Development of a Bioluminescent Resonance Energy Transfer-Based Photodynamic Therapy Model for the Treatment of Cancer

Photodynamic therapy (PDT) is a minimally invasive treatment modality that utilises a photoactive drug and visible light to induce cytotoxicity and cell death. PDT is in clinical use for the treatment of several cancers. However, its implementation as a universal option for cancer treatment is obstructed by two key limitations: Poor tissue-penetration of light and the inability to treat occult metastases. Several studies attempt to overcome the issue of tissue-penetration by employing bioluminescence (BL); however, a solution for treatment of metastasised cancers remains unknown. In addition, the selectivity of conventional PDT is lower in published BL-approaches. We therefore aim to combine the use of BL with knowledge of cancer-specific gene expression to create a novel, bioluminescent resonance energy transfer- (BRET-) based PDT system. In our approach, the source of light will be BL genetically engineered to only “switch on” in cancer cells. This system will be applicable to both primary cancers and occult metastases, regardless of how deep in a tissue they might be located. Thus, the modality will overcome the limitations of current PDT, while preserving the positive features such as effectiveness and cancer cell selectivity. Using androgen receptor- (AR-) positive prostate cancer as a model, we engineered AR-positive cell lines that expressed luciferase enzymes from either the firefly (Fluc) or the deep-sea shrimp (Nluc) transcriptionally controlled by the promoters of the AR-target genes prostate-specific antigen (PSA) or probasin (PB). Following BL-PDT experimentation, we found that cell lines expressing Nluc controlled by the PB promoter exhibited a 39 % reduction in cell survival compared to the control cell line not producing BL. With the preliminary results of this study, proof of concept is thereby produced for our BL-PDT approach. Additionally, to enable implementation of our treatment modality to other cancer types in the future, bioinformatical analysis of expression levels in estrogen receptor- (ER-) positive breast cancers has been performed