Improved Tumor-Type informed compared to Tumor-Informed Mutation Tracking for ctDNA Detection and Microscopic Residual Disease Assessment in Epithelial Ovarian Cancer

Background Epithelial ovarian cancer (EOC) is a leading cause of cancer mortality in women, often diagnosed at advanced stages. While first-line treatments improve survival, relapses remain common, with 5-year survival rates below 40%. Circulating tumor DNA (ctDNA) is a promising biomarker for non-invasive EOC detection and monitoring. It may help assess treatment response, notably microscopic residual diseases. Our objective was to compare two ctDNA characterization strategies in EOC for assessing tumor burden during first-line treatment: a tumor-informed approach based on somatic mutations and a tumor-type informed approach utilizing DNA methylation patterns. Methods In the tumor-informed approach, whole exome sequencing (WES) was performed on EOC tumor DNA and matched PBMCs from 12 patients aiming to identify tumor-specific mutations. Custom panels were designed to target patient’s specific tumor mutations, which were then tracked in cfDNA from plasma samples. In the tumor-type informed approach, differentially methylated loci (DMLs) were identified by comparing EOC samples, healthy ovarian tissues, and PBMCs. A unique custom methylation panel was designed, and a support vector machine classifier was trained to distinguish between healthy and cancerous plasma samples. Plasma samples were collected from 47 advanced-stage EOC patients during neoadjuvant chemotherapy, alongside plasma from healthy subjects. Results For the tumor-informed approach, WES identified an average of 74 somatic mutations per patient. CtDNA was detected in 11 out of 12 patients at baseline (mean VAF: 1.29%). For the tumor-type informed approach, 52,173 DMLs were identified. The classifier trained on these DMLs detected ctDNA in baseline plasma samples for 11 out of the 12 patients demonstrating equivalent sensitivity (mean VAF: 1.17%. In end-of-treatment samples, the tumor-type-informed approach detected ctDNA twice as often as the tumor-informed method. Detection using this more sensitive approach correlated with relapse and shorter progression-free survival (log-rank p = 0.017, Hazard ratio = 8.24; 95% CI [1.06–64.4]) and was associated with poorer overall survival (log-rank p = 0.036). Conclusion The tumor-type informed classifier demonstrated sensitivity and specificity for ctDNA detection, outperforming the tumor-informed approach in monitoring EOC progression. Requiring fewer sequencing data, it offers a practical, efficient solution for clinical management of EOC.