Next-generation sequencing (NGS) and its application to breast cancer research is discussed. NGS allows for comprehensive profiling of microRNA (miRNA) expression, which can provide insights into tumorigenesis pathways. The document outlines the NGS process, including template preparation, sequencing/imaging, and complex data analysis using statistical methods and software. Key applications are identifying miRNA involvement in cancer driver genes and exploring miRNA expression patterns to discover potential diagnostic or prognostic biomarkers for breast cancer subtypes.

1. Next
Generation
sequencing in
Breast Cancer
Research
By: Seham Alshehri
MSc Biochemical research

2. OUTLINES
 MicroRNA (miRNA) and breast cancer.
 Next generation sequencing (NGS)
 Data analysis

3. 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

4. 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!

5. 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

6. 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

7. miRNA & NGS application

8. Next Generation Sequencing
(NGS)

9. NGS main steps
 Template preparation
 Sequencing and imaging
 Data analysis

10. 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.

11. Which protocol fits your trial!

12. NGS methodology cascade
www.nature.com/jid/journalhttp://
a.html2013248/full/jid8/n133/v

13. Gene Expression Analysis Involves:
 High quality RNA extracted from available
biological samples.
 large scale microarrays.

14. Reading Arrays:
STATISTICAL Genetics
Analysis

15. You may need to read this :
http://www.transcriptome.ens.fr/sgdb/contact/download/200602_StatsP
uces_INAPG.pdf

16. 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

17. 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…

18. 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.

19. 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/

20. 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

21. 2-:Identify models: Clustering
 Cluster Analysis
Hierarchical
K-means
Self-organizing maps
Maximum likelihood/mixture models
 Graphical Displays
Dendrogram
Heatmap
Multidimensional scaling plot

22. Modules
Dynamic Tree
cut strategy
Tree Dendrogram
Fixed height

23. (System biology)
Functional enrichment
 Interpretation of genome-scale data
often includes looking for the biological
functions that are enriched in lists of
genes.

24. /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

25. Discussing the outcomes
 Results
 Discussion
 Future Plan

26. More Statistics
There are much more steps that by the end give an
initial clues, guiding the researcher to shift in system
biology

28. Further investigations: Again to
trails!!