Download Lung Cancer

Lung Cancer—
Molecular Network Disease
Cheng Shujun
Cancer Institute, Chinese Academy of Medical
Sciences, Peking Union Medical College
Fortune Magazine, March 22,2004
The five-year survival rate did not improve when a cancer has spread
The challenge we faced in cancer
therapy may be related to the
complexity of gene network changes in
lung cancer cells, especially at late
stages.
(Li Ding et al.
Nature, 2008, Oct. 455: 1069-)
DNA sequencing of 623 genes in 188 lung adenocarcinomas.
26 genes are mutated at significantly high frequencies .
Several important pathway involved in lung adenocarcinoma
A small-cell lung cancer genome with complex signatures of tobacco
exposure
/nature Published online 16 December 2009
.
They sequenced a small-cell lung cancer cell line, NCI-H209, NCI-BL209 (an
Epstein–Barr-virus-transformed lymphoblastoid line has been generated from the
patient. ) to explore the mutational burden associated with tobacco smoking.
A total of 22,910 somatic substitutions
(including 134 in coding exons ) were identified in
a small-cell lung cancer cell line .
They estimated one mutation for every 15
cigarettes smoked.
What we may learn from the recent studies:
Pathway rather than individual genes appear to
govern the course of tumorigenesis.
The wide variation in tumor behavior and
responsiveness to therapy may relate to the diversity of
gene function abnormalities (network) in different
patients from the same type of tumor.
The acquisition of numerous somatic
mutations, each with a small fitness advantage,
may also drive tumourigenesis ?
Previous report indicated that many
cancer genes play critical roles in
cellular development and growth
Cancer might be a molecular network
disease caused by cellular abnormal
growth and differentiation, which may
be related to developmental genome
disorder
During the past two yeas, we
investigated gene expression
profiles in different time of human
lung embryonic development and
lung cancer tissues
Mid FL
AduL
Adjacent
lung tissue
Developmental landscape
Early FL
Lung Cancer
Embud
We projected all the embryonic tissue samples
(Embud, early and middle fetal lung ( Early FL & Mid FL) and the mature lung samples (AduL)
adjacent lung tissues (Adjacent Lung) and the lung cancer tissues (Lung Cancer )onto a two
dimensional space with the principle component analysis (PCA) to construct the developmental
landscape. Every spot represents one sample. The color of the spot indicates its tissue type. .
(cycle direction; wide distribution for cancer(hetrogenecity)
Gene-expression in human fetal
lung tissues and lung cancers
P53 signaling pathway
DNA replication
NOSTRIN mediated
eNOS trafficking
DNA replication
preinitiation
Metabolism of nitric oxide
DNA strand elongation
E2F mediated regulation of
DNA replication
G1/S transition
G2/M checkpoints
G2/M DNA damage
checkpoint
Metaphase/anaphase
transition
Mammalian Wnt
signaling pathway
10
Inhibition of matrix
metalloproteinases
5
TGFBR
0
Signaling events mediated
by HDAC Class III
-5
RNA polymerase I
transcription initiation
Mitotic prometaphase
Mitotic prophase
Mitotic spindle
checkpoint
Mitotic telophase /cytokinesis
Early
E
胎
肺
Middle
Adjacent
Norm
al
肺
tissue
Lung
cancer
Cheng et al. unpublished data
RNA polymerase I
transcription
RNA polymerase I
promoter clearance
34
The dynamic gene expressing patterns in
human developmental process
We take a bundle of
genes (Embryfeature)
to test their clinical
significance.
Embryfeature enriched
in following GO terms:
M Phase
M/G1 Transition
Mitotic Metaphase/Anaphase Transition
Mitotic Prometaphase
Mitotic Prophase
Mitotic Spindle Checkpoint
Mitotic Telophase /Cytokinesis
DNA Replication
DNA Replication Pre-Initiation
DNA strand elongation
E2F mediated regulation of DNA replication
E2F transcriptional targets at G1/S
FOXM1 transcription factor network
FoxO family signaling
G1/S Transition
G2/M Checkpoints
G2/M DNA damage checkpoint
G2/M Transition
Clinical Significance of Embryfeature
• The expression level of Embryfeature was
correlated with the survival time of cancer patients.
Such as
• Lung adenocarcinoma （353 samples)
– 4 independent data sets: 49, 117, 125, 62 samples
• Glioma（371 samples）
– 3 independent data sets: 100, 191,80 samples
• Breast Cancer（1300 samples）
– 7 independent data sets:
159, 286, 204, 189, 136, 77, 249 sampels
We divided the 49
lung ADC patients
into two groups
according to the
expression level of
Embryfeature in their
cancer tissues.
Survival analysis
49 ADC patients
Survivalofanalysis
100
L group
90
H group
80
70
60
SMC4_group
H
L Survival
50
40
P = 0.0407
P = 0.041
30
20
0
1
2
3
4
5
6
7
Time (years)
Number at risk
Group:
H
Overall
survival analysis of 49 lung
25
22
15
8
3
0
0
0
ADC
patients(from
our
cancer
hospital)
Group: L
analysis
showed that the
prognosis of the
Embryfeature higher
patients (H group, red
line) was significantly
worse than that of
lower ones (L group,
black line).
Overall survival analysis of
Overall
survival
GSE13213_ADC patients
117
lunganalysis
ADC of
patients
Survival probability (%)
100
90
80
70
60
50
p = 0.0016
40
0
2
4
6
8
10
Relapse-free survival analysis
of 125 lung
ADCofpatients
Relapse-free
survival
PNAS_ADC patients
100
90
80
70
SMC4_group
60
H
L50
40
30
20
10
0
p = 0.0019
2
Time (years)
Number at risk
The Hsame result was
Group:
59 in other
49
33
22
confirmed
three
Group: L
independent
58
55 lung
47
29
adenocarcinoma data sets.
The microarray data
and patients’ clinical
information were
downloaded from GEO
database of NCBI.
4
6
8
10
Time (years)
6
0
10
1
Number at risk
Group: H
62
36
Group: L
63
44
p = 0.0001
Relapse-free
16survival
5
0
0 of
analysis
62
31
13 lung
2 ADC1
patients
L group
H group
Survival analysis of 191
Glioma patients
Survival analysis of Glioma
patients : grouped by their
Embryfeature expression level.
events
100
80
L group
60
H group
We analyzed 3 independent sets of glioma
patients (371 samples) with the expression
level of Embryfeature in their cancer tissues.
Survival analysis showed that the prognosis
of the Embryfeature higher patients (H
group, red line) was significantly worse
than that of lower ones (L group, black
line).
Group_SHA_86
H
L
40
P = 0.0299
20
0
0
2
4
6
8
10
0
0
0
7
2
1
Time
Number at risk
Group: H
100
Group: L
91
Survival analysis of 80
Glioma patients
17
6
25
13
EVENTS
evnets
100
100
90
90
80
80
L group
Survival probability (%)
70
60
50
40
H group
Overall
survival
analysis
of 77
Glioma
patients
L group
70
H group
SHA_86 60
H
L 50
SHA_86_GROUP
H
L
40
P = 0.0009
30
p = 0.0044
30
20
20
10
10
0
1
2
3
4
Time
Number at risk
5
6
7
0
2
4
6
Time
8
10
Overall Survival analysis of 159
Breast Cancer patients
Overall Survival analysis of 249
Breast Cancer patients
Events
100
95
Overall survival analysis of all 249 patients
100
P = 0.0003
90
85
P = 0.0004
80
75
L group
70
H group
65
60
Survival probability (%)
90
SHA_Group
H
L
L group
L group
H group
80
medi
H group
70
60
0
2
4
6
8
10
Time
50
0
2
4
6
8
10
12
14
Time
L group
H group
The expression level of Embryfeature was
associated with the relapse-free and
overall survival of the breast cancer
patients, which was confirmed in 7
independent datasets, involving 1,300
samples. Here the survival curves (K-M
curve) of four datasets were shown.
The hub genes in the interaction network constituted a
7-node sub-network shown as below. Extensive
research on the interaction among these hub genes
may provide more hints on understanding human lung
carcinogenesis. Further analysis is under way.
CCNH
IRAK4
MET
CDKN1B
HSP90AA1
RIMS2
RAD50
The embryfeature gene may predict the
prognosis of several types of tumor (breast
cancer, glioma, lung adenocarcinoma)located at
different organs, It may indicate that the clinic
features of human cancer may not only depend
on their location, perhaps also on their
developmental original memory?
The gene network in cancer cells can
overcome (compensate) the effect of singleagent intervention. ( as reported, the
amplification of Met gene can reactivate
PI3K/AKT pathway Inhibited by Iressa).
The development of drug resistance in
cancer cells may also relate to their gene
network response.
• Lung cancer is a molecular network
disease caused by cellular abnormal
growth and differentiation related to
developmental genome.
• It will be difficult to cure cancer at late
stage with single drug (single gene).
• Multidrug treatments (network drug) are
needed for cancer therapy in the future
• Key steps for lung cancer
research in the future
• To intensify clinical investigation
on human lung cancer and set up
tumor tissue banks.
• To establish high-throughput
platforms for fast analysis of
cancer samples through a
synthetic approach.
• Systematic analysis of both
clinical and basic research data
with bioinformatics.
Acknowledgements
Dr. Zhang kaitai
Dr. Gao yanning
Dr. Fung Lin
Dr. Xiao Ting
Dr. Liu Yu
DR. Cao bangrong
Dr. Sun Wenyue
Dr. Xiao Tin
Dr. Liu Yan
Ms. Guo supin
Ms. Hun Naijun
Mr. Di Xuebing
Dr. Se Xiaoyu
Beijing Haidian Women- Children Hospital
Department of Oncology ,Capital Medical
University
Thank you