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A Multivariate Cell-Based Liquid Biopsy for Lung Nodule Risk Stratification: Analytical Validation and Early Clinical Evaluation

ABSTRACT: The iCAP is a tool for blood-based diagnostics that addresses the low signal-to-noise ratio of blood biomarkers by using cells as biosensors. The assay exposes patient serum to standardized cells in culture and classifies disease by AI analysis of gene expression readouts from the cells. This method simplifies the complexity of blood into a concise readout in a scalable cell-based assay. We developed the LC-iCAP as a rule-out test for nodule management in CT-based lung cancer screening. The assay achieved an AUC of 0.63 (95% CI 0.50-0.75) in blind temporal validation with retrospective samples. When integrated with CT data after validation, it demonstrated 90% sensitivity, 67% specificity and 95% NPV using an estimated 25% prevalence, significantly outperforming the Mayo Clinic model and potentially reducing unnecessary follow-up procedures. Analytical validation demonstrated LC-iCAP reproducibility, identified unwanted variation from long-term sample storage and indicated enrichment of hypoxia signaling in the differential assay readout.

ORGANISM(S): Homo sapiens

PROVIDER: GSE275699 | GEO | 2025/08/07

REPOSITORIES: GEO

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Project description:Rationale There is a need for new and better biomarkers for hypertrophic cardiomyopathy (HCM) which correlate more closely with disease progression as determined by clinical imaging and biohumoral information. We have used a combination of heart tissue and plasma proteomics to identify potential biomarkers for HCM and developed them into an exploratory targeted proteomic assay. Objective To identify informative staging biomarkers for HCM and develop them into a blood test. The test using 10 µl of plasma, was developed into a 10 min liquid chromatography-tandem/mass spectrometry (LC-MS/MS) assay to analyze multiple candidate biomarkers and evaluate their association with clinical phenotypes in patients with HCM. Methods and Results Myocardial tissue and plasma samples from patients with HCM and healthy volunteers (controls) were screened using a combined gel- and nano-LC quadrupole time of flight MS approach. Twenty-six potential biomarkers were identified from the proteomics screens and developed into a multiplexed targeted proteomic assay. Their association with clinical phenotypes was tested in plasma samples collected from 207 prospectively recruited participants: 110 patients with HCM (50.1 ± 15.0 years, 70% male; 48 [44%] with identified genetic mutations) and 97 controls (49.6 ± 13.4 years, 58% male), randomly split into training (80 HCM, 67 controls) and validation datasets (30 HCM, 30 controls). Six markers (Aldolase Fructose-Bisphosphate A, Complement C3, Glutathione S-Transferase Omega 1, Ras Suppressor Protein 1, Talin 1, and Thrombospondin 1) were significantly increased (P<0.006) in the plasma of HCM patients compared to controls in the training dataset. These markers correlated with left ventricular (LV) wall thickness, LV mass and % myocardial scar on cardiovascular magnetic resonance imaging. Using supervized machine learning (ML) this panel differentiated HCM from controls (area under the curve: 0.89 in the training dataset, sensitivity 96%, 95% confidence interval [CI] 77–93; specificity 87%, 95%CI 77–94; and 0.87 in the validation dataset, sensitivity 97%, 95%CI 83–100; specificity 77%, 95%CI 58–90). Four of the biomarkers as well as the composite ML score of the plasma proteome correlated with the presence of nonsustained ventricular tachycardia and the estimated 5-year risk of sudden cardiac death. Conclusion By developing a high-throughput, multiplex, and targeted proteomic plasma assay we identified 6 biomarkers that correlate with the presence of disease and with clinical risk score for sudden cardiac death.

Project description:Background: Lung cancer (LC) is the leading cause of cancer deaths worldwide with more than 1.7 million deaths each year. Early detection of LC may be crucial to achieve efficient treatment and to increase survival. However, there are limitations to current methods for early detection of LC as they are too invasive and represent potential health hazards, necessitating improved tools to detect LC at an early stage. Blood-based gene expression profiling is an alternative, or additional tool for diagnostic purposes as blood samples are easily available, essentially non-invasive, and can be collected at a low cost. However, as the blood collection method can affect measured gene expression, the aim of this study was to identify mRNAs that are robust biomarkers for LC in blood. How: We sampled blood from 123 LC patients at the St. Olavs university hospital, and 180 controls from two different biobanks sampled on two different blood tubes (PAXgene and Tempus) and used Illumina (HT-12 v4) microarrays to measure whole blood gene expression. We did three analyses: (i) finding relevant gene expression differences between cases and controls, (ii) finding a robust set of genes to be used as a panel to identify presence of LC in blood, and (iii) identifying differences between patients with different pathological traits such as stage and histology. We collected phenotype data from questionnaires and hospital medical records, and evaluated the potential effects of CRP, tumour size, gender, and smoking habits. Results: By comparing cases and controls sampled on the same RNA sampling system (training set), we identified 355 significant genes (Bonferroni adjusted p < 0.05) with biological relevance. When evaluating these on our test set comprising of the same cases as used in the training set but analysed against controls from a different biobank sampled on a different RNA sampling system (test set), we found 50 genes that were robust as they were unaffected by either technical issues in sampling systems, biobank differences, gender, or pathological traits such as LC subtype or stadium. These findings were confirmed in three validation sets and the robust list’s diagnostic potential was validated on RNA sequencing data from a new cohort. By analysing the cases separately, we found seven genes distinguishing squamous cell carcinoma (SCC) from other LC subtypes, and six genes distinguishing early from late-stage LC patients. Pathway analyses and a literature survey indicated that the identified genes show biological relevance for cancer development and lung related diseases.

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A Multivariate Cell-Based Liquid Biopsy for Lung Nodule Risk Stratification: Analytical Validation and Early Clinical Evaluation

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