DeepFRAG: a method for cancer detection based on DNA fragmentomics and deep learning

Cancer screening using liquid biopsy technology has become standard in modern clinical and preventive oncology. This method analyzes cell-free DNA (cfDNA) circulating in a patient's bloodstream. While mutation-based diagnostics using deep exome sequencing are highly sensitive and specific, an alternative approach involves examining cfDNA fragment size distribution profiles. This method is less expensive and can be derived from low-depth whole genome sequencing (WGS).

Our study presents DeepFRAG: a new cancer detection method based on deep learning analysis of cfDNA fragment size distribution profiles using wavelet transform. We utilized two independent cohorts comprising 73 patients with stage III and IV cancers (breast, colorectal, pancreatic, lung, and liver) and 80 healthy individuals. We introduced an original data augmentation technique specific to WGS fragment size data, ensuring sufficient data for training the deep learning model. The proposed method demonstrated high accuracy, with a median test AUROC (area under the receiver operating characteristic curve) of 0.974 and a sensitivity of 96.1% at 98.8% specificity. Our approach offers several advantages, including high accuracy, cost-effectiveness, robustness, and suitability for detecting major cancer types. This method represents a promising advancement in cancer screening technology, expanding the options available for noninvasive cancer detection, with the goal of improving patient outcomes.

Data and source code are available at https://github.com/andreykoch/DeepFRAG.