A perception-based nanosensor platform to detect cancer biomarkers

Conventional molecular recognition elements, such as antibodies, present issues for developing biomolecular assays for use in certain technologies, such as implantable devices. Additionally, antibody development and use, especially for highly multiplexed applications, can be slow and costly. We developed a perception-based platform based on an optical nanosensor array that leverages machine learning algorithms to detect multiple protein biomarkers in biofluids. We demonstrated this platform in gynecologic cancers, often diagnosed at advanced stages, leading to low survival rates. We investigated the detection of protein biomarkers in uterine lavage samples, which are enriched with certain cancer markers compared to blood. We found that the method enables the simultaneous detection of multiple biomarkers in patient samples, with F1-scores of similar to 0.95 in uterine lavage samples from patients with cancer. This work demonstrates the potential of perception-based systems for the development of multiplexed sensors of disease biomarkers without the need for specific molecular recognition elements.

Automated summary

This study presents a perception-based platform using machine learning algorithms to detect multiple protein biomarkers in gynecologic cancers, without the need for specific molecular recognition elements. The method enables simultaneous detection of various biomarkers in uterine lavage samples, demonstrating the potential for developing multiplexed sensors of disease biomarkers.