Abstract 5277: Defining the enhancer logic of breast cancer metastasis using transcriptional and chromatin accessibility profiling

Abstract Breast cancer metastasis poses a significant challenge in cancer treatment, underscoring the need to understand the molecular mechanisms involved. In this project, we generated a resource dataset that includes ATAC-seq and RNA-seq data from ER positive primary breast tumors and matched liver and lung metastases. We collected samples from four patients at diagnosis in addition to samples from eight patients at autopsy following cancer-related death, allowing us to gain insights into the molecular mechanisms driving metastasis. Our research centered on the hypothesis that cancer cells undergo dedifferentiation, reverting to a more stem-like state, which may facilitate their spread to other parts of the body. For ATAC-seq, we used the PEPATAC pipeline for processing, and analyzed differential chromatin accessibility with DiffBind while identifying metastatic-specific peaks using ChiPseeker. For RNA-seq, reads were aligned using STAR and quantified with HTSeq. DESeq2 was employed to identify differentially expressed genes while controlling for batch effects. We established peak-to-gene correlations between chromatin accessibility and gene expression within a 0.5 Mbp window, assessing significance through a conservative null model. Pathway analysis utilized the KEGG and Gene Ontology databases. Ongoing work involves validating candidate genes in metastasis using CRISPR/Cas9. Notably, we discovered that metastatic cells exhibit a higher number of open chromatin regions compared to early-stage cancer cells, suggesting a role in promoting metastatic mechanisms. By identifying unique peaks in the ATAC-seq data and linking them to gene expression results from RNA-seq, we elucidated how changes in chromatin accessibility relate to gene activity in metastatic breast cancer cells. For instance, we found that there are changes in chromatin accessibility leading to higher expression of genes (CHD4, SNAI1, APH1A). These genes are linked to enhanced activity in the EMT pathway, which is critical for metastasis. Additionally, we leveraged our data to infer the activity of transcription factors using DNA sequence-aware motif analysis. By focusing on the specific variations present in the patient-derived sequences, we can identify transcription factor binding sites that are more accurately reflective of the individual tumor biology. Prioritizing these transcription factors based on their activity in the context of patient-derived genomes can lead to more effective and personalized treatment strategies, enhancing our ability to combat the complexities of metastasis. Citation Format: Frances L. Heredia Negron, Homer W. Fogle, Michael R. Kelly, Kamila Wisniewska, Lisa A. Carey, Hector L. Franco. Defining the enhancer logic of breast cancer metastasis using transcriptional and chromatin accessibility profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5277.