Abstract P4-09-19: Exploring cellular heterogeneity of localised breast cancers

Abstract Breast cancer exhibits heterogeneity at various levels, from subtype differences between patients (inter-patient heterogeneity) to diverse malignant cell compositions, variability in hormone receptor (HR) expression, and cellular makeup within single breast cancer samples (intra-tumour heterogeneity). Despite the availability of numerous effective therapies, many patients still experience incomplete treatment responses and subsequent relapse. These adverse outcomes are often due to the critical but frequently overlooked factor of cellular heterogeneity. To gain deeper insights into intra-tumour heterogeneity, we applied single-cell technologies to a cohort of 250 primary, untreated breast cancers. We optimized tissue cryopreservation methods, eliminating the need for fresh sample processing, and developed multiplex tissue profiling techniques. These cost-efficient processes improve handling of small tissue sizes, such as biopsies, and reduce batch effects. To ensure accurate and reliable data processing, we created a scalable computational workflow that includes benchmarked methods for sample SNP-demultiplexing, doublet detection, high-resolution cell annotation, and cellular integration. Additionally, we extended our existing methods to study the cellular heterogeneity of breast cancers. Our method, scSubtyper, examines the phenotypic differences between malignant cells within tumours by comparing each single cell to features associated with different molecular subtypes and assigning each cell to one of these subtypes. Our previous study and preliminary results from this project revealed that over 90% of samples exhibit a mix of malignant cells of different subtypes, and 50% of samples contain cells with characteristics of all subtypes, demonstrating that cellular heterogeneity exists not only between malignant cells but also within malignant cells of a tumour. Our second approach, known as ecotyping, assesses patterns of cell type frequencies across samples and groups them based on the similarity of these co-occurrences. Preliminary results have revealed the existence of five ecotypes that lack significant associations with clinical subtypes. Application of the same approach exclusively within the HR-positive samples identified four ecotypes characterized by distinct abundances of immune and stromal cells. This analysis revealed that ecotypes are not simple surrogates for clinical and molecular subtypes, but their presence could influence different responses to treatment. Together, our high-throughput tissue processing and computational approaches to study intra-tumour heterogeneity are now being applied to our large, well-annotated clinical cohort. Supported by preliminary results, we hypothesize that this study will play a vital role in optimizing breast cancer patient stratification to improve treatment management and outcomes. Citation Format: Beata Kiedik, Daniel L. Roden, Kate Harvey, Ghamdan Al-Eryani, Sunny Z. Wu, Mun N. Hui, Sandra O’Toole, Elgene Lim, Charles M. Perou, Alexander Swarbrick. Exploring cellular heterogeneity of localised breast cancers [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P4-09-19.