Breast cancer is a global health issue affecting 2.3 million women per year, causing death in over 600,000. Mammography (and biopsy) is the gold standard for screening and diagnosis. Whilst effective, this test exposes individuals to radiation, has limitations to its sensitivity and specificity and may cause moderate to severe discomfort. Some women may also find this test culturally unacceptable. This proof-of-concept study, combining bottom-up proteomics with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection, explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears. A cohort of 15 women with either benign breast disease (n = 5), early breast cancer (n = 5) or metastatic breast cancer (n = 5) were recruited from a single UK breast unit. Fingertips smears were taken from each patient and from each of the ten digits, either at the time of diagnosis or, for metastatic patients, during active treatment. A number of statistical analyses and machine learning approaches were investigated and applied to the resulting mass spectral dataset. The highest performing predictive method, a 3-class Multilayer Perceptron neural network, yielded an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes. These findings support the need for further research into the use of sweat deposits (in the form of fingertip smears or fingerprints) for non-invasive screening of breast cancer
