Abstract 2692: 3D in vitro models for identifying primary vs. metastatic tumors in targeted breast cancer treatment

Abstract Metastatic disease is responsible for over 90% of cancer-related deaths, with breast cancer patients exhibiting a survival rate of only 5% once bone metastases develop. Traditional 2D in vitro models often fail to accurately replicate the tumor microenvironment (TME) and drug responses observed in vivo. To address these limitations, we utilized advanced 3D in vitro models to better simulate primary and metastatic tumors, enabling the study of cancer-stromal interactions and drug efficacy. Patient-derived cancer cells (PDCs) from three molecular subtypes of breast cancer were used to model both primary tumors (using a hanging drop system) and bone metastases (using a bioengineered bone-mimetic scaffold). Stromal components, including endothelial cells (ECs), macrophages, and cancer-associated fibroblasts (CAFs), were co-cultured with PDCs to mimic TME more closely. As expected, drug sensitivity profiles generated within 48 hours revealed significant differences between 2D and 3D models when treated with paclitaxel (PTX), doxorubicin (DOX), lapatinib (LAPA), and 4-hydroxy tamoxifen (4-OH TAM). PDCs at bone metastatic sites exhibited higher survival rates post-treatment, as indicated by elevated BCL2 and Ki67 expression and lower CAS9 expression compared to primary tumor sites. This suggests site-specific resistance mechanisms, emphasizing that bone metastases may require higher drug doses to achieve comparable efficacy. Stromal interactions significantly influenced angiogenic capacity. While drug treatments substantially altered network formation when ECs and PDCs were cultured together, the presence of CAFs produced opposing effects. These findings underscore the complexity of bone TME and suggest that higher drug doses or combination therapies may be necessary to effectively target metastatic tumors. Additionally, we are incorporating macrophages into the TME in ongoing experiments to further elucidate their contributions to angiogenesis and therapeutic resistance. Moving forward, we will employ single-cell sequencing of 3D models to investigate tumor heterogeneity and refine dose-specific treatments, advancing precision medicine strategies to combat metastasis and improve patient outcomes. This work is currently in progress. Our single-cell sequencing data aims to reveal distinct gene expression differences between primary tumor sites and metastatic sites within the tumor microenvironment. By identifying unique gene expression profiles in cancer cells, stromal cells, and immune cells at each site, we aim to uncover critical pathways driving metastasis. These findings will guide the development of targeted therapies that specifically address metastatic tumors, thereby preventing metastasis and minimizing off-target effects. Citation Format: Shrinwanti Ghosh, Sangdeuk Ha, Anu Gaba, Kalpana Katti, Jiha Kim. 3D in vitro models for identifying primary vs. metastatic tumors in targeted breast cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2692.