Lung Cancer—Lung Cancer—Molecular Network DiseaseMolecular Network Disease

Cheng ShujunCheng Shujun

Cancer Institute, Chinese Academy of Medical Cancer Institute, Chinese Academy of Medical Sciences, Peking Union Medical CollegeSciences, Peking Union Medical College

Fortune Magazine, March 22,2004The 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

Developmental landscape 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)

Adjacent lung tissue Lung CancerAduL

Mid FLEarly FL

Embud

P53 signaling pathway

NOSTRIN mediated eNOS trafficking

Metabolism of nitric oxide

Mammalian Wnt signaling pathway

Inhibition of matrix metalloproteinases

TGFBR

Signaling events mediated by HDAC Class III

RNA polymerase I transcription initiation

RNA polymerase I transcription

RNA polymerase I promoter clearance

DNA replication

DNA replication preinitiation

DNA strand elongation

E2F mediated regulation of DNA replication

G1/S transitionG2/M checkpoints

G2/M DNA damage checkpoint

Metaphase/anaphase transition

Mitotic prometaphase

Mitotic prophase

Mitotic spindle checkpoint

Mitotic telophase /cytokinesisCheng et al. unpublished data

Gene-expression in human fetal lung tissues and lung cancers

34

Early Middle Normal

Adjacenttissue

Lung

cancerE胎肺

肺

10

5

0

-5

DNA ReplicationDNA Replication Pre-InitiationDNA strand elongationE2F mediated regulation of DNA replicationE2F transcriptional targets at G1/SFOXM1 transcription factor networkFoxO family signalingG1/S TransitionG2/M CheckpointsG2/M DNA damage checkpointG2/M Transition

M PhaseM/G1 TransitionMitotic Metaphase/Anaphase TransitionMitotic PrometaphaseMitotic ProphaseMitotic Spindle CheckpointMitotic Telophase /Cytokinesis

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:

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

P = 0.041

Overall survival analysis of 49 lung ADC patients(from our cancer

hospital)

Survival analysis

0 1 2 3 4 5 6 7

100

90

80

70

60

50

40

30

20

Time (years)

Surv

ival

pro

babi

lity

(%)

Number at riskGroup: H

25 22 15 8 3 0 0 0Group: L

24 23 19 14 6 3 2 1

SMC4_groupHL

Survival analysis

0 1 2 3 4 5 6 7

100

90

80

70

60

50

40

30

20

Time (years)

Surv

ival

pro

babi

lity

(%)

Number at riskGroup: H

25 22 15 8 3 0 0 0Group: L

24 23 19 14 6 3 2 1

SMC4_groupHL

P = 0.0407

Survival analysis of 49 ADC patients

L group

H group

We divided the 49 lung ADC patients

into two groups according to 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).

Overall survival analysis of 117 lung ADC patients

Overall survival analysis of 117 lung ADC patientsOverall survival analysis of GSE13213_ADC patients

0 2 4 6 8 10

100

90

80

70

60

50

40

Time (years)

Surv

ival

pro

babi

lity

(%

)

Number at riskGroup: H

59 49 33 22 6 0Group: L

58 55 47 29 10 1

SMC4_groupHL

p = 0.0016

Relapse-free survival of PNAS_ADC patients

0 2 4 6 8 10

100908070605040302010

Time (years)

Surv

ival

pro

bab

ilit

y (%

)

Number at riskGroup: H

62 36 16 5 0 0Group: L

63 44 31 13 2 1

H groupL group

p = 0.0019

Relapse-free survival analysis of 125 lung ADC patients

L group

H group

Relapse-free survival analysis of

62 lung ADC patients

The same result was confirmed in other three

independent lung adenocarcinoma data sets. The microarray data

and patients’ clinical information were

downloaded from GEO database of NCBI.

events

0 2 4 6 8 10

100

80

60

40

20

0

Time

Su

rviv

al p

rob

abili

ty (

%)

Number at riskGroup: H

100 17 6 0 0 0Group: L

91 25 13 7 2 1

Group_SHA_86HL

Survival analysis of 191 Glioma patients

P = 0.0299

EVENTS

0 1 2 3 4 5 6 7

100

90

80

70

60

50

40

30

20

10

Time

Sur

viva

l pro

babi

lity

(%)

Number at riskGroup: H

40 13 6 4 3 2 1 0Group: L

40 28 18 9 6 4 1 1

SHA_86HL

Survival analysis of 80Glioma patients

P = 0.0009

Survival analysis of Glioma patients : grouped by their

Embryfeature expression level.

Overall survival analysis

of 77 Glioma

patients

Overall survival analysis

of 77 Glioma

patients

L group

H group

L group

H group

evnets

0 2 4 6 8 10

100

90

80

70

60

50

40

30

20

10

Time

Su

rviv

al p

rob

ab

ilit

y (

%)

SHA_86_GROUPHL

evnets

0 2 4 6 8 10

100

90

80

70

60

50

40

30

20

10

Time

Su

rviv

al p

rob

ab

ilit

y (

%)

SHA_86_GROUPHL

p = 0.0044

L group

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).

Overall Survival analysis of 249 Breast Cancer patients

Overall survival analysis of all 249 patients

0 2 4 6 8 10 12 14

100

90

80

70

60

50

Time

Su

rviv

al p

rob

abili

ty (%

)

median_GroupHL

Overall survival analysis of all 249 patients

0 2 4 6 8 10 12 14

100

90

80

70

60

50

Time

Su

rviv

al p

rob

abili

ty (%

)

median_GroupHL

P = 0.0003

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.

L group

H group

L group

H group

L group

H group

MET

RIMS2

CDKN1BHSP90AA1

CCNH

RAD50

IRAK4

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.

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 single-agent 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.

AcknowledgementsDr. Zhang kaitai Dr. Gao yanningDr. Fung Lin Dr. Xiao TingDr. Liu Yu DR. Cao bangrongDr. Sun Wenyue Dr. Xiao Tin Dr. Liu Yan Ms. Guo supinMs. Hun Naijun Mr. Di XuebingDr. Se XiaoyuBeijing Haidian Women- Children HospitalDepartment of Oncology ,Capital Medical University

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