Comprehensive network and pathway analysis of RNA
sequencing of triple negative breast cancers "
Jeffrey Solzak, MS1 • Rutuja Atale, MS1 • Brad Hancock, BS1 • Jean-Noel Billaud, PhD2 • Milan Radovich, PhD1 !
1Indiana University School of Medicine & Melvin and Bren Simon Cancer Center,!
Department of Surgery; 2Ingenuity Systems, a Qiagen Company!
!
Background!
Results!
Triple-negative breast cancers (TNBCs) account for 15% of all
breast cancers cases and are defined by an absence of
actionable therapeutic targets (ER-,PR-,HER2-). Using RNA-seq
data, we compared TNBCs to microdissected normal breast
epithelium from healthy volunteers and to normal tissue adjacent
to tumor followed by comprehensive network and pathway
analysis. "
Master
Regulator
HSPG2
Vegf
PARP16
F3-F7
KLKB1
EIF2AK3
CLNK
Materials and Methods!
cDNA libraries from 20 normal breast tissues from the Susan G.
Komen Tissue Bank at the IU Simon Cancer Center and 10 TNBC
tumors were sequenced on a Life Technologies Ion Torrent Proton
sequencer. Mapping of reads to the human genome (hg19) was
performed using the LifeScope software. RNA-seq data from an
additional 84 TNBCs was downloaded from The Cancer Genome
Atlas (TCGA) data portal. Differential gene expression was
analyzed using Partek Genomics Suite; and network, pathway,
and transcription factor analysis was performed using Ingenuity
Systems IPA using a p-value of .001. "
Figure 2: Top hits for canonical pathways within TNBC. The
columns represent p-values while the orange points represents the
ratio of the number of genes that meet the cutoff criteria / the
number of genes that make up that pathway. While cancer related
pathways are at the top of the list, more characteristic pathways of
TNBC such as “Estrogen-mediated S phase entry” were observed
to have both significant p-values and a high gene ratio. "
Category
Infectious Disease
Organismal
Development
size of body
Cellular Movement
cell movement
Cell Death and
Survival
cell survival
Infectious Disease
Viral Infection
Cellular Movement
migration of cells
Cell Death and
Survival
cell viability
Cellular Movement
invasion of cells
Cell Death and
cell viability of tumor cell
Survival
lines
Cellular Movement
homing
Cellular Movement
homing of cells
Organismal Survival
organismal death
Developmental
Disorder
Growth Failure
Organismal Survival
death of embryo
Embryonic
Development
death of embryo
Cardiovascular
Disease
Heart Disease
Neurological Disease
seizures
Infectious Disease
infection of mammalia
p-Value
2.88E-09
Predicted
Activation
State
Increased
9.44E-14
1.66E-28
Increased
Increased
9.384
8.401
206
466
2.39E-22
5.86E-23
1.89E-28
Increased
Increased
Increased
8.305
8.234
8.011
327
390
428
9.01E-22
1.16E-18
Increased
Increased
7.888
6.709
306
198
6.07E-16
2.08E-15
8.15E-16
5.72E-27
Increased
Increased
Increased
Decreased
6.504
6.5
6.436
-15.4
183
145
143
534
5.46E-09
1.63E-07
Decreased
Decreased
-8.57
-4.92
139
35
1.63E-07
Decreased
-4.92
35
2.79E-08
8.80E-08
2.30E-18
Decreased
Decreased
Decreased
-4.16
-3.7
-3.65
200
95
110
Activation
#
z-score Molecules
9.964
183
Activated
11.32
1.55E-41
transcription
regulator
complex
group
transporter
Activated
Activated
Activated
Inhibited
11.32
11.28
11.07
-12
3.30E-41
3.77E-41
2.09E-45
1.68E-25
transcription
regulator
other
Inhibited
Inhibited
-11.6
-11.6
3.38E-29
1.54E-42
FOXJ1
transcription
regulator
Inhibited
-11.5
3.38E-29
MAFK
2.74
transcription
regulator
Inhibited
-11.2
3.06E-41
15.25
2.348
peptidase
other
other
other
Inhibited
Inhibited
Inhibited
Inhibited
-10.5
-10.4
-10.2
-10.1
1.08E-19
3.94E-25
3.35E-21
2.45E-17
-1.4
-2.2
transmembrane
receptor
enzyme
Inhibited
Inhibited
-10.1
-10.1
2.45E-17
1.61E-25
LIMD1
HOXB4
Ap1
Nfat (family)
ATP2B2
FOXD1
TRIM45
1.693
1.121
2.436
-1.89
SARM1
IMPDH2
Figure 8: Mechanistic network driven by TP53 predicted to be
inhibited by the upstream regulator analysis. The colors indicate
activity, orange being predicted activation and blue being
predicted inhibition."
Figure 5: The top predicted activated and inhibited master
regulators by the causal network analysis of TNBC. The causal
network analysis helps discover candidate upstream genes,
RNAs, and proteins and their expression that may explain the
results of the data set and propose potentially new set of
connections between these regulators and downstream molecules
in the dataset."
Figure 3: Overlap of canonical pathways. This network of
canonical pathway highlights the relationships between signaling
pathways in TNBC. "
Upstream
Regulator
CSF2
TNF
IL1B
Vegf
Fold
Change
2.795
5.257
TGFB1 4.829
NFkB
(complex)
IFNG
HGF
CD3
CD28
IL1RN
Figure 1: The table above represents characteristics of our TNBC
data set. Using activation z-score, IPA was able to predict which
cellular aspects significantly affect TNBC. Here we see the usual
increase in development and survival while observing a decrease
in cell death and growth failure. The plot below is a heat map of
downstream effects of the data set. The size of each square
denotes significance in p-value while the colors, orange is positive
and blue is negative, shows the change of the function."
p-value of overlap
1.01E-35
1.73E-35
1.41E-22
7.27E-30
6.66E-35
1.15E-21
1.56E-41
transcription
regulator
5430435G22
Rik
Cbp
SERPINA1
CACTIN
Diseases & Functions!
Diseases or Functions
Annotation
infection of cells
Predicted
Activation Activation zFold
Change Molecule Type
score
State
2.08
enzyme
Activated
13.2
group
Activated
13.17
1.154
other
Activated
12.84
complex
Activated
12.52
peptidase
Activated
12.1
-1.07
kinase
Activated
11.94
other
Activated
11.67
RB1
Alpha
catenin
APOE
Rb
KDM5B
-3.74
Molecule Type
Predicted
Activation
State
Activation zscore
p-value of overlap
cytokine
cytokine
cytokine
group
Activated
Activated
Activated
Activated
10.12
9.323
8.573
8.557
7.39E-42
1.81E-22
2.2E-20
5.69E-21
growth factor
Activated
8.443
3.58E-45
complex
cytokine
Activated
Activated
8.335
8.225
2.81E-14
2.97E-29
growth factor
complex
Activated
Inhibited
7.857
-6.56
6.19E-27
3.15E-24
transmembrane
receptor
3.26
Inhibited
30.89
cytokine
Inhibited
transcription
regulator
1.034
Inhibited
4.179
1.787
group
transporter
group
transcription
regulator
Inhibited
Inhibited
Inhibited
Inhibited
-5.18
-5.08
8.7E-14
2.55E-11
-5.03
3.05E-16
-4.85
-4.71
-4.33
2.59E-06
8.7E-09
2.35E-11
-4.29
Figure 9: PI3K pathway analysis using MAP (Molecule Activity
Predictor). The molecules outlined in pink are part of the PI3K
canonical pathway. MAP helps to interrogate networks or
Canonical Pathways and to generate hypotheses by selecting a
molecule of interest, indicating up or down regulation, and
simulating directional consequences of downstream molecules
and the inferred activity upstream in the network or pathway."
Figure 6: Causal network analysis identified several new
regulators, including HOXB4, that may be acting in TNBC. "
Predicted Activation
State
Transcription Factor
TP53
Regulation z-score
p-value
-1.309
3.60E-51
CDKN2A
Inhibited
-4.062
2.74E-20
RB1
Inhibited
-5.031
3.05E-16
E2F1
Activated
4.892
4.82E-24
MYC
Activated
2.917
4.69E-23
FOXM1
Activated
4.132
2.45E-12
PI3K (complex)
Activated
5.925
1.03E-12
4.68E-09
Figure 4: 16 of the most activated and inhibited upstream
regulators in TNBC predicted by the upstream regulators analysis.
These regulators may be responsible for gene expression changes
seen in our data set and are founded on known biology between
these regulators and molecules in the dataset."
Figure 7: Upstream regulator analysis confirmed expression levels
characteristic of TNBC. The upstream regulator analysis identifies
the cascade of transcriptional regulators that can help clarify the
observed gene expressions in the data set."
Conclusions!
•  Using disease and function analysis, many of the hallmarks of
cancer were present including low cell death, high survival, and
high cellular movement. Observed however, were aberrant
pathways regarding the immune system, making these molecules
potential targets for therapy. "
"
• Canonical pathway analysis also displayed many trademarks of
cancer, but also gave new thoughts of potential targets such as
IL-6 signaling"
"
•  Upstream regulator analysis confirmed many of the observed
activated and inhibited molecules, including TP53 and RB1. The
predicted activity of these two molecules are characteristic of
TNBC and are indicative of loss of function mutations."
• Causal network analysis provided several novel molecules
affected in TNBC including HOXB4, a developmental gene
responsible for expanding hematopoietic stem and progenitor
cells."
"