Abstract Background: Epigenetic and genetic alterations synergistically drive cancer initiation and progression, yet one blood draw rarely yields enough cell-free DNA (cfDNA) to profile both modalities. Current co-detection strategies either demand high tumor burden or custom chemistry and cannot be retro-applied to existing datasets. We present MMcall, a computational tool that reconstructs the original four-base genome from conventional bisulfite-sequencing reads and simultaneously detects mutations and methylation variants without new benchwork, delivering fully integrated genomic/epigenomic signatures from one single library. Methods: MMcall reconstructs the original four-base genome from standard bisulfite-converted reads by jointly modeling complementary top- and bottom-strand base counts. Because the G nucleotide on the strand opposing a C is unaffected by bisulfite conversion, the strand-specificity principle is used to restore pre-conversion sequence. A machine-learning error-suppression module is then applied to suppress the high technical noise inherent to bisulfite treatment. The algorithm simultaneously outputs methylation-variant allele frequency (MVAF) and somatic-variant allele frequency (SVAF) from the same library, enabling epigenetic and genetic profiling without additional wet-lab steps. Results: Benchmarked against 0 -1% tumor-fraction serial dilutions of Seraseq® ctDNA Reference Material and an in-house standard (OverC Monitor panel; 1000X methylation depth, 20000X mutation depth), MMcall demonstrated near-perfect concordance with expected methylation levels (R20.99) and detected mutations down to 0.25%. At 0.5-1% VAF, SVAF measurements by MMcall closely matched those obtained by ultra-deep sequencing (HS-UMI, 35000X), yielding 99.7% NPA (95% CI: 99.3-99.9%) and 86.9 % PPA (95 % CI: 77.8-93.3%). No false-positive calls were observed across predefined hotspot loci in any negative control, confirming robust suppression of technical noise. Conclusion: MMcall jointly calls mutations and methylation variants at single-base resolution without additional bench steps. The method offers a cost-effective route to richer molecular information for early cancer detection and minimal residual disease monitoring. Citation Format: Xingyu Yang, Jing Su, Chen Yang, Xiaoling Li, Si Zhang, Xianrong Chen, Bingsi Li. Integrated, base-resolution profiling of genetic and epigenetic signatures in cell-free DNA abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 111.
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Yang et al. (Fri,) studied this question.
www.synapsesocial.com/papers/69d1fc70a79560c99a0a201e — DOI: https://doi.org/10.1158/1538-7445.am2026-111
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