Abstract LB119: An integrated DNA methylation-based and multidimensional risk factor model for predicting breast cancer progression

Abstract Background: Breast cancer remains a leading cause of cancer-related mortality, with approximately 20-30% of patients with early-stage disease developing metastatic recurrence. Conventional serum tumor markers, such as CA15-3 and CEA, have limited sensitivity for monitoring disease progression. Circulating DNA methylation biomarkers offer a promising approach for dynamic assessment of tumor burden. This study aimed to develop an integrated blood-based prediction model combining cancer specific methylated GCM2 and TMEM240 with tumor characteristics and multidimensional patient-related risk factors to improve breast cancer progression monitoring. Methods: In this prospective cohort study, 200 breast cancer patients in Taiwan were enrolled and followed for 6 to 65 months after diagnosis, with 87. 7% of patients followed for more than three years. Baseline demographic, anthropometric, reproductive, hormonal, psychosocial, dietary, and lifestyle variables were systematically collected at enrollment. Blood samples were obtained every three months for quantitative methylation analysis of GCM2 and TMEM240, together with conventional tumor marker assessments. Disease-free survival was used as the primary outcome. A multivariable regression model integrating methylation biomarkers, tumor stage, breast cancer subtype, hormone receptor status, tumor markers, and patient-related risk factors was constructed to estimate individual breast cancer progression risk. Results: The integrated multivariable model demonstrated that both circulating DNA methylation biomarkers and patient-related factors independently and collectively contributed to breast cancer progression risk. Factors associated with increased risk included triple-negative breast cancer (TNBC), elevated body mass index (BMI), and a first-degree family history of other malignancies. Reproductive and endogenous hormonal factors, including number of live births and exclusive breastfeeding for ≥ 6 months, were associated with differential risk profiles. In addition, persistent poor sleep quality without effective improvement strategies, psychosocial stress, and dietary patterns characterized by frequent intake of sweets or highly processed foods were identified as contributory risk factors. Incorporation of methylated GCM2 and TMEM240 significantly improved the predictive performance of the progression model beyond conventional tumor markers alone. Conclusions: This study demonstrates that combining circulating DNA methylation biomarkers (GCM2 and TMEM240) with tumor characteristics and multidimensional patient-related risk factors enables a comprehensive and dynamic assessment of breast cancer progression risk. Importantly, patients identified as higher risk for disease progression by the multivariable regression model may benefit from intensified surveillance, including serial blood-based methylation monitoring of GCM2 and TMEM240 at three-month intervals, to facilitate earlier detection of disease progression and more timely clinical intervention. This integrated approach shows strong potential for clinical utility in precision monitoring of treatment response and tumor burden, supporting further validation and translation into clinical practice. Citation Format: Chin-Sheng Hung, Ruo-Kai Lin. An integrated DNA methylation-based and multidimensional risk factor model for predicting breast cancer progression abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (8Suppl): Abstract nr LB119.