Comprehensive Summary
Aptamers are recognition elements synthesized for biosensing but are limited due to enzyme degradation. To combat this, researchers created the personalized protein corona (based on individual serum), which stabilizes aptamers added afterwards to protect them as well as enriches less abundant cancer biomarkers onto the nanoparticle surface. This highly specific protein-aptamer corona was then analyzed using an 8-channel Orthogonal Multiplexed Electrochemical (OMEC) chip to measure binding signatures of many aptamers simultaneously. The signals received from the eight channels was then integrated into machine learning algorithms to give a clear diagnostic output. The developed algorithm was found to have high diagnostic accuracy when testing against ovarian cancer and non-small cell lung cancer cohorts, allowing for accurate diagnosis.
Outcomes and Implications
This research allows for early detection techniques to be utilized in oncology via blood tests as it can easily be integrated and test sera of individuals to detect cancer. Moreover, this platform's high accuracy allows for enhanced diagnosis and therapeutic development strategies to be developed and refined. Personalized medicine can be further developed using an individual's sample to be analyzed for diagnosis, prognosis, and monitoring treatment response. Overall, this modality of diagnosis is very stable and robust in the clinical setting, allowing for more accurate and scalable diagnosis across the globe in the future.