Distinguishing malignant from benign microscopic skin lesions using desorption electrospray ionization mass spectrometry imaging

Significance Timely detection of microscopic tumors is of utmost importance in cancer diagnostics. We show that desorption electrospray ionization mass spectrometry imaging (DESI-MSI) can successfully locate microscopic aggregates of a common skin cancer, basal cell carcinoma (BCC), and distinguish them from adjacent normal skin. DESI-MSI unveils an altered chemical profile in BCC region, including lipids and metabolites, and does not rely on visual identification of histopathologic features. We processed specimens from 86 Mohs micrographic surgeries, with nearly 60% of tumors sized less than 1 mm in diameter. By applying the statistical method of least absolute shrinkage and selection operator (Lasso) on collected DESI-MSI data, we were able to achieve up to 94.1% diagnostic accuracy compared with pathological evaluation of BCC. Detection of microscopic skin lesions presents a considerable challenge in diagnosing early-stage malignancies as well as in residual tumor interrogation after surgical intervention. In this study, we established the capability of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) to distinguish between micrometer-sized tumor aggregates of basal cell carcinoma (BCC), a common skin cancer, and normal human skin. We analyzed 86 human specimens collected during Mohs micrographic surgery for BCC to cross-examine spatial distributions of numerous lipids and metabolites in BCC aggregates versus adjacent skin. Statistical analysis using the least absolute shrinkage and selection operation (Lasso) was employed to categorize each 200-µm-diameter picture element (pixel) of investigated skin tissue map as BCC or normal. Lasso identified 24 molecular ion signals, which are significant for pixel classification. These ion signals included lipids observed at m/z 200–1,200 and Krebs cycle metabolites observed at m/z < 200. Based on these features, Lasso yielded an overall 94.1% diagnostic accuracy pixel by pixel of the skin map compared with histopathological evaluation. We suggest that DESI-MSI/Lasso analysis can be employed as a complementary technique for delineation of microscopic skin tumors.