Abstract / Summary
Background: Liquid biopsy-based monitoring of circulating tumor DNA (ctDNA) holds prom-ises for real-time assessment of tumor burden and treatment response in precision oncology. However, mutation-based approaches alone show limited sensitivity, particularly in low-shedding tumors. We evaluated whether integrating tumor-naive epigenomic biomarkers, specifically LINE-1 retrotransposon (L1PA) hypomethylation and copy number variation (CNV), with standard mutation-based ctDNA analysis could improve cancer detection and longitudinal monitoring in patients enrolled in the SHIVA02 precision oncology trial. Methods: We performed a retrospective analysis of 32 patients with 11 different types of ad-vanced or metastatic solid tumors and who received molecularly matched targeted therapies within the SHIVA02 trial (NCT03084757). Plasma samples were collected at baseline, longitu-dinally every two months and at progression. Multimodal ctDNA profiling was performed using the DRAGON targeted NGS panel covering SNVs, indels and focal CNVs in 571 genes and the DIAMOND assay profiling L1PA methylation and genome-wide CNV. A three-step classifi-cation algorithm integrating maximum variant allele frequency (MaxVAF), L1PA methylation-based cancer probability (MethPCancer), and genome-wide CNV scores was developed to maximize ctDNA detectability. Results: At baseline, mutation-based profiling detected ctDNA in 62.5% of patients. L1PA hypomethylation alone identified ctDNA in 90.6% of patients, including cases with undetectable mutations. The three-step integrative model increased overall detectability to 96.9%. The three modalities showed limited pairwise correlation at baseline, supporting their complemen-tarity. Longitudinal analysis demonstrated that changes in MaxVAF, MethPCancer, and L1PA CNV scores over time were informative on treatment response across tumor types, with ctDNA detected in 97.3% of samples collected at disease progression. In selected patients, ctDNA alterations preceded radiological progression by two to four months. Conclusions: Multimodal ctDNA profiling integrating mutation analysis, CNV profiling, and tumor-naive LINE-1 hypomethylation substantially improves ctDNA detection at baseline and during treatment in a pan-cancer setting. These complementary genomic and epigenomic biomarkers provide a more comprehensive and dynamic assessment of tumor burden than single-modality approaches, supporting prospective validation in larger cohorts for integration into precision oncology workflows.