Abstract 5150: Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection

Abstract Introduction: Genomic scale copy number, methylation, and fragmentation aberrations are proven genetic and epigenetic biomarkers of circulating tumor DNA (ctDNA). We developed a novel cell-free DNA (cfDNA) methylome sequencing assay that allows for integrative analysis of these genomic features for sensitive detection of multiple types of cancer. Methods: Whole methylome sequencing (WMS) libraries were generated from enzymatically converted cfDNA. Low-pass (~2X) paired-end NGS sequencing was performed on WMS libraries and paired whole genome sequencing (WGS) libraries of unconverted cfDNA for technical comparison and analytical validation. For development of cancer detection models, we profiled the genome-wide methylation density (MD), fragment size index (FSI), fragment end motif (motif) and chromosome instability (CIN) based on WMS data from a discovery cohort of 352 healthy controls and 559 newly diagnosed cancer patients (45 breast, 105 colorectal, 44 esophageal, 79 gastric, 79 liver, 110 lung, 83 pancreatic, and 14 others), 34.5% of which were at stage I or II. Machine learning models, including KNN, SVM, LR, GBDT, and random forest were trained and tested for individual biomarker types, with a final ensemble classifier to integrate all biomarkers. Performance of the predictive model was confirmed on an independent validation cohort consisting of 145 healthy controls and 236 cancer patients (21 breast, 45 colorectal, 18 esophageal, 35 gastric, 34 liver, 47 lung, and 36 pancreatic), among which 31.8% were at early stages (I or II). Results: WMS and WGS data from 512 cfDNA samples showed high concordance in CIN (R=0.988, 95% CI: 0.986-0.990) and FSI (R=0.961, 0.954-0.967) profiles. On the independent validation cohort, the optimal model selected for each of individual genomic features achieved following area under the ROC curve (AUC) values for cancer detection: MD-KNN, 0.830 (0.789-0.870); FSI-SVM, 0.904 (0.874-0.933); motif-SVM, 0.943 (0.920-0.966); and CIN-PAscore, 0.812 (0.770-0.854). The ensemble classifier based on linear SVM outperformed individual biomarkers, with an AUC value of 0.952 (0.934-0.971), which translated to, at 95% specificity, detection sensitivity of 66.7% for breast, 77.8% for colorectal, 83.3% for esophageal, 62.9% for gastric, 82.4% for liver, 66.0% for lung, and 77.8% for pancreatic cancers. Noteworthily, the overall sensitivity on early-stage cancer was 74.7%. Conclusions: These results demonstrate the first proof of principle on the feasibility of integrating multiple genomic cancer markers on the same WMS technical platform. Low-pass WMS on plasma cfDNA from 10ml of blood with integrative multimodal analysis of methylation, fragmentation, and CNV profiles yields in satisfactory sensitivity and specificity for detection of multiple types of cancer, warranting a forthcoming prospective study to further assess its clinical performance in a larger cohort. Citation Format: Yulong Li, Fenglong Bie, Fengwei Tan, Tiancheng Han, Shunli Yang, Fang Lv, Peiyao Nie, Qi Zhang, Yuanyuan Hong, Zhijie Wang, Ji He, Weizhi Chen, Liang Zhao, Shugeng Gao. Multimodal analysis of plasma cell-free DNA methylome for sensitive multi-cancer detection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5150.