Meta-Analysis for Multi-Cancer Early Detection Biomarker Discovery

Cancer poses a significant worldwide burden where ongoing efforts are targeted towards improving patient outcomes in which a significant contribution results from cancer screening. Multi-cancer early detection tests have been introduced which measure a series of biomarkers to detect signals that may indicate carcinogenesis in its earliest stages and work in tandem with other diagnostic techniques to localize and verify tumor formation across multiple cancer types. Molecular biomarkers such as autoantibodies are promising candidates for early detection across multiple cancers. This study identifies autoantibodies that are aberrantly expressed across multiple cancer types that may be used to discriminate between healthy individuals and those with cancer from a single serum sample. Multiple datasets are integrated from prior studies to examine 8,200 sera autoantibodies from 5 cancer types including lung adenocarcinoma, basal-like breast cancer, advanced colorectal cancer, ovarian cancer, and HER2+ breast cancer. The diagnostic utility of these autoantibodies is assessed for combined cancer types by meta-receiver operating characteristic (ROC) curve analysis. A meta-analysis data processing pipeline is utilized for processing each biomarker with statistical analysis performed across ROC metrics for each meta-curve including partial area under the curve and sensitivity at a 90% specificity threshold. Results identified 26 autoantibody biomarkers that are useful for multi-cancer detection and may be developed for future clinical applications in cancer screening.