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.

Non-invasive Breast Cancer Screening Using Fingertip Smears

This study explores the potential of a non-invasive technique for early detection of breast cancer using fingertip smears and bottom-up proteomics combined with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection. The study involved 15 women with benign breast disease, early breast cancer, or metastatic breast cancer. Machine learning approaches were applied to the resulting mass spectral dataset, and a 3-class Multilayer Perceptron neural network achieved an accuracy score of 97.8% in classifying unseen MALDI MS spectra and correctly differentiating women with metastatic breast cancer, early breast cancer and benign pathology. The results support further research into the use of sweat deposits for non-invasive screening of breast cancer, as it could provide a pain-free and culturally acceptable alternative to mammography.

This research is important for several reasons. Breast cancer is a global health issue affecting a large number of women, causing death in over 600,000 per year. The study explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears, which could be a valuable alternative to the current gold standard of mammography and biopsy or used as a preliminary screening to reduce the number of women that undergo these tests. Mammography 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. Therefore, the development of a non-invasive, non-painful technique for breast cancer screening could increase screening and survival rates, while eliminating the risk of unnecessary radiation exposure.

Key Takeaways:

1. The study explores the potential for a non-invasive technique for the early detection of breast cancer from fingertip smears.

2. Mammography, the current gold standard for breast cancer screening, has limitations such as exposure to radiation, discomfort, and cultural unacceptability.

3. The study used a bottom-up proteomics approach combined with Matrix Assisted Laser Desorption Ionisation Mass Spectrometry (MALDI MS) detection to analyse fingertip smears from patients with benign breast disease, early breast cancer, or metastatic breast cancer.

4. A 3-class Multilayer Perceptron neural network was the highest performing predictive method, yielding an accuracy score of 97.8% when categorising unseen MALDI MS spectra as either the benign, early or metastatic cancer classes.

5. The 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.