Abstract LB100: Performance of a multi-analyte, multi-cancer early detection (MCED) blood test in a prospectively-collected cohort
Authors: Gainullin Vladimir , Hwang Hee Jung , Hogstrom Larson , Arvai Kevin , Best Amira , Gray Melissa , Kumar Madhav , Manesse Mael , Chen Xi , Uren Philip , Mazloom Amin , Cerqueira Gustavo , Garces Jorge , Beer Tomasz M. , Mcelhinny Abigail , Diehl Frank 
Published: April 2024

Abstract
Background: A multi-analyte blood test has potential for robust sensitivity in detecting a broad range of cancer types and stages. Previously, using retrospectively collected samples, we trained and independently assessed in a case-control study the performance of various biomarker classes for the detection of cancers (Douville, Annals of Oncology,2022:pS575). The aim of this study was to further assess two of the four original biomarker classes (methylation and protein) using samples from a large, multi-center, prospectively collected study: Ascertaining Serial Cancer patients to Enable New Diagnostic 2 (ASCEND 2).
Methods: The study included 6,314 samples (1,426 cancer and 4,888 non-cancer) collected in LBgard® tubes. The training cohort included 3,026 samples (654 cancers and 2,372 non-cancer controls) and the test set included 3,288 samples (772 cancers and 2,516 non-cancer controls). By measuring biomarkers that capture shared cancer signals, the test is designed to detect a broad range of cancer types. In this analysis, cancer stages were well-represented across 21 solid and hematologic tumor organ sites that collectively represent &gt;85% of incident cancers. We combined DNA methylation and protein biomarkers in a single overarching classifier. Machine learning models were calibrated using stratified cross-validation to reach optimal cancer detection at a combined specificity &gt;99.0%.
Results: The specificity threshold identified in training resulted in an overall test set sensitivity of 49.9% and 99.0% specificity, with sensitivities of 23.6% (n=364), 68.6% (n=191), 84.6% (n=182), and 40.0% (n=35) for stages I/II, III, IV, and unknown, respectively. We will also report the cross-validation results of the multi-analyte classifiers in this newly enrolled case-control study.
Conclusions: In ASCEND2, a large prospectively collected case-control study that included &gt;85% of cancer burden, new training data and updated models demonstrated robust performance of methylation and protein biomarkers. These results demonstrate reproducibility of the multi-biomarker class approach to cancer detection in the ASCEND 2 cohort and represent an evolution in cancer classifier design.
Citation Format: Vladimir Gainullin, Hee Jung Hwang, Larson Hogstrom, Kevin Arvai, Amira Best, Melissa Gray, Madhav Kumar, Mael Manesse, Xi Chen, Philip Uren, Amin Mazloom, Gustavo Cerqueira, Jorge Garces, Tomasz M. Beer, Abigail Mcelhinny, Frank Diehl. Performance of a multi-analyte, multi-cancer early detection (MCED) blood test in a prospectively-collected cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB100.