Abstract 1108: Performance of an optimized methylation-protein multi-cancer early detection (MCED) test classifier

Abstract Background: Multi-cancer early detection (MCED) tests can detect several cancer types and stages. We previously developed a methylation and protein (MP V1) MCED classifier with a target specificity of ≥98.0% (Gainullin, medRxiv, 2025.08.24.25334244). Herein, we describe a refined MP V2 classifier that was identified based on the evaluation of classifier model architectures that improved performance. Methods: Compared to MP V1, the MP V2 classifier was trained to achieve increased early-stage sensitivity at a case-control target specificity of ≥97.0%. MP V2 classifier architecture was developed using a training set (654 cancer; 2,373 non-cancer) partitioned into 5-fold cross validation and mini-holdout sets. Locked candidate models were compared using an independent mini-holdout test set (110 cancer; 509 non-cancer). MP V1 and MP V2 classifier performance were compared using a previously described test set (729 cancer; 2,434 non-cancer), and MP V2 performance was also evaluated in an independent clinical validation test set (324 cancer; 800 non-cancer) that was designed to more closely mimic the intended use population. Results: Compared to MP V1, MP V2 (specificity of 97.4%) demonstrated a 7.3% increase in overall sensitivity, with sensitivity increases of 7.6%, 9.2%, 8.3%, 8.0%, 3.8%, and 13.3% for stages I, II, stages I/II, III, IV, and unknown, respectively, in the test set (Table 1). In the independent validation set, MP V2 (specificity 97.4%; 95% CI: 96.0-98.3%) overall sensitivity was 41.4%, with sensitivities of 16.0%, 31.3%, 22.8%, 52.9%, and 83.1% for stages I, II, stages I/II, III and IV, respectively. Excluding breast and prostate cancers, MP V2 overall sensitivity was 55.6%, with sensitivities of 26.8%, 42.9%, 34.8%, 63.6%, and 89.3% for stages I, II, stages I/II, III, and IV, respectively, in the validation set. Conclusion: In a case-control setting, the MP V2 classifier offered improved sensitivity for early-stage cancers at a lower specificity target. Citation Format: Vladimir G. Gainullin, Melissa Gray, Madhav Kumar, Stephen Luebker, Amy Lehman, Darl D. Flake, Avinash Shanmugam, Kevin Cortes, Emily Chang, Philip J. Uren, Amin Mazloom, Jorge Garces, Gerard A. Silvestri, David W. Chesla, Robert W. Given, Tomasz M. Beer, Frank Diehl. Performance of an optimized methylation-protein multi-cancer early detection (MCED) test classifier [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 1108.