Breast cancer is the most common cancer type that affects women and although several therapy improvements have been made in the last decades, it is still estimated that nearly 11,400 women die each year of this type of cancer in the United Kingdom alone. At early stages of the disease the options for treatment and likelihood of survival are much higher. For that reason, it is crucial to develop better strategies for early detection.

Marta's PhD examined the biophysical characteristics of cancer associated fibroblasts which are the major component of the tumour microenvironment and have been shown to play an important role in regulating cancer progression. The research used fluorescence microscopy and atomic force microscopy to estimate the mechanical properties of individual cells from cancer patients and to combine with artificial intelligence and machine learning to develop diagnostic biomarkers.

The results suggest that key parameters related to the appearance of the cytoskeleton in cancer associated fibroblasts may be used as biophysical biomarkers to identify the cancer subtype associated with an individual patient. This provides a potential new diagnostic toolkit for early detection and stratification of breast cancer sub types.

Marta's PhD was supervised by Dr Nuria Gavara and more recently by Prof Martin Knight within the Centre for Bioengineering in the School of Engineering and Materials Science at Queen Mary University of London.

Following her PhD, Marta is now working as a Data Scientist at 'Knight Frank' here in London.

Updated by: Martin Knight