Abstract PD2-04: Tumor Heterogeneity in the transATAC Breast Cancer Spatial Single Cell Atlas

Abstract Aims: Primary breast cancer (BC) is highly heterogeneous and this drives divergent clinical outcomes. Many molecular features have significant prognostic value in hormone receptor positive (HR+) BC, and several commercial multigene prognostic assays currently guide chemotherapy decision making. The translational study from the monotherapy arms of the Arimidex, Tamoxifen, Alone or in Combination trial (transATAC), has been pivotal for the validation and regulatory approval of multiple prognostic assays. Spatial single cell analysis may lead to new insights into molecular features and tumor microenvironment (TME) that associate with prognosis and allow the future development of the next generation of prognostic assays. We present preliminary analysis of the transATAC spatial single-cell atlas of BC heterogeneity. We assess whether single cell analysis of a tissue micro-assay (TMA) core, sampling of ∼1000 cells per tumor, is sufficient to recapitulate the major features of the tumor. Methods: TMA blocks of 0.6mm diameter cores were generated from tumors of 1710 patients. Cores were taken from two or more distinct tumor regions in 27 patients. Every region/tumor had 3 spatially distant cores taken and spread across separate TMA blocks. We present initial analysis of single cell spatial transcriptomic analysis of 6108 genes using a CosMx spatial molecular imager from two TMAs. The preliminary analysis was generated data for 160 cores from 155 transATAC patients with data combined per patient to generate 155 tumor samples that would better represent molecular data used in prognostic assays. OncotypeDX recurrence scores on a surgery excision were available for 133 HR+ tumors. Cell type was determined by semi-supervised clustering with InSituType using BC specific reference profiles. Pseudo-bulk gene expression profiles were created by summing the total gene expression from all cells and separately all tumor cells. Large-scale chromosomal copy number alterations (CNA) in individual tumor cells were detected by InferCNV and used to understand tumor clonality. Results: We analysed 155 transATAC HR+ and HR- tumours. A mean of 952 cells were analysed, with 967 transcripts from 603 genes. Across the full dataset, cell typing assigned 59% of cells to tumor cells, 21% to immune cells (T-cells, B-cells and Myeloid cells) and 20% other cells (Endothelial, Fibroblast and Pericytes). Single cell CNA analysis of individual tumors cells suggested only one patient had multiple clones (with >50 tumors cells per clone) and this patient had cores taken from 2 regions of the tumor. Recurrence scores generated from single cell pseudo-bulk data were significantly correlated with Oncotype Dx assay recurrence scores generated from surgery excisions (Pearson r=0.76; p