The ELEANOR noncoding RNA expression contributes to cancer dormancy and predicts late recurrence of estrogen receptor-positive breast cancer.

The recurrence risk of estrogen receptor (ER)-positive breast cancer remains high for a long period of time, unlike other types of cancer. Late recurrence reflects the ability of cancer cells to remain dormant through various events, including cancer stemness acquisition, but the detailed mechanism is unknown. ESR1 locus enhancing and activating noncoding RNAs (ELEANORS) are a cluster of nuclear noncoding RNAs originally identified in a recurrent breast cancer cell model. Although their functions as chromatin regulators in vitro are well characterized, their roles in vivo remain elusive. In this study, we evaluated the clinicopathologic features of ELEANORS, using primary and corresponding metastatic breast cancer tissues. The ELEANOR expression was restricted to ER-positive cases and well-correlated with the ER and progesterone receptor expression levels, especially at the metastatic sites. ELEANORS were detected in both primary and metastatic tumors (32% and 29%, respectively), and frequently in postmenopausal cases. Interestingly, after surgery, patients with ELEANOR-positive primary tumors showed increased relapse rates after, but not within, 5 years. Multivariate analysis showed that ELEANORS are an independent recurrence risk factor. Consistently, analyses with cell lines, mouse xenografts, and patient tissues revealed that ELEANORS upregulate a breast cancer stemness gene, CD44, and maintain the cancer stem cell population, which could facilitate tumor dormancy. Our findings highlight a new role of nuclear long noncoding RNAs and their clinical potential as predictive biomarkers and therapeutic targets for late recurrence of ER-positive breast cancer.

Modeling population size independent tissue epigenomes by ChIL-seq with single thin sections.

Recent advances in genome-wide technologies have enabled analyses using small cell numbers of even single cells. However, obtaining tissue epigenomes with cell-type resolution from large organs and tissues still remains challenging, especially when the available material is limited. Here, we present a ChIL-based approach for analyzing the diverse cellular dynamics at the tissue level using high-depth epigenomic data. "ChIL for tissues" allows the analysis of a single tissue section and can reproducibly generate epigenomic profiles from several tissue types, based on the distribution of target epigenomic states, tissue morphology, and number of cells. The proposed method enabled the independent evaluation of changes in cell populations and gene activation in cells from regenerating skeletal muscle tissues, using a statistical model of RNA polymerase II distribution on gene loci. Thus, the integrative analyses performed using ChIL can elucidate in vivo cell-type dynamics of tissues.