STTR Phase I: Portable single cell cytology and predictive analysis platform for the early detection of epithelial cancers

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will address the need for an accessible method to identify early-stage epithelial cancers, with high accuracy, earlier and at lower cost than is currently available. In 2020, the total cost of cancer care was nearly $210 billion. Due to the nature of current cancer diagnostics, most cancers are diagnosed and treated during late-stages. This results in a large economic burden to patients, families, healthcare providers, and facilities. To advance the health and welfare of the public and reduce the nation’s healthcare burden, there is a need for cancer screening, diagnostic, and monitoring devices that are non-invasive, cost-effective, easy-to-use, and accurate. The proposed platform for the early detection of multiple types of epithelial cancers 1) addresses the lack of effective non-invasive portable screening devices; 2) provides faster, more discriminatory assessments in near real-time; 3) yields the most precise and accurate results to identify cancers earlier, when interventions are more impactful, less expensive, less invasive, and more likely to improve patient outcomes. This Small Business Technology Transfer (STTR) Phase I project seeks to establish the feasibility of developing the first portable, programmable, single cell cytology platform for early detection of multiple types of epithelial cancers, suitable for use at the point-of-care. The proposed technology will uniquely combine microfluidics and artificial intelligence (AI) to act as a sensor and provide predictive analysis, allowing for the accurate classification of potentially cancerous tissue. The platform will support near real-time, multiparameter, single-cell cytology measurements and will provide a method for automated analysis of a plurality of key metrics. Proof of concept has been established for the application area of oral cavity cancers, with the approach demonstrating superior performance metrics compared to other diagnostics (tissue reflectance, tissue auto fluorescence, salivary testing, and cytology testing). It is the only adjunct that can distinguish between mild, moderate, and severe dysplasia. The key objectives for this project are to develop methodologies to link different clinical specimen types to the microfluidics environment, and a biomarker discovery process to identify biomarkers for different applications that are amenable to the platform. The successful completion of this project will enable the platform to recognize and assess various levels of dysplasia across multiple epithelial cancer types. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.