Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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