Clinical validation of a metabolomics-based multi-cancer early detection test.

Abstract

e22512 Background: Multi-cancer detection (MCD) tests can improve the efficacy of existing screening tests by detecting a greater number of cancers. For exerting a clinical impact, however, it is important that the MCD is able to accurately detect early-stage cancers, a feature that is limited in currently available tests. We had previous developed an alternate approach that combined serum metabolomics with machine learning-powered data analytics. Using this approach we had developed an MCD test that could concurrently detect 30 cancers with high accuracy. The goal of the present study was to clinically validate the performance of this test. Methods: A prospective, multicenter, observational study was conducted in which de-identified blood samples were collected from 10,074 participants with ( n = 7246, 72%) and without ( n = 2828, 28%) cancer. A blinded arm was also included to validate test performance. Sensitivity and specificity of cancer detection, and the accuracy of tissue of origin (TOO) identification was measured. Results: The overall sensitivity obtained for cancer detection was 98.55% while the specificity was >99%. Importantly, detection of early-stage cancers (i.e. Stage I and II) was also achieved with high sensitivity, which ranged from 95% to 100% for the different cancers. The overall sensitivity obtained for TOO was 95.62%, with accuracies ranging from 90% to 100% for the early-stage cancers. Conclusions: This study validates that our serum-based MCD test is uniquely capable of detecting early-stages of diverse cancers with high sensitivity and specificity, while also ascribing TOO with high fidelity. This test can, therefore, potentially complement existing single-cancer screening tests and contribute significantly to the detection of early-stage cancers. Clinical trial information: CT/MD/2024/000007.