Abstract 1124: cfDNA fragmentomics enables sensitive early detection and tissue-of-origin prediction in gynecologic cancers

Abstract Background: Early detection of gynecologic cancers remains challenging due to nonspecific symptoms and limited sensitivity of conventional biomarkers. We aimed to develop and validate cfDNA-based models for cancer detection and tissue-of-origin (TOO) classification. Methods: We prospectively enrolled 1,007 participants from two hospitals, of whom 763 passed eligibility and quality control. The training set (N=363; 173 cancer, 190 non-cancer) was used to develop models integrating four cfDNA features reflecting fragmentation, chromatin architecture, and epigenetic regulation via machine learning. The internal test set (N=158; 86 cancer, 72 non-cancer) and an independent external test set (N=242; 127 cancer, 115 non-cancer) were used for validation. Results: The diagnostic model achieved area under the curve (AUC) values of 0.974 (95% confidence interval CI: 0.954-0.994) and 0.975 (95% CI: 0.959-0.992) in the internal and external cohorts, with sensitivities of 83.7% and 82.7% at 98% specificity. High performance was observed across ovarian (AUC: 0.992 and 0.999), cervical (AUC: 0.972 and 0.989), and endometrial (AUC: 0.948 and 0.937) cancers, including stage I disease (AUC: 0.955 and 0.961). The model detected over 77% of cancers that were missed by CA125. Interception modeling projected a 26.4-68.9% increase in stage I diagnoses and 11.6-37.8% 5-year survival gains. The TOO model achieved 73% overall accuracy, with the highest accuracy for ovarian (81.3-86.7%), followed by cervical (70.7-73.3%) and endometrial (59.1-62.7%) cancers. Analytical validation demonstrated robust performance even at ultra-low sequencing depths of 1x, supporting scalability for population screening. Conclusions: cfDNA fragmentomics enables sensitive detection and tissue-of-origin classification of gynecologic cancers, complementing conventional biomarkers. These models hold promise for cost-effective, population-level early detection and risk stratification. Citation Format: Jin Li, Xun Zhang, Song Wang, Xiaoying Wu, Jinpeng Zhang, Hua Bao, Shanhui Liang, Xiaotian Han, Jiangchun Wu, Hao Wen, Hairong Bao, Haimeng Tang, Xue Wu, Xiaohua Wu, Zhao Wu, Xiaoqiu Li. cfDNA fragmentomics enables sensitive early detection and tissue-of-origin prediction in gynecologic cancers 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 1124.