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

TL;DR

This study employs Surface Enhanced Raman Scattering combined with machine learning to distinguish between healthy and tumor DNA, as well as different tumor subtypes, based on their physical and biological properties.

Contribution

It introduces a novel SERS-based platform with learning models for accurate classification of tumor subtypes from genomic DNA.

Findings

Successful discrimination of healthy and tumor DNA

Effective classification of melanoma and colon cancer subtypes

Potential for early cancer detection and personalized therapy

Abstract

An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that physical properties, including structural conformation, stiffness, and shape, as well as biological processes, such as methylation, can be pivotal to recognize DNA modifications. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated--silicon nanowires, to investigate genomic DNA from subtypes of melanoma and colon cancers and to efficiently discriminate tumor and healthy cells, as well as the different tumor subtypes. The diagnostic information is obtained by performing label--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 the specific and distinct interaction of healthy and tumor DNA molecules with nanowires.