ER+ breast cancer in 3D scaffold ex vivo for transcriptomics-based functional drug screening and personalized medicine

Breast cancer (BC) is the most diagnosed cancer and a leading cause of cancer-related mortality for women worldwide (Bray Bsc et al. 2024). It is a heterogeneous disease with 19 special histopathological subtypes and 5 molecular subtypes (Tavassoli and Devilee 2003; Rakha EA et al. 2019), where more than 70% of BC cases are ER+ and receive endocrine therapy as the primary treatment (Harbeck and Gnant 2017). Although the classification of BC subtypes has effectively stratified treatment regimens to improve therapeutic outcomes, the variability among patients due to different hormone sensitivities, diverse genetic backgrounds, and mutational landscapes has highlighted the importance of personalized medicine (Harbeck and Gnant 2017; Tremont, Lu, and Cole 2017; Harbeck et al. 2019). To advance personalized medicine for ER+ BC, development of appropriate ex vivo models that faithfully recapitulate the disease is crucial yet challenging due to loss of hormone receptor expressions when cultured ex vivo. Previous work in the lab demonstrated that maintaining cell-to-cell interactions and tissue architecture preserved hormone receptor signaling in freshly dissected reduction mammoplasty tissue microstructures (Tanos et al. 2013). Collaborative research showed that embedding ER+ tumor samples in alginate extends viability and hormone receptor status in ex vivo systems (Cartaxo et al. 2020). Yet, more refined ex vivo models excluding external growth factors and serum needs to be established. Here, we developed an ex vivo model with defined biomaterial for both normal human breast epithelium and estrogen receptor-positive (ER+) patient-derived breast tumors. The model adequately preserved cell viability, proliferation, apoptosis levels, hormone receptor expressions and intact ER signaling in response to ER agonist and antagonists. We established a high-throughput transcriptome-based hormone and drug testing platform based on this ex vivo model for functional drug efficacy testing. We demonstrated reliability and precision of this drug testing platform in identifying effective drugs: 1) transcriptome-based drug screening revealed commonalities in response to families of compounds and heterogeneity in response to specific compounds from 8 patient-derived ER+ BC microstructures ex vivo; 2) drug responses are consistent with drug potency stemming from different modes of action, patient mutational profiles, disease stage and treatment history; 3) interestingly, drug response and resistance patterns from patient-derived tumors in 3D ex vivo model screened on our platform aligned well with the actual patient response and resistance observed in the clinic; 4) drug screening identified potential new target for endocrine resistance patient. We also demonstrated the feasibility of downscaling the drug screening platform to microfluidic workflow that can manipulate tissue a micron-scale with high uniformity and less tissue consumption, which is particularly advantageous