Multimodal ctDNA profiling for cancer detection and monitoring in pan-cancer patients with advanced disease enrolled in the SHI-VA02 trial
- 1Research Institute for Environmental and Occupational Health
Abstract
BackgroundLiquid biopsy-based monitoring of circulating tumor DNA (ctDNA) holds promises 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.
MethodsWe performed a retrospective analysis of 32 patients with 11 different types of advanced or metastatic solid tumors and who received molecularly matched targeted therapies within the SHIVA02 trial (NCT03084757). Plasma samples were collected at baseline, longitudinally 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 classification algorithm integrating maximum variant allele frequency (MaxVAF), L1PA methylation-based cancer probability (MethPCancer), and genome-wide CNV scores was developed to maximize ctDNA detectability.
ResultsAt 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 complementarity. 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.
ConclusionsMultimodal 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.
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BibTeX
@misc{nedara2026multimodal,
title = {{Multimodal ctDNA profiling for cancer detection and monitoring in pan-cancer patients with advanced disease enrolled in the SHI-VA02 trial}},
author = {K. Nedara and M. Gorse and K. von Grafenstein and J. masliah-planchon and S. Antonio and C. Bianchi and M. Sene and P. Du Rusquec and O. Mariani and M. KAMAL and A. Hamza and I. Bieche and C. LE TOURNEAU and C. Dupain and C. Proudhon},
howpublished = {medRxiv},
year = {2026},
month = jul,
doi = {10.64898/2026.07.24.26358660},
url = {https://www.medrxiv.org/content/10.64898/2026.07.24.26358660v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Multimodal ctDNA profiling for cancer detection and monitoring in pan-cancer patients with advanced disease enrolled in the SHI-VA02 trial
AU - K. Nedara
AU - M. Gorse
AU - K. von Grafenstein
AU - J. masliah-planchon
AU - S. Antonio
AU - C. Bianchi
AU - M. Sene
AU - P. Du Rusquec
AU - O. Mariani
AU - M. KAMAL
AU - A. Hamza
AU - I. Bieche
AU - C. LE TOURNEAU
AU - C. Dupain
AU - C. Proudhon
PY - 2026
DA - 2026/07/24
PB - medRxiv
DO - 10.64898/2026.07.24.26358660
UR - https://www.medrxiv.org/content/10.64898/2026.07.24.26358660v2
AB - BackgroundLiquid biopsy-based monitoring of circulating tumor DNA (ctDNA) holds promises 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. MethodsWe performed a retrospective analysis of 32 patients with 11 different types of advanced or metastatic solid tumors and who received molecularly matched targeted therapies within the SHIVA02 trial (NCT03084757). Plasma samples were collected at baseline, longitudinally 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 classification algorithm integrating maximum variant allele frequency (MaxVAF), L1PA methylation-based cancer probability (MethPCancer), and genome-wide CNV scores was developed to maximize ctDNA detectability. ResultsAt 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 complementarity. 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. ConclusionsMultimodal 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.
ER -