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
PreCyte, Inc., Seattle, WA, USA.
Seattle Children's Research Institute, Seattle, WA, USA.
J Liq Biopsy. 2025 Jul 26;9:100313. doi: 10.1016/j.jlb.2025.100313. eCollection 2025 Sep.
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.
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 (72 % Stage I, 22 % Stage II cancer).
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 AUC = 0.64 (95 % CI: 0.51-0.76). Post hoc integration with CT imaging features yielded a combined model with 90 % sensitivity, 64 % specificity, and 95 % negative predictive value at 25 % prevalence, suggesting clinical utility and surpassing performance of existing rule-out tests.
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.
指示细胞分析平台（iCAP）是一种用于血液诊断的新型工具，它利用活细胞作为生物传感器来整合和放大患者血清中存在的微弱、多价疾病信号。在该平台中，标准化细胞暴露于少量患者血清中，然后使用机器学习工具分析产生的转录组反应，以开发疾病分类器。
我们开发了一种肺癌特异性iCAP（LC-iCAP），作为低剂量CT筛查检测到的不确定肺结节管理的排除测试。这包括测定参数化、分析重现性测试，以及选择固定的85基因特征集用于未来的临床验证和监管开发。使用包含176个样本的前瞻性样本收集、回顾性盲法评估（PRoBE）研究设计来评估临床性能。分类器变体通过使用85个基因的子集进行嵌套交叉验证进行训练，选定的变体通过使用39个对照和40个病例样本（72%为I期，22%为II期癌症）进行时间盲法验证进行评估。
该测定在各种条件和细胞谱系中显示出优异的重现性，病例与对照转录组信号富含缺氧反应基因，与已知的肺癌生物学一致。两个模型在盲法验证中表现出判别能力，其中一个模型的AUC = 0.64（95% CI：0.51 - 0.76）。事后与CT成像特征整合产生了一个联合模型，在患病率为25%时，灵敏度为90%，特异性为64%，阴性预测值为95%，表明具有临床实用性且优于现有排除测试的性能。
本研究确立了LC-iCAP的分析重现性和生物学相关性。虽然临床验证是初步的，但结果支持该测定在肺结节管理中的潜在效用。该研究引入了一种新的范式，即使用可扩展且经济高效的基于细胞的生物传感器测定进行液体活检。通过多变量读数，该平台适用于多癌症早期检测等精准医学应用。
