Non-invasive screening of breast cancer from fingertip smears-a proof of concept study.

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.

Exploring the problem of determining human age from fingermarks using MALDI MS-machine learning combined approaches.

For over a century fingerprints have been predominantly used as a means of biometric identification. Notwithstanding, the unique pattern of lines that can contribute to identifying a suspect is made up of molecules originating from touch chemistry (contaminants) as well as from within the body. It is the latter class of molecules that could provide additional information about a suspect, such as lifestyle, as well as physiological, pharmacological and pathological states. An example of the physiological state (and semi-biometric information) is the sex of an individual; recent investigations have demonstrated the opportunity to determine the sex of an individual with an 86% accuracy of prediction based on the peptidic/protein profile of their fingerprints. In the study presented here, the first of its kind, a range of supervised learning predictive methods have been evaluated to explore the depth of the issue connected to human age determination from fingermarks exploiting again the differential presence of peptides and small proteins. A number of observations could be made providing (i) an understanding of the more appropriate study design for this kind of investigation, (ii) the most promising prediction model to test within future work and (iii) the deeper issues relating to this type of determination and concerning a mismatch between chronological and biological ages. Particularly resolving point (iii) is crucial to the success in determining the age of an individual from the molecular composition of their fingermark.