Significant associations between driver gene mutations and DNA methylation alterations across many cancer types

<div><p>Recent evidence shows that mutations in several driver genes can cause aberrant methylation patterns, a hallmark of cancer. In light of these findings, we hypothesized that the landscapes of tumor genomes and epigenomes are tightly interconnected. We measured this relationship using principal component analyses and methylation-mutation associations applied at the nucleotide level and with respect to genome-wide trends. We found that a few mutated driver genes were associated with genome-wide patterns of aberrant hypomethylation or CpG island hypermethylation in specific cancer types. In addition, we identified associations between 737 mutated driver genes and site-specific methylation changes. Moreover, using these mutation-methylation associations, we were able to distinguish between two uterine and two thyroid cancer subtypes. The driver gene mutation–associated methylation differences between the thyroid cancer subtypes were linked to differential gene expression in JAK-STAT signaling, NADPH oxidation, and other cancer-related pathways. These results establish that driver gene mutations are associated with methylation alterations capable of shaping regulatory network functions. In addition, the methodology presented here can be used to subdivide tumors into more homogeneous subsets corresponding to underlying molecular characteristics, which could improve treatment efficacy.</p></div

Driver gene–associated methylation patterns can be used to identify tumor molecular subtypes.

<p>Heat maps for (A) thyroid carcinoma (THCA) and (B) uterine corpus endometrial carcinoma (UCEC) depict hierarchical clustering of methylation values of the union set of the 500 probes most significantly associated with each of the three dominant driver genes in each cancer type. Each column represents a sample, and each row represents a probe. Mutation status and subtype classification are shown in the upper sidebars. The sidebars on the left indicate gene-probe associations and CpG subsets, as well as average methylation levels across normal samples. The arrow in (B) indicates methylation probes that display more hypomethylation in samples where <i>PTEN</i> and <i>CTNNB1</i> mutations co-occur than in samples with <i>PTEN</i> mutations alone.</p

Driver gene–methylation associations and CpG subsets.

<p>(A) The total number of probes associated with any driver gene is shown for each cancer type (<i>top of each column</i>). Each point represents the fraction of corresponding probes associated with a driver gene (y-axis). Names are shown for each of the top three driver genes if they account for more than 10% of total probes (<i>dotted line</i>). See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005840#pcbi.1005840.t001" target="_blank">Table 1</a> for cancer type abbreviations. (B) Driver genes with the most probe associations in each cancer type (<i>gene names in panel</i>). The bar plots show the proportion of associated probes in each of the three CpG subsets [CpG islands (CGIs), shores and shelves (SSs), or open sea], stratified by the direction of association (+/-). Dashed lines indicate the divisions expected if associations were proportionally distributed. No probes were associated with driver genes in BLCA, LUSC, and READ.</p

Differential expression of JAK and STAT family genes is correlated with differential methylation in thyroid cancer subtypes.

<p>(A) Shown are scatter plots for gene expression levels (y-axis) and methylation levels (x-axis) for <i>STAT1</i>, <i>STAT3</i>, <i>STAT4</i>, <i>STAT5A</i>, <i>JAK1</i>, and <i>JAK3</i>, plotted with <i>BRAF</i>-mutated (<i>red</i>), <i>HRAS</i>-mutated (<i>blue</i>), <i>NRAS</i>-mutated (<i>green</i>), and normal (<i>grey</i>) samples. Gene names and Spearman rho values (with p-values) for the correlation between gene expression and methylation among tumor samples are shown on top of the plots. Probe names (where methylation levels were measured) and genomic locations are shown on the bottom of the plots. (B) Snapshots from the UCSC genome browser for <i>STAT1</i> and <i>JAK3</i>, with CpG islands (CGIs) indicated below (<i>green arrow</i>: CGIs; <i>red arrow</i>: probes flanking the CGIs). Methylation levels of the indicated regions are shown in panel (C). (C) Box plots show methylation levels (y-axis) at probes in <i>STAT1</i> and <i>JAK3</i> for <i>BRAF</i>-mutated, <i>HRAS</i>-mutated, <i>NRAS</i>-mutated, and normal samples. The shown probes fall in the north shores and shelves [or SSs, indicated by red arrows in (B)] of the <i>STAT1</i> promoter CGI and the 3’ CGI of <i>JAK3</i> [indicated by green arrows in (B)].</p

Mutated driver genes that exhibit primarily positive or negative probe associations and correspond to a high HyperZ or HypoZ index in particular cancer types.

<p>Mutated driver genes that exhibit primarily positive or negative probe associations and correspond to a high HyperZ or HypoZ index in particular cancer types.</p

Number of tumor samples, normal samples^a, and driver genes across 18 cancer types.

<p>Number of tumor samples, normal samples<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005840#t001fn001" target="_blank">^a</a>, and driver genes across 18 cancer types.</p

Associations between mutated driver genes and HyperZ and HypoZ indices or site–specific methylation alterations.

<p>Associations between mutated driver genes and HyperZ and HypoZ indices or site–specific methylation alterations.</p

Proportion of positive and negative associations with methylation for 17 recurrently mutated driver genes.

<p>(A) Bar plots show the proportion of methylation probes for each driver gene (<i>labels at bottom</i>) and cancer type (<i>labels at top</i>) displaying positive and negative associations. Positive associations are plotted above the horizontal line, negative associations below the horizontal line. Associations are further stratified by CpG subset: CpG islands (CGI), shores and shelves (SS), and open sea (regions outside CGIs and SSs). Driver genes were classified into three groups based on the directionality of their predominant associations (<i>negative</i>, <i>positive</i>, <i>inconsistent</i>). All genes shown were associated with more than 1,000 probes, in at least two cancer types. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005840#pcbi.1005840.t001" target="_blank">Table 1</a> for cancer type abbreviations. (B) Plotted as in (A), using: (1) positively associated and hypermethylated probes and (2) negatively associated and hypomethylated probes. *Hyper- or hypomethylated probes were not identified for glioblastoma (GBM), stomach adenocarcinoma (STAD), skin cutaneous melanoma (SKCM), and testicular germ cell tumor (TGCT) due to a lack of normal samples.</p

Driver gene mutations are significantly associated with DNA methylation in various cancers.

<p>(A) Examples of mutations in 15 driver genes that display an uneven distribution along the first principal component (PC1) of DNAm, biased toward either the positive extreme (+) or the negative extreme (-). Tumor samples are ordered vertically by their coordinates on PC1, from small (-, <i>bottom</i>) to large (+, <i>top</i>). A black line indicates the presence of the mutated driver gene in a sample, whereas a white line indicates its absence. Note that a sample’s presence at an extreme (+/-) of a PC does not necessarily correspond to high or low methylation. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005840#pcbi.1005840.t001" target="_blank">Table 1</a> for cancer type abbreviations. (B) Example of a cancer with driver gene mutations unevenly distributed on PC1, resulting in distinct methylation patterns: <i>ARID1A/PIK3CA</i>-mutated stomach adenocarcinomas (STADs) display a methylation pattern distinct from <i>TP53</i>-mutated STADs. Shown is a heat map of methylation levels for the top 1,000 most heavily weighted probes in PC1. Each column represents a sample ordered by its PC1 coordinate, from small (-, <i>left</i>) to large (+, <i>right</i>). Each row represents a probe site. The three column sidebars on the top indicate mutation status for <i>TP53</i>, <i>PIK3CA</i>, and <i>ARID1A</i>. The row sidebar indicates the CpG subsets: CpG island (CGI), shores and shelves (SS; the 4-kb regions flanking the CGIs), or open sea (i.e., probes outside of CGIs and SSs). <i>TP53</i>-mutated STADs display lower methylation levels at the selected CpG sites than the majority of <i>ARID1A</i>/<i>PIK3CA</i>-mutated STADs. (C) In 15 of 18 cancer types examined, mutated driver genes were associated with one or more of the top five methylation PCs, shown as rows. The three driver genes most significantly associated with each PC are reported. Driver genes associated with the negative extreme of a PC are in blue, whereas associations with the positive extreme are in red. *Some of these mutated driver genes were previously reported in association with DNAm subtypes (corresponding references listed at the top).</p

<i>ERBB4</i> gene structure with a focus on a novel variant within a “bleeding” exon 20.

<p><i>ERBB4</i> gene structure with a focus on a novel variant within a “bleeding” exon 20.</p