Abstract PS17-01: Spatial transcriptomics identifies molecular patterns predictive of response to neoadjuvant chemotherapy in triple-negative breast cancer

Abstract Background: Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer and only ∼40% of TNBC patients achieve pathological complete response (pCR) following neoadjuvant chemotherapy (NACT). Since molecular heterogeneity of TNBC contributes to NACT response, we investigated their histology and spatial transcriptomics (ST) patterns. Methods: In total, 97 core biopsies, taken prior to (n = 92) or during (n = 5) treatment, and 44 post-treatment surgical resections were collected from 96 women with TNBC receiving NACT from archival samples and the ongoing FORCE (NCT03238144) clinical trial at Guy’s Hospital, London, UK. Each tissue section was H&E stained, imaged, and annotated by a pathologist. A 6-digit classifier was designed to capture the histology of each region of interest (ROI), characterising the tumour region, epithelial type and immune cell localisation, population, distribution, and abundance. The annotations were used to guide ST profiling of serial sections, using the GeoMx Digital Spatial Profiler. Tissue sections were stained with fluorescent antibodies against PanCK and CD45. Tumour and immune-enriched compartments, defined as PanCK+/CD45- and PanCK-/CD45+, respectively, were acquired. Data analysis was performed in R 4.3.1, including quality control, batch correction, differential and topographical gene expression, immune cell deconvolution and gene set enrichment analysis (GSEA). Epithelial states (ES) were defined by identifying gene modules of co-expressed genes and annotating these with enriched pathways. RESULTS: To date, we have performed ST profiling of 120 TNBCs from 85 patients (40 Non-Responders; 45 Responders), totalling 4506 tumour or immune-cell enriched regions. Genes signatures known to predict pCR of NACT in women with TNBC were first assessed by generating a pseudo-bulk dataset produced in silico by combining gene counts from all regions. Two gene signatures, namely the PAM50 proliferation signature and the GeparSixto Immune signature, were found to be predictive (PAM50: OR = 0.12, p-value = 0.06; GeparSixto: OR= 0.19, p-value = 0.03) in our data. Seven transcriptionally distinct epithelial states (ES), each underpinning unique molecular functions, were defined by identifying gene modules of tumour-enriched regions. The relative abundance of ES2 (DNA repair mechanisms) and ES4 (deregulated ECM, increased epithelial-mesenchymal transition and TGF-b signaling) in pre-NACT core biopsies was significantly associated with pCR (OR=1.03, p-value = 0.02) and residual disease (OR = 0.98, p-value = 0.05), respectively. Within the immune compartment, seven clusters of tumour-immune microenvironments (TIMEs), were obtained by unsupervised hierarchical clustering of immune cell proportions. A high relative abundance of TIME1 (naïve CD4 T cells and plasma cells) and TIME5 (naïve CD4 T cells, naïve B cells, memory B cells and plasma cells) were significantly associated with pCR (TIME1 OR = 1.05, p-value = 0.01; TIME5 OR = 1.04, p-value = 0.05). We further investigated rates of co-occurrence between each ES and TIME, identifying combinations that were statistically most probable to occur and identified TIME as a confounding factor in the relative risk associated with the presence of individual ES. Moreover, we identify potential ligand-receptor interactions between distinct ES and TIMEs which may provide new insights into the mechanisms driving differential responses to NACT in TNBC. Conclusion: To date, we have collected one of the largest spatial transcriptomics data sets with detailed histological annotation. The abundance of epithelial states and tumour-immune microenvironments is predictive for pCR in NACT TNBC. The spatial location, colocalization and ligand-receptor interactions in ES and TIME provide novel insights into the molecular heterogeneity of TNBCs and mechanisms regulating responses to NACT. Citation Format: Isobelle Wall, Jelmar Quist, Sarah Pinder, KCL Biobank, Sheeba Irshad, Cheryl Gillet, Victoria Seewaldt, David Frankhouser, Shankar Subramanium, Maddy Parsons, and Anita Grigoriadis. Spatial transcriptomics identifies molecular patterns predictive of response to neoadjuvant chemotherapy in triple-negative breast cancer [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 PS17-01.