A deep learning approach for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA

Abstract

AbstractEarly cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments require tedious data analytical procedures. Here, we present a Deep-learning-based approach for Early Cancer Interception and DIAgnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on feature representation learning of DNA fragments and weakly supervised learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer-type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC, n = 1574), hepatocellular cell carcinoma (HCC, n = 1181), lung cancer (n = 654) and non-cancer control (n=1980). DECIDIA achieves an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in ten-fold cross validation settings on the CRC dataset, outperforming benchmarked methods that are based on fragmentation profiles and methylation intensities. Noticeably, DECIDIA achieves an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieves a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95%CI, 81.8% - 83.9%). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.