Profiling DNA mutation patterns by SERS fingerprinting for supervised cancer classification

Abstract

Profiling DNA mutation patterns for cancer classification plays an essential role in precision and personalized medicine. Conventional PCR-based mutation assay is limited by the extensive labour on target amplification. We herein create an amplification-free surface enhanced Raman spectroscopy (SERS) biochip which enables direct and simultaneous identification of multiple point mutations in tumor cells. Without pre-amplifying the target sequences, the SERS assay reads out the presence of cellular mutations through the interpretation of Raman fingerprints. The SERS sensor is integrated into a microfluidic chip, achieving one-step multiplex analysis within 40 min. Importantly, by combining SERS spectra encoding technique with supervised learning algorithm, a panel of nucleotide mixtures can be well distinguished according to their mutation profiles. We initially demonstrate an excellent levels of classification in samples from colorectal cancer and melanoma cell lines. For final clinical validation, the system performance is verified by classifying cancer patient samples, which shows an accuracy above 90%. Due to the simplicity and rapidness, the SERS biosensor is expected to become a promising tool for clinical point-of-care diagnosis towards precision medicine.

Keywords: DNA mutation; Multiplex detection; Spectral encoding; Supervised cancer classification; Surface enhanced Raman spectroscopy (SERS).