Chromosome X genomic and epigenomic aberrations and clinical implications in breast cancer by base resolution profiling

The Price of Human Evolution: Cancer-Testis Antigens, the Decline in Male Fertility and the Increase in Cancer

The increasing frequency of general and particularly male cancer coupled with the reduction in male fertility seen worldwide motivated us to seek a potential evolutionary link between these two phenomena, concerning the reproductive transcriptional modules observed in cancer and the expression of cancer-testis antigens (CTA). The phylostratigraphy analysis of the human genome allowed us to link the early evolutionary origin of cancer via the reproductive life cycles of the unicellulars and early multicellulars, potentially driving soma-germ transition, female meiosis, and the parthenogenesis of polyploid giant cancer cells (PGCCs), with the expansion of the CTA multi-families, very late during their evolution. CTA adaptation was aided by retrovirus domestication in the unstable genomes of mammals, for protecting male fertility in stress conditions, particularly that of humans, as compensation for the energy consumption of a large complex brain which also exploited retrotransposition. We found that the early and late evolutionary branches of human cancer are united by the immunity-proto-placental network, which evolved in the Cambrian and shares stress regulators with the finely-tuned sex determination system. We further propose that social stress and endocrine disruption caused by environmental pollution with organic materials, which alter sex determination in male foetuses and further spermatogenesis in adults, bias the development of PGCC-parthenogenetic cancer by default.

Thorough statistical analyses of breast cancer co-methylation patterns

Background

Breast cancer is one of the most commonly diagnosed cancers. It is associated with DNA methylation, an epigenetic event with a methyl group added to a cytosine paired with a guanine, i.e., a CG site. The methylation levels of different genes in a genome are correlated in certain ways that affect gene functions. This correlation pattern is known as co-methylation. It is still not clear how different genes co-methylate in the whole genome of breast cancer samples. Previous studies are conducted using relatively small datasets (Illumina 27K data). In this study, we analyze much larger datasets (Illumina 450K data).

Results

Our key findings are summarized below. First, normal samples have more highly correlated, or co-methylated, CG pairs than tumor samples. Both tumor and normal samples have more than 93% positive co-methylation, but normal samples have significantly more negatively correlated CG sites than tumor samples (6.6% vs. 2.8%). Second, both tumor and normal samples have about 94% of co-methylated CG pairs on different chromosomes, but normal samples have 470 million more CG pairs. Highly co-methylated pairs on the same chromosome tend to be close to each other. Third, a small proportion of CG sites’ co-methylation patterns change dramatically from normal to tumor. The percentage of differentially methylated (DM) sites among them is larger than the overall DM rate. Fourth, certain CG sites are highly correlated with many CG sites. The top 100 of such super-connector CG sites in tumor and normal samples have no overlaps. Fifth, both highly changing sites and super-connector sites’ locations are significantly different from the genome-wide CG sites’ locations. Sixth, chromosome X co-methylation patterns are very different from other chromosomes. Finally, the network analyses of genes associated with several sets of co-methylated CG sites identified above show that tumor and normal samples have different patterns.

Conclusions

Our findings will provide researchers with a new understanding of co-methylation patterns in breast cancer. Our ability to thoroughly analyze co-methylation of large datasets will allow researchers to study relationships and associations between different genes in breast cancer.

Methylation Profile of X-Chromosome-Related Genes in Male Breast Cancer

Background: Androgen receptor (AR) has been described to play a prominent role in male breast cancer (MBC). It maps on chromosome X, and recent reports indicate that X-chromosome polysomy is frequent in MBC. Since the response to anti-androgen therapy may depend on AR polysomy and on its overexpression similarly to prostate cancer, the aim of the present study was to investigate the DNA methylation level of AR and its coregulators, especially those mapped on the X-chromosome, that may influence the activity of AR in MBC.

Methods: The DNA methylation level of AR, MAGEA2, MAGEA11, MAGEC1, MAGEC2, FLNA, HDAC6, and UXT, mapped on the X-chromosome, was evaluated by quantitative bisulfite-NGS. Bioinformatic analysis was performed in a Galaxy Project environment using BWA-METH, MethylDackel, and Methylation Plotter tools. The study population consisted of MBC (41 cases) compared with gynecomastia (17 cases).

Results:MAGEA family members, especially MAGEA2, MAGEA11, MAGEC, and UXT and HDAC6 showed hypomethylation of several CpGs, reaching statistical significance by the Kruskal–Wallis test (p < 0.01) in MBC when compared to gynecomastia. AR showed almost no methylation at all.

Conclusions: Our study demonstrated for the first time that MAGEA family members mapped on the X-chromosome and coregulators of AR are hypomethylated in MBC. This may lead to their overexpression, enhancing AR activity.

CORAL: Building up QSAR models for the chromosome aberration test

A high level of chromosomal aberrations in peripheral blood lymphocytes may be an early marker of cancer risk, but data on risk of specific cancers and types of chromosomal aberrations are limited. Consequently, the development of predictive models for chromosomal aberrations test is important task. Majority of models for chromosomal aberrations test are so-called knowledge-based rules system. The CORAL software (http://www.insilico.eu/coral, abbreviation of “CORrelation And Logic”) is an alternative for knowledge-based rules system. In contrast to knowledge-based rules system, the CORAL software gives possibility to estimate the influence upon the predictive potential of a model of different molecular alerts as well as different splits into the training set and validation set. This possibility is not available for the approaches based on the knowledge-based rules system. Quantitative Structure–Activity Relationships (QSAR) for chromosome aberration test are established for five random splits into the training, calibration, and validation sets. The QSAR approach is based on representation of the molecular structure by simplified molecular input-line entry system (SMILES) without data on physicochemical and/or biochemical parameters. In spite of this limitation, the statistical quality of these models is quite good.

Comprehensive characterization, annotation and innovative use of Infinium DNA methylation BeadChip probes

Illumina Infinium DNA Methylation BeadChips represent the most widely used genome-scale DNA methylation assays. Existing strategies for masking Infinium probes overlapping repeats or single nucleotide polymorphisms (SNPs) are based largely on ad hoc assumptions and subjective criteria. In addition, the recently introduced MethylationEPIC (EPIC) array expands on the utility of this platform, but has not yet been well characterized. We present in this paper an extensive characterization of probes on the EPIC and HM450 microarrays, including mappability to the latest genome build, genomic copy number of the 3΄ nested subsequence and influence of polymorphisms including a previously unrecognized color channel switch for Type I probes. We show empirical evidence for exclusion criteria for underperforming probes, providing a sounder basis than current ad hoc criteria for exclusion. In addition, we describe novel probe uses, exemplified by the addition of a total of 1052 SNP probes to the existing 59 explicit SNP probes on the EPIC array and the use of these probes to predict ethnicity. Finally, we present an innovative out-of-band color channel application for the dual use of 62 371 probes as internal bisulfite conversion controls.

X-chromosome inactivation and escape

X-chromosome inactivation, which was discovered by Mary Lyon in 1961 results in random silencing of one X chromosome in female mammals. This review is dedicated to Mary Lyon, who passed away last year. She predicted many of the features of X inactivation, for e.g., the existence of an X inactivation center, the role of L1 elements in spreading of silencing and the existence of genes that escape X inactivation. Starting from her published work here we summarize advances in the field.