Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis

Abstract Digital medicine leverages digital biomarkers by algebraically integrating multiple biomarkers to reflect disease status. Colorimetric analysis offers an intuitive readout, but colorimetric-based digital medicine remains underexplored. Here we show an Enzymatic Colorimetric Encoding-based Digital Medicine platform (EnCODE). By harnessing enzyme-catalyzed multicolor encoding in tandem with the programmability of DNA technology, EnCODE converts multidimensional miRNA information into recognizable optical signals. We demonstrate that these signals are decodable and can be interpreted by visual inspection or spectral analysis, facilitating dimensionality reduction and visualization of disease states. Additionally, EnCODE integrates a continuous weighting mechanism that enables accurate mapping of digital biomarkers. In a cohort of 163 pancreatic cancer clinical samples, EnCODE achieves 96% detection sensitivity and 90% overall accuracy—comparable to the 96% sensitivity and 91% overall accuracy with conventional molecular diagnostic methods. We increase data density through three-dimensional color encoding and hyperspectral imaging-based analysis, enabling an intuitive color-coded molecular readout.