Zeynep Madak-Erdogan

Endocrine therapies targeting estrogen receptor alpha (ERα), expressed in ~70% of breast cancers, remain the standard of care for ER+ disease. However, 30-40% of patients experience recurrence and metastasis, with 5-year survival rates of only 31.9%. Using the Carle Foundation Hospital cohort and liver metastatic patient-derived xenograft models, we identified upregulated lipid metabolism and acetyl-CoA production as metabolic vulnerabilities. We demonstrate that combining Fulvestrant (Fulv) with an inhibitor of Acyl-CoA Synthetase Short Chain Family Member 2 (ACSS2) synergistically reduces metastatic breast cancer cell viability. Through isotope tracing, CUT&RUN sequencing, immunofluorescence, western blot, and RNA sequencing, we show that Fulv increases ACSS2 expression and acetate utilization, redirecting acetate flux from the TCA cycle toward fatty acid synthesis. Nuclear ACSS2 chromatin occupancy increases with Fulv treatment, expanding ERα/ACSS2/H3K27ac co-occupancy at tumor progression genes, an effect abolished by ACSS2 inhibition. RNA sequencing revealed that ACSS2 inhibition suppresses Fulv-induced metabolic and oncogenic transcriptional programs. In a therapy-resistant xenograft model, combination treatment reduced Fulv-dependent metastatic burden. These findings establish ACSS2 as a driver of endocrine resistance through nuclear acetyl-CoA provision for epigenetic reprogramming, representing a novel therapeutic target in metastatic breast cancer.

Gene set enrichment analysis result on shared nearest neighbor (SNN) clusters enriched with human transcripts in metastatic tumors from xenograft model

Correlation heatmap of hallmark pathway gene set coregulation analysis (GESECA) z-scores in primary and metastatic breast tumor samples

Comparison of local spatial autocorrelation results based on bivariate local Moran’s I at raw p-value < 0.05 and adjusted p-value < 0.05

Mapping of local spatial autocorrelation based on bivariate local Moran’s I of hallmark pathways in primary and metastatic breast tumor samples

Characteristics of patient tumor samples collected for Visium analysis

The tumor microenvironment comprises different cell populations that interact, contributing to tumor heterogeneity and therapy response. Spatial transcriptomics offers valuable insights into the transcriptional complexity and heterogeneity of the tumor microenvironment. In this study, we established a geographic information system (GIS)–augmented in silico reconstruction of tumor architecture (GIS-ROTA), a biologically informed analytic framework that integrates pathway or cell type–based enrichment analysis with spatial autocorrelation measurement to uncover functional spatial domains. The approach considers biological functions prior to identifying any spatial domains, providing direct interpretability and minimizing the subjectivity of interpreting clusters observed from conventional analytic methods. The application of GIS-ROTA to a Visium spatial transcriptomics dataset of primary and metastatic estrogen receptor–positive breast tumor samples revealed extensive colocalization of estrogen response with metabolic pathway gene sets and mutual exclusivity with metastasis-related and specific immune-related pathway gene sets. Overall, the GIS-ROTA framework integrates biological knowledge first, yielding spatial patterns with functional relevance and enabling the identification of novel targets for the development of therapeutic strategies.Significance:GIS-ROTA, a spatial transcriptomics analytical framework that maps tissues with biologically curated pathways and signatures followed by spatial localization analysis, yields biologically relevant spatial domains, enabling cancer target and biomarker discovery.

Pathologist annotation and mapping of spatial features, shared nearest neighbor (SNN) clustering, and cell type deconvolution in primary and metastatic breast tumor samples

Gene set enrichment analysis result on shared nearest neighbor (SNN) clusters in primary and metastatic tumor samples

Application of conventional analytic approaches and GIS-ROTA framework to metastatic tumors from xenograft model