Introduction: Conventional 2D imaging is the standard for many cancer diagnoses; however, its limitations become evident when considering the complexity of the 3D tumour microenvironment (TME). In 2D imaging, valuable spatial information about the TME is lost, which can impact diagnostic accuracy and prognostication. The advent of 3D imaging pipelines—such as DIPCO, iDISCO, and TRIC-DISCO—enables biopsies to be visualised in 3D, capturing details previously unavailable with 2D methods, at both the RNA and protein level. This expanded view is particularly valuable in rare cancers like vulvar cancer, where established prognostic markers are scarce. Vulvar cancer is a gynaecological malignancy often associated with severe complications, and while lymph node metastasis is the primary prognostic indicator, reliable markers to predict metastasis remain limited. A comprehensive 3D analysis may reveal novel markers linked to metastasis, though they have not yet been tested in vulvar cancer. Therefore, our objective was to assess whether routine markers of interest are effective in 3D models of vulvar cancer.
Methods: Primary vulvar cancer and lymph node samples were collected from patients diagnosed with vulvar squamous cell carcinoma (SCC). Using advanced 3D imaging techniques, we labelled vulvar SCC and associated lymph node samples for CD34 (blood vessels), LYVE1 (lymphatic vessels), and p40 (SCC cells).
Results: In primary vulvar cancer specimens, we successfully detected and modelled CD34, LYVE1, and p40 in 3D. CD34 and LYVE1 demonstrated distinct vascular patterns, while p40 displayed a nuclear pattern consistent with traditional 2D staining. Furthermore, CD34 and LYVE1 were identified in lymph node biopsies, with p40 found exclusively in nodes with metastatic vulvar cancer, as anticipated.
Conclusions: Our findings demonstrate the potential of 3D imaging as a valuable tool for advancing our understanding of vulvar cancer.