Facilitating Selection with Artificial Intelligence-Based Glypican-3 Expression Quantification in Patients with Solid Tumors

Background:Glypican-3 (GPC3), a cell surface glycoprotein, regulates cell growth and exhibits increased expression in hepatocellular carcinoma (HCC) and squamous non-small cell lung cancer (SQ-NSCLC). This study developed an artificial intelligence (AI) algorithm for predicting GPC3 expression to accelerate clinical trial enrollment, comparing it with manual immunohistochemistry (IHC) scoring. Methods:Using 167 NSCLC and 133 HCC formalin-fixed paraffin-embedded tumor blocks, GPC3 expression was quantified via IHC assays. Machine learning (ML) models were trained on digitized NSCLC whole slide images to identify GPC3-positive tumor areas, applying data-driven cutoffs for classification. Association between GPC3 and programmed cell death-ligand 1 (PD-L1) IHC expression in NSCLC sample was explored. Results:GPC3 expression peaked in HCC (63.9%), followed by SQ-NSCLC (52.6%) and adeno-NSCLC (lung adenocarcinoma) (10.0%). No significant correlation was found between GPC3 and PD-L1 expression in SQ-NSCLC. AI-based screening surpassed clinical pathologists by 10% in precision, achieving 100% recall at a 1% cutoff. ML model quantification aligned well with pathologist consensus. Profiling GPC3 expression emphasized its prevalence in HCC and SQ-NSCLC. Conclusion:Our AI platform standardizes, scales, and reproducibly characterizes GPC3 in NSCLC, supporting patient selection in clinical studies.