Visualizing and identifying the DNA methylation

markers in breast cancer tumor subtypes

By

Islam Ibrahim AminThe 5th International Conference on Innovations in Bio-Inspired Computing and Applications, June 23-25, 2014

Scientific Research Group in Egyptwww.egyptscience.net

Agenda Introduction DNA Methylation Analysis

Non-specific Filtering specific Filtering

Formal Concept Analysis Applied FCA for Breast Cancer Subtypes

Formal Context Concept Lattice

Conclusion and future work

IntroductionDNA (Deoxyribonucleic acid)

IntroductionCentral Dogma

Introduction DNA Methylation

DNA methylation plays a very important role in the gene expression regulation, causing many diseases such as cancers. Methylation in a specific region of genes called promoter, can inhibit genes to be expressed. Methylation occurs at a specific regions in the genome called CPG sites (CPGs), the ”P” in CPG refers to phosphodiester bond between the guanine (G) and the cytosine (C).

Hypermethylated referred to that regions are become methylated but hypomethylated referred to that the regions are less methylated.

IntroductionCancer

Cancer refers to a group of diseases characterized by uncontrolled cell growth.

There are two main types of genes that play a role cancer in development:

Oncogenes tell cells when to divide.

Tumor suppressor genes tell cells when not to divide.

Hypermethylated

Tumor suppressor

genes

Hypomethylated

Oncogenes

IntroductionMeasuring DNA Methylation

How Can we

measure DNA

methylation?

Introduction Illumina High Throughput Arrays

On the chip, there are two bead types for each CpG site per locus. One of the bead types will correspond to the methylated cytosine locus and the

other will correspond to the unmethylated cytosine locus, this type of probe is known as an Allele specific oligonucleotide.

Introduction

Introduction Breast cancer can be classified according to receptor

status. Receptors are proteins existed on the surface of a cell, in the cytoplasm and nucleus. These receptors play an important role in receiving chemical signals to take from outside into the inside of the cell.

Breast Cancer Subtypes: Basal-like ERBB2+ Luminal A Luminal B

Introduction

Introduction we analyze the DNA methylation data from 28 breast

cancer subtypes paired samples, The normal tissue is located at least 2 Centimeters away from site of the tumor. The methylation data reported in this paper have been previously deposited in NCBIs Gene Expression Omnibus (GEO) and are accessible through GEO Series accession number [GEO: GSE22135]. The methylation level measured as a continuous values start from zero (completely unmethylated) to one (completely methylated).

Introduction DNA Methylation Data

The methylation level measured as a continuous values start from zero (completely unmethylated) to one (completely methylated).

It is logical to suppose that samples with a methylation value greater than |0.2| are candidate to be methylated markers

Introduction

Introduction Illumina High Throughput Arrays

By using Illumina methylation microarray, our experiment analyzed DNA methylation level in 1505 CPG loci sites from the regulatory regions of 806 cancer related genes (one to five CPG sites per gene)

DNA Methylation Analysis Non-Specific Filtering : This phase is determining rows (CPG

sites) which are candidate to be demonstrate a differential change of their methylation level. The using of |∆β| can determine the level of cut off, where |∆β| is the absolutely value of the difference between the mean of methylation level for cancer samples with the mean of methylation level for the corresponding adjacent normal tissue.

Specific Filtering : This phase aims to determine the most differential DNA methylation markers by using the most appropriate statistical test after testing the normality of methylation data. A one sample Kolmogorov-smirnov test used to determine which test will be used a parametric or a non-parametric. For a paired sample the t-test is using as a parametric test otherwise using a Wilcoxon signed rank test as non-parametric test. False discover rate (FDR) is used to adjusted the raw p-value to reduce the false positives that arise from multiple testing.

DNA Methylation Analysis

DNA Methylation Analysis

Formal Concept Analysis Formal concept analysis

was introduced as a mathematical theory modelling by WILLE (1982). Formal concept analysis is very helpful for the analysis of data, also it has been applied in many applications. Visualizing the data is one of the useful objective of FCA. The concept lattice provides this visualization.

Formal Concept Analysis

To distinguish between hypomethylated and hypermethylated, we refere to hypomethylated genes by adding plus (+) in their names (e.g. Aim2(+)), also we refere to hypermethylated genes by adding minus (-) in their name (e.g. BCR(-))

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Conclusions and Future Work Finally, DNA methylation have been associated with

cancer in several investigations. Hypermethylated or hypomethylated of CPG islands can affect the expression of genes, therefore there is a need of mining and visualizing the DNA methylation status among breast cancer molecular subtypes. In future work we will use FCA for mining DNA methylation status by using data obtained from Illumina Infinium HumanMethylation27 BeadChip, this BeadChip allows researchers to interrogate 27,578 targeted CpGs sites in total, spread across promoter regions of 14,495 genes, therefore we can identify a new relationship among cancer tumors subtypes.

For further For further questions:questions:Islam AminIslam Amin

Eng.IslamAmin@gmail.coEng.IslamAmin@gmail.comm