Dear Editor, The molecular determinants that drive breast cancer progression to metastasis are complex and partly controlled by nucleic acid epi-modifications [1]. Although DNA-methyl modifications in breast cancer metastasis have been well described, there is limited understanding of the role of RNA methylation in advanced disease [2]. This study aimed to provide an understanding on the role of the epitranscriptome in estrogen receptor-positive (ER+) breast cancer progression to metastasis.Numerous RNA modifications have been described to date, of which N 6 -methyladenosine (m 6 A) modifications are the most common [3]. Here, we mapped dynamic global m 6 A-specific methylated RNA immunoprecipitation sequencing (MeRIPseq), with corresponding RNA-sequencing (RNA-seq) and mass spectrometry, in cell models of disease progression to metastasis: endocrine-sensitive (MCF7/luminal A), endocrineresistant (LY2/luminal B) and endocrine-resistant brain metastatic (T347/patient-derived luminal B metastatic) (Figure 1A). The relevance of this model system was verified using comparative analysis of patient-matched brain metastatic samples [4] with RNA-seq data from our cells (LY2 vs. MCF7 and T347 vs. MCF7) [4] (Supplementary Table S1). Consistent differential gene expression in key oncogenic pathways such as Kirsten rat sarcoma virus (KRAS), nuclear factor-κB (NF-κB

The cover image is based on the Correspondence Dynamic epi‐transcriptomic landscape mapping with disease progression in estrogen receptor‐positive breast cancer by Stephen Keelan et al., https://doi.org/10.1002/cac2.12407.

Introduction Breast cancer is the most frequently diagnosed malignancy in women worldwide. Heterogeneity is a characteristic of tumour aggression and metastatic progression in breast cancer. With the power of single-cell analysis we uncovered key subpopulations within this heterogeneous landscape and specific progressive metastatic characteristics. Methods Single cell RNA sequencing was carried on three endocrine resistant ER positive xenograft tumours with varying levels of metastatic burden. Data analysis was performed using the Seurat pipeline for filtering, horizontal data integration, cell cycle regression and unsupervised clustering. Individual clusters were characterized through differentially expressed markers and PAM50 molecular subtypes distribution. Results Data integration of scRNA sequencing from primary tumours revealed seven unsupervised clusters, six of which are present in all three mice, highlighting the strong heterogeneity of breast cancer independent of metastatic outcome. PAM50 classification showed a higher distribution of luminal A cells associated with good metastatic outcome. We observed a significant difference in PAM50 distributions between the individual clusters. One cluster diverges significantly from all others and shows a decrease in luminal subtype and an increase in basal like cells. Further analysis of this cluster shows an association of selected differentially expressed markers to metastatic progression. Co-occurrence and decreased expression of several EMT markers was observed with disease progression. Conclusion High tumour heterogeneity is independent of metastatic outcomes. Downregulation of EMT related genes can be essential in metastatic progression when selected markers are co-occurring in a subpopulation. Take-home message High tumour heterogeneity is present in breast cancer independent of metastatic outcomes. Downregulation of EMT related genes can be essential in metastatic progression when selected markers are co-occurring in a subpopulation.