Background: Breast cancer is the most common malignancy among women worldwide, with an estimated 2.3 million new cases diagnosed in 2022. A key challenge in clinical oncology is the inability to reliably predict patient-specific therapeutic responses prior to treatment. Patient-derived organoids (PDOs) or three-dimensional ‘mini-tumours’ serve as a clinically-relevant model that recapitulate the complexity and heterogeneity of patient tumours, with emerging studies demonstrating their potential as a tool to guide treatment decisions and improve patient outcomes.
Objectives: To establish and validate a biobank of PDO models for two high-risk breast tumour subtypes: human epidermal growth factor receptor-2 (HER2) positive and triple negative breast cancer (TNBC) and investigate their potential to predict patient-specific drug responses ex vivo.
Methods: Treatment-naïve primary breast tumour tissues donated by a cohort of New Zealand patients undergoing surgical resection were used to generate a cryopreserved bank of PDOs. Histological characterization was carried out to confirm recapitulation of key tumour features. The organoid drug response against clinically-relevant targeted therapy and/or chemotherapy was assessed at early passage using Cell Titre Glo® 3D cell viability assay and confocal microscopy, with responses compared against available clinical information.
Results and Discussion: A collection of breast tumour organoids (n=30) have been established in our laboratory with ~ 80% success rate. Case studies from n=5 patients (one HER2+, two HER2-low and two TNBC) are presented here. These organoid models have been characterised, and their sensitivity to adjuvant therapy received by each donor patient in real-time has been assessed. Comparing organoid drug responses with clinical observations has provided encouraging evidence for the predictive potential of organoids and suggests the need for large follow-up studies. Overall, patient-derived tumour organoids provide a powerful translational platform with the potential to support more tailored clinical decisions in the future.