Development of a Machine Learning Model for Aspyre Lung Blood: A New Assay for Rapid Detection of Actionable Variants From Plasma in Patients With Non–Small Cell Lung Cancer
Implementation of the model for liquid biopsy sample analysis enables running of these samples alongside tissue in a single workflow with high sensitivity, specificity, and accuracy. These results demonstrate that the Aspyre Lung assay, powered by a robust machine learning algorithm, offers a reliable and scalable solution for molecular testing in NSCLC, enabling a diverse range of laboratories to confidently perform high-sensitivity, high-specificity testing on both tissue and liquid biopsy samples.
Authors
- Sam Abujudeh (ORCID: https://orcid.org/0000-0002-1127-4299)
- Justyna M Mordaka
- Ryan Evans (ORCID: https://orcid.org/0009-0005-6289-5107)
- Alessandro Tomassini (ORCID: https://orcid.org/0000-0001-5645-6910)
- Julia N. Brown (ORCID: https://orcid.org/0000-0003-1912-0961)
- Rebecca N Palmer
- Iyelola Turner
- Alejandra Collazos
- Magdalena Stolarek-Januszkiewicz
- Katherine Elizabeth Knudsen
- Ana-Luisa Silva
- Barnaby Balmforth
- Christina Xyrafaki
- Simonetta Andreazza (ORCID: https://orcid.org/0000-0001-8816-4955)
- Candace King
- Eleanor R. Gray (ORCID: https://orcid.org/0000-0002-6515-839X)
- Kristine von Bargen
- Robert J. Osborne (ORCID: https://orcid.org/0000-0002-1914-1239)
- Nicola D Potts
- Elizabeth Gillon-Zhang
- Cory Kiser
- Jinsy Jose
- Chau Ha Ho
- Dilyara Nugent
- Mary Beth Rossi
- Prarthna Barot
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1200/cci-25-00050
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 0.00