Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing.

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung.

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.