Pcr plus and predictor 2013

Sample & Assay Technologies

Assess pathway activity and cellular toxicity with
biomarker signature genes
Raed N Samara, PhD
QIAGEN


Outline
Introduction
R&D Development Process
Pathway Activity Score
Specificity of Signature Biomarker Genes
Application Data

2


Outline
Introduction
Application Data

3


Gene expression for biological functional readout
Use real-time PCR technology to develop a
numerical score for biological functional readout.

protein activity

pathway activity

toxicity

physiological
function

pathological
condition

gene expression change

Pathway and Toxicity Gene Profiling

4


Pathway and Toxicity Gene Profiling

Gene profiling assesses
whether genes in a
certain pathway are
upregulated or
downregulated. But did
pathway activity change?

Title, Location, Date

5


Where to measure pathway activity?

physiological

pathological

WNT

WNT

Fzd

Fzd

L
R
P

Dsh

Pathway
Activity?

APC Axin
GSK3β
β
P

TCF/
LEF1

Dsh
APC

Axin

GSK3β
β
P

β -catenin

β -catenin

L
R
P

β -catenin
β -catenin

Myc

TCF/
LEF1

CCND1

Myc
CCND1

Downstream effect!
Traditional indicators provide insufficient information of the regulatory status of the whole
pathway signaling
Multi-gene expression based methods measure pathway alteration as a function of the
downstream effect of pathway regulation on multiple gene expression changes

6


Gene expression for biological functional readout
Use real-time PCR technology to develop a numeric
score for biological functional readout.

protein activity

pathway activity

toxicity

physiological
function

pathological
condition

gene expression change

Signature gene gene panel

gene panel

gene panel

gene panel

gene panel

measure by real-time PCR
Specific gene panel with companion score to monitor individual phenotype

7


Pathway and Toxicity Gene Profiling & Activity

RT2 Profiler Plus


RT2 Predictor

8


Outline
Introduction
Application Data

9


Signaling pathway


Toxicity pathway


Signature Genes and Mathematical Model Derivation

Bootstrap resampling method was used to randomly split training samples set into
two parts to evaluate classifier model performance without selection bias.
Classification performance was estimated by using the out-of-bootstrap-sample
activity scores from the 250 iterations with their corresponding top 16 genes and
classifier models.
The median rank of each gene was calculated from 250 iterations and the top 16
genes were selected as the final signature biomarker gene set for assessing
specific pathway activity.


Current available product
RT2 Profiler Plus PCR Array
Human WNT Signaling Pathway Plus
Human IL6/STAT3 Signaling Pathway Plus
Human TGFb/BMP Signaling Pathway Plus
Human Notch Signaling Pathway Plus
Human NFkB Signaling Pathway Plus
Human Oxidative Stress Plus
Human Hypoxia Signaling Pathway Plus
Human Unfolded Protein Response Plus
Human Mitochondrial Energy Metabolism Plus

RT2 Predictor PCR Array
Human WNT Signaling Pathway Activity
Human IL6/STAT3 Signaling Pathway Activity
Human TGFb Signaling Pathway Activity
Human Notch Signaling Pathway Activity
Human NFkB Signaling Pathway Activity
Human Oxidative Stress Toxicity
Human Hypoxia Signaling Pathway Activity
Human Unfolded Protein Response Toxicity
Human Mitochondrial Energy Metabolism Toxicity
12


Product Formats – RT2 Profiler Plus PCR Array (96)

Profiling
Same workflow as current RT2 Profile PCR array
Content (per sample)
Signature genes (16)
Pathway relevant genes (68)
HK gene (5)
GDC (1)
RTC (3)
PPC (3)
Data analysis
Profiling
Pathway activity readout

Profiling
+ SCORE

13


Product Formats – RT2 Profiler Plus PCR Array (384)

Profiling
Same workflow as current RT2 Profile PCR array
Signature genes (16)
Pathway relevant genes (68)
HK gene (5)
GDC (1)
RTC (3)
PPC (3)
Data analysis
Profiling

Profiling
+ SCORE
14


Product Formats – RT2 Predictor PCR Array (96 well)

SCORE Only
Same workflow as current

RT2

PCR array

Signature gene (16)
HK gene (5)
GDC (1)
RTC (1)
PPC (1)
Data analysis


15


Product Formats – RT2 Predictor PCR Array (384 well)

SCORE Only
Same workflow as current

RT2

PCR array

Signature gene (16)
HK gene (5)
GDC (1)
RTC (1)
PPC (1)
Data analysis


16


Workflow – RT2 Profiler Plus PCR Array

Purify RNA

Prepare cDNA from purified
RNA using RT2 First Strand Kit
Add cDNA to RT2 SYBR Green
Mastermix
Aliquote mix into RT2 Profile
Plus PCR Arrays
Cycle in real-time cycler

Analyze results

17


Workflow – RT2 Predictor PCR Array

Purify RNA (FFPE samples
NOT recommended)

Prepare cDNA from purified
RNA using RT2 First Strand Kit

Add cDNA to RT2 SYBR Green
Mastermix
Aliquote mix into RT2 Profile
Plus PCR Arrays

Cycle in real-time cycler

Analyze results

18


Outline
Introduction
Application Data

19


Pathway Activity Data Analysis Principle
Calculate ∆Ct of each signature gene vs average of HK genes of
each sample
Calculate ∆∆Ct of each signature gene between experimental
and control samples
Compare ∆∆Ct of signature genes in experiment sample to ∆∆Ct
of signature genes in training data set
A probability of pathway activity regulation is determined based
on similarity between experimental sample and training data
For Toxicity pathways:
0 to 0.3: – no toxicity
0.3 to 1: Toxicity
For Signaling pathways:
-1 to -0.3: Pathway activity repressed
-0.3 to 0.3: No change in activity
0.3 to 1: Pathway activity stimulated
20


Data analysis Set Up – RT2 Profiler Plus & Predictor

Upload data

Select Plus
Array catalog

Upload Ct in excel format

Data analysis
(Plus Array)


21


Prediction of Pathway Activity in Experimental Sample

A new set of 50 bootstrapped samples were drawn and used to
train the final model ensemble with selected 16 biomarker
signature genes.
The resulting 50 models are used to predict the pathway activity
score of test samples.
All 50 predictions for a test sample are shown as a box-andwhisker plot.

22


Data analysis Output – RT2 Profiler Plus & Predictor

Summary of Pathway Activity Scores
Sample

Pathway activity score

P value
value*

A

-0.925142

0.01

B

0.904624

0.01

C

0.146092

0.82

Positive score indicates stimulation of pathway activity while negative score
indicates repression.
*Samples with P value <0.05 as well as probability score >0.3 or <-0.3 indicate
significant pathway activity change in experimental sample as compared to control
sample.
Training data used to derive biomarker signature genes and classifier algorithm
23


Data analysis Output – RT2 Profiler Plus & Predictor

Summary of Pathway Activity Scores
Sample


P value
value*

A

0.146092

0.56

B

0.745142

0.001

C

0.474624

0.001

*Samples with P value <0.05 and probability score >0.3 indicate significant pathway
activity change in experimental sample as compared to control sample.
Training data used to derive biomarker signature genes and classifier algorithm
24


Array

Interpretation of score

Score
-1 to -0.3

Signaling

-0.3 to 0.3
0.3 to 1
0 to 0.3

Toxicity
0.3 to 1


P value

Prediction

P<0.05

Pathway repressed

P>0.05

Unchanged

P<0.05

Unchanged

P>0.05

Unchanged

P<0.05

Pathway activated

P>0.05

Unchanged

P<0.05

No toxicity induced

P>0.05

Unchanged

P<0.05

Toxicity induced

P>0.05

Unchanged

25


Outline
Introduction
Application Data

26


Notch
+
-

+

Wnt

-

+

IL6

-

NFκB
+
-

+

TGFβ

1.2
0.9

Wnt
Array

0.6


0.3
0
-0.3
-0.6
-0.9
-1.2
1.2
0.9
0.6
0.3
0
-0.3

Notch
Array

-0.6
-0.9
-1.2

Possible cross talk between Notch and NFκB, Wnt and TGFβ
Overall signatures seem to be specific to individual signaling pathway
It is expected to have some degree of cross talks given the nature of interactions between
different signaling pathways

-


Outline
Introduction
Application Data

28


Application of Gene Expression Signature
Application Data 1: Pharmacodynamic marker
Efficacy measurement after drug treatment
Target validation after drug treatment
Application Data 2: Pathway activity changes during neural stem
cell differentiation
Application Data 3 & 4: Dissection of mechanisms that regulate
pathway activity
Crosstalk between pathways
Study cancer stem cells

29


Application Data 1
What can the gene expression signature be used for and
how is it compared to traditional method?

30


Application Data 1

HCT116 or 293H cells were treated with CCT036477 to inhibit WNT pathway activity or CHIR99021 to stimulate WNT
pathway activity, respectively
Human WNT Signaling Pathway Predictor Arrays and Western blots to detect active β -catenin were used to assess
pathway activity changes

Gene expression signature successfully indicated dose-response activation or repression of pathway activity after
CHIR99021 or CCT036477 treatment, respectively
Detection of decreased Wnt/β-catenin signaling activity by gene expression signature preceded the detection of downβ
regulation of active β -catenin protein levels by Western blot after CCT036477 treatment
31


Pathway activity
changes during neural
stem cell
differentiation

Application Data 2
NSC (undifferentiated)

NSC (differentiated 10d)

Notch
Wnt
IL6/STAT3
NFkB

TGFβ

Neural stem cells were spontaneously differentiated for 10 days by withdrawing FGF and EGF from
culture medium
RT2 Predictor array indicted activation of Notch and TGFβ pathway activity while repression of Wnt
β
signaling during this differentiation process
32


Application Data 3
What is the benefit of profiling changes in the expression of
pathway-relevant genes coupled with pathway activity
scores?

33


Application Data 3

Notch signaling has been shown to be able to interact with WNT signaling
U105MG cells were treated with Notch NICD adenovirus and WNT Signaling Pathway Plus Array was used to
exam WNT pathway activity and associated mechanism

Expression profiling indicated the upregulation of WNT target genes, Myc and LEF1, suggesting the activation of
WNT/β-catenin pathway activity.
β
Was the pathway activated?
Pathway activity score indicated that WNT/β-catenin pathway activity was marginally activated in U105MG cells
β
after NICD treatment
What was the mechanism involved?
Gene expression profiling indicated upregulation of FZD4, a receptor for WNT signaling
However, the upregulation of WNT11, a non canonical WNT ligand, suggesting further study is needed to examine
the exact mechanism
34


Application Data 4
Wnt pathway activity confers chemoresistance to cancer
stem-like cells in a neuroblastoma cell line

CD133-

Tumour Biol. 2012 Aug 11. [Epub ahead of print]

Wnt Signaling RT2 PCR Array Plus uncovered:
Differential Wnt pathway gene expression profiles
associated with CD133+ (stem like) and CD133neuroblastoma cells
CD133+

Activation of Wnt pathway signaling in CD133+ cells
(sample 1) while repression of Wnt signaling in
CD133- cells (sample 3) upon Dox treatment.

35


Benefits of Profiler Plus & Predictor

Profiler Plus
Ability to profile and determine pathway activity score
Pathway activity score is experimentally verified
Determine pathway activity score in a medium-throughput
manner
Simple workflow

Predictor
Ability to determine pathway activity score
Pathway activity score is experimentally verified
Determine pathway activity score in a high-throughput
manner
Simple workflow


36


Questions

Assess biological function and cellular toxicity using
gene expression analysis
Raed N Samara, PhD
QIAGEN

Pcr plus and predictor 2013

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