1. health and fitness
2. disease
3. cancer

Sample & Assay Technologies
IPA 系統生物學分析軟體暨資料庫
進階操作課程
Academia Sinica
2015 March
Gene(陳冠文)
Supervisor and IPA certified analyst
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Sample & Assay Technologies
Review for Introductory Training course
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利用IPA進行搜尋
使用IPA進行分子模型建構
繪製訊息傳遞路徑
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Sample & Assay Technologies
Searching

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Searching Basics
Gene/chemical search and results
Function/Disease search and results
Drug target search and results
Advanced search: Limiting results to a molecule type, family or subcellular
location
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Sample & Assay Technologies
Build Tools 的功能
Build Tools包含下列數個建構pathway圖型的工具:
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Grow: 依照使用者的篩選以及參數設定，找出與Pathway圖型目標分子下有關係的其他分
子
Path Explorer: 此工具可以找出兩群分子的最短關係途徑
Connect: 依照使用者的條件設定，迅速將Pathway圖型內的各分子關係找出並連結
Trim: 依照使用者的條件設定，移除Pathway圖型的分子
Keep: 依照使用者的條件設定，保留符合條件的Pathway圖型內的分子
Add Molecule/Relationship: 讓使用者加入自行訂定名稱以及相關註解的資訊到Pathway
圖型裡面，但此資訊只限定在使用者自己的帳號內可使用
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Sample & Assay Technologies
Build and Grow Networks of Molecules
Grow Upstream from AKT1 to
kinases and phosphatases
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Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
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Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
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Sample & Assay Technologies
IPA的資料上傳與分析功能介紹
Ingenuity Pathways Analysis的分析的結果回傳
 與實驗資料相關的生物功能或是疾病分析
 所影響的Signaling Pathway與Metabolic Pathway以及裡面的組成分子
 受影響的Transcription regulator的種類以及相關基因與蛋白
 實驗資料中的分子關係如何形成的網路
分析功能種類:
IPA-Core Analysis 分析mRNA, miRNA或是protein的實驗資料
IPA-Tox Analysis: 分析後得到毒性學相關結果
IPA-Metabolomics Analysis: 主要用於分析代謝體(Metabolomics)實驗相關資料
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Sample & Assay Technologies
Workflow for Dataset Analysis
IPA
Genomic, exon, miRNA,
SNP, protein arrays;
Any molecule lists;
Other proteomic &
metabolomic assays
Identify functions,
diseases, and canonical
pathways associated with
your data
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Sample & Assay Technologies
Upload
Data
IPA Data Analysis Workflow
Run Core
Analysis
Pathways
(overlay)
Functional
Effects
Save
Export
Experiment
approval
IPA
Transcription
Regulators
Research
Genes of
Interest
IPA
User platform
General Analysis Workflow in IPA
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Sample & Assay Technologies
Key Terminology
Observation:
 An experimental condition such as a time point, disease subtype, or compound
concentration
Expression Value:
 Numerical value indicating level of expression, significance, or other assay result for a
specific identifier (gene, RNA, protein, or chemical)
Reference Set:
 The set of molecules used as the universe of molecules when calculating the
statistical relevance of biological functions and pathways with respect to a
dataset file. The set of molecules are the user's dataset or molecules in Ingenuity's
Knowledge Base (genes, endogenous chemicals, or both).
Focus Molecule:
 Molecules that are from uploaded list, pass filters are applied, and are available for
generating networks
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Sample & Assay Technologies
準備IPA分析用的Dataset
必須有一欄放入ID
Replicates
Average
Other observations
(Comparison)
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Sample & Assay Technologies
準備IPA分析用的Dataset
 重複性實驗的數值平均、p-vlaue或fold-change等統計計算，要先在
IPA分析之前完成。
 將實驗資料用 Excel 表格檔案儲存，檔案裡面只能有一個Sheet存在。

Excel Sheet當中必須要有一欄是列出分子的ID (如Gene Symbol, Refseq number,
Uniprot number, HMDB等常用命名皆支援)

每個Excel Sheet 最多可以放入 20個 observations (即20個實驗變因的資料欄的意思)

每個Observation可以有3個不同的表現值種類 (ex. p-Value，fold-change等)

表格欄位最上方只能有一個Head row (首行)

資料上傳到IPA後，可以在cut-off 值欄位進行設定，讓使用者決定門檻來決定表現顯著
有差異的生物分子。意味著原始實驗資料中有些分子的數值不夠顯著，可以用cut-off值
作為門檻排除於分析運算中。那些通過cut-off值的分子們在IPA中稱之為AnalysisReady Molecules。
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Sample & Assay Technologies
分析用的Dataset的範例格式
這表格為標準IPA分析用資料表格範例，裡面的數值類型是 Log Ratio
這組實驗資料裡面有三個Observation :
• Observation 1 : Smokers vs. NonSmokers
• Observation 2 : Early COPD vs. NonSmokers
• Observation 3 : COPD vs. NonSmokers
* 不 同 observation 的
重複實驗數值已經在
先前經過平均才放入
此表格
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Sample & Assay Technologies
After Today, You Should Be Able To:
Define the following IPA terms
 Reference Set
 Observation
 Expression Value
 Network Eligible Molecules
 Functions/Pathways/Lists Eligible Molecule
 Focus Molecule
Upload a dataset
Run an analysis using IPA Best Practices
Access the Analysis Summary Page
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Sample & Assay Technologies
Live Demo
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Sample & Assay Technologies
1. 自[NEW]選擇任一
項Analysis功能
進行分析的步驟
或是從 [Quick Start Menu]這邊開
始一項Analysis功能
2. 選擇要從桌面上傳一份新
資料進行分析或是選取已經
上傳到IPA的資料進行
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Sample & Assay Technologies
Data 上傳與執行IPA 分析設定
3. 定義 Platform type 以及資料中
Identifier type (ID)的命名種類
4. 指派observation, ID, Identifier type,或是
expression value 到各欄位屬性
5. Save & Create
analysis
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Sample & Assay Technologies
1. 設定分析過程的條件
分析前的相關設定
2. Cutoff 數值設定規範資料中那些
分子與數值會讓IPA分析
3. 設定好cutoff值，可以按
下[Recalculate]重新計算
Analysis-Ready的數量，大
約1000個左右比較顯著。
4. 開始分析
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Sample & Assay Technologies
開啟分析結果
分析的摘要可在此輸出為PDF
IPA的分析結果存放於使用者
預先指定的資料夾內，在日
後於計劃管理視窗點擊2次中
開啟。
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Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
21
Sample & Assay Technologies
IPA 分析結果
Functions analysis: 呈現因為分子變化而受影響的生物功能、疾病與毒性學結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究
文獻預測它們是否是被啟動或是被抑制。
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay
Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
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Sample & Assay Technologies
IPA 分析結果
Functions analysis: 呈現因為分子變化而受影響的生物功能、疾病與毒性學結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究
文獻預測它們是否是被啟動或是被抑制。
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay
Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
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Sample & Assay Technologies
Interpret Downstream Biological Functions
Identify over-represented biological functions and predict how those
functions are increased or decreased in the experiment
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Sample & Assay Technologies
Downstream Effects Analysis介紹
方塊代表受實驗影響的生物功能與疾病，顏色可
以用[Color by]指定是z-score, -log (p-value),
或是 # of genes上色。如果是用z-score上色的
話，藍色區塊是預測被減低的功能，橘色則是此
功能會增加。是根據實驗資料做出的演算。
每個矩形可以經由點擊進入下一層分區: Midlevel functional category (level 2) 與
Specific functions (level 3)
Click to show bar chart
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Sample & Assay Technologies
Disease and Molecules relationships
 Powerful functionality enables you to understand causal connections between
molecules and diseases.
 Interactive visual exploration of causality between molecules and disease,
function, or phenotypes from a network or My Pathway
Visualize the impact of genes on
diseases or biological functions in
Downstream Effects Analysis.
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Sample & Assay Technologies
Disease or Function View
 provides details associated with the disease or biological function such as
molecules associated with that disease or function, known drug targets, drugs
known to target those molecules, and more.
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Sample & Assay Technologies
IPA 分析結果
Functions analysis: 呈現因為分子變化而受影響的生物功能、疾病與毒性學結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究
文獻預測它們是否是被啟動或是被抑制。
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay
Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
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Sample & Assay Technologies
Canonical Pathway Analysis
Canonical Pathways結果標籤:
受影響的Signaling Pathway與Metabolic Pathway 依照顯著性用條狀圖排列
click
點擊特定Canonical Pathway
名稱上方的Bar條，下方視窗
會出現dataset 中有參與組成
該 pathway的分子 ID
點擊“Open Pathway” 則可以
展開那個Canonical Pathway，
實驗資料中的分子會用顏色提示。
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Sample & Assay Technologies
Canonical Pathways
Understanding the biology of your data in an established signaling and metabolic
context
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Sample & Assay Technologies
IPA 分析結果
Functions analysis: 呈現因為分子變化而受影響的生物功能、疾病與毒性學結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究
文獻預測它們是否是被啟動或是被抑制。
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay
Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
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Sample & Assay Technologies
Upstream Regulator Analysis: How does it work?
Use experimentally observed relationships (vs.
Predicted event) between Upstream Regulators and
genes to predict potential regulator and activation
Predict activation or inhibition of regulator to explain
the changes in gene expression in your dataset
Calculates two complementary statistical measures:
 Activation z-score
 Overlap p-value
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Sample & Assay Technologies
Upstream Regulator Analysis: How does it work?
Can we predict the activation state (activated/inhibited) of a
potential regulator from expression data?
Approach: Two complementary statistical measures:
Activation z-score and Overlap p-value
TR  target edge types considered:
- Expression
- Transcription
- Protein-DNA binding
Upstream Regulatorregulated genes in
Ingenuity Knowledge
Base
Evaluate the perturbed
genes in the dataset that
are known targets of a
particular regulator
Data set
(differentiallyexpressed genes)
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Sample & Assay Technologies
P-value from Fisher’s Exact test
Molecules both in the dataset
AND the regulator neighborhood
Genes
in the dataset but
not in the
regulator
neighborhood
Genes
in the
regulator
neighborhood but not
dataset
Genes in the
reference “universe” but not in
dataset or regulator neighborhood

The statistical test looks for
an unexpectedly large
overlap given the number
of genes in each category

p-values should be
insignificant (>0.05) for
random datasets

Gene direction is ignored
in this calculation
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Upstream Analysis Activation z–score
Sample & Assay Technologies
Statistical measure of correlation between the transcription
regulator (TR) and resulting gene expression
N = 8 genes
TR
TR effect on downstream genes
(Literature)
Differential gene expression
(Uploaded Data)
1
1
1 -1 1
1
0
1
1
z-score > 2 or < -2 is considered significant
Actual z-score can be weighted by relationship types, relationship bias, data bias
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Sample & Assay Technologies
Identify cross-talk between Upstream Regulators

Automatically
generate a directed
TR-target network

Add relationships to
the regulatory
network, e.g.
upstream signaling
molecules, or to
disease, biological
process associations

See published
evidence for the
regulatory
interactions
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Sample & Assay Technologies
Mechanistic Networks
Upstream Regulator Results:
?
?
?
Which predicted upstream
regulators might work together to
explain the expression changes in
this dataset?
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Sample & Assay Technologies
Mechanistic Network Algorithm
Algorithm seeks large overlaps between an upstream regulator’s
targets and a more downstream regulator’s targets
Upstream molecule likely to operate
thru this more downstream regulator
Upstream molecule less likely to operate
thru this more downstream regulator
A
B
Shares 6 of 7 of the more downstream
regulator’s targets
A
B
Shares 1 of 7 of the more downstream
regulator’s targets
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Sample & Assay Technologies
Concept of “Regulator Effects” - Spring 2014
Hypotheses for how activated or inhibited upstream regulators
cause downstream effects on biology
Upstream Regulators
Simplest Regulator Effects result
A
A
Algorithm
Molecules in the dataset
First iteration
Disease or
Function
Disease or
Function
Displays a relationship between the
regulator and disease/function if it exists
Downstream Effects Analysis
Causally consistent networks score higher
The algorithm runs iteratively to merge additional regulators with diseases and functions
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Sample & Assay Technologies
IPA 分析結果
Functions analysis: 呈現因為分子變化而受影響的生物功能、疾病與毒性學結果
Canonical Pathways : 列出受實驗影響的Signaling Pathway與Metabolic Pathway
Upstream Analysis: 列出與資料中變動分子有關的Upstream molecules，以及根據研究
文獻預測它們是否是被啟動或是被抑制。
Networks : 呈現實驗資料中的分子間的網路關係 。並且可以利用Build Tool與Overlay
Tool進行延伸與知識的拓展，以上各分析結果都是用來解釋實驗觀察到的現象的重要依據。
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Sample & Assay Technologies
Networks in IPA
Purpose:
 To show as many interactions between user-specified molecules in a given
dataset and how they might work together at the molecular level
Why are Ingenuity networks biologically interesting?
 Highly-interconnected networks are likely to represent significant biological
function
 Networks involve molecules you don’t see in your data set. This allows
genes you have assayed to be linked to metabolites and chemicals that you
couldn’t have assayed for, to imply a regulation network that is meaningful.
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Sample & Assay Technologies
How Networks Are Generated
1. Focus molecules are “seeds”
2. Focus molecules with the most
interactions to other focus molecules
are then connected together to form a
network
3. Non-focus molecules from the
dataset are then added
4. Molecules from the Ingenuity’s
Knowledge Base are added
5. Resulting Networks are scored and
then sorted based on the score
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Sample & Assay Technologies
Network types in IPA
Upstream Regulator
Mechanistic Network of Upstream Regulators
Upstream
Regulator
Dataset Molecules
Other upstream
regulators
Dataset Molecules
Regulator Effect Network
Causal Network
Interaction Network
Any
Dataset Molecules
Diseases / functions
Sample & Assay Technologies
After Today, You Should Be Able To:
Define the terms: Focus Molecule, Functional Category, Function, and
Effect on Function
Access Functional Analysis for a dataset
Customize the bar charts according to your preferences
Describe Ingenuity’s Functional Categorization
Describe the significance values calculated for Functions/ Pathways
Access Canonical Pathways and Network for a dataset
View the molecules involved in a Canonical Pathway and Network
View the Canonical Pathway diagram with data overlaid
Sample & Assay Technologies
Live Demo
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Sample & Assay Technologies
1.
Hands-on Exercises I
Upload a dataset into IPA. You may use your own or we can provide you
with an example.
2.
What is the top function associated with your dataset?
3.
How can you find out what main functions a Canonical Pathway (or
group of Canonical Pathways) is involved in?
4.
What are the functions of the top network in this analysis?
Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
47
Sample & Assay Technologies
Bringing together multiple types of genomic data
Research AIM:
 To attain a systems biology understanding of your research by bringing multiple
types of genomic data together (SNP, CNA, mRNA, microRNA, proteomics, etc.).
Challenge:
 Data types measured different molecular status in experiment
 Too much data, some data types may have extra ‘noise’(i.e. arrays)
 Venn Diagram-type comparison excludes ‘A affects B’ information
Solution:
 Identify phenotypes, disease associations, and pathways that are common themes
for multiple data types using Comparison Analysis
 Interactive pathways overlay multiple data types and find genes up or down-stream
that change in the various data types.
 Pathway tools find regulatory connections between molecules of interest and the
various data types
 microRNA Target Filter can link microRNAs and targets from miRNA and target data
sets
How do you integrate multiple data types now?
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Sample & Assay Technologies
Single Experiment
• Time Course
• Dose Response
Multi Experiment
• System biology
• Combining SNP, CNA, mRNA, microRNA, proteomics,
etc.
Set Analysis
• Exploring Common Molecules across one or more
experiment (s)
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Sample & Assay Technologies
Core Comparison Analysis
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Sample & Assay Technologies
Single Experiment
• Time Course
• Dose Response
Multi Experiment
• System biology
• Combining SNP, CNA, mRNA, microRNA, proteomics,
etc
Set Analysis
• Exploring Common Molecules across one or more
experiment (s)
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Sample & Assay Technologies
Mutations
IPA: A Point of Data Integration
mRNA
Expression
CNA
/CNV
Methylation
ChIP-Seq
Phosphorylation
miRNA
Expression
IPA
Protein
Expression
Biological Interpretation
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Sample & Assay Technologies
Mutations
IPA: A Point of Data Integration
mRNA
Expression
CNA
/CNV
Methylation
ChIP-Seq
Phosphorylation
miRNA
Expression
IPA
Protein
Expression
Biological Interpretation
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Sample & Assay Technologies
Example of Core Analysis with 3-Data Types
Mutations
CNAs
mRNAs
File Name
GBM paper mutation GBM paper CNA
data
GBM vs Norm
Expression
ID
Gene Symbol
Gene Symbol
Gene Symbol
Observation
1
frequency of nonsilent mutation
across samples Pct.
Sample/Other
frequency of CNA
Log2 ratio
across samples
change, p-value
[Pct/Other],
increase or
decrease in copy
number
[Amp/Other], and [qvalue/p-value]
Core Analysis Frequency of
mutation ≥ 2%
Keep in mind
p-value < 0.05
Log ratio ≥ |1.5|
To set the same Reference Set across the 3 core analyses
To check the Expression value type used for coloring the nodes
Sample & Assay Technologies
What do you want out of this comparison?
Review your workflow – What are your goals?
mRNA
data
Core
Analysis
CNA
data
Core
Analysis
mutation
data
Core
Analysis
Comparison
Analysis
?
Pathways?
Export?
References?
Lists?
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Sample & Assay Technologies
Comparison of Functions for 3 data sets:
1. Sorted by 1st data type (mRNA); re-order or review whole
table for Functions significant for other data types
2. Look for Functions common to mRNA, CNA, mutations
from glioblastoma
3. Table may be customized or exported
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Sample & Assay Technologies
Single Experiment
• Time Course
• Dose Response
Multi Experiment
• System biology
• Combining SNP, CNA, mRNA, microRNA, proteomics,
etc
Set Analysis
• Exploring Common Molecules across one or more
experiment (s)
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Sample & Assay Technologies
Genes Overlap
Gene Exp
CNA
2282
47
1
29
18
16
Mutated
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Sample & Assay Technologies
Compare Tool
59
Sample & Assay Technologies
C
C
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Sample & Assay Technologies
IPA Core Analysis:
Upstream
Analysis
Functions
Mechanistic
Networks
Canonical
Pathways
Regulator
Effects
Networks
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Sample & Assay Technologies
Live Demo
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Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
63
Sample & Assay Technologies
miRNA
Data
88 data
points
miRNA
Target Filter
13,690
targets
Filter Datasets for Biomarkers or miRNA Targets
Molecule
Type
Pathways
1,090
targets
333
targets
(Cancer/
Growth)
mRNA
39
targets
↑↓
↓↑
?
32
targets
Use Pathway tools to build hypothesis for microRNA to mRNA target association
Sample & Assay Technologies
Using Biological Context in miRNA Target ID
 Goal: Utilize newly discovered microRNAs to better understand biology around
potential mRNA targets/disease
 Challenge: New and rapidly evolving field with different measurement
techniques and prediction algorithms leading to variability in data
 Need: Identify mRNA targets to microRNAs using biological and experimental
information, correlate microRNA and mRNA target expression, specify easy to
use confidence levels of interaction predictions, annotate mRNA targets with
biological context, pathways, species, etc., all within a single workflow
 Outcome: Reduce time of identification of relevant mRNA targets from months
to minutes
Sample & Assay Technologies
(1 Target Scan search) x (each microRNA in your data set) =
A LOT of targets
Sample & Assay Technologies
Live Demo
67
Sample & Assay Technologies
Hands-on Exercises II
Overall Exercises:
Use the COPD analytical results in exercises I.
1. What is the observed effect on the Xenobiotic Metabolism Signaling
Canonical Pathway in the Early COPD group?
2.
In the COPD group, focus on the function Cellular Movement. Select these
genes and add them to a new My Pathway in your IPA account. How many of
the proteins in this pathway are enzymes?
3.
In Early COPD vs NonSmokers observation Upstream Regulators chapter,
filter the Molecule Type to only Transcription Factors, which molecule is
predicted to be Inhibited with the lowest z-score?
Sample & Assay Technologies
Hands-on Exercises II cont.
Overall Exercises:
5.
In studies of nicotine metabolism in smokers, it has been estimated that 70%
of a nicotine dose is metabolized to cotinine. Which group express the
highest effect on the Nicotine Degradation pathway?
6.
In observation Upstream Regulators chapter. Which molecule is predicted to
be activated in Both of early and late COPD groups?
Sample & Assay Technologies
A.
大綱
Data Upload and How to Run a Core Analysis
上傳實驗資料並使用IPA分析功能
B.
Functional Interpretation in IPA
IPA分析結果介紹

Hands-on Exercises
C.
Comparison Analyses
比較分析結果的差異
D. Using IPA to Explore microRNA Impacts on Molecular Mechanisms of
Disease
快速篩選microRNA的潛在標的
E.
Data Analysis & Interpretation in IPA

F.
High resolution analysis of copy number alternations and associated
expression change in ovarian tumors
Q&A
70
Sample & Assay Technologies
Comparison of IPA and open-source data analysis
workflows
Conservative estimate of time for IPA data analysis is 20.87 h., including high value
functional effects and transcription regulator prediction. Open-source workflow
requires 3 tools to conduct a complete analysis including functions and pathways,
view pathways with data overlay, and follow-up with manual gene research to interpret
effects.
RNA-Seq NGS Data, Analysis and Applications
Sample & Assay Technologies
Raw NGS Data
Access
NGS Data
Generation
Data Normalization,
Mapping to Gene
Models, Signal
Calculation
Biological Analysis
And Interpretation
•
•
•
•
•
•
•
•
Illumina
Life
Technologies
Roche
Helicos
Ion Torrent
•
•
Short Read
Archive (SRA)
Gene
Expression
Omnibus
(GEO)
Proprietary
data
•
•
•
•
•
•
CLC bio
Geospiza
DNAnexus
Genome
Quest
Spiral
Genetics
Partek
•
•
•
•
•
•
Pathways
Biological
Functions
Networks
Toxicity
Biomarkers
Therapeutic
targets
Mechanistic
hypotheses
Drug
repurposing
Basic Research, Predictive and Diagnostic Biomarker Discovery, Mechanistic Studies,
Drug Efficacy, Metabolism and Toxicity, Drug Target Discovery, Developmental
Biology, Infectious Diseases…and many more applications
Sample & Assay Technologies
Prioritization of Candidate Ovarian Cancer Genes with IPA
• Survey copy number alterations (CNAs) in ovarian tumors using Affymetrix 500K SNP
Chip
• Profile the expression patterns of these tumors and whole ovary normal samples with
Affymetrix U133A and B chips
Sample & Assay Technologies
Goals of study initial study, extended analysis in IPA
DNA copy number variations are frequently observed in ovarian
cancer.
 What are the most relevant alterations (recurrent CNAs)?
 Causal genes in those regions?
 Which copy number variations are functionally relevant to oncogenesis?
Identification of causal genes provides candidate therapeutic
targets and biomarkers
 Help find genes with tumor-driving roles in ovarian cancer
 What functions are these genes associated with?
 Can we infer that modulation of those functions may be a major driving factor in the
selection of CNAs?
 Prioritize those candidates based on biomarker, drug target, cellular and disease
knowledge
Sample & Assay Technologies
Prioritize Candidate Genes from CNA Study
Which are implicated in ovarian cancer? (search Ingenuity Knowledge Base)
What molecular interactions exist – and do those interactions represent a collective
biological function? Potential driver for carcinogenesis? (IPA Core Analysis)
Does an assay exist to measure key, carcinogenesis-relevant gene products in a
clinical setting?
 Identify exploratory clinical biomarkers (Overlay Biomarkers)
Narrow in on key genes, relationships that are relevant in multiple contexts, datasets
 MECOM/SMARCA2/CCNE1
Sample & Assay Technologies
Prioritize Candidate Genes from CNA Study
Which are implicated in ovarian cancer? (search Ingenuity Knowledge Base)
What molecular interactions exist – and do those interactions represent a collective
biological function? Potential driver for carcinogenesis? (IPA Core Analysis)
Does an assay exist to measure key, carcinogenesis-relevant gene products in a
clinical setting?
 Identify exploratory clinical biomarkers (Overlay Biomarkers)
Narrow in on key genes, relationships that are relevant in multiple contexts, datasets
 MECOM/SMARCA2/CCNE1
Sample & Assay Technologies
Validation, Therapeutic Relevance in Ovarian Cancer
• 13 of the genes identified
in this study have been
implicated in previous
ovarian cancer gene
expression studies – with
similar up/down
regulation patterns
• Several are targets of
ovarian cancer drugs:
• SPINT2
• EPCAM
• PTGER1
• PRKC1
• CKD4
• HDAC10
Sample & Assay Technologies
Additional Evidence Linking CNA Genes to Ovarian Cancer
Hint!
Find the ovarian cancer genes in IPA and overlap with the CNA study
 Prepare datasets by using search box and up load dataset/my list tools in IPA
 Select interest genes by using highlight and select functions
 Overlay CNA expression dataset
78
Sample & Assay Technologies
Prioritize Candidate Genes from CNA Study
Which are implicated in ovarian cancer? (search Ingenuity Knowledge Base)
What molecular interactions exist – and do those interactions represent a collective
biological function? Potential driver for carcinogenesis? (IPA Core Analysis)
Does an assay exist to measure key, carcinogenesis-relevant gene products in a
clinical setting?
 Identify exploratory clinical biomarkers (Overlay Biomarkers)
Narrow in on key genes, relationships that are relevant in multiple contexts, datasets
 MECOM/SMARCA2/CCNE1
Sample & Assay Technologies
Analyze CNA dataset on context of Networks, Pathways, Functions
Hint!
• Upload dataset into IPA
• All CNA-specific gene expression changes were considered significant by
authors (relative to normal ovarian tissue)
• Focus analysis on genes that are known to be expressed either in ovarian
tissue or ovarian cancer cell lines (according to IPA tissue and cell line body
atlas data)
Sample & Assay Technologies
Pre-analysis settings
Hint!
How to focus on specific tissue/cell line?
Stringent filter
A
Relaxed filter
B
A is approved that expressed in the selected tissue/cell line
B is approved that expressed in the selected tissue/cell line
A to B is approved that expressed in the selected tissue/cell
line
A
B
A is approved that expressed in the selected tissue/cell line
B is approved that expressed in the selected tissue/cell line
A to B is approved that expressed in other tissues/cell lines
81
Sample & Assay Technologies
Analysis Summary
Cell Death, Cell Cycle networks and
Functional groups
Carcinogenesis and Apoptosis pathways
Sample & Assay Technologies
Top scoring network for CNA-specific gene expression changes
Points to key biological process that may be a driver of carcinogenesis in
ovarian tissue. Provides a pool of candidate genes for further prioritization,
validation (mRNA, protein levels, functional validation).
Sample & Assay Technologies
Summary Report for Apoptosis Network
Highlights therapeutic relevance, expands biological understanding of network.
Sample & Assay Technologies
Apoptosis network: Relevance to exploratory ovarian cancer
biomarkers
Strong connections between
ovarian cancer biomarkers
and CNA apoptosis network.
Many in ovarian cell line
context:
• IL8 – MAPK12
• IL8 – CASP8
• ERBB2 – MDM2
• P53 – MDM2
• EGFR – FOXO1
85
Sample & Assay Technologies
Apoptosis network: Relevance to exploratory ovarian cancer
biomarkers
Hint!
Find the relationship between two interest group
 Select Network
 Change to Path designer mode
 Load biomarker list and build relationship by Path explorer tool
Sample & Assay Technologies
Initial Conclusions from Pathway Analysis
Collection of expression changes that are specific to ovarian cancer
tumors and regions of high copy number alteration
 Strong association with apoptosis
 Biological process and genes may be potential drivers of carcinogenesis
 Contains known anti-neoplastic drug targets: TNFRSF10B, IGF1R, VEGF,
MAPK11/12, CASP8
Sample & Assay Technologies
Prioritize Candidate Genes from CNA Study
Which are implicated in ovarian cancer? (search Ingenuity Knowledge Base)
What molecular interactions exist – and do those interactions represent a collective
biological function? Potential driver for carcinogenesis? (IPA Core Analysis)
Does an assay exist to measure key, carcinogenesis-relevant gene products in a
clinical setting?
 Identify exploratory clinical biomarkers (Overlay Biomarkers)
Narrow in on key genes, relationships that are relevant in multiple contexts, datasets
 MECOM/SMARCA2/CCNE1
Sample & Assay Technologies
What methods, assays exist? Identify exploratory clinical
biomarkers
Sample & Assay Technologies
Understand method, application of exploratory biomarker
ELISA exists to measure IGFR1 levels and activity in clinical samples.
CASP8 is being used as a secondary outcome marker (impact on apoptosis
and cell cycle arrest) for CDDO anti-tumor therapy.
Sample & Assay Technologies
Prioritize Candidate Genes from CNA Study
Which are implicated in ovarian cancer? (search Ingenuity Knowledge Base)
What molecular interactions exist – and do those interactions represent a collective
biological function? Potential driver for carcinogenesis? (IPA Core Analysis)
Does an assay exist to measure key, carcinogenesis-relevant gene products in a
clinical setting?
 Identify exploratory clinical biomarkers (Overlay Biomarkers)
Narrow in on key genes, relationships that are relevant in multiple contexts, datasets
 MECOM/SMARCA2/CCNE1
Sample & Assay Technologies
Integration of multiple lines of evidence highlights MECOM
• Upregulated in CNA-specific gene expression analysis of ovarian
tumors
• Upregulated in other ovarian cancer studies (Findings in Ingenuity
Knowledge Base)
• Target of miRNA upregulated in ovarian cancer (Dahiya et al, Johns
Hopkins )
• Binds SMARCA2
 downregulated in CNA-specific gene expression analysis
 target of miRNA downregulated in OC
 binds ovarian cancer markers
Sample & Assay Technologies
MECOM/SMARCA2 hypothesis
CCNE1, SMARCA2, MECOM (EVI1), MBD3 relationship plays
an important role in cell proliferation, growth arrest
checkpoints.
Deregulation of transcript levels and miRNA in ovarian tumors
suggest important area for validation studies
Validate mRNA, protein levels & role as drivers of carcinogenic
processes
Sample & Assay Technologies
Conclusions
Evaluation of Copy Number Alteration (CNA) – specific gene
expression changes provide valuable insight into genes that are
potential drivers of carcinogenesis
Analysis of CNA-specific gene expression changes in IPA identified
key processes, pathways that may be driven by these genes
 Apoptosis networks
 Molecular Mechanisms of Cancer, Death Receptor Signaling pathways
Provides an initial pool of candidate genes that may be useful as
markers of carcinogenesis in ovarian tissue
Examination of candidate genes in the context of multiple lines of
evidence narrows in on subset of genes for validation studies:
validation of protein levels, functional validation
 Highlighting exploratory clinical biomarkers, therapeutic targets
 Overlaying additional mRNA, miRNA datasets
Sample & Assay Technologies
Q&A
95
Sample & Assay Technologies
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Office: +886-2-2795-1777#1635
Fax: +886-2-2793-8009 EXT 1022
My E-mail: Genechen@gga.asia
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