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

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

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

Gene Expression Profiles Accurately Predict Outcome Following Liver Resection in Patients with Metastatic Colorectal Cancer

<div><p>Purpose</p><p>The aim of this study was to build a molecular prognostic model based on gene signatures for patients with completely resected hepatic metastases from colorectal cancer (MCRC).</p><p>Methods</p><p>Using the Illumina HumanHT-12 gene chip, RNA samples from the liver metastases of 96 patients who underwent R0 liver resection were analyzed. Patients were randomly assigned to a training (n = 60) and test (n = 36) set. The genes associated with disease-specific survival (DSS) and liver-recurrence-free survival (LRFS) were identified by Cox-regression and selected to construct a molecular risk score (MRS) using the supervised principle component method on the training set. The MRS was then evaluated in the independent test set.</p><p>Results</p><p>Nineteen and 115 genes were selected to construct the MRS for DSS and LRFS, respectively. Each MRS was validated in the test set; 3-year DSS/LRFS rates were 42/32% and 79/80% for patients with high and low MRS, respectively (<i>p</i> = 0.007 for DSS and p = 0.046 for LRFS). In a multivariate model controlling for a previously validated clinical risk score (CRS), the MRS remained a significant predictor of DSS (<i>p</i> = 0.001) and LRFS (<i>p</i> = 0.03). When CRS and MRS were combined, the patients were discriminated better with 3-year DSS/LRFS rates of 90/89% in the low risk group (both risk scores low) vs 42/26% in the high risk group (both risk scores high), respectively (<i>p</i> = 0.002/0.004 for DSS/LRFS).</p><p>Conclusion</p><p>MRS based on gene expression profiling has high prognostic value and is independent of CRS. This finding provides a potential strategy for better risk-stratification of patients with liver MCRC.</p></div

Risk stratification by combination of CRS and MRS for DSS and LRFS.

<p>A. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate, and low risk groups among the training set cohort (N = 60) B. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate and low risk group among the test set cohort (N = 36) C. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for patients in the high, intermediate and super-low risk group among the entire cohort (N = 96).</p

Patient demographics, tumor characteristics and perioperative chemotherapy.

<p>CRS: Sum of points for each variable marked as *on the table, ≥3 considered as high risk.</p>**<p>training set vs test set, ^#Among overall cancer death.</p

Study profile.

<p>Ninety-six samples were selected from our departmental tissue bank. The patients were randomly assigned to the training set and test set by 2∶1. The molecular score was constructed based on the data in the training set cohort and validated using the data in the test set cohort.</p

Association of clinicopathological variables with high- or low-risk signature for cancer death and liver recurrence in training and test cohort patient (N = 60/36).

<p>Association of clinicopathological variables with high- or low-risk signature for cancer death and liver recurrence in training and test cohort patient (N = 60/36).</p

Disease-specific survival (DSS) and Liver recurrence-free survival (LRFS) of patients following curative liver resection stratified by molecular risk scores (MRS).

<p>A. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for the patients in high-risk and low-risk groups among the training set cohort (N = 60) B. Kaplan-Meier estimates of DSS (left panel) and LRFS (right panel) for the patients in high-risk and low-risk groups among the test set cohort; Of note, the threshold values to discriminate the high-risk and low-risk group were the same as used in the analysis for the training set cohort.</p

Univariate and multivariate analysis of DSS and LRFS.

<p>UV, univariate, MV, multivariate.</p