Researchers have identified a new class of orphan non-coding RNAs, called oncRNAs, that appear across various cancer types and form unique molecular signatures. These molecules not only identify cancer type and subtype with high accuracy but also drive tumor growth in some cases. Their presence in the bloodstream offers potential for simple blood tests to monitor treatment response and predict patient survival.
The discovery began in 2018 with T3p, a small RNA molecule found in breast cancer but absent in normal tissue. This unusual finding sparked a six-year research effort to explore similar orphan non-coding RNAs, termed oncRNAs, in major cancer types. The study, published in Cell Reports Medicine, involved analyzing small RNA sequencing data from The Cancer Genome Atlas across 32 cancer types, revealing approximately 260,000 cancer-specific small RNAs present in every type examined.
Each cancer displayed distinct oncRNA expression patterns. For instance, lung cancers exhibited different oncRNAs compared to breast cancers. Machine learning models using these patterns classified cancer types with 90.9% accuracy on the initial dataset and 82.1% on a separate group of 938 tumors. Within breast cancer, oncRNA patterns differed between basal and luminal subtypes, acting as "digital molecular barcodes" that capture tumor identity, subtype, and cellular state.
To assess functional roles, researchers screened about 400 oncRNAs from breast, colon, lung, and prostate tumors. Using lentiviral vectors in cancer cells implanted into mice, they found that roughly 5% influenced tumor growth. Two breast cancer oncRNAs were studied in detail: one induced epithelial-mesenchymal transition, aiding metastasis, while the other activated E2F target genes to promote proliferation. Both accelerated tumor growth and metastatic colonization in models, with similar pathway changes observed in patient tumor data from TCGA.
A key clinical insight emerged from oncRNAs' release into the bloodstream. Analysis of cell-free RNA from 25 cancer cell lines across nine tissue types showed about 30% were actively secreted. In serum samples from 192 breast cancer patients in the I-SPY 2 trial, high residual oncRNA levels after neoadjuvant chemotherapy correlated with nearly four-fold worse overall survival, even after adjusting for standard clinical indicators.
This approach addresses challenges in monitoring minimal residual disease, where RNA secretion may provide clearer signals than DNA. The team, including Hani Goodarzi, is collaborating with Exai Bio to develop oncRNA-based diagnostics using AI models. The findings highlight oncRNAs as both disease drivers and biomarkers, with resources made openly available for further research.
