A Methyl-Deviator Epigenotype of Estrogen Receptor–Positive Breast Carcinoma Is Associated with Malignant Biology

Materials and Methods

Results

The methylation array profiles of 351 individual breast cancers and 46 noncancer tissues were included in the analysis (Table 1 and Figure 1; see also Supplemental Table S1 at http://ajp.amjpathol.org). Dynamic data exploration of 312 primary invasive carcinomas and 32 TDLUs yielded 242 CpG target variables when the variance filter was tuned to 0.5. Hierarchical clustering revealed an out-group comprising roughly 25% of cancers and manifesting maximal deviation from baseline (Figure 2A). Target deflections from baseline TDLU included both hypomethylations and hypermethylations, and the out-group was subsequently referred to as the methyl-deviator group (Figure 2A). Annotation of the clustered samples for ER status and Nottingham histological grade further suggested that the deviator out-group is substantially enriched for high-grade ER⁺ cancers (Figure 2A). The least methyl-deviant cancers form a neighboring branch to TDLU and appear to be enriched for ER⁻ cancers (Figure 2A).

Subsequently, we calculated an MDI for each sample to use as a metric in group comparisons and survival analyses. The MDI is calculated as the global sum of target methyl deviations in a cancer relative to TDLU baseline, for all targets that meet generic TDLU homogeneity and cancer heterogeneity variance thresholds. By summing the absolute values of target methylation difference between a cancer sample and the baseline, both positive and negative deflections from baseline positively contribute to the MDI score. The MDI captures both the amplitude and frequency of methyl deviation across the cancer genome, while suppressing signals from neutral epigenetic polymorphisms. In our initial comparative analysis of MDI across various sample groups (Figure 2B), the baseline TDLU variance filter was set to <0.1, whereas the cancer filter was set to >0.7, yielding an overlap set of 109 CpG targets (MDI_109) distributed among 85 discrete genes.

Summary statistics of MDI_109 values in clinically relevant cancer subclasses are shown in Figure 2B. This analysis confirmed the impression from the hierarchical clustering that ER⁺ and ER⁻ cancer groups manifest significant differences in global methylation reprogramming. Notably, ER⁺ cancers have a greater MDI (P < 0.001), whereas the ER⁻ cancers are the most normal, as in this parameter. Thus, the data exploration revealed significant contrast in global deviation between ER⁻ and ER⁺ tumors. Coupled with clinical and biological insight that typically regards ER⁺ and ER⁻ cancers as distinct entities, ER⁺ and ER⁻ groups were subsequently treated separately for further clinicopathological correlation of methyl deviation. The analysis focused on ER⁺ cancers revealed a significantly higher MDI among tumors with high-grade histological features and a poor prognosis (Figure 2B and Table 2). A high tumor proliferative index based on Ki-67 staining was correlated with a higher MDI, with borderline significance (P = 0.06). We did not observe a correlation between MDI and Her-2 amplification status of ER⁺ cancers (P = 0.9).

Tuning of the cancer and baseline variance cutoffs was performed to include between 3.5% (MDI_53) and 85% (MDI_1268) of array targets in the MDI calculation (Figure 3A). These adjustments to variance cutoffs had little effect on the performance of MDI as a prognostic metric. For example, the ROC AUC for MDI-based prognosis is approximately 0.78 (Figure 3A), whether the cancer variance is titrated to be more target inclusive (MDI_1268: variance = 0.0, AUC = 0.78) or target restrictive (MDI_53: variance = 0.8, AUC = 0.78). Moreover, all MDI target sets were significantly prognostic for ER⁺ cancer survival (P < 0.001). In contrast to this relative insensitivity to adjusting the variance filters, prognostic performance is substantially undermined when the TDLU baseline reference is removed and crude methylation levels are summed, as the AUC decreases to 0.60 (Figure 3A, MBI_1505). Even more important, failure to evaluate survival separately for ER⁺ and ER⁻ groups shifts the AUC to 0.49, totally effacing the prognostic performance of MDI. Thus, we find the following: counting cancer hypomethylations as positive contributors to methyl-deviance computation has a substantial positive impact for methylation-based prognostication; and it is essential to perform MDI-based prognosis separately for ER⁺ and ER⁻ cancers. Kaplan-Meier survival analysis further showed a significant difference in time to distant recurrence between MDI-low and MDI-high ER⁺ cancers (Figure 3B).

Because MDI target summation captures methyl deviation in cancer as a global process, it does not determine the statistical significance of any given target for association with aggressive cancer biological features. Therefore, MDI targets were individually tested by analysis of variance P values and FDR-based Q scores for significant differences between short- and long-survival ER⁺ patient groups (Table 3). Indeed, >70% of MDI_109 targets are significantly different, and 80% of the top 50 analysis of variance–derived targets were identified through MDI analysis.

Returning to ER⁻ cancers, we identified no significant association between MDI and survival status (Figure 2B, P = 0.263). There was no difference in MDI between basallike and nonbasallike ER⁻ cancers (P = 0.4). In addition, as previously noted, failure to exclude ER⁻ cancers from evaluation of MDI as a prognostic marker undermines the performance of MDI-based prognosis; this result can be explained by the combination of significantly lower MDI in ER⁻ cancers and the lack of correlation in that group of MDI with survival. Because the ER⁻ cancers were predominantly of intermediate to high histological grade, we could not effectively compare low- with high-grade ER⁻ cancers for MDI status. However, high-grade ER⁻ cancers have even lower MDI than low–Nottingham histological grade/long-survival ER⁺ cancers (Figure 2B).

Next, MDI was tested for independent significance in a multivariate regression analysis of ER⁺ cancer survival. The univariate analysis identified the following variables to be significantly associated with survival (P < 0.05): cancer stage, MDI_109, histological grade, MI_44 (see later), and Her-2 amplification (Table 2). The Ki-67 index was borderline significant (P = 0.064). In the multivariate analysis of significant variables from the univariate analysis, MDI_109 and clinical stage retained independent significance (Table 2).

Next, we investigated the biological logic of target methylation reprogramming in breast cancer by testing MDI targets for enrichment of certain biological annotations, including CpG islands (CGIs), polycomb group targets (PGCTs), and estrogen-responsive genes. There was no specific targeting of CGI because more than one third of MDI targets are off island and there is no enrichment for cancer CGI versus non-CGI methylation when adjusted for proportions of CGI and non-CGI array targets in the TDLU epigenomic space available for de novo methylation. This result is reminiscent of our finding in follicular lymphoma that CGIs are not specifically targeted for methylation relative to non-CGIs.⁸ PGCTs were significantly enriched among MDI targets: 43% of MDI_109 are PGCTs, as defined by single occupancy of SUZ12, EED, or H3K27me3 in human embryonic stem cells,^7,26 whereas 22% of all array targets are PGCTs. Thus, we observed a significant moderate twofold enrichment for polycomb group targets (P < 10E⁻⁶). Regarding methylation reprogramming of estrogen-responsive genes, we measured the overlap of the MDI_109 with the published whole-genome ER-α binding site cartograph of Lin et al.²⁷ Interestingly, there is only a single gene common to MDI_109 targets and the 234 estrogen-responsive genes that neighbor estrogen response elements, as derived from MCF-7 ChIP-Seq data. This indicates that the methylated targets in breast cancer tissues are not the estrogen-responsive genes in MCF-7 cells.

Next, we investigated whether group MDI differences are substantially affected by heterogeneity for benign tissue–specific epigenetic markers (eg, because of the presence of tumor-infiltrating lymphoid cells or mesenchymal cells). The LIs and MIs (ie, LI_59 and MI_44, respectively) were calculated from array data (see Materials and Methods and Table 4). The overlap of MDI_109 with LI_59 or MI_44 is only two and three targets, respectively, indicating MDI is largely not measuring lymphoid and mesenchymal background.

Regarding possible dilutional hypodeviation among ER⁻ cancers, we tested whether lymphoid and/or mesenchymal cells in that group suppress the measurement of deviant methylation relative to the ER⁺ group. A few outliers with notably high LI were identified in the ER⁻ group (Figure 4B), and a review of the histological features revealed characteristic features of the lymphoid-rich medullary carcinoma variant^28,29 (Figure 4C). Except for these relatively rare medullary cancers,^18,28,29 the difference of LI means between ER⁻ and ER⁺ cancer LIs is <0.02 and cannot account for the significant difference in MDI (Figure 4B). Similarly, the difference in mean MI between ER⁺ and ER⁻ cancers was <0.02 (Figure 4E). Thus, background tissue-specific epialleles in breast cancers do not explain ER⁻ cancer MDI suppression or ER⁺ cancer MDI elevation; contrasting epigenomic reprogramming is likely an intrinsic property of the breast malignant epithelial cell genome.

Although differences of LI and MI between the ER⁺ and ER⁻ groups do not account for MDI differences, there is heterogeneity of LI and MI within these groups (Figure 4, B and E); therefore, we looked for possible correlations of LI or MI with survival. Interestingly, among ER⁺ tumors, a high MI associates with longer survival (Figure 4H). Specifically, the ROC AUC for MI_44 prognostic performance is 0.31 (P < 0.001), indicating a fairly robust anticorrelation of MI with subsequent distant recurrence. Moreover, when ER⁺ cancers are divided evenly into MI-low and MI-high groups, the Kaplan-Meier curves show a significant difference between MI-low and MI-high time to distant recurrence (Figure 4). Consistent with this finding, recently, an increased histological mesenchymal component was determined to be a favorable breast cancer prognostic marker.³⁰ Among ER⁻ cancers, LI was not significantly different between short- and long-survival classes (P = 0.2); this result will be further discussed.

Finally, we compared breast carcinoma distant metastases with primary tumors for conservation of these epigenomic distinctions (Figure 5). Comparisons included the following: i) eight matched pairs of primary tumors and their metastases, ii) 23 ER⁺ metastases and 39 ER⁺ primary tumors with subsequent distant recurrence, and iii) four ER⁻ metastases and 19 ER⁻ primary tumors with subsequent distant recurrence. First, in hierarchical clustering of the matched pairs of primary and metastatic lesions (Figure 5, A and B), seven of eight pairs cosegregate, whereas the eighth pair is slightly less similar, indicating epigenomic stability overall. Second, the median MDI_109 values of the ER⁻ primary tumors and metastases are 0.13 and 0.11, respectively, whereas those of the ER⁺ cancers are 0.39 and 0.44, respectively (Figure 5C). These findings suggest that the bulk of methylation reprogramming may occur early during tumorigenesis, particularly in ER⁻ cancers. Although the MDI is moderately greater in ER⁺ metastases than primary carcinomas (Figure 5C), we find little evidence for a concerted process of progression-target methylation subsequent to primary tumorigenesis because only two CpG targets were significant between these two cancer groups (data not shown). In sum, much ER⁺ breast cancer prognosis-related genomic methylation reprogramming is already established in primary lesions and remains stable through progression.

Discussion

The main finding in this study is that a genomic index of deviant DNA methylation (ie, the MDI) is readily measurable from routine FFPE breast pathology samples and correlates with aggressive cancer biological features, including time to distant recurrence. MDI is informative to estimate disease prognosis for ER⁺ primary invasive carcinomas. More important, we found that deviant methylation must be measured relative to TDLU baseline for optimal prognostic performance. Prior studies^3–6 have also observed correlation of breast cancer clinical features with methylation status of gene targets. In accord with prior studies,^31,32 we identified several reported markers of ER⁺ breast cancer prognosis. These markers include CCND2, APC, and RASSF1. Notably, the latter two genes were detectable in the serum of patients with breast cancer and carried prognostic significance.³¹ One recent analysis of candidate gene expression subtypes of breast cancer^1,33 noted higher methylation levels in samples classified as luminal B versus luminal A and basal.⁴ Interestingly, we found in our study that nearly 60% of reported basal-type methylation markers are consistent with tissue-specific lymphoid markers and could derive from tumor-infiltrating lymphocytes (data not shown).

Going beyond prior studies, we observe global epigenomic remodeling in breast cancer, suggesting that perhaps hundreds of robust methylation biomarkers of ER⁺ disease prognosis are readily accessible in routine breast biopsy specimens. Furthermore, our computation of a TDLU baseline reference from numerous individuals and quantification of methylation array–based lymphoid and MIs constitute additional advances over prior studies. We found that epigenomic array-based quantification of nonepithelial constituents, such as mesenchymal background within ER⁺ breast carcinoma lesions, may have prognostic value. In addition, among ER⁻ cancers, we found no difference in LI between survival classes. This result is in accord with recent work by Teschendorff et al³⁴ that suggests the prosurvival immune response gene expression signature (“IR⁺”) among ER⁻ cancers derives intrinsic to the cancer epithelial cells and is not because of extrinsic LI.

Given the many samples and the convincing prognostic signal achieved in this study, global methylation profiling of FFPE samples from clinical trial samples is warranted to validate these findings and further pursue predictive methyl biomarkers for a therapeutic response, such as adjuvant chemotherapy in the treatment of ER⁺ cancers.

Beyond these diagnostic ramifications, this study indicates a fundamentally different process of epigenomic remodeling between ER⁺ and ER⁻ cancers. Curiously, the ER⁻ cancers have the least globally deviant methylome because their biological features may be considered to deviate the most from TDLU. For instance, ER⁻ cancers are among the most metastatic and least hormonally responsive, whereas TDLU epithelial cell proliferation is localized and under hormonal regulation. Finally, the observed conservation of primary tumor methylation patterns in subsequent metastases further underlines the biological distinction between ER⁺ and ER⁻ groups and indicates the potential utility of methylation profiling at multiple stages of disease evolution.

Supplementary data