overview on next generation sequencing in breast csncer

Next

Generation

sequencing in

Breast Cancer

Research

By: Seham Alshehri

MSc Biochemical research

OUTLINES

MicroRNA (miRNA) and breast cancer.

Next generation sequencing (NGS)

Data analysis

Cancer overview

Cancer is genetic disease

Is a group of diseases characterized by

uncontrolled cell division leading to a growth of

abnormal cells

Oncogenes=accelarators pedals

TS genes=breake pedals

miRNA in Cancer Recently discovered, miRNA is small RNA

molecules that play important roles in gene expression.

These small genes make much smaller RNA that don’t make protein products but they react controlling the expression of other genes

Some new researches showed that miRNA play roles in controlling TS & oncogenes expression!

miRNA in BC

miRNAs dysregulation in BC was first

described in 2005.

Some studies address the potential of miRNAs

as diagnostic marker for BC subtypes and as

prognostic markers for patient outcomes.

Most of these studies were conducted using

microarrays or RT-PCR and thus limited to a

subset of miRNAs.

WeiWu.Hani Choudhry, Next generation

sequencing in cancer

research.Chrpter12,springer2013

MicroRNAs

miRNAs regulate many genes critical for

tumorgenesis.

Studies claim expression of miRNA in BC

could unfold mysteries of tumorgenesis

pathways and identify potential prognostic

and diagnostic markers

miRNA & NGS application

Next Generation Sequencing

(NGS)

NGS main steps

Template preparation

Sequencing and imaging

Data analysis

Choose your protocol There is a collection of next-generation sequencing (NGS)

sample preparation protocols, was compiled from the scientific

literature to demonstrate the wide range of scientific questions

that can be addressed by Illumina’s sequencing by synthesis

technology.

it will inspire researchers to use these methods or to develop

new ones to address new scientific challenges.

These methods were developed by users, so readers should

refer to the original publications for detailed descriptions and

protocols.

Which protocol fits your trial!

NGS methodology cascade

www.nature.com/jid/journalhttp://a.html2013248/full/jid8/n133/v

Gene Expression Analysis Involves:

High quality RNA extracted from available

biological samples.

large scale microarrays.

Reading Arrays:

STATISTICAL Genetics

Analysis

You may need to read this :

http://www.transcriptome.ens.fr/sgdb/contact/download/200602_StatsP

uces_INAPG.pdf

Statistical Methods for Microarray Data

Analysis is going through:

1-Genetics

2- Experimental Design

3-Data Normalization

4- Gene Clustering

5-Differintial Analysis

6- Survival Classification

http://www.transcriptome.ens.fr/sgdb/contact/d

ownload/200602_StatsPuces_INAPG.pdf

Material & Methods

Data Generation

Ex: Phenotype Data, Genotype Data, Gene

Expression Data…

Statistical Analysis

Ex: Filtering, software package( WGCNA),

Gene Network mapping, functional

enrichment analysis of network…

Statistical Analysis:1- Filtering

FILTERING METHOD: used to isolate active genes from inactive genes by ranking them according to their expression.

FILTRING CRITERIA:

Mean: ranking genes based on their average level of expression in the population.

Variance: ranking genes based on the variability of their expression.

Sum Covariance: incorporating both diagonal and off-diagonal elements of the covariance matrix of gene expression.

WGCNA Weighted gene co-expression network analysis is a

systems biology method for describing the correlation

patterns among genes across microarray samples.

Weighted correlation network analysis (WGCNA) can

be used for :

-Finding clusters (modules) of highly correlated

genes,

-Summarizing such clusters using the module

eigengene or an intramodular hub gene,

- Relating modules to one another and to external

sample traits (using eigengene network methodology),

- And for calculating module membership measures

http://labs.genetics.ucla.edu/horv

ath/CoexpressionNetwork/Rpacka

ges/WGCNA/

2- Explore and recognize Key

genes by Statistical computing: R

1) Construct a gene co-expression network

- Correlation, topological overlap

2) Identify modules

- Clustering, Dynamic Tree cut

3) Relate modules to external information

- Gene Ontology enrichment, correlation to

phenotype/linkage analyses

2-:Identify models: Clustering

Cluster Analysis

Hierarchical

K-means

Self-organizing maps

Maximum likelihood/mixture models

Graphical Displays

Dendrogram

Heatmap

Multidimensional scaling plot

Modules

Dynamic Tree

cut strategy

Tree Dendrogram

Fixed height

(System biology)

Functional enrichment

Interpretation of genome-scale data

often includes looking for the biological

functions that are enriched in lists of

genes.

/http://bioinfow.dep.usal.es/coexpression

3:Relate Modules to external

information: Mapping

Mapping clusters to the genome at

different loci:

• High LOD score for cluster (s)

mapped to a lucs!!!

• Physical position of a QTL lucs on

the genome(quantitative trait

lucs)

• SNPs on the QTL

• Correlation analysis of the cluster

with phenotype

Discussing the outcomes

Results

Discussion

Future Plan

More Statistics There are much more steps that by the end give an

initial clues, guiding the researcher to shift in system

biology

Further investigations: Again to

trails!!