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We broadly profiled DNA methylation in breast cancers (n = 351) and benign parenchyma (n = 47) for correspondence with disease phenotype, using FFPE diagnostic surgical pathology specimens. Exploratory analysis revealed a distinctive primary invasive carcinoma subclass featuring extreme global methylation deviation. Subsequently, we tested the correlation between methylation remodeling pervasiveness and malignant biological features. A methyl deviation index (MDI) was calculated for each lesion relative to terminal ductal-lobular unit baseline, and group comparisons revealed that high-grade and short-survival estrogen receptor–positive (ER+) cancers manifest a significantly higher MDI than low-grade and long-survival ER+ cancers. In contrast, ER− cancers display a significantly lower MDI, revealing a striking epigenomic distinction between cancer hormone receptor subtypes. Kaplan-Meier survival curves of MDI-based risk classes showed significant divergence between low- and high-risk groups. MDI showed superior prognostic performance to crude methylation levels, and MDI retained prognostic significance (P < 0.01) in Cox multivariate analysis, including clinical stage and pathological grade. Most MDI targets individually are significant markers of ER+ cancer survival. Lymphoid and mesenchymal indexes were not substantially different between ER+ and ER− groups and do not explain MDI dichotomy. However, the mesenchymal index was associated with ER+ cancer survival, and a high lymphoid index was associated with medullary carcinoma. Finally, a comparison between metastases and primary tumors suggests methylation patterns are established early and maintained through disease progression for both ER+ and ER− tumors.
Breast cancer is a heterogeneous disease, manifesting variation at the clinical, biological, histopathological, and molecular levels. Profiling studies
of gene expression and DNA copy number have identified molecular markers that can be used to distinguish clinically relevant tumor subtypes. DNA methylation analysis is emerging as a promising avenue for cancer classification; several studies
point toward the potential for DNA methylation markers to identify distinct breast cancer phenotypes using candidate gene measurements and microarray analyses. As a robust biomarker conserved in routinely processed clinical specimens, DNA methylation is amenable to high-throughput microarray-based discovery,
providing a justification for translational epigenotype-phenotype correlation in routine breast cancer pathological samples.
In the current study, we present a large-scale DNA methylation analysis of primary invasive breast cancers for deviation from the epigenetic state of the normal mammary terminal ductal-lobular unit (TDLU). The TDLU is the structural and functional unit of the mammary gland and is generally considered the origin of breast carcinomas.
In addition to providing a normal tissue epigenetic baseline, the TDLU profile defined by DNA methylation targets invariant among numerous unrelated patients permits filtration of array signals potentially arising from neutral genetic and epigenetic polymorphisms.
The use of archival diagnostic formalin-fixed, paraffin embedded (FFPE) pathological samples for biomarker discovery provides multiple unique benefits, including an indication of potential biomarker applicability to clinical practice.
Contributions to measurements of cancer versus normal tissue epigenetic deviation may arise from both within and outside the cancer cell nucleus. Intrinsic to the cancer cell, de novo methyltransferase activity may generate divergent epialleles; not to be overlooked, faithful maintenance methylation of conserved cell lineage–specific marks,
coupled with malignant cell population enrichment, could also manifest as differential methylation between benign and cancer tissues. Meanwhile, cancer-lesion epigenetic distinctions may be extrinsic to the cancer cell, arising from characteristic microanatomical embedding of benign elements among cancer epithelial cells that often determine histopathological classification.
Pathologic findings from the National Surgical Adjuvant Project for Breast Cancers (protocol no. 4), X: discriminants for tenth year treatment failure.
For example, the microarchitecture of breast medullary carcinoma displays syncytial cords of high-grade malignant epithelial cells interwoven with channels of benign lymphoid cells
; and the subtype-specific molecular signature of the lesion will derive from both compartments. By contrast, nonspecific heterogeneity across biological subclasses may arise from benign glandular and inflammatory elements and possibly iatrogenic effects (ie, needle-core-biopsy–related changes). Therefore, microscopy-based histological control and molecular quantification of constituent benign lymphoid and mesenchymal epialleles may be beneficial for understanding cancer tissue differential methylation signatures.
Subsequent to primary invasive tumorigenesis, the fidelity of maintenance and de novo DNA methylation during disease progression is incompletely understood. Archival pathological specimens provide an opportunity to compare primary tumors with longitudinal recurrences to probe the status of these processes. Thus, finally, in our study, we compare primary tumors with matched longitudinal recurrences to obtain a global snapshot of the methylome at different tumor stages and to investigate the stability of DNA methylation patterns during disease evolution.
Materials and Methods
Samples
FFPE breast cancer (n = 351), benign breast TDLU (n = 32), reactive lymph node (n = 9), and benign mesenchyme (fibromuscular tissue, n = 5) samples were retrieved from the pathology department archives of Suburban Hospital, Bethesda, MD (Table 1 and Figure 1). To reduce case selection bias, we included all available archival breast cancers from a consecutive 2-year period in the analysis. Available clinical registry data included cancer stage, follow-up interval, and time to distant recurrence. Survival analyses were based on the end point of distant recurrence. Specimens and corresponding clinical data were deidentified according to the NIH Office of Human Subjects Research policy.
Table 1Patient and Sample Characteristics
Characteristics by type of tissue
No. affected
Breast carcinoma
351
Primary invasive breast carcinoma
312
Age at primary diagnosis (median, 60 years) (years)
Figure 1Representative photomicrographs of tissues used in the study. Top: TDLU, reactive lymph node, and fibromuscular tissue. Bottom: Low-, intermediate-, and high-grade primary invasive breast carcinomas. (Images are shown from left to right.)
Review and Processing of Specimen Pathological Features
Histological sections were reviewed by a pathologist (J.K.K.) for characteristic pathological features and scored for cancer grade according to the Nottingham system.
Pathological prognostic factors in breast cancer, I: the value of histological grade in breast cancer: experience from a large study with long-term follow-up.
The region of characteristic tumor histological features with maximal tumor-cell fraction was marked on the slide section, and the target region was then manually dissected from the homologous region of the corresponding FFPE tissue block using a 1- to 2-mm needle micropunch (J.K.K.). Similarly, benign TDLUs, lymph nodes, and mesenchymal muscle and fibrous elements were needle dissected from paraffin blocks under histological guidance. Tissue cores were lysed by incubation at 65°C for 2 to 3 days in 200 μL of FFPE tissue lysis solution (160 μL of Qiagen ATL + 20 μL of Qiagen proteinase K + 20 μL of Dako target retrieval solution), and lysates were processed to yield 1 to 2 μg of bisulfite-modified DNA using the EZ DNA methylation kit (Zymo Research, Irvine, CA). The yield of bisulfite-converted DNA was measured by Nanodrop (ThermoScientific, Wilmington, DE).
Immunophenotyping
From available paraffin blocks with residual tumor, adjacent 2-mm cores to those used for methylation profiling were taken to construct TMAs for immunophenotyping. TMA slide sections were immunostained for estrogen receptor (ER), progesterone receptor, Her-2, CK5/6, pan-CK, Ki-67, and epidermal growth factor receptor in a diagnostic pathology laboratory using a Ventana autostainer (Ventana Medical Systems, Inc., Tucson, AZ) with antibody clones SP1, 1E2, 4B5, D5/16B4, AE1AE3, 30-9, and 2-18C9, respectively. The cutoff for Ki-67 low versus high proliferative index was positive staining of 10% cancer cell nuclei.
Bisulfite-converted DNA, 250 ng, was assayed using the GoldenGate Cancer Panel I methylation assay (Illumina, Inc., San Diego, CA), as previously described.
Briefly, this assay measures DNA methylation at 1505 distinct CpG targets distributed among 807 genes. Sample target methylation β values that approximate percentage methylation within the sample homogenate were extracted in BeadStudio (Illumina, Inc.) from raw Cy3 and Cy5 signal intensities. Samples that did not pass array internal controls were excluded. The lesion β is the average β of any samples that were technical replicates (DNA or needle cores) derived from a single patient lesion. Methylation β data are provided in Supplemental Table S1 (available at http://ajp.amjpathol.org). Methylation data may also be retrieved from Gene Expression Omnibus.
Data Analysis
Dynamic data exploration and discovery analyses were performed using Qlucore Omics Explorer version 2.1 (Qlucore AB, Lund, Sweden), as follows. The 1505 array target methylation β values from 32 TDLU and 312 primary carcinoma lesions were extracted from BeadStudio and imported to QOE. Data normalization was set as follows: mean = 0 and variance = 1; the hierarchical clustering module was set to maximum linkage, and the variance filter was dynamically tuned while observing sample and variable clustering. The variance was set to 0.5 to yield the set of 242 target variables shown in Figure 2A.
Figure 2Exploratory data analysis and observation of the methyl-deviator subclass. A: Hierarchical clustering of target methylation β of benign breast parenchymal TDLU (n = 32) and primary breast cancers (n = 312) (242 CpG targets, see Material and Methods) segregates methyl-deviator breast cancer subgroup from TDLU and other breast cancers. Green, black, and red heat map shades correspond to target methylation β continuous scores of 0 to 0.5 to 1. B: Box plot summary statistics comparing the distribution of MDI_109 in various clinicopathological breast cancer groups. NHG indicates Nottingham histological grade; ERS, ER status; short survival, primary cancers later followed by distant recurrence; and long survival, primary cancers not followed by distant recurrence, with at least 7 years of follow-up.
Target methyl deviation was calculated as the methylation β difference between sample and TDLU baseline. The baseline target β is the TDLU group average β from 32 different individuals.
Target methyl deviation values were summed to compute the methyl deviation index (MDI) of each lesion:
Of 1505 array targets, 237 had a baseline variance >0.1, and these targets were excluded from calculations of MDI that implement a baseline uniformity filter. Target MDI rank from highest to lowest is the SD of the target within the group. For example, the top 100 MDI targets in the ER+ cancer group are the 100 targets with the greatest SD in that group.
Alternative to MDI, the methylation β index was calculated as the sum of all target methylation levels within the lesion without reference to a baseline:
The performances of multiple different arbitrary cancer variance cutoffs for MDI-based survival prognostication were compared using receiver operating characteristic (ROC) area under the curve (AUC) analysis, as was the performance of MDI versus methylation β index.
The statistical significance (P values and false-discovery-rate–corrected Q scores) of MDI target measures between long- versus short-survival ER+ breast cancers was calculated by two-group comparison of array CpG target variables using analysis of variance in QOE. The MDI_72sig refers to the intersection of statistically significant survival targets between long- and short-survival ER+ cancers (P < 0.05), with the top 100 MDI targets in the ER+ cancer group.
To calculate the lymphoid index (LI), statistically significant lymphoid-specific methyl markers relative to TDLU (analysis of variance, P < 0.05) were identified in QOE. The input target variables were the 1268 conforming TDLU targets (variance <0.1, as previously indicated), and the samples were the 32 TDLU and the 9 female lymphoid tissues. Next, the LI_59 was calculated for each primary cancer lesion after setting the variance filter to 0.5 (to enrich for lymphoid-specific markers of highest contrast) and dividing by 59, the number of targets in the resulting cassette:
The same concept was used to calculate the lesion mesenchymal index (MI) by summing mesenchyme-specific methylation markers relative to TDLU:
MDI, LI, and MI were treated as continuous variables and were not stratified or discretized for ROC and pairwise analyses. For the Kaplan-Meier survival analysis and Cox regression analyses, patients with ER+ unilateral primary invasive carcinomas and ≥7 years of follow-up (n = 157) were assigned to low-, middle-, and high-risk groups based on MDI_109 rank (bottom, 30%; middle, 40%; and top, 30%; respectively) and low- and high-risk groups based on MI_44 and LI_59 rank (bottom, 50%; and top, 50%; respectively).
Box plot graphs, ROC calculations, and survival analyses were performed using SigmaPlot11.2 (Systat Software, Inc., Chicago, IL) and R. Heat plots were generated in QOE. Target significance for ER+ cancer survival (P values and Q scores) was measured in QOE using analysis of variance.
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).
Table 2Cox Multivariate Regression Analysis of Prognostic Factors for Breast Carcinoma
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).
Figure 3A: ROC curves demonstrate prognostic performance of several variance cutoffs in the calculation of the MDI. The MDI is the summation of target differences from TDLU baseline for targets meeting tunable cancer heterogeneity and baseline homogeneity cutoffs. The methylation β index (MβI) is derived solely from summation of array target methylation measures without reference to baseline. ER+_MDI and ER+_MβI curves show superior performance of MDI to MβI for ER+ cancer prognostication. ER+_MDI_72SIG shows a modest increase to AUC by adding a statistical significance filter to top 100 MDI targets. ERU_MβI_1505 (AUC = 0.49) indicates the prognostic performance on primary carcinomas unselected for hormone receptor status and shows that failure to evaluate methyl deviation in ER+ and ER− cancers separately severely undermines MDI-based prognostication. A within the figure indicates AUC. B: Kaplan-Meier plot shows a statistically significant survival difference between low-, intermediate-, and high-risk distant metastasis groups, defined by MDI. P < 0.01 for all group comparisons.
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.
Table 3MDI_109 Targets Ranked by Statistical Significance
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.
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,
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.
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.
Table 4LI_59 and MI_44 Targets and Their Statistical Significance
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
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.
Figure 4Deconstruction of cancer tissue lymphoid and mesenchymal constituents. A: Hierarchical cluster of TDLU (n = 32) and female-only lymph node samples (n = 9) using 59 lymphoid tissue–specific methylation targets (see Materials and Methods for LI_59 rule). B: Summary statistics (box plot graph) showing the similarity of the ER+ and ER− groups for the LI; red spheres denote ER− cancer outliers with exceptionally high LI and histological features of medullary carcinoma. C: Representative photomicrograph from high-LI outlier ER− cancer showing histological features of the medullary subtype of breast carcinoma. D: Hierarchical cluster of TDLU (n = 32) and female-only mesenchymal samples (n = 5) using 44 mesenchymal tissue–specific methylation targets. E: Summary statistics (box plot) showing the similarity of ER+ and ER− groups for MI. F: Photomicrograph from the highest MI cancer case, showing histologically pronounced mesenchymal stroma. G: ROC curves indicate MI has prognostic value in ER+ breast cancer prognosis and is anticorrelated to disease distant recurrence. By contrast, LI has minimal prognostic value. LI was also not significant for ER− survival (P = 0.2, ROC curve not shown). A within figure indicates AUC. H: The Kaplan-Meier curve shows longer survival time to distant recurrence in MI-high ER+ cancers.
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.
Pathobiology of small invasive breast cancers without metastases (T1a/b. N0, M0): National Surgical Adjuvant Breast and Bowel Project (NSABP) protocol B-21.
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.
Figure 5Conservation of the methylation profile among primary breast cancers and their metastases. A: A hierarchical cluster of eight matched primary metastasis (P/M)–tumor pairs (targets filtered for variance only) reveals conservation of the primary methylation signature in its metastasis. Barbells link matched primary tumors and metastasis. B: Representative photomicrographs of a matched primary tumor–metastasis tumor pair, in this case showing the primary breast carcinoma (top) and distant scalp metastasis (bottom). C: Summary statistics of conservation of MDI in primary tumors and metastases (Mets). ERS indicates ER status.
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
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.
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 al
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.
Acknowledgment
We thank Marie Mueller and Dr. Eugene Passamani for facilitating archival pathology research.
Pathologic findings from the National Surgical Adjuvant Project for Breast Cancers (protocol no. 4), X: discriminants for tenth year treatment failure.
Pathological prognostic factors in breast cancer, I: the value of histological grade in breast cancer: experience from a large study with long-term follow-up.
Pathobiology of small invasive breast cancers without metastases (T1a/b. N0, M0): National Surgical Adjuvant Breast and Bowel Project (NSABP) protocol B-21.
Tested and informative samples in each category.
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