A novel approach to detect RNA modification patterns in patient blood samples may be a promising tool for the early detection of colon cancer, as detailed in a recent study published in Nature Biotechnology.
Wei Zhang, PhD, professor of Preventive Medicine in the Division of Cancer Epidemiology and Prevention and a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University, was a co-author of the study.
Emerging approaches that detect cancer-associated molecular signals in the blood have demonstrated significant advantages, including being non-invasive and clinically convenient, as compared to existing methods for the early detection of cancer, such as a screening colonoscopy for detecting colon cancer.
“A blood-based test with high sensitivity and specificity could improve screening compliance and patient survival because to date, early detection followed by curative surgery is still the only viable way to enhance clinical outcomes for colorectal cancer patients,” Zhang said.
Previous work, in which Zhang was also a co-author, demonstrated the potential of epigenetic features on circulating cell-free DNA as a cancer biomarker to detect liver cancer earlier than other methods. Cell-free RNA is a class of RNA molecules that can be isolated in blood plasma and is comprised of a diverse collection of RNA molecules, including messenger RNA (mRNA), transfer RNA (tRNA) and ribosomal RNA (rRNA).
In the case of human cancer, cell-free RNA may reflect alterations in gene expression associated with cancer development as well as the body’s response to the cancer, positioning cell-free RNA as a promising cancer biomarker, according to Zhang.
“The ultimate goal is to identify molecular signals in cell-free RNA from clinically feasible samples that can help distinguish cancer patients from non-cancer/healthy individuals,” Zhang said.
In the current study, the investigators developed a novel non-invasive approach called LIME-seq (low-input multiple methylation sequencing) that can simultaneously detect RNA modifications at nucleotide resolution across multiple RNA species while also monitoring quantification changes or differential levels of these RNA modifications.
“The LIME-seq uses the human HIV reverse transcriptase to make complementary DNA (cDNA) copies from cell-free RNA. The RNA–cDNA ligation strategy in LIME-seq ensures the capture of all short RNA species like tRNA in plasma, which are often lost in typical RNA-seq libraries that use commercial kits. Of note, commercial RNA-seq kits cannot be used to quantify and map RNA methylations as well,” Zhang said.
When the investigators used LIME-seq in cell-free RNA samples, they found that tRNAs are major components of cell-free RNA in human plasma, in addition to other RNA species such as rRNAs. The method could also capture human tRNA-derived methylation signals as well as microbial genome-derived signals.
“This is especially exciting for colorectal cancer detection because this new method would allow us to evaluate the potential of early cancer detection through monitoring dynamic status of host microbiomes which, compared to mutational signals, are more likely to reflect early signs of cancer development,” Zhang said.
When comparing tRNA in plasma samples from 27 patients with colon cancer and 36 healthy controls, the scientists discovered noticeable methylation changes between the two groups of patients.
“We reasoned that methylation changes in microbiome-derived cell-free RNA could reflect the activity of the host microbiota, likely making this test more sensitive to the changes in tumor microenvironment,” Zhang said.
In the future, Zhang said larger clinical studies are needed to develop and validate these new biomarkers for colon cancer detection and expand to other human cancers, as well.
“The new LIME-seq represents a revolutionary approach that opens up opportunities to explore cell-free RNA in cancer biomarker discovery,” Zhang said.
Xiaolong Cui, PhD, research assistant professor of Preventive Medicine, was also a co-author of the study.
This work was supported in part by the National Institute of Health grants RM1 HG008935, R33 CA269100 and U01 CA217078.
