Why do mutant allele frequencies in oncogenes peak around .40 and rapidly decrease?

<p>The mutant allele frequencies in oncogenes peak around .40 and rapidly decrease. In this article, we explain why this is the case. Invoking a key result from mathematical analysis in our model, namely, the inverse function theorem, we estimate the selection coefficients of the mutant alleles as a function of germline allele frequencies. Under complete dominance of oncogenic mutations, this selection function is expected to be linearly correlated with the distribution of the mutant alleles. We demonstrate that this is the case by investigating the allele frequencies of mutations in oncogenes across various cancer types, validating our model for mean effective selection. Consistent with the population genetics model of fitness, the selection function fits a gamma-distribution curve that accurately describes the trend of the mutant allele frequencies. While existing equations for selection explain evolution at low allele frequencies, our equations are general formulas for natural selection under complete dominance operating at all frequencies. We show that selection exhibits linear behaviour at all times, favouring dominant alleles with respect to the change in recessive allele frequencies. Also, these equations show, selection behaves like power law against the recessive alleles at low dominant allele frequencies.</p

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

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

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