Abstract PO5-21-05: An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients

Abstract

Abstract Key words: breast cancer; hormone receptor-positive; predictive model; prognosis; endocrine therapy; drug resistance Hormone receptor positive (HR+) breast cancer is the most common type of breast cancer, accounting for approximately 70% of all breast cancer, in whom endocrine therapy plays a vital role. However, the limited effectiveness of endocrine therapy due to intricate mechanisms of drug resistance poses a significant challenge in treating certain patients. Therefore, it is imperative to identify novel indicators of endocrine resistance and develop new models for predicting recurrence-free survival (RFS) in patients with HR+ breast cancer. In this study, we successfully established an endocrine resistance-related risk model based on multicenter RNA sequencing data. This model could represent tumor microenvironment characteristics and provide valuable insights into predicting the prognosis of HR+ breast cancer patients. A seven gene risk model reflecting endocrine therapy resistance in 208 early HR+ breast cancer patients from two centers was constructed by LASSO analysis and multivariate cox regression. Based on the median value of risk score, patients were divided into high-risk group and low-risk group. Compared with the low-risk group, the high-risk group exhibited a significantly worse prognosis, which showed consistent across two external validation cohorts (TCGA-BRCA and METABRIC cohort). Time dependent ROC curve showed excellent capacity of gene model in predicting 1,3 and 5-year RFS. The IC50 analysis revealed that patients in low-risk group exhibited greater sensitivity to endocrine drugs, such as tamoxifen, fulvestrant and palbociclib, which strongly suggested that the identified gene signature can effectively differentiate between endocrine-resistant and -sensitive status. Gene-set enrichment analysis revealed regulation of cell killing, positive regulation of inflammatory response and T cell mediated cytotoxicity were enriched in low risk group, suggesting immune activation and a higher immune infiltration status. Conversely, the high-risk group demonstrated associations with collagen metabolic process, extracellular matrix structural constituent and collagen fibril organization, which were the indicative of the location and function of cancer-associated fibroblasts (CAFs). As expected, ssGSEA results revealed elevated expression levels of immune cells (e.g., natural killer cells, tumor-infiltrating lymphocytes, CD8+ T cells) and immune functions (e.g., T cell co-stimulation, inflammation-promoting, cytolytic activity) in the low-risk group. However, three diverse acknowledged methods (TIDE, EPIC and MCP-counter) consistently identified CAFs as predominantly content in the high-risk groups. Furthermore, considering that CD8+ T cell is the primary cell type responsible for exerting cytotoxic effects, we observed a negative correlation between CD8+ T cells and CAFs. This suggests that CAFs may inhibit the infiltration of CD8+ T cells, thereby weakening their cytotoxic capabilities, leading to a shorter RFS in high-risk patient groups. In conclusion, a novel gene signature constructed with 7 endocrine resistance response-related genes can be used for prognostic prediction and reflecting the tumor microenvironment status in HR+ early breast cancer. The low-risk group exhibited enhanced anti-tumor immunity, resulting in a favorable prognosis. In contrast, the high-risk group displayed pronounced infiltration of CAFs, indicating their potential to impede immune cell infiltration and impact the efficacy of endocrine drugs through the formation of physical barriers and recruitment of immunosuppressive factors. Consequently, patients in the high-risk group experienced a worse prognosis. Therefore, inhibiting these genes may be a therapeutic option for overcoming endocrine therapy resistance in HR+ breast cancer patients. Citation Format: Xiyu Kang, Jiaxiang Liu, Xin Wang. An Endocrine Resistant-Related Gene Signature Revealing the Tumor Microenvironment to Predict the Prognosis of Hormone Receptor-Positive Breast Cancer Patients [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-21-05.