Faculty, Staff and Student Publications
Publication Date
11-1-2024
Journal
British Journal of Cancer
Abstract
Background: Advanced epithelial ovarian cancer (EOC) has high recurrence rates due to disseminated initial disease presentation. Cytotoxic phototherapies, such as photodynamic therapy (PDT) and photoimmunotherapy (PIT, cell-targeted PDT), have the potential to treat disseminated malignancies due to safe intraperitoneal delivery.
Methods: We use in vitro measurements of EOC tumour cell and T cell responses to chemotherapy, PDT, and epidermal growth factor receptor targeted PIT as inputs to a mathematical model of non-linear tumour and immune effector cell interaction. The model outputs were used to calculate how photoimmunotherapy could be utilised for tumour control.
Results: In vitro measurements of PIT dose responses revealed that although low light doses (<10 J/cm2) lead to limited tumour cell killing they also increased proliferation of anti-tumour immune effector cells. Model simulations demonstrated that breaking up a larger light dose into multiple lower dose fractions (vis-à-vis fractionated radiotherapy) could be utilised to effect tumour control via stimulation of an anti-tumour immune response.
Conclusions: There is promise for applying fractionated PIT in the setting of EOC. However, recommending specific fractionated PIT dosimetry and timing will require appropriate model calibration on tumour-immune interaction data in human patients and subsequent validation of model predictions in prospective clinical trials.
Keywords
Humans, Immunotherapy, Female, Photochemotherapy, Carcinoma, Ovarian Epithelial, Ovarian Neoplasms, Cell Line, Tumor, T-Lymphocytes, Models, Theoretical, ErbB Receptors, Ovarian cancer, Computational science, Nonlinear dynamics
DOI
10.1038/s41416-024-02844-y
PMID
39261715
PMCID
PMC11473784
PubMedCentral® Posted Date
9-11-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Included in
Bioinformatics Commons, Biomedical Informatics Commons, Genetic Phenomena Commons, Medical Genetics Commons, Oncology Commons