DEcancer: Machine learning framework tailored to liquid biopsy based cancer detection and biomarker signature selection

Cancer is a leading cause of mortality worldwide. Over 50% of cancers are diagnosed late, rendering many treatments ineffective. Existing liquid biopsy studies demonstrate a minimally invasive and inexpensive approach for disease detection but lack parsimonious biomarker selection, exhibit poor cancer detection performance and lack appropriate validation and testing. We established a tailored machine learning pipeline, DEcancer, for liquid biopsy analysis that addresses these limitations and improved performance. In a test set from a published cohort of 1,005 patients including 8 cancer types and 812 cancer-free individuals, DEcancer increased stage 1 cancer detection sensitivity across cancer types from 48 to 90%. In addition, with a test set cohort of patients from a high dimensional proteomics dataset of 61 lung cancer patients and 80 cancer-free individuals, DEcancer’s performance using a 14-43 protein panel was comparable to 1,000 original proteins. DEcancer is a promising tool which may facilitate improved cancer detection and management. [Display omitted] •DEcancer multi-cancer detection over double sensitivity of existing cited approaches•DEcancer successfully identifies distinct cancer types•DEcancer selects a succinct panel of biomarkers indicative of cancer type, including lung•DEcancer provides a foundation for clinical blood-based tests for cancer detection Diagnostics; Cancer; Machine learning