Abstract PD5-02: PD5-02 An Organoid Model System to Study Resistance Mechanisms, Predictive Biomarkers, and New Strategies to Overcome Therapeutic Resistance in Early-Stage Triple-Negative Breast Cancer

Abstract

Background: While new treatments and improved subtyping schemas are anticipated to improve treatment response in triple-negative breast cancer (TNBC) patients, therapeutic resistance remains a significant challenge. Moreover, there is an urgent need for additional research model systems to study resistance and residual disease in breast cancer, including aggressive subtypes of breast cancer. Organoid culture is a promising technology used for growing breast cancer cells with high efficiency; however, the extent to which treatment resistance can be modeled using this system is unknown. This research used patient-derived organoid cultures in the context of computational analyses of large molecular and clinical datasets to study resistance mechanisms, biomarkers, and alternative treatment strategies to overcome drug resistance in early-stage TNBC. Methods: Organoid cultures were derived from breast tumor samples (taken from lumpectomy, mastectomy, or core biopsy samples), digested to the organoid level using collagenase, and grown in three dimensional cultures using a basement membrane extract and a fully-defined organoid medium (Dekkers et al. Nat Protoc 2021). An evaluation of all available I-SPY2 biomarker data (Wolf et al. Cancer Cell 2022) was performed focusing on genes, proteins, and pathways associated with resistance. These were then used to study whether resistance biomarkers identified in patient tumors are also present in organoids propagated from breast cancer post-treatment residual disease. To this end, bulk RNA sequencing data of organoids were normalized and merged with the TCGA dataset (Hoadley et al. Cell 2018) to enable analysis in a larger context, and immunofluorescence staining of organoids was performed. A high-throughput 386 anti-cancer drug compound screen and subsequent synergy testing with the most promising compounds were performed to identify and predict alternative treatment strategies. Additional assays to explore kinome activity in this organoid model are in progress. Results: A TNBC organoid biobank was established (n=31), which was enriched for inflammatory breast cancer (IBC; n=15), an aggressive form of breast cancer. Most organoids were derived from residual disease after neoadjuvant therapy. Bulk RNA sequencing analysis performed on 10 TNBC organoids revealed 3 subsets that were characterized predominantly by either normal-like/luminal androgen receptor or basal-like features or expressed distinct gene expression profiles, with IBC cases present in all 3 subsets. Intriguingly, the IBC organoids were characterized by higher expression of a number of immune-related signatures, despite an absence of clear immune cells in culture. A residual disease IBC/TNBC organoid resistant to chemotherapy was used to perform the 386-drug compound screen. The organoid model showed resistance to veliparib-cisplatin, consistent with the expression of gene/protein biomarkers predictive of drug resistance found in I-SPY2 (low PARPi7 levels and high pFOXO1 and pMEK1/2 expression). In addition, the screen identified multiple classes of inhibitors as promising synergistic/additive candidates for overcoming resistance to cisplatin. Conclusion: In this proof-of-principle study, we demonstrated the utility of matching I-SPY2 resistance biomarkers and signatures to residual disease tumor organoid cultures. We show that tumor organoid cultures modeling drug resistance states are a useful complement to existing research models of breast cancer and can be used for compound testing. We have developed a pipeline to propagate residual tumors from patients enrolled in I-SPY2 into organoid cultures to create a broader platform for preclinical drug testing informed by tumor biology with the ultimate goal of enabling faster, more successful translational studies and increased treatment options for resistant disease. Citation Format: Tam Binh V. Bui, Denise M. Wolf, Kaitlin Moore, Isaac S. Harris, Pravin Phadatare, Christina Yau, Lamorna A. Brown Swigart, Laura J. Esserman, Jean-Philippe Coppe, Julia Wulfkuhle, Emanuel F. Petricoin, Michael Campbell, Laura M. Selfors, Deborah A. Dillon, Beth Overmoyer, Filipa Lynce, Laura Van ’t Veer, Jennifer Rosenbluth. PD5-02 An Organoid Model System to Study Resistance Mechanisms, Predictive Biomarkers, and New Strategies to Overcome Therapeutic Resistance in Early-Stage Triple-Negative Breast Cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD5-02.