Abstract 111: Integrated, base-resolution profiling of genetic and epigenetic signatures in cell-free DNA.

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 (R2>0.99) 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.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-111
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 111: Integrated, base-resolution profiling of genetic and epigenetic signatures in cell-free DNA.

Si Min Zhang, Bingsi Li, C. Yang, X. Li et al.
Cancer Research
Cancer Genomics and Diagnostics
article

Abstract 111: Integrated, base-resolution profiling of genetic and epigenetic signatures in cell-free DNA.

Si Min Zhang, Bingsi Li, C. Yang, X. Li, X. Yang, J. Su, Xianrong Chen
article en

Abstract

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 (R2>0.99) 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.

Cancer ResearchVol. 86(7_Supplement)
Burning Rock Biotech (China) (CN)
Openalex Percentile: Top 12%
Cancer Genomics and Diagnostics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.