Researchers at UCSF identified something unusual with T3p, a small RNA present in breast cancer but absent from normal tissue. First described in 2018, this molecule launched a six-year effort to systematically map orphan non-coding RNAs, known as oncRNAs, across all major cancer types, determine which ones actively drive disease, and demonstrate their potential for patient monitoring through simple blood tests.

In work published recently, the team describes how they progressed from mining large cancer genomics datasets to building machine learning classifiers, conducting large-scale functional screens in mouse models, and ultimately validating oncRNAs as blood-based biomarkers in nearly 200 breast cancer patients.

Orphan RNAs are widespread in cancer

One of the first major discoveries was that the phenomenon observed in breast cancer was not unique. By systematically analyzing small RNA sequencing data from The Cancer Genome Atlas across 32 cancer types, the researchers identified approximately 260,000 cancer-specific small RNAs, termed oncRNAs, distributed across every cancer examined.

The presence of these RNAs is highly structured rather than random, with each cancer type producing a distinct oncRNA expression pattern. For instance, lung cancers express a different set of oncRNAs than breast cancers. These patterns can be leveraged by machine learning models to classify cancer types with 90.9% accuracy, a result that was independently validated in a cohort of 938 tumors, achieving 82.1% accuracy.

Additional variation was observed within individual cancer types. Basal breast tumors exhibited oncRNA patterns distinct from luminal tumors, suggesting the existence of previously unrecognized subdivisions. Together, these findings indicate that oncRNA patterns reflect fundamental cancer cell states. In effect, the presence or absence of specific oncRNAs acts as a digital molecular barcode that captures cancer identity at the level of cancer type, subtype, and cellular state.

Some oncRNAs actively drive cancer

Beyond their value as biomarkers, the researchers sought to determine whether cancer cells exploit certain oncRNAs to promote tumor progression. To investigate this, they constructed large-scale screening libraries encoding roughly 400 oncRNAs derived from breast, colon, lung, and prostate cancers. These libraries were introduced into cancer cells using lentiviral vectors, either by overexpressing oncRNAs or suppressing them using Tough Decoy constructs.

The modified cells were then implanted into mice to assess tumor growth. Approximately 5% of oncRNAs produced clear phenotypes in xenograft models. Two breast cancer oncRNAs were studied in depth. One promoted epithelial-mesenchymal transition, a key process in cancer invasion and metastasis, while the other activated E2F target genes, driving cell proliferation. Both significantly enhanced tumor growth and metastatic colonization across independent cancer cell line models.

Importantly, analysis of patient tumor datasets showed that cancers expressing these oncRNAs exhibited the same pathway changes observed in experimental models. The concordance between patient data and functional models strengthened confidence that these oncRNAs play biologically meaningful roles in cancer.

OncRNAs are released into the bloodstream

Perhaps the most clinically significant finding is that cancer cells actively release oncRNAs into the bloodstream, making them accessible for noninvasive monitoring. Profiling of cell-free RNA from 25 cancer cell lines across nine tissue types revealed that approximately 30% of oncRNAs are actively secreted.

To validate their clinical relevance, the researchers analyzed serum samples from 192 breast cancer patients enrolled in the I-SPY 2 neoadjuvant chemotherapy trial. Blood samples were collected before and after treatment, and changes in total oncRNA burden were calculated. This single metric proved highly predictive. Patients with elevated oncRNA levels after chemotherapy had nearly four-fold worse overall survival, even after controlling for established clinical measures such as pathologic complete response and residual cancer burden.

The ability to detect meaningful signals from just one milliliter of serum was particularly notable. While oncRNAs were known to circulate in blood, their performance as patient monitoring markers had remained uncertain until this analysis.

These findings address a longstanding clinical challenge. Monitoring minimal residual disease in breast cancer using cell-free DNA is difficult because early-stage tumors shed little DNA. In contrast, cancer cells actively secrete RNA, suggesting that RNA-based monitoring may offer a more sensitive alternative.

Looking ahead

Despite these advances, many biological and clinical questions remain. The precise molecular mechanisms by which functional oncRNAs exert their effects are not yet fully understood. It remains unclear whether they primarily interact with proteins, other RNAs, or additional regulatory systems. Their potential use in real-time treatment guidance, early recurrence detection, and patient stratification will require larger prospective studies.

Nonetheless, the core finding that oncRNAs provide cancer-specific signals in blood is already advancing toward clinical translation. The research team is closely involved with the biotechnology company Exai Bio, co-founded by Hani Goodarzi, which is developing oncRNA-based diagnostic tools and building large AI-driven datasets for cancer detection and classification.

Translational research of this scale relies on contributions from many disciplines, yet each dataset ultimately represents real patients who volunteered samples in hopes of advancing care. Maintaining that perspective continues to motivate the research team.

Overall, oncRNAs emerge as a new class of cancer-associated molecules that function both as oncogenic drivers and clinically actionable biomarkers. By making this resource openly available, the researchers aim to catalyze new directions across cancer biology, diagnostics, and precision oncology.

“We think oncRNAs represent a new class of cancer-emergent molecules that function as both drivers and biomarkers. We hope that this resource, which we’ve made open source, opens new directions for the field.” –  Jeffrey Wang and Hani Goodarzi

Source – Eurekalert