Integrated microfluidic platform with MEMS cantilever for the early detection of prostate cancer using dry mass sensing

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Prostate cancer is the most prevalent cancer in men. In the UK alone, more than 40'000 cases are diagnosed every year (www.nhs.uk). The current accepted clinical gold standard for the diagnosis of prostate cancer is the measure of the prostate-specific antigen (PSA) levels in blood. However, it is now accepted that PSA test are unreliable and can lead to over-diagnosis. These false-positive results mean that many men are subjected to invasive and painful biopsies for no reason. Our pro-ject aims to address these limitations through the development of a minimally invasive, low-cost biosensing platform to detect PCA3, a promising urinary diagnosis biomarker for prostate cancer. In order to provide a viable alternative to ultra-sensitive, yet more complex and time-consuming assays, our platform should be capable of detecting, within 15 minutes, PCA3 down to concentra-tions of 1 ng/mL in urine. This goal will be achieved through the implementation of a simple and novel concept to enable high-resolution measurement, using ultra-sensitive mechanical sensors integrated in microfluidic devices. The concept of measurement air pockets, proposed here for the first time, will permit the capture of the analyte molecules in the liquid phase and their measurement in the gas phase; through the introduction of air pockets as measurement chambers. The measurement of liquid borne analytes in air will minimize viscous damping losses and thereby enable high-resolution, high-sensitivity measurement. In order to facilitate the deployment of the platform in clinical settings, we will test it using clinical samples from the early development stage and we will ensure that it can be fabricated using ac-cepted manufacturing methods. After a proof-of-principle device has been demonstrated we will seek further funding, in collaboration with prostate cancer clinicians, to evaluate the platform in clin-ical settings. It is noted that the platform could be adapted for the diagnosis of other conditions and that the simple concept proposed here could be applied to other sensor for a range of applications including environmental monitoring and food safety.