Lung Cancer—Molecular Network DiseaseCheng ShujunCancer 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 Early FL Adjacent lung tissue Embud Lung Cancer AduL 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)
Gene-expression in human fetal lung tissues and lung cancers 10 5 0 -5 Early Middle Adjacent tissue Lung cancer E胎肺 Normal 肺 Cheng et al. unpublished data 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:
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, 62samples • Glioma（371samples） • 3 independent data sets: 100, 191,80 samples • Breast Cancer（1300 samples） • 7 independent data sets: 159, 286, 204, 189, 136, 77, 249 sampels
L 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). H group Survival analysis of 49 ADC patients P = 0.0407 P = 0.041 Overall survival analysis of 49 lung ADC patients(from our cancer hospital)
L group Relapse-free survival analysis of 125 lung ADC patients H group Overall survival analysis of 117 lung ADC patients p = 0.0019 p = 0.0016 p = 0.0001 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. Relapse-free survival analysis of 62 lung ADC patients
Survival analysis of Glioma patients :grouped by their Embryfeature expression level. L group L group L group H group H 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). Survival analysis of 191 Glioma patients Survival analysis of 80 Glioma patients Overall survival analysis of 77 Glioma patients p = 0.0044 P = 0.0299 P = 0.0009
L group L group L group L group H group H group H group H group Overall Survival analysis of 249 Breast Cancer patients Overall Survival analysis of 159 Breast Cancer patients P = 0.0003 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. P = 0.0004
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 RAD50 HSP90AA1 CDKN1B RIMS2
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.