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An early detection of different tumor subtypes is crucial for an effectiveguidance to personalized therapy. While much efforts focus on decoding thesequence of DNA basis to detect the genetic mutations related to cancer, it isbecoming clear that physical properties, including structural conformation,stiffness, and shape, as well as biological processes, such as methylation, canbe pivotal to recognize DNA modifications. Here we exploit the Surface EnhancedRaman Scattering (SERS) platform, based on disordered silver coated--siliconnanowires, to investigate genomic DNA from subtypes of melanoma and coloncancers and to efficiently discriminate tumor and healthy cells, as well as thedifferent tumor subtypes. The diagnostic information is obtained by performinglabel--free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat,and leveraging the classification ability of learning models to reveal thespecific and distinct interaction of healthy and tumor DNA molecules withnanowires.

Learning models for classifying Raman spectra of genomic DNA from tumor subtypes