Evaluating a data-driven approach to biomarker discovery for tumor-targeted imaging in epithelial ovarian cancer

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Introduction: In epithelial ovarian cancer (EOC), surgical outcome is the strongest prognostic factor for survival. However, estimating intra-abdominal tumor burden to plan optimal treatment strategies remains challenging. Moreover, metastases can remain undetected during surgery via visual and tactile inspection. Tumor-targeted molecular imaging has the potential to improve tumor cell identification pre- and intraoperatively. While targeting folate receptor-alpha (FRα) shows promise, other specific biomarkers are needed. Methods: This study evaluates a novel, data-driven approach using RNA expression data to identify new target proteins for tumor-targeted imaging in EOC. A knowledge platform was utilized to search omics-databases for membrane proteins expressed in EOC but absent or minimally expressed in surrounding tumor-negative and inflammatory cells. Results: Differential gene expression analysis identified highly expressed genes, which were validated through immunohistochemistry. Two new genes were identified: VTCN1 and AQP5, encoding for proteins B7-H4 and AQP5, respectively. Immunohistochemical validation showed that B7-H4 expression aligned with RNA levels, indicating its potential as a new target. In contrast, there was a discrepancy in AQP5 expression at the protein level compared to its gene counterpart. Discussion: While this approach was valuable in identifying novel targets for tumor targeted imaging of EOC, immunohistochemistry or cell studies remain imperative for validation of RNA expression results.

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