Non-invasive detection of breast cancer from fingertip smears

Breast cancer is the most common cancer worldwide, accounting for 11.7% of all cancer diagnoses in 2020, with 150 new cases/per day in the UK. Despite the threat, mammography, the current gold standard screening method, has variable uptake and faces accessibility barriers amongst other.

We have developed a groundbreaking, non-invasive method to detect breast cancer from simply swiping a fingertip on a metal plate thus offering an accessible and painless alternative to traditional screening methods. Using a combination of proteomics, Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI) and artificial intelligence, unique protein profiles in sweat have been recovered which can differentiate between benign, early cancer and metastatic breast cancer with the 98% of accuracy.

This innovative PhD project aims to further develop the method developed as proof-of-concept, by refining sample preparation and machine learning techniques applied to data acquired on state-of-the-art mass spectrometry equipment, to enhance the detection of breast cancer biomarkers from fingertip smears and improve detection accuracy whilst reducing false positives/negatives.

You will gain interdisciplinary experience in bioanalytical mass spectrometry, proteomics, data science and cancer biology, supported and guided by a Sheffield Hallam University and the University of Sheffield collaborative team of experts in forensic bioanalytics, surgical oncology, proteomics and data science.

Your contribution to develop a faster, more reliable, and cost-effective breast cancer screening method has the potential to transform early detection practices, enabling easier access to screening and improving outcomes for patients worldwide.