New blood test detects tumor-derived cell-free RNA with high sensitivity

Somewhere in the body of a patient, a small clump of cells, growing undetected, has begun to form a tumor. It has yet to cause pain or visible symptoms of illness. Several months from now, or perhaps years, those first signs will prompt a doctor’s inquiry, a referral to a specialist, and an eventual diagnosis. Treatment will depend on how long the cancer has gone unnoticed and how far it has spread.

There were early signs, though not ones the patient or doctor could have noticed. Small fragments of RNA, cast off from dying cells or spit out of the tumor’s twisted transcriptions, floating about in the bloodstream—early signals of a tissue in distress.

A new method developed by Stanford researchers aims to bring the moment of detection much closer to the beginning. They have developed a blood-based method called RARE-seq that detects tumor-derived cell-free RNA with around 50 times the sensitivity of standard sequencing techniques.

RARE-seq showed an ability to identify lung cancer signatures in patients at various disease stages, outperforming commonly used DNA-based approaches. RNA from vaccines and many transcripts associated with non-cancer-related conditions were also detectable.

Blood-based liquid biopsies offer a non-invasive approach to capture cancer-related changes by identifying circulating tumor DNA. This allows early detection, genotyping, and monitoring of disease, even when specific tumor locations are unknown or would require surgical biopsy to investigate.

Cell-free RNA, fragments of RNA released into the bloodstream by dying or active cells, presents a broader diagnostic view of gene activity throughout the body. Detection requires precision because most RNA circulating in the bloodstream originates from blood-forming cells rather than tumors. Rare tumor-derived RNA molecules are often masked by the background signal of these hematopoietic transcripts.

In the study, “An ultrasensitive method for detection of cell-free RNA,” published in Nature, researchers designed RARE-seq (random priming and affinity capture of cell-free RNA fragments for enrichment analysis by sequencing), a method optimized for the detection of cell-free RNA.

Samples analyzed in the study included 437 plasma collections from 369 individuals. Participants represented a range of cancer stages, non-malignant conditions, and healthy controls drawn from multiple clinical centers.

Researchers optimized the full experimental workflow for analyzing cell-free RNA in plasma. Pre-analytical variables such as blood collection, RNA extraction, and sample storage were systematically evaluated to reduce variability.

Steps in library preparation were adapted for low RNA input, including enzymatic removal of contaminating DNA, improved complementary DNA synthesis, and end-repair protocols to enhance efficiency.

A central feature of the method involves selective enrichment of transcripts using a capture panel during library preparation. Researchers used molecular probes to isolate 4,737 rare abundance genes and 50 housekeeping genes. These genes were selected because they are typically low or absent in healthy plasma and are more likely to reflect tissue-specific or disease-related RNA.

A computational model was developed to eliminate expression noise caused by residual platelet RNA. This model identified gene patterns linked to platelet contamination and adjusted for them using data from healthy reference samples.

RARE-seq detected expression signatures associated with non-small-cell lung cancer in 101 out of 139 participants with previously confirmed lung cancer. Detection rates increased by cancer stage, with 30% in stage I, 63% in stage II, 67% in stage III, and 83% in stage IV.

In a head-to-head comparison using matched plasma samples, RARE-seq identified cancer in 34% of cases that were missed by ctDNA analysis, while no samples were detected by ctDNA alone.

RARE-seq also identified somatic driver mutations in 28% of patients with lung adenocarcinoma as well as 1% of controls. Known variants such as EGFR, KRAS, and RET were among those detected through RNA sequencing.

RARE-seq revealed cell-free RNA expression profiles associated with histological transformation, MET amplification, and drug resistance in patients treated with EGFR-targeting therapies. In one patient, cell-free RNA markers of small-cell transformation diminished following chemotherapy, reflecting a shift in tumor state.

For samples unrelated to cancer, RNA from mRNA vaccines was detected for up to six weeks after administration. Transcripts linked to COVID-19 infection, lung injury, and mechanical ventilation were also found, particularly in individuals with recent tobacco exposure or active pulmonary disease.

In this preliminary validation, RARE-seq achieved levels of detection sensitivity beyond those of current ctDNA-based methods, identifying tumor-derived RNA at extremely low concentrations. Detection of gene activity patterns and cancer-related mutations from a single blood sample enabled detailed molecular characterization of lung tumors and resistance mechanisms.

Results imply a much broader utility for cell-free RNA analysis in both cancer and non-cancer conditions. RARE-seq introduces a foundation for future blood tests that capture gene expression changes in a wide range of clinical scenarios. Diagnostic use will require clinical trials in early-stage cancer and expanded reference datasets for cell-free RNA.

© 2025 Science X Network

Somewhere in the body of a patient, a small clump of cells, growing undetected, has begun to form a tumor. It has yet to cause pain or visible symptoms of illness. Several months from now, or perhaps years, those first signs will prompt a doctor’s inquiry, a referral to a specialist, and an eventual diagnosis. Treatment will depend on how long the cancer has gone unnoticed and how far it has spread.

There were early signs, though not ones the patient or doctor could have noticed. Small fragments of RNA, cast off from dying cells or spit out of the tumor’s twisted transcriptions, floating about in the bloodstream—early signals of a tissue in distress.

A new method developed by Stanford researchers aims to bring the moment of detection much closer to the beginning. They have developed a blood-based method called RARE-seq that detects tumor-derived cell-free RNA with around 50 times the sensitivity of standard sequencing techniques.

RARE-seq showed an ability to identify lung cancer signatures in patients at various disease stages, outperforming commonly used DNA-based approaches. RNA from vaccines and many transcripts associated with non-cancer-related conditions were also detectable.

Blood-based liquid biopsies offer a non-invasive approach to capture cancer-related changes by identifying circulating tumor DNA. This allows early detection, genotyping, and monitoring of disease, even when specific tumor locations are unknown or would require surgical biopsy to investigate.

Cell-free RNA, fragments of RNA released into the bloodstream by dying or active cells, presents a broader diagnostic view of gene activity throughout the body. Detection requires precision because most RNA circulating in the bloodstream originates from blood-forming cells rather than tumors. Rare tumor-derived RNA molecules are often masked by the background signal of these hematopoietic transcripts.

In the study, “An ultrasensitive method for detection of cell-free RNA,” published in Nature, researchers designed RARE-seq (random priming and affinity capture of cell-free RNA fragments for enrichment analysis by sequencing), a method optimized for the detection of cell-free RNA.

Samples analyzed in the study included 437 plasma collections from 369 individuals. Participants represented a range of cancer stages, non-malignant conditions, and healthy controls drawn from multiple clinical centers.

Researchers optimized the full experimental workflow for analyzing cell-free RNA in plasma. Pre-analytical variables such as blood collection, RNA extraction, and sample storage were systematically evaluated to reduce variability.

Steps in library preparation were adapted for low RNA input, including enzymatic removal of contaminating DNA, improved complementary DNA synthesis, and end-repair protocols to enhance efficiency.

A central feature of the method involves selective enrichment of transcripts using a capture panel during library preparation. Researchers used molecular probes to isolate 4,737 rare abundance genes and 50 housekeeping genes. These genes were selected because they are typically low or absent in healthy plasma and are more likely to reflect tissue-specific or disease-related RNA.

A computational model was developed to eliminate expression noise caused by residual platelet RNA. This model identified gene patterns linked to platelet contamination and adjusted for them using data from healthy reference samples.

RARE-seq detected expression signatures associated with non-small-cell lung cancer in 101 out of 139 participants with previously confirmed lung cancer. Detection rates increased by cancer stage, with 30% in stage I, 63% in stage II, 67% in stage III, and 83% in stage IV.

In a head-to-head comparison using matched plasma samples, RARE-seq identified cancer in 34% of cases that were missed by ctDNA analysis, while no samples were detected by ctDNA alone.

RARE-seq also identified somatic driver mutations in 28% of patients with lung adenocarcinoma as well as 1% of controls. Known variants such as EGFR, KRAS, and RET were among those detected through RNA sequencing.

RARE-seq revealed cell-free RNA expression profiles associated with histological transformation, MET amplification, and drug resistance in patients treated with EGFR-targeting therapies. In one patient, cell-free RNA markers of small-cell transformation diminished following chemotherapy, reflecting a shift in tumor state.

For samples unrelated to cancer, RNA from mRNA vaccines was detected for up to six weeks after administration. Transcripts linked to COVID-19 infection, lung injury, and mechanical ventilation were also found, particularly in individuals with recent tobacco exposure or active pulmonary disease.

In this preliminary validation, RARE-seq achieved levels of detection sensitivity beyond those of current ctDNA-based methods, identifying tumor-derived RNA at extremely low concentrations. Detection of gene activity patterns and cancer-related mutations from a single blood sample enabled detailed molecular characterization of lung tumors and resistance mechanisms.

Results imply a much broader utility for cell-free RNA analysis in both cancer and non-cancer conditions. RARE-seq introduces a foundation for future blood tests that capture gene expression changes in a wide range of clinical scenarios. Diagnostic use will require clinical trials in early-stage cancer and expanded reference datasets for cell-free RNA.

© 2025 Science X Network