Novel Blood Test Significantly Improves Cancer Detection by Leveraging RNA “Dark Matter”

Detecting cancer in its early stages is vital for effective treatment, creating the need for innovative and non-invasive diagnostic methods. Liquid biopsies, which involve a simple blood draw, are emerging as a technology for non-invasive cancer testing through DNA or RNA sequencing of blood samples. Researchers are harnessing signals from RNA "dark matter," a lesser-explored genome area, to develop more accurate and powerful liquid biopsy techniques. Now, new research reveals that this genetic material is present in the blood of individuals with cancer, and its identification can facilitate the early diagnosis of specific cancer types like pancreatic, lung, and esophageal cancers.

Researchers at UC Santa Cruz (Santa Cruz, CA, USA) have developed an RNA liquid biopsy platform that is capable of detecting both protein-coding RNA and RNA dark matter in blood samples. This approach significantly enhances the performance of liquid biopsies for cancer diagnosis. Unlike the prevalent focus on DNA-based liquid biopsies, this approach zeroes in on RNA "dark matter," particularly noncoding and repetitive RNA. Most of the human genome's three billion base pairs are transcribed into RNA, collectively termed the transcriptome. While RNA's primary function is protein coding, 75% of the human genome generates noncoding RNA that doesn't code for proteins. A considerable portion of these noncoding RNAs originates from repetitive elements, some of which exit the cell of origin and enter the bloodstream. Ordinarily, a healthy individual's blood contains minimal repetitive noncoding RNAs. However, the researchers at UC Santa Cruz demonstrated that even during the earliest cancer stages, many of these repetitive RNAs are secreted by cancer cells, serving as potent biomarkers for early-stage disease.

The RNA liquid biopsy technology developed by the researchers employs "cell-free RNA" sequencing from patient blood samples to detect the presence of both protein-coding and repetitive noncoding RNA. The team has developed the COMPLETE-seq cell-free RNA sequencing and analysis platform that identifies repetitive noncoding RNAs which are usually overlooked. This approach analyzes the sample for all annotated transcriptome regions—tens of thousands of well-documented RNAs—as well as the five million noncoding repetitive elements that the researchers focus on. Other existing liquid biopsy tests have displayed limited sensitivity for early-stage cancers, sometimes missing up to 75% of stage I cancers due to their low biological signal linked to small tumor size. The novel research underscores that incorporating repetitive RNA into the liquid biopsy platform amplifies the biological signal, enhancing the performance of machine learning models in cancer identification. For instance, COMPLETE-seq enhanced the sensitivity for identifying colorectal cancer to 91%.

The study findings indicate the potential of this technology for identifying diverse cancer types. The team’s initial study focused on pancreatic cancer due to its critical need for early detection, given its unfavorable outcomes when detected late. Additionally, pancreatic cancer is understood to be driven by KRAS gene mutations, which is also a focus area of the research team. Following verification in pancreatic cancer, the researchers extended their investigations to various other cancers and plan to further explore a wide range of cancer types using samples across progressive cancer stages. Their ultimate goal is to develop an RNA liquid biopsy test for early detection of multiple cancers, utilizing the wealth of information from repetitive RNAs to achieve precise and sensitive disease identification and diagnosis. This platform aspires not only to diagnose cancer at its earliest stages but also to guide tailored, individualized treatment strategies when cancer is most treatable. Moreover, the test's potential extends to identifying cancer recurrence and diagnosing other diseases altering the repetitive RNA landscape, such as Alzheimer's disease.

“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 multi-cancer early detection tests,” said Daniel Kim, Assistant Professor of Biomolecular Engineering at UC Santa Cruz.

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