Overexpression of CD36 in mammary fibroblasts suppresses colony growth in breast cancer cell lines.

Human breast tumors are not fully autonomous. They are dependent on nutrients and growth-promoting signals provided by the supporting stromal cells. Within the tumor microenvironment, one of the secreted macromolecules by tumor cells is activin A, where we show to downregulate CD36 in fibroblasts. Downregulation of CD36 in fibroblasts also increases the secretion of activin A by fibroblasts. We hypothesize that overexpression of CD36 in fibroblasts inhibits the formation of solid tumors in subtypes of breast cancer models. For the first time, we show that co-culturing organoid models of breast cancer cell lines of MDA-MB-231 (e.g., a triple-negative line) or MCF7 (e.g., a luminal-A line) with CD36+ fibroblasts inhibit the growth and normalizes basal and lateral polarities, respectively. In the long-term anchorage-independent growth assay, the rate of colony formation is also reduced for MDA-MB-231. These observations are consistent with the mechanism of tumor suppression involving the downregulation of pSMAD2/3 and YY1 expression levels. Our integrated analytical methods leverage and extend quantitative assays at cell- and colony-scales in both short- and long-term cultures using brightfield or immunofluorescent microscopy and robust image analysis. Conditioned media are profiled with the ELISA assay.

YY1 is a cis-regulator in the organoid models of high mammographic density.

MOTIVATION: Our previous study has shown that ERBB2 is overexpressed in the organoid model of MCF10A when the stiffness of the microenvironment is increased to that of high mammographic density (MD). We now aim to identify key transcription factors (TFs) and functional enhancers that regulate processes associated with increased stiffness of the microenvironment in the organoid models of premalignant human mammary cell lines. RESULTS: 3D colony organizations and the cis-regulatory networks of two human mammary epithelial cell lines (184A1 and MCF10A) are investigated as a function of the increased stiffness of the microenvironment within the range of MD. The 3D colonies are imaged using confocal microscopy, and the morphometries of colony organizations and heterogeneity are quantified as a function of the stiffness of the microenvironment using BioSig3D. In a surrogate assay, colony organizations are profiled by transcriptomics. Transcriptome data are enriched by correlative analysis with the computed morphometric indices. Next, a subset of enriched data are processed against publicly available ChIP-Seq data using Model-based Analysis of Regulation of Gene Expression to predict regulatory transcription factors. This integrative analysis of morphometric and transcriptomic data predicted YY1 as one of the cis-regulators in both cell lines as a result of the increased stiffness of the microenvironment. Subsequent experiments validated that YY1 is expressed at protein and mRNA levels for MCF10A and 184A1, respectively. Also, there is a causal relationship between activation of YY1 and ERBB2 when YY1 is overexpressed at the protein level in MCF10A. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of epigenetically regulated genes in breast cancer cell lines.

BACKGROUND: Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profiles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profiles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fixed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. RESULTS: Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically significant negative correlation between methylation profiles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identified 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. CONCLUSIONS: Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators.