Remodeling of the Methylation Landscape in Breast Cancer Metastasis

<div><p>The development of breast cancer metastasis is accompanied by dynamic transcriptome changes and dramatic alterations in nuclear and chromatin structure. The basis of these changes is incompletely understood. The DNA methylome of primary breast cancers contribute to transcriptomic heterogeneity and different metastatic behavior. Therefore we sought to characterize methylome remodeling during regional metastasis. We profiled the DNA methylome and transcriptome of 44 matched primary breast tumors and regional metastases. Striking subtype-specific patterns of metastasis-associated methylome remodeling were observed, which reflected the molecular heterogeneity of breast cancers. These divergent changes occurred primarily in CpG island (CGI)-poor areas. Regions of methylome reorganization shared by the subtypes were also observed, and we were able to identify a metastasis-specific methylation signature that was present across the breast cancer subclasses. These alterations also occurred outside of CGIs and promoters, including sequences flanking CGIs and intergenic sequences. Integrated analysis of methylation and gene expression identified genes whose expression correlated with metastasis-specific methylation. Together, these findings significantly enhance our understanding of the epigenetic reorganization that occurs during regional breast cancer metastasis across the major breast cancer subtypes and reveal the nature of methylome remodeling during this process.</p></div

Chromosome characterization of subtype-specific methylation change in metastasis.

<p><b>A</b>. Chromosome view of smoothed averaged paired differential methylation between metastasis and primary tumors shown for luminal A (purple), luminal B (orange), basal-like (red) and Her2-enriched (green) subtypes along human chromosome 2. CpG islands (CGI) are shown in grey below. <b>B</b>. Methylation profile of a 20 Mb region is shown. Location of RNAseq transcripts for+and – strands is shown above. CGIs, lamin B1-associated domains (LAD), and peaks for H3K4-trimethylated (H3K4me3), H3K4-monomethylated (H3K4me1) and H3K9-acetylated chromatin marks from human mammary endothelial cells (HMEC) are shown (from <a href="http://www.genome.ucsc.edu/cgi-bin/hgTables" target="_blank">http://www.genome.ucsc.edu/cgi-bin/hgTables</a>). <b>C</b>. Volcano plots of differentially methylated sites between metastasis and primaries by subtype-specific ANOVA. β-value difference is shown on the x-axis, -log10 of FDR-corrected p-value is on the y-axis. β-values of top three loci from luminal A, luminal B and basal primaries and metastases are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103896#pone.0103896.s001" target="_blank">figure S1</a>.</p

Methylome remodeling in metastasis across molecular subtypes of breast cancer.

<p><b>A</b>. Hierarchical clustering of top 1000 differentially methylated loci (defined by SAM). Primary tumor or metastasis is noted along the top. PAM50 subtype classification is labeled. Color scale indicates normalized β-value. <b>B</b>. Box plots of metastasis-specific methylation change across top 25% differentially methylated probes common to all molecular subtypes. Y-axis, mean beta-value change. The median, 1 standard deviation (box plot), and 10–90 percentile (whiskers) are indicated in the graph. <b>C</b>. Frequency of differentially hypermethylated and hypomethylated loci as a function of relationship to CGIs (top panel), functional location (middle panel) and location within core promoter (bottom panel). <b>D</b>. Gene set enrichment analysis (GSEA) plot. GSEA was performed with PRC2 target list from Lee et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103896#pone.0103896-Lee1" target="_blank">[37]</a> The graph on the bottom represents the ranked, ordered, non-redundant list of genes (by SAM). Genes on the far left (red) correlated the most with metastases, and genes on the far right (blue) correlated the most primary samples. The vertical black lines indicate the position of each of the genes of the studied gene set in the ordered, non-redundant data set. The green curve corresponds to the ES (enrichment score) curve, which is the running sum of the weighted enrichment score obtained from GSEA software.</p

KingClonalityData

Number log-ratio data for the 31 samples in the manuscript. It is a gzipped text file

KingClonalityCode

This file contains the R code used to preprocess the SNP array files (not submitted due to privacy issues) as well as the clonality analysis using the submitted log-ratio data

Additional file 4: of Clonal relationships between lobular carcinoma in situ and other breast malignancies

This table compares the p values obtained from the clonality tests based on copy number profiling derived using comparative genomic hybridization and the exome arrays. Although there is clearly considerable variation in the actual p values observed, there is consistency in identifying strong clonality signals (p < 0.01), except for case 48 (LCIS2-ILC). (DOCX 28 kb

Additional file 1: of Clonal relationships between lobular carcinoma in situ and other breast malignancies

Separate plots are provided for copy number comparisons of all pairs of lesions ascertained in the study, including those comparisons not presented in the main text because one or other of the tumors in the pair was considered to have insufficient quality. We used two quality metrics: percent gained or lost; 75th percentile of |height| of gains or losses (at least ten markers long) divided by the median absolute deviation of the residuals. The quality was considered to be sufficiently good if either the percent gained or lost was >10 % or if the |height| percentile was >1.75 median absolute deviation. The figures display the log ratios for each marker ordered across the genome, side by side for each tumor in the pair. The blue lines indicate regions of allelic gain, and the red lines indicate regions of allelic loss, as determined by the segmentation algorithm used [21]. (PDF 19100 kb

Noninvasive Detection of Inflammatory Changes in White Adipose Tissue by Label-Free Raman Spectroscopy

White adipose tissue inflammation (WATi) has been linked to the pathogenesis of obesity-related diseases, including type 2 diabetes, cardiovascular disease, and cancer. In addition to the obese, a substantial number of normal and overweight individuals harbor WATi, putting them at increased risk for disease. We report the first technique that has the potential to detect WATi noninvasively. Here, we used Raman spectroscopy to detect WATi with excellent accuracy in both murine and human tissues. This is a potentially significant advance over current histopathological techniques for the detection of WATi, which rely on tissue excision and, therefore, are not practical for assessing disease risk in the absence of other identifying factors. Importantly, we show that noninvasive Raman spectroscopy can diagnose WATi in mice. Taken together, these results demonstrate the potential of Raman spectroscopy to provide objective risk assessment for future cardiometabolic complications in both normal weight and overweight/obese individuals