Epigenetic and genetic burden measures are associated with tumor characteristics in invasive breast carcinoma

<p>The development and progression of invasive breast cancer is characterized by alterations to the genome and epigenome. However, the relationship between breast tumor characteristics, disease subtypes, and patient outcomes with the cumulative burden of these molecular alterations are not well characterized. We determined the average departure of tumor DNA methylation from adjacent normal breast DNA methylation using Illumina 450K methylation data from 700 invasive breast tumors and 90 adjacent normal breast tissues in The Cancer Genome Atlas. From this we generated a novel summary measure of altered DNA methylation, the DNA methylation dysregulation index (MDI), and examined the relation of MDI with tumor characteristics and summary measures that quantify cumulative burden of genetic mutation and copy number alterations. Our analysis revealed that MDI was significantly associated with tumor stage (<i>P</i> = 0.017). Across invasive breast tumor subtypes we observed significant differences in genome-wide DNA MDIs (<i>P</i> = 4.9E–09) and in a fraction of the genome with copy number alterations (FGA) (<i>P</i> = 4.6E–03). Results from a linear regression adjusted for subject age, tumor stage, and estimated tumor purity indicated a positive significant association of MDI with both MCB and FGA (<i>P</i> = 0.036 and <i>P</i> < 2.2E–16). A recursively partitioned mixture model of all 3 somatic alteration burden measures resulted in classes of tumors whose epigenetic and genetic burden profile were associated with the PAM50 subtype and mutations in <i>TP53, PIK3CA, and CDH1</i>. Together, our work presents a novel framework for characterizing the epigenetic burden and adds to the understanding of the aggregate impact of epigenetic and genetic alterations in breast cancer.</p

Placental <i>FKBP5</i> Genetic and Epigenetic Variation Is Associated with Infant Neurobehavioral Outcomes in the RICHS Cohort

<div><p>Adverse maternal environments can lead to increased fetal exposure to maternal cortisol, which can cause infant neurobehavioral deficits. The placenta regulates fetal cortisol exposure and response, and placental DNA methylation can influence this function. FK506 binding protein (FKBP5) is a negative regulator of cortisol response, <i>FKBP5</i> methylation has been linked to brain morphology and mental disorder risk, and genetic variation of <i>FKBP5</i> was associated with post-traumatic stress disorder in adults. We hypothesized that placental <i>FKBP5</i> methylation and genetic variation contribute to gene expression control, and are associated with infant neurodevelopmental outcomes assessed using the Neonatal Intensive Care Unit (NICU) Network Neurobehavioral Scales (NNNS). In 509 infants enrolled in the Rhode Island Child Health Study, placental <i>FKBP5</i> methylation was measured at intron 7 using quantitative bisulfite pyrosequencing. Placental <i>FKBP5</i> mRNA was measured in a subset of 61 infants by quantitative PCR, and the SNP rs1360780 was genotyped using a quantitative allelic discrimination assay. Relationships between methylation, expression and NNNS scores were examined using linear models adjusted for confounding variables, then logistic models were created to determine the influence of methylation on membership in high risk groups of infants. <i>FKBP5</i> methylation was negatively associated with expression (<i>P</i> = 0.08, r = −0.22); infants with the TT genotype had higher expression than individuals with CC and CT genotypes (<i>P</i> = 0.06), and those with CC genotype displayed a negative relationship between methylation and expression (<i>P</i> = 0.06, r = −0.43). Infants in the highest quartile of <i>FKBP5</i> methylation had increased risk of NNNS high arousal compared to infants in the lowest quartile (OR 2.22, CI 1.07–4.61). TT genotype infants had increased odds of high NNNS stress abstinence (OR 1.98, CI 0.92–4.26). Placental <i>FKBP5</i> methylation reduces expression in a genotype specific fashion, and genetic variation supersedes this effect. These genetic and epigenetic differences in expression may alter the placenta’s ability to modulate cortisol response and exposure, leading to altered neurobehavioral outcomes.</p></div

Methylation and Genotype are associated with FKBP5 Expression.

<p>Gene expression results were quantified in a subset (N = 61) of all placentas sequenced <b>A.</b>Gene expression as quantified as <i>FKBP5</i>/<i>SDHA</i> counts stratified by genotype, divided by the mean value of the average of the CC group (<i>P</i> = 0.07, ANOVA, Tukey test CT vs. CC, <i>P</i> = 0.53. CC vs TT <i>P</i> = 0.41, TT vs CT <i>P</i> = 0.05) <b>B.</b> Correlation of <i>FKBP5/SDHA</i> counts vs. <i>FKBP5</i> Intron 7 Methylation (r = −0.22, <i>P</i> = 0.08<b>). C–E.</b> Correlation of <i>FKBP5/SHDA</i> counts vs. <i>FKBP5</i> Intron 7 methylation stratified by genotype.*P≤0.1 ** = P≤0.05.</p

Linear regression model of <i>FKBP5</i> methylation, rs1360780 Genotype and NNNS outcomes.

<p>*Also adjusted for birth weight group, maternal age, gender and random effect of conversion plate.</p

Logistic regression model of <i>FKBP5</i> methylation quartiles and 95^th percentile arousal and stress abstinence scores.

<p>Logistic regression model of <i>FKBP5</i> methylation quartiles and 95^th percentile arousal and stress abstinence scores.</p

GCT methylation status in context of methylation during germ cell development.

<p>The top and bottom line charts depict normal germ cell development in female and male respectively (stages specified in the middle black bar). Methylation status during normal germ cell development is depicted for the global genome, ICRs and chromosome X (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#sec010" target="_blank">Discussion</a>). Putative cells of origin of the various types of GCTs are indicated in the brown boxes. ICR_P/M = ICR regulating paternally/maternally expressed genes. Bimodal indicates a methylation pattern peaking 0 and 100% with the exception of SE/DG (between 0 and ≈50). The table (bottom) provides a summary of the results, mainly Figs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g002" target="_blank">2</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g006" target="_blank">6</a>. Abbreviations: pf = primordial follicle. Type I tumors are indicated with their type (I), sex (m = male, f = female) and location (s = sacral, t = testis, o = ovary). Other GCT subtypes are indicated with their type (I, II, IV) and the abbreviation of each histological class, which are explained in the main text. Gradient bars indicate percentages of methylation (0→100%, green-white-grey-red) analogous to the gradient used in the other figures.</p

Methylation of imprinting control regions and the X chromosome.

<p>Analogous to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g002" target="_blank">Fig 2</a> the differences in methylation status between histological GCT subtypes is illustrated by two methods. Firstly, the methylation pattern is visualized using the distribution of the methylation percentage β. Next, the discriminatory power of the methylation pattern for each individual sample is shown using principal component analysis. <b>(A)</b> All probes associated with paternally expressed genes (ICR_P). <b>(B)</b> All probes associated with maternally expressed genes (ICR_M). <b>(C)</b> All probes located on the X chromosome. <b>(D)</b> Distribution of methylation in individual TE samples ordered by sex and localization. To compare type I and II TE the n = 3 type II pure TEs from the mNS were included in this visualization. Methylation levels of all probes, and probes associated with ICRs (P/M) and probes on the X chromosome are subsequently shown. <b>(Distribution plots of methylation percentage.)</b> Violin plots: grey areas indicate a kernel density plot of the methylation percentage (β) of all probes in all samples in a certain category. The boxplot indicates the interquartile range (black bars) and median (white squares). X-axis labels indicate histological subgroup according to Fig <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1A</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1B</a>. TE indicates type I TE only. (<b>Principal Component Analysis.)</b> The first two principal components (PC) are plotted to evaluate the discriminative power of the methylation pattern between the subtypes. Abbreviations of histological subtypes are explained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1A</a>. CL indicates cell lines. Please note that in the legend of the PCA the TE group is subdivided based on gender and localization: I = type I; II = type II/formally part of the mNS group, s = sacrum, t = testis, o = ovary, m = male, f = female.</p

Functional enrichment of DMPs.

<p>DMPs were classified according to their functional genomic location (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1C</a>). Statistical over- and underrepresentation of probes in certain categories provides clues to differences between GCT subtypes in regarding function of methylation. Enrichment was assessed by comparing the number of probes in a functional category in a subset of DMPs with the that in the total dataset (Fisher’s Exact test, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#sec011" target="_blank">Materials & Methods</a> section). Results are shown for four pairwise (A vs B) comparisons of histological subtypes: <b>(A)</b> SE/DG versus EC/mNS; <b>(B)</b> SE/DG vs type I TE; <b>(C)</b> EC/MNS vs type I TE and <b>(D)</b> SE/DG vs SS. <b>(LEFT)</b> The number (n) of DMPs identified in either the DMP[<u><b>A</b></u>-B] (hypermethylated in A, green) or DMP[A-<u><b>B</b></u>] (hypermethylated in B, red) group. <b>(MIDDLE/RIGHT)</b> Functional enrichment in the DMP[<u><b>A</b></u>-B] and DMP[A-<u><b>B</b></u>] group respectively. X-axis: positive numbers indicate a significant overrepresentation of DMPs in a functional category compared to non-DMPs while negative numbers indicate a significant underrepresentation. Depicted is the log2 ratio of (1) the % of either DMP group assigned to a category and (2) the % of non-DMPs assigned to that category. Only significant enrichments are depicted (2-sided Fisher’s Exact test, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#sec011" target="_blank">Methods</a> section for Bonferroni corrected α threshold). DMPs[se/dgvs<u><b>SS</b></u>].IMPR_P1500 showed significant underrepresentation, but could not be plotted on log scale (0 probes in DMP group). Details of calculations and raw counts and percentages are presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.s008" target="_blank">S2 Table</a>. Y-axis: functional categories as specified in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1C</a>.</p

Methylation patterns in GCT subtypes and cell lines.

<p>To illustrate differences in methylation status between histological GCT subtypes two (visualization) methods were applied. Firstly, the methylation pattern over the whole genome and specific functional categories (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1C</a>) is visualized using the distribution of the methylation percentage β in all samples of a certain GCT subtype. Next, the discriminatory power of the methylation pattern for each individual sample is shown using principal component analysis. <b>(A) Distribution of methylation percentage.</b> Violin plots: grey areas indicate a kernel density plot of the methylation percentage (β) of all probes in all samples in a certain category. The boxplot indicates the interquartile range (black bars) and median (white squares). X-axis labels indicate histological subgroup according to Fig <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1A</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">1B</a>. TE indicates type I TE only. <b>(B) Principal Component Analysis.</b> The first two principal components (PC) are plotted to evaluate the discriminative power of the methylation pattern between the subtypes. Abbreviations of histological subtypes are explained in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.g001" target="_blank">Fig 1A</a>. CL indicates cell lines. Please note that in the legend of the PCA the TE group is subdivided based on gender and localization: I = type I; II = type II/formally part of the mNS group, s = sacrum, t = testis, o = ovary, m = male, f = female. A more detailed visualization of the TE classes is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122146#pone.0122146.s002" target="_blank">S2 Fig</a>, which also includes the full series of 18 functional categories, bootstrap validation of the PCA and an estimation of the variance explained by the first two principal components.</p

GCT (methylation) associated genes.

<p>GCT (methylation) associated genes.</p