Performance of an Optimized Methylation-Protein Multi-Cancer Early Detection (MCED) Test Classifier
Gainullin, V. G.; Gray, M.; Kumar, M.; Luebker, S.; Lehman, A. M.; Choudhry, O. A.; Roberta, J.; Flake, D. D.; Shanmugam, A.; Cortes, K.; Chang, E.; Uren, P. J.; Mazloom, A.; Garces, J.; Silvestri, G. A.; Chesla, D. W.; Given, R. W.; Beer, T. M.; Diehl, F.
Show abstract
Multi-cancer early detection (MCED) tests can detect several cancer types and stages. We previously developed a methylation and protein (MP V1) MCED classifier. In this study, we present a refined MP V2 classifier, developed by evaluating model architectures that improved performance in prospectively enrolled case-control cohorts under standard testing conditions. The newly developed MP V2 classifier was trained to be more generalizable and achieve increased early-stage sensitivity at a target specificity of [≥]97.0%. MP V1 and MP V2 classifier performances were compared using a previously described test set, and MP V2 performance was also evaluated in a new independent clinical validation set. Compared to MP V1, the MP V2 classifier demonstrated a 7.3% increase in overall sensitivity, with sensitivity increases of 7.6%, 9.2%, and 8.3% for stages I, II, and stages I/II, respectively, in the intended use (breast and prostate cancers excluded) test set. In an independent validation intended use set, the MP V2 classifier showed an overall sensitivity of 55.6%, with sensitivities of 26.8%, 42.9%, and 34.8% for stages I, II, and stages I/II, respectively. In a case-control setting, the MP V2 classifier offered improved sensitivity for early-stage cancers at a lower specificity target.
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