Nanopore based RNA methylation profiling of a circulating lung cancer biomarker

The characterisation of circulating nucleic acid biomarkers in liquid biopsy based diagnostics holds great potential to transform the landscape of early cancer detection and screening. These tests are increasingly incorporating information beyond the primary sequence, to include epigenetic, fragmentomic, and other chemical properties to boost performance. Chemical modifications of RNA offer a rich, and currently underutilised source of biomarker signal. We aimed to develop a single-molecule method to profile 2′-O-methylation of a ribosomal RNA fragment previously linked to lung cancer, and to evaluate its diagnostic value in blood. We have designed a targeted capture strategy that ligates structure-guided adapters to a ~22-nucleotide ribosomal RNA fragment to enable sequencing of native molecules on a nanopore platform. Raw ionic-current signals were used to train and validate machine-learning classifiers to detect methylation states of synthetic oligonucleotides and cell culture derived ribosomal RNA fragments. Finally, we applied these methods to liquid biopsy samples collected from a 43-patient cohort of individuals undergoing investigation for suspected lung cancer. Here we show that single molecules of the target fragment are sequenced, and their methylation states can be accurately (92%) and quantitatively (Pearson r = 0.997) measured. In clinical liquid biopsy samples, it reveals a differential pattern of methylation in lung cancer that yields a diagnostic classifier with an area under the receiver-operating characteristic curve of 0.84. This approach enables direct, single-molecule methylation profiling of small RNAs in blood and identifies a lung cancer–associated methylation pattern with diagnostic potential. It is readily compatible with multi-modal liquid biopsy assays to enhance performance. Blood tests for the early detection of cancer show great promise. These rely on the analysis of millions of molecules in the blood and the detection of differences that indicate the presence of cancer. RNA molecules are typically assessed by reading the letters of their genetic code (A, U, C, G), however we now realise that these letters can undergo chemical modifications, akin to changing their font. We have built a method that can read these molecules and identify their font using artificial intelligence. We used this method to investigate one RNA molecule in blood samples and found a difference between the fonts used in healthy compared to patients with lung cancer. This method could add a new layer to blood-based cancer tests and may help us find lung cancer earlier. Sanchez-Delgado, Frank et al. develop a method for single molecule, single nucleotide profiling of small RNA biomarker methylation status. They apply this method to lung cancer liquid biopsy samples to identify a differential pattern of methylation of a ribosomal RNA fragment with diagnostic potential.