Abstract 4561: ShieldingUltra: A novel approach for enhanced minimal residual disease detection through the integration of mutation, copy number variation, and fragmentomics

Abstract Introduction: Minimal Residual Disease (MRD) detection using circulating tumor DNA (ctDNA) has significant clinical value in cancer therapy. However, existing methods primarily focus on mutations, restricting their broader applicability. Personalized panels, while targeted, are limited by prolonged turnaround times (TAT) and reliance on central labs. Fixed-panel approaches provide standardized, efficient solutions with strong potential for in vitro diagnostic kit development. Method: We present ShieldingUltra, a fixed-panel MRD solution targeting hotspot mutations across 2, 365 genes using ultra-deep unique molecular identifier sequencing. Using over 1, 000 clinical plasma samples, cutting-edge AI-driven models were designed to reduce sequencing background error and clonal hematopoiesis interference, and improve sensitivity by integrating mutations, copy number variations, and fragmentomics. ShieldingUltra demonstrates exceptional performance validated across diverse clinical settings. Result: ShieldingUltra demonstrated exceptional performance, achieving over 70% and 90% presurgical plasma positivity in early- and late-stage pan-cancer samples, respectively, with >99% specificity in healthy plasma. We further evaluated ShieldingUltra in a few most challenging clinical scenarios, rarely studied before with poor outcomes, and achieved unparalleled performance. In a cohort of lung cancer patients, ShieldingUltra achieved the most sensitive MRD detection with 1-week postsurgical landmark plasma (61.5%, 16/26), with sensitivity rising to 96% (25/26) using longitudinal plasma. In another challenging and unresolved scenario aimed at further stratifying advanced non-small cell lung cancer (NSCLC) patients who exhibited a partial response upon completion of immunotherapy (IO), as well as identifying those unable to maintain prolonged progression-free survival, ShieldingUltra successfully identified over 50% (18/34) of individuals who quickly relapsed. This highlights ShieldingUltra’s potential to pinpoint patients most likely to ultimately benefit from IO, which marks an innovative frontier in MRD research. Additionally, ShieldingUltra was explored for perioperative MRD tracking and the potential to guide the treatment strategies in an advanced stage ovarian cancer cohort, including evaluating neoadjuvant therapy for surgical decisions, assessing the need for adjuvant therapy post-surgery, and monitoring treatment efficacy and MRD status after surgery. Conclusion: ShieldingUltra combines a fixed panel with multiple genomic features and AI modeling for exceptional MRD detection. Its standardized design minimizes TAT, eliminates custom setup delays, and ensures reliable, cost-effective results, making it ideal for time-sensitive clinical decisions and broad clinical adoption. Citation Format: Hao Zhang, Ningyou Li, Wanxiangfu Tang, Jinfeng Zhang, Xunbiao Liu, Peng He, Baihan Zhu, Shuang Chang, Zilin Wang, Zhili Chang, Dongqin Zhu, Rui Liu, Xiaoxi Chen, Haimeng Tang, Hua Bao, Xue Wu, Yang Shao. ShieldingUltra: A novel approach for enhanced minimal residual disease detection through the integration of mutation, copy number variation, and fragmentomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4561.