BACKGROUND: The Indicator Cell Assay Platform (iCAP) is a novel tool for blood-based diagnostics that uses living cells as biosensors to integrate and amplify weak, multivalent disease signals present in patient serum. In the platform, standardized cells are exposed to small volumes of patient serum, and the resulting transcriptomic response is analyzed using machine learning tools to develop disease classifiers. METHODS: We developed a lung cancer-specific iCAP (LC-iCAP) as a rule-out test for the management of indeterminate pulmonary nodules detected by low-dose CT screening. This included assay parameterization, analytical reproducibility testing, and selection of a fixed 85-gene feature set for future clinical validation and regulatory development. Clinical performance was estimated using a prospective-specimen-collection, retrospective-blinded-evaluation (PRoBE) study design comprising 176 samples. Classifier variants were trained by nested cross validation using subsets of the 85 genes, and selected variants were evaluated by temporal blind validation using 39 control and 40 case samples (72Â % Stage I, 22Â % Stage II cancer). RESULTS: The assay showed excellent reproducibility across various conditions and cell lineages, and case versus control transcriptomic signals were enriched for hypoxia-responsive genes, consistent with known lung cancer biology. Two models demonstrated discriminative ability in blind validation, one with AUCÂ =Â 0.64 (95Â % CI: 0.51-0.76). Post hoc integration with CT imaging features yielded a combined model with 90Â % sensitivity, 64Â % specificity, and 95Â % negative predictive value at 25Â % prevalence, suggesting clinical utility and surpassing performance of existing rule-out tests. CONCLUSION: This study establishes the analytical reproducibility and biological relevance of the LC-iCAP. While clinical validation is preliminary, the results support the assay's potential utility in lung nodule management. The study introduces a new paradigm of using scalable and cost-effective cell-based biosensor assays for liquid biopsies. With a multivariate readout, the platform is amenable to precision medicine applications such as multi-cancer early detection.
A multivariate cell-based liquid biopsy for lung nodule risk stratification: Analytical validation and early clinical evaluation.
阅读:17
作者:Berndt Jason D, Duffy Fergal J, D'Ascenzo Mark D, Miller Leslie R, Qi Yijun, Whitney G Adam, Danziger Samuel A, Vachani Anil, Massion Pierre P, Deppen Stephen A, Lipshutz Robert J, Aitchison John D, Smith Jennifer J
| 期刊: | J Liq Biopsy | 影响因子: | 0.000 |
| 时间: | 2025 | 起止号: | 2025 Jul 26; 9:100313 |
| doi: | 10.1016/j.jlb.2025.100313 | ||
特别声明
1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。
2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。
3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。
4、投稿及合作请联系:info@biocloudy.com。
