead author Maximilian Diehn, MD, PhD, the Jack, Lulu, and Sam Willson Professor and professor of radiation oncology.
Enhanced Cancer Detection Through Rare Gene Analysis
The researchers achieved significant improvements in cancer detection by focusing their analysis on approximately 5,000 genes that are not typically expressed in healthy blood. This approach, targeting so-called rare abundance genes, increased the test's ability to correctly identify cancer by over a factor of 50. In clinical testing, the method detected lung cancer RNA in 73% of lung cancer patients, including those at early stages.
"Analysis of the rare abundance genes lets us focus on the most relevant subset of RNA for detecting disease, just like archaeologists who want to learn about what people ate might focus on a subset of artifacts such as food containers or utensils," explained co-lead author Ash Alizadeh, MD, PhD, the Moghadam Family Professor and a professor of medicine, oncology, and hematology.
Monitoring Treatment Resistance Without Genetic Changes
The test's ability to detect cell-free messenger RNA (search) enables monitoring of cancer treatment resistance that doesn't involve genetic mutations, addressing a significant clinical challenge. Many cancer patients experience treatment failure due to cellular adaptations that alter cell behavior or appearance without genetic changes.
"Unfortunately, a significant fraction of our patients who are being treated for cancer go on to have their therapy stop working, and that resistance is often based on adaptations that do not involve genetic changes, but instead altering how the cells behave or even how the cells look under a microscope," Alizadeh explained. "Our non-invasive approach has the potential of avoiding surgical biopsies and identifying these common types of resistance earlier before substantial disease burden shows up on scans or presents with symptoms like pain, providing an earlier opportunity to change treatment and improve outcomes."
Overcoming Technical Challenges
The research team addressed a major technical obstacle by developing molecular and computational strategies to overcome platelet contamination. Platelets, blood cells responsible for clotting that contain RNA but not DNA, previously obscured cancer signals in blood tests. The computational approach allows the method to work on both newly collected samples and previously stored specimens.
"This approach means that the test can be used to examine blood samples currently in the freezer from a completed clinical trial, for example, and could help find a molecular signature that predicts who responded to a drug and who did not," Diehn said. "We can save time by using historical samples to discover a biomarker that can then be applied in real time to patients moving forward."
Applications Beyond Cancer
The cell-free RNA (search) method demonstrates utility for non-cancer applications, detecting high levels of normal lung RNA in blood samples from patients with acute respiratory distress syndrome who required intubation and ventilator support. The test also showed that the amount of normal lung RNA in COVID-19 (search) patients' blood samples correlated with disease severity. Additionally, researchers found normal lung RNA in healthy smokers' blood, potentially indicating microscopic lung injury from smoking.
Collaborative Research Effort
The study was conducted by a collaborative team including researchers from Stanford Medicine, Massachusetts General Hospital, Harvard Medical School, Memorial Sloan Kettering Cancer Center, Fred Hutchinson Cancer Center, and the University of Washington. Monica Nesselbush, a postdoctoral scholar at the Stanford Cancer Institute, Bogdan Luca, a postdoctoral scholar in pathology, and Young-Jun Jeon, a former Stanford Medicine postdoctoral scholar now at Sungkyunkwan University, served as joint first authors.
The research received support from multiple sources, including the National Institutes of Health, National Science Foundation of Korea, Stand Up to Cancer Phil Sharpe Award, Tobacco-Related Disease Research Program, Virginia and D.K. Ludwig Fund for Cancer Research, Susan D. Wojcicki and Dennis Troper Family Foundations, and Lungstrong Foundation.