A fragment-based algorithm for identifying pan-cancer differential methylation markers from cfDNA.

e22507 Background: Methylation-based sequencing has been developed as a promising approach for Multi-Cancer Early Detection (MCED). Conventionally, cancer-specific methylation markers were identified by comparing paired tumor and adjacent normal tissues using traditional algorithm DSS (Dispersion shrinkage for sequencing data). However, the clinical applicability is limited by the unsatisfactory sensitivity in early-stage disease. Hence, we included blood specific markers derived from paired tissue and blood samples and developed a novel fragment-based algorithm in cfDNA with a better signal to noise ratio (SNR) than DSS to improve the sensitivity of the assay. Methods: A total of 391 plasma samples and 433 tissue samples from 7 cancer types were subjected to whole genome bisulfite sequencing (WGBS) (Table). The proportion of stage I-III and stage IV was 3.5:1 among 7 cancer types. The average sequencing depth was 30X. For each bin, the proportion of fully methylated (M) / un-methylated (U) fragments, defined with methylation level ≥85% / ≤15%, was computed individually. One-sided Wilcoxon rank sum test was employed to test whether cancer cfDNA have significantly (FDR < 0.001) higher proportion of U/M fragments than control cfDNA. Results: To evaluate the performance of fragment-based algorithm in cfDNA, the false positive was assessed by resampling from healthy subjects. The healthy cfDNA were randomly assigned 20 times as pseudo case-control groups of 30 /170. The number of markers among healthy samples was recorded as N False Positive (N FP ), while the number of markers between cancer and healthy samples was recorded as N Positive (N P ). SNR was defined as (N P -N FP )/N FP . We calculated SNR using the fragment-based algorithm and achieved SNR of 135.2 for colorectal cancer, compared to SNR of 2.2 using DSS. The improved SNR was also reproduced in other cancer types. Concordance between plasma and tissue markers was 30.09%-95.92% (median 66.72%) among 7 cancer types, which was related to the proportion of ctDNA for each cancer type. Plasma markers which show weak signals in tissue are defined as blood-specific markers. These markers were enriched in immune-related pathways, suggesting a correlation to the immune response of cancer patients. Conclusions: We developed a novel fragment-based algorithm that achieves much higher SNR than DSS in cfDNA. We also identified blood-specific methylation markers which may improve the sensitivity of early cancer detection in clinical. Table: see text