Approximately 10-15% of all breast cancers are triple-negative (TNBC), defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 (HER2) expression. TNBC represents a biologically aggressive subtype associated with worse prognosis compared with other breast cancer subtypes. In high-risk early-stage TNBC, the addition of immune checkpoint inhibitors to neoadjuvant chemotherapy significantly improves pathological complete response and overall survival, and it is now incorporated into international treatment guidelines. Despite these advances, reliable predictive biomarkers for response to neoadjuvant immunotherapy remain lacking. According to international guidelines, ER expression <1% is classified as ER-negative. However, studies indicate that tumors with low ER expression share biological characteristics and clinical outcomes more closely resembling ER-zero than ER-high disease. This project aims to study the cancer-associated fibroblast (CAFs) in breast tumors with ER expression across the continuum spanning from ER-zero (including TNBC) to ER-high, with the ultimate aim of improving patient selection for immunotherapy and endocrine therapy. Although population-based studies have described the clinical landscape of ER-zero and ER-low breast cancer, important knowledge gaps remain regarding the molecular mechanisms underlying ER-low and ER-intermediate tumors and their response to treatment. We will perform spatial profiling in a well-characterized cohort spanning ER-zero, ER-low, ER-intermediate, and ER-high breast cancers, with specific focus on the microenvironment and cancer-associated fibroblast. We have performed spatial transcriptomic analysis of 40 breast tumors using the 10x Genomics Xenium 5K gene panel. After QC, we generated around 3 million high-quality single-cell resolution spatial transcriptomic data containing immune and stromal populations. We will need the computing capacity powered by Dardel@PDC to support the integrative analysis of such a large dataset, which is important to understand the microenvironment and spatial distribution of such ER expression in relation to histology features.