The project investigates how tumor-derived RNA circulating in blood can be used to accurately predict survival outcomes in patients with non-small-cell lung cancer (NSCLC). While the approach has clear prognostic aims, the insights gained may also contribute to the development of future diagnostic applications. Importantly, the project does not rely on AI alone. It leverages SmartComplete, a novel, highly efficient RNA-sequencing method recently developed by Berezikov’s team. SmartComplete enables sensitive detection of a broad spectrum of RNA species from minimal input material, offering a more comprehensive molecular portrait than conventional cfRNA methods. Combining this advanced sequencing technology with state-of-the-art machine-learning approaches, the study aims to analyze 1,000 patient samples to build robust models for survival prediction. The project will also receive support from Rewire, which will provide a research trainee to collaborate on the application of AI models to the newly generated data.
