Framework to Select Multi-Cancer Detection Assays in the National Cancer Institute's Vanguard Study.
Year: 2026
Type: article
Abstract: BACKGROUND: The Cancer Screening Research Network (CSRN) is a new clinical trials network funded by the National Cancer Institute. The first CSRN study, the Vanguard Study, will assess the feasibility of using multi-cancer detection (MCD) tests in future randomized controlled trials. METHODS: This paper describes the framework NCI developed to engage MCD assay developers, evaluate emerging technologies using biobank reference sets, and select fit-for-purpose MCD assays for inclusion in the Vanguard Study. RESULTS: NCI evaluated twenty-three technologically diverse MCD assays, all utilizing machine learning and artificial intelligence components. Nine assays underwent blinded performance evaluations, using specimens from three biobanks. Assay developers were provided with independent assessments of their assays, which enabled them to make further assay refinements to enhance performance. The assay selection process resulted in participation by two assay companies in the Vanguard Study. CONCLUSIONS: NCI created a fair and transparent process to streamline the evaluation of assay performance and to select promising assays for clinical research and public health initiatives. IMPACT: Significant resources are required for large scale cancer screening trials, therefore promising technologies must be prioritized for inclusion in a definitive trial. NCI's assay selection framework can be used and repurposed by other networks and institutions.
Source: UNC Libraries
DOI: 10.17615/k6nz-z224
Authors: Harris, Lyndsay, Ransohoff, David, Martin, Albine, Ong, Mary Jane C, LeeVan, Elyse, Rubinstein, Wendy S, Pinsky, Paul F, Temkin, Sarah M, Prorok, Philip C, Patriotis, Christos, Skarlupka, Amanda L, Bolton, Wade, Dickherber, Anthony, Minasian, Lori M, Katki, Hormuzd A, Lavik, Erin B, Edelman, Daniel C
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7204656895