Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry is a powerful tool for rapidly generating protein expression data (peptide and protein profiles) from a large number of samples. However, as with any technology, it must be optimized and reproducible for one to have confidence in the results. Using a classical statistical method called the fractional factorial design of experiments, we assessed the effects of 11 different experimental factors. We also developed several metrics that reflect trace quality and reproducibility. These were used to measure the effect of each individual factor, and the interactions between factors, to determine optimal factor settings and thus ultimately produce the best possible traces. Significant improvements to output traces were seen by simultaneously altering several parameters, either in the sample preparation procedure or during the matrix preparation and application procedure. This has led to the implementation of an improved method that gives a better quality, reproducible, and robust output.

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3D cell culture models of cancer are currently being developed to recapitulate in vivo physiological conditions and to assess therapeutic responses. However, most models failed to incorporate the biochemical and biophysical stimuli from fluid flow. In this study, a three-dimensional scaffold, SeedEZ was applied within the PerfusionPal perfused culture system to investigate how perfusion, and blood-like oxygen delivery influenced breast cancer cell growth and their responses to a commonly used breast cancer drug tamoxifen. Our results showed that breast cancer cells could be maintained over 3 weeks in PerfusionPal with increased cell viability compared to static 3D culture in fully humanised conditions. This platform also supported examining the effect of tamoxifen on breast cancer cell lines and in primary patient-derived breast cancer samples. Future work is warranted to further the adaption for fully humanised assessment of drug effectiveness in a patient personalized approach with the aim to reduce the burden of animal use in cancer research and increase the degree of human pre-clinical data translation to clinic.

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Through translational research, the outcomes for women (and men) diagnosed with breast cancer have improved significantly, with now over 80% of women surviving for at least 5 years post-diagnosis. Much of this success has been translated from the bench to the bedside using laboratory models. Here, we outline the types of laboratory models that have helped achieve this and discuss new approaches as we move towards animal-free disease modelling.