With a single drop of blood, orphan non-coding RNA (oncRNA) expression patterns could be used to identify cancer subtypes and monitor disease progression in real time. In a study published today in Cell, scientists systematically mapped oncRNAs across 32 major cancer types, investigated their role in disease progression and metastasis, and validated their potential as prognostic biomarkers.
“We think oncRNAs represent a new class of cancer-emergent molecules that function as both drivers and biomarkers,” write the authors of the study, led by Hani Goodarzi, PhD, associate professor at the University of California, San Francisco and core investigator at the Arc Institute. “We hope that this resource, which we’ve made open source, opens new directions for the field.”
Goodarzi and colleagues first reported the discovery of oncRNAs back in 2018, defining them as small non-coding RNAs that are expressed by cancer cells but absent from healthy tissues. While their previous work delved into the role one of these molecules plays in promoting breast cancer metastasis, the new study analyzed RNA sequencing data across 32 cancer types, obtained from The Cancer Genome Atlas.
More than 260,000 oncRNAs were identified, with results showing that each cancer type produces distinct patterns of oncRNA expression. These differences were leveraged to develop a machine learning algorithm that could classify cancer types with over 90% accuracy. In an independent cohort including samples from over 900 tumors, the algorithm achieved 82% accuracy.
The analysis further revealed variations between cancer subtypes. For instance, basal breast tumors were shown to produce different oncRNA patterns than luminal breast cancers. “This suggests that these molecules are telling us something fundamental about cancer cell state,” state Goodarzi and colleagues. “In other words, patterns of oncRNA presence and absence serve as ‘digital molecular barcodes’ that capture cancer cell identity, from cancer type to subtype and even cellular states.”
Next, the researchers looked into whether oncRNAs play an active role in cancer progression. Using large-scale genetic screenings in xenograft mice, they found that about 5% of the 400 oncRNAs tested were able to boost tumor growth. Two of them were then selected to be studied in more detail; one was found to promote epithelial-mesenchymal transition, a process critical during metastasis, while another activated E2F genetic pathways that drive cancer cell proliferation. Both were linked to increased tumor growth and metastasis in human cell lines and patient tumor data.
“Most clinically exciting is that cancer cells release these RNAs into the bloodstream, and tracking them can tell you how patients are doing,” write the researchers. “We knew these RNAs showed up in blood, but whether they’d be informative in actual patient samples was unknown. The fact that we needed only one milliliter of serum and still got a strong signal was surprising.”
After profiling RNA from 25 cancer cell lines spanning across nine different tissue types, the team found that 30% of oncRNAs are actively secreted by cancer cells. These results were validated in samples from 192 breast cancer patients enrolled in the I-SPY 2 neoadjuvant chemotherapy trial, where blood samples were collected before and after treatment to calculate the oncRNA burden. Changes in oncRNA burden were shown to predict both short- and long-term clinical outcomes, and patients with high levels of residual oncRNA after chemotherapy showed nearly four times lower overall survival.
“This addresses a real clinical problem: monitoring minimal residual disease in breast cancer with markers such as cell-free DNA is challenging because tumors don’t shed much DNA, especially in early stages,” the authors note. “RNA-based monitoring might offer a way forward because cancer cells actively secrete RNA rather than passively shedding DNA.”
The translation of these findings into clinical applications is already ongoing through Exai Bio, a biotechnology company co-founded by Goodarzi tasked with developing oncRNA-based diagnostics leveraging artificial intelligence to improve cancer detection and patient stratification. Future work will delve deeper into identifying oncRNA signatures specific to cancer subtypes as well as investigating potential links to treatment responses in larger patient populations.
