Cancer is most treatable in its early stages, so finding innovative and noninvasive methods to diagnose cancer early on is crucial for fighting the disease. Liquid biopsies, which require just a simple blood draw, are an emerging technology for noninvasively testing for cancer by sequencing nucleic acids from a patient’s blood.
Daniel Kim, PhD, assistant professor of biomolecular engineering, and his lab are developing more accurate and powerful liquid biopsy technologies that take advantage of signals from RNA “dark matter,” an understudied area of the genome. Kim’s new research shows that this genetic material is present in the blood of people with cancer and can be identified to diagnose specific cancer types such as pancreatic, lung, esophageal, and others, early in the course of the disease.
Kim’s lab developed an RNA liquid biopsy platform that detects both protein-coding RNA and RNA dark matter in the blood, and showed that this new approach significantly improves the performance of liquid biopsy for cancer diagnosis. This research was published recently in the Nature Biomedical Engineering.
What is the “dark matter” in cells?
While most researchers and companies are pursuing DNA-based liquid biopsy for cancer diagnosis, Kim’s approach is unique in its focus on RNA “dark matter,” specifically noncoding and repetitive RNA.
Most of the three billion base pairs of DNA that make up the human genome are transcribed into RNA: All of the RNA is collectively known as the transcriptome. The most commonly recognized function of RNA is to code for proteins in the body, but 75 percent of the human genome generates RNA that does not code for proteins (noncoding).
A substantial portion of these noncoding RNAs are derived from repetitive elements. These RNAs can travel out of the cell from which they originate and enter the bloodstream. A healthy individual’s blood typically would have very few of these repetitive noncoding RNAs. However, Kim’s research has shown that even at the earliest stages of cancer, many of these repetitive RNAs are secreted out of cancer cells, making them potent biomarkers of early-stage disease.
Cell-free, noncoding RNA as biomarkers
RNA liquid biopsy technology developed by the Kim lab aims to detect cancer by sequencing “cell-free RNA” in a patient’s blood to test for the presence of both protein-coding and repetitive noncoding RNA. Kim’s lab created a cell-free RNA sequencing and analysis platform, called COMPLETE-seq, to identify repetitive noncoding RNAs that are typically overlooked.
After a patient’s blood is drawn, this comprehensive approach analyzes the sample for all of the annotated areas of the transcriptome—the tens of thousands of RNAs that have already been well-documented—plus all of the five million noncoding repetitive elements that Kim’s lab also focuses on.
“If you look at these different cancers, each has its own characteristic cell-free RNA profile, but a lot of these RNAs are coming from the millions of repeat elements that are found throughout the genome,” said Kim. “What we found was that when we trained machine learning models for cancer classification, the models perform better when you introduce these repetitive cell-free RNAs as additional features. We see higher sensitivity in terms of detecting cancer, so we think that these repeat elements are actually providing a lot of rich cell-free RNA information that people previously hadn't looked for.”
Honing liquid biopsy tests
Other existing liquid biopsy tests have not been very sensitive for early-stage cancer detection—with some tests missing up to 75 percent of stage I cancers—when the biological signal is low due to the small tumor size. Kim’s study shows that incorporating repetitive RNA into their liquid biopsy platform greatly increases the biological signal and boosts the performance of machine learning models tasked to identify cancer.
As an example, using COMPLETE-seq improved performance to 91 percent sensitivity for identifying colorectal cancer.
“The value of our study is that we've now shown the potential of these repeat elements for diagnosing disease, so hopefully there'll be a lot of interest in leveraging repetitive RNAs to boost the sensitivity of these multicancer early detection tests,” said Kim.
Diagnostic applicability of noncoding RNA-led liquid biopsies
The research findings show that this technology can be used to identify a variety of cancer types. The lab initially focused on pancreatic cancer for this study, as there is an urgent clinical need for pancreatic cancer early detection and late detection leads to worse outcomes for patients.
After verifying findings in pancreatic cancer, the researchers also looked at a variety of other cancers, and plan to look at many more cancer types with additional samples across the progressive stages of cancer. The team is interested in collaborating with clinicians and companies to do this.
Kim’s goal is to develop an RNA liquid biopsy test for multicancer early detection, using the rich information from repetitive RNAs to identify and diagnose diseases with high sensitivity and specificity. He hopes his platform will not only diagnose cancer at the earliest stages but also help guide individualized, precise treatment strategies when the cancer is more treatable.
Moreover, his test could help to identify a recurrence of cancer, and also be used to study aging and to diagnose other types of diseases that alter the repetitive RNA landscape, such as Alzheimer’s disease.
The researchers also used nanopore sequencing to read the cell-free RNAs floating in the blood, which helped generate long reads and determine the true length of these cell-free RNAs. Kim believes his lab is the first to use nanopore sequencing for RNA liquid biopsies to diagnose cancer and to determine the full length of these cell-free RNAs.
Nanopore sequencing holds promise for carrying out cancer screening in remote or resource-poor settings where larger, more expensive sequencers may not be readily available.
- This press release was originally published on the University of California-Santa Cruz website