Abstract IA017: A liquid biopsy fingerprint of disease via nanoengineering

Abstract Serum biomarkers are often insufficiently sensitive or specific to facilitate cancer screening or diagnostic testing. In many cancers, including high-grade serous carcinoma (HGSC), biomarkers fail to detect early-stage cancer detection or to substantially impact mortality rates. We developed a perception-based sensing method that captures a biomarker-agnostic ‘disease fingerprint’ of HGSC using serum. Instead of measuring individual biomarkers, the method collects large data sets of molecular binding interactions to a diverse array of moderately-selective sensors, which were used to train machine learning algorithms. In an initial study using serum from 264 patients, we built a prediction model of nanosensor responses that reliably identified HGSC substantially better than CA125. We believe that the improvement of such a method, using larger nanosensor arrays, more sophisticated AI algorithms, and larger patient cohorts, could improve the detection of HGSC and facilitate biomarker discovery efforts. Citation Format: Daniel A. Heller, Mijin Kim, Lakshmi Ramanathan, Kara Long Roche. A liquid biopsy fingerprint of disease via nanoengineering abstract. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84 (5 Suppl₂): Abstract nr IA017.