Analysis of tumoral, stromal and glycolitic markers in the response basal cell carcinoma and Bowen disease to photodynamic therapy in real life - L Bernal-Masferrer; T Gracia-Cazaña; L Najera-Botello; MC Gomez-Mateo; P Cerro; MC Matei; M Gallego-Rentero; S González; A Juarranz; Y Gilaberte (Photodiagnosis and Photodynamic Therapy)
Background: Photodynamic therapy (PDT) is a widely-used non-surgical treatment for non-melanoma skin cancers, including basal cell carcinoma (BCC), actinic keratoses (AK), and Bowen's disease (BD). PDT has high success rates, but various factors, can influence treatment response. This study investigates the clinical, histological, and molecular factors that affect the efficacy of methyl aminolevulinate PDT (MAL-PDT) for BCC and BD. Methods and Patients: Prospective observational multicentric study performed between May 2019 and January 2021 with 64 patients included. Clinical data such as tumor thickness, location, and histological subtype were recorded. Immunohistochemical analysis was performed on tumor samples to assess the expression of biomarkers including p53, β-catenin, and GLUT1. Results: Tumor thickness was found to be a critical determinant of MAL-PDT response, with thicker nodular BCCs showing reduced response rates compared to thinner, superficial BCCs and BD lesions. Immunohistochemical analysis revealed that p53 positivity was associated with better treatment outcomes, while increased β-catenin and cytoplasmic GLUT1 expression correlated with resistance to PDT. On the other hand, the metabolic profile of the tumors indicated that tumors with higher glycolytic activity were less responsive to treatment, therefore, using metformin, a glycolytic inhibitor, as a potential adjuvant therapy to improve outcomes in resistant tumors should be considered. Conclusion: This study emphasizes the importance of personalized approaches in the use of MAL-PDT, tailoring treatment according to tumor-specific characteristics. Biomarkers such as p53, β-catenin, and GLUT1 can serve as predictive tools for PDT response, helping clinicians identify patients who may benefit from alternative or combined treatments to enhance therapeutic efficacy.