An embryonic stem cell-like signature identifies poorly-differentiated lung adenocarcinoma, but not squamous cell carcinoma (original) (raw)

Clin Cancer Res. Author manuscript; available in PMC 2010 Oct 15.

Published in final edited form as:

PMCID: PMC2787085

NIHMSID: NIHMS143924

Department of Internal Medicine (KAH, GPK, MW), Thoracic Surgery, Department of Surgery (GC, DGB), Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan

*Authors equally contributed to this work.

Requests for Reprints: Khaled A. Hassan C363 Med Inn. Bldg. 1500 E. Medical Center Drive Ann Arbor, MI 48109-0848 Tel: 734-615-1623 Fax: 734-615-2109 ude.hcimu.dem@hdelahk

Supplementary Materials

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Abstract

Purpose

An embryonic stem cell profile correlates with poorly differentiated breast, bladder and glioma cancers. In this manuscript, we assess the correlation between the embryonic stem cell profile and clinical variables in lung cancer.

Experimental Design

Microarray gene expression analysis was done using Affymetrix Human Genome U133A on 443 samples of human lung adenocarcinoma and 130 samples of squamous cell carcinoma. To identify gene-set enrichment patterns we used the Genomica software.

Results

Our analysis showed that an increased expression of the embryonic stem cell gene set and decreased expression of Polycomb target gene set identified poorly-differentiated lung adenocarcinoma. In addition, this gene expression signature was associated with markers of poor prognosis and worse overall survival in lung adenocarcinoma. However, there was no correlation between this embryonic stem cell gene signature and any histological or clinical variable assessed in lung squamous cell carcinoma.

Conclusions

This work suggests that not all poorly-differentiated non-small cell lung cancers exhibit a gene expression profile similar to ESC, and that other characteristics may play a more important role in the determination of differentiation and survival in squamous cell carcinoma of the lung.

Keywords: Embryonic genes, stem cell, Affymetrix, lung, cancer

Introduction

The cancer stem cell theory postulates the existence of a distinct population of undifferentiated cells responsible for tumor initiation and maintenance (1). In a seminal paper, Kim et al described a rare population of bronchioalveolar stem cells (BASCs) in adult mice. This population possesses the ability of self-renewal and multipotent differentiation and is crucial in lung repair after injury (2). The BASC population was found in the precursor lesions of a mouse model of adenocarcinoma (3). In human lung cancer, several studies have shown the presence of clonogenic populations that possess cancer stem cell properties using different markers, including Hoechst 33342, uPAR, CD133 and ALDH (4–7). Cancer stem cells have the capacity for self-renewal, multipotency, and unlimited proliferation. These traits also characterize embryonic stem cells (ESC), thus suggesting probable overlap in the molecular signature between ESC and cancer stem cells.

ESC lines were first identified in 1998 and their molecular profiles have been determined in various studies (8). A meta-analysis identified 38 original studies analyzing the transcriptome of human ESC lines derived from human blastocysts (9). Genes that were consistently over-expressed or under-expressed in ESC as compared to differentiated cells were identified. Twenty ESC gene lists were collected from these studies and 380 genes were found to be commonly overexpressed in five of them. Furthermore, Polycomb (10), Nanog (11), Oct4 (12), Sox2 (13) and their target genes play a major role in controlling ESC and seem to be involved in different cancer types. The expression of these genes and possible correlation with differentiation status and outcome was assessed by Ben-Porath et al in various human tumors (14). They showed that an increase in the expression of the ESC gene set and a decrease in the expression of the Polycomb target gene set, identified poorly-differentiated breast cancer, glioma and bladder cancer. In addition, patients whose tumors possessed such an expression profile had worse overall survival as compared to others. This was intriguing, as ESC regulatory genes seem to be crucial in determining differentiation and prognosis in multiple cancers. In this work, we attempted to establish whether these findings can be generalized to other cancers, namely the adenocarcinoma and squamous cell carcinoma subtypes of non-small cell lung cancer.

Materials and Methods

Specimens and gene sets

Details of the adenocarcinoma specimens, criteria for inclusion, mRNA processing and hybridization, pathological and clinical data are all available from Shedden et al. (15). Similarly, the squamous cell carcinoma details are available from Raponi et al. (16). A summary of the clinical variables in 443 adenocarcinomas and 130 squamous cell lung cancers used in this study is provided in Supplemental Table 1. In addition, the correlation of clinical variables with survival was provided in Supplemental Table 2. The original gene sets of embryonic stem (ES) cell, Polycomb (PRC) targets, Nanog, Oct4 and Sox2 (NOS) targets and Myc targets were obtained from Ben-Porath et al (14). We matched the original gene name to the Affymetrix Human Genome U133A gene name, and we focused on gene sets ES exp 1, PRC2 targets, NOS targets and Myc targets. The gene list is provided in Supplemental Table 3.

Gene expression data and analysis of gene set enrichment

Microarray gene expression data on 443 human lung adenocarcinomas (15) and 130 squamous cell lung cancers (16) were downloaded from websites described by original papers. Raw data was processed by log2 transformation of the expression values, and the mean center expression level for each gene across all samples was determined. The expression was represented relative to the mean of each gene. The processed expression data is provided as Supplement Table 4 and Table 5. To identify gene-set enrichment patterns, we used the Genomica software used by Ben- Porath et al (16), which was downloaded from http://genomica.weizmann.ac.il/. In brief, we identified genes that were over- or under-expressed in each sample, determined genes whose expression was at least two-fold above or below the mean expression level, and calculated a P value. A threshold of P < 0.05 was used as a cutoff for significant enrichment. We determined the gene set to which each differentially expressed gene in a specific sample belonged. Then, for all samples showing enrichment for a particular gene set, we determined the correlation between the samples and each clinical variable annotation, and assigned a P value according to the hypergeometric distribution. We used a more stringent threshold of P < 0.01 for this calculation.

Real-time RT-PCR

In order to validate the ES cell gene expression of the microarray data, we performed real-time PCR experiments using Custom TaqMan Low Density Arrays (Applied Biosystems) on 47 lung cancers. A total of 109 genes were randomly picked from ES, PRC2 and other gene lists used in this study. A standard RT-PCR technique was run on the Applied Biosystems 7900HT Fast Real-Time PCR System. For detailed information on TaqMan arrays as well as card set-up and data analysis, refer to the TaqMan Low Density Array Getting Started Guide (P/N 4319399) which can be downloaded from the ABI website: http://www.appliedbiosystems.com.

Statistical analysis

Statistical analyses were done using R package (http://www.r-project.org/). Individual tumors enriched for overexpression of the ES exp1 set were considered to have an ES signature. P values were calculated using the log-rank test and Kaplen-Meyer survival curves comparing the group of individuals with tumors showing the ES signature to all other individuals. Survival-related genes were selected by Cox regression model and differentiation-related genes were obtained using t-test by comparing well-differentiated to poorly-differentiated lung tumors. Spearman correlation was used for the correlation analysis of ES genes between real-time PCR and microarray data.

Results

ESC and Polycomb gene set expression correlate with differentiation status in lung adenocarcinoma

We performed microarray gene expression analysis using Affymetrix Human Genome U133A on 443 samples of human lung adenocarcinoma (15). Utilizing the Genomica software as used by Ben-Porath et al, we analyzed the expression of the ESC, NOS, Myc and Polycomb-regulated gene sets according to various clinical features. Increased ESC gene set expression (p = 1× 10−10) and decreased Polycomb gene set expression (p = 6.3×10−9), was detected in histological poorly-differentiated tumors (Fig. 1a). This association was independent of proliferation and remained significant even after eliminating proliferation-related genes from both ESC (p = 1.2 ×10−5) and Polycomb (p = 0.01) gene sets. This indicates that poorly-differentiated tumors express genes that are related to those of ESC and that such tumors may include a more robust cancer stem cell population.

Poorly-differentiated lung adenocarcinomas possess an ESC expression pattern that correlates with poor prognosis. a) Expression pattern of gene sets (rows) in 443 lung adenocarcinoma samples. Red and green indicate overexpressed or underexpressed gene sets, respectively. Brown bars (bottom) indicate each sample annotation for grade where 1 is a well-, 2 is moderately-, 3 is poorly-differentiated tumor. Gene set expression (right) with or without proliferation genes in lung adenocarcinoma samples stratified by tumor grade. b) Gene set expression stratified by smoking status, TNM classification and stage. c) Kaplan-Meier curve analysis of overall survival in patients with lung adenocarcinoma with over-expressed or under-expressed ESC gene set. d) Kaplan-Meier curve analysis of overall survival in lung adenocarcinoma patients based on tumor grade with p value indicating significance of survival difference between grade 1 and grade 3 tumors.

ESC gene set expression associates with poor clinical variables

Patients with advanced stage disease (T2, T3 and T4) had increased expression of the ESC gene set as compared to patients with T1 disease who had a decreased expression (Fig. 1b). Similarly, patients with lymph node involvement (N1 and N2) had increased expression of the ESC gene set as compared to patients with no lymph node involvement (N0). Current smokers also had increased expression of the ESC gene set (Fig. 1b). Clinically, current smokers and patients with advanced stage disease or lymph node involvement have poor outcome; this suggests that ESC gene set expression correlates with markers of poor prognosis in lung adenocarcinoma.

Poor prognosis is associated with ESC gene set expression

To determine whether the ESC gene set expression correlates with poor prognosis, we performed Kaplan-Meier and log-rank test analysis of overall survival. This analysis showed that patients whose tumors had increased expression of the ESC gene set had a worse 5-year overall survival than patients with decreased expression (p = 0.005) (Fig. 1c). Kaplan-Meier analysis of overall survival based on differentiation showed a non-significant trend toward worse 5-year overall survival in patients with poorly-differentiated tumors as compared to patients with moderately or well-differentiated (p = 0.06) tumors (Fig. 1d). This analysis shows that poorly-differentiated lung adenocarcinomas possess a molecular signature that is similar to the ESC profile, and that patients with such a profile have a poor prognosis. This may also indicate that such tumors possess a larger cancer stem cell population as compared to well or moderately-differentiated tumors.

ESC gene set expression in squamous cell lung cancer

To assess whether these findings apply to squamous cell lung cancer, we further analyzed the expression of ESC and Polycomb target gene sets in 130 samples of lung squamous cell carcinoma (SCC) (16). There was no correlation between the expression of these gene sets and any histological or clinical variable assessed, including differentiation and survival (Fig. 2a). In an attempt to understand these unexpected results, we performed a Cox regression model or t-test-based analyses of Polycomb, NOS and Myc target genes for survival and differentiation in the lung adenocarcinoma and SCC samples, and these analyses detected no significant difference (results not shown). Further, the percentage of survival-related genes expressed in the ESC gene set was 28.6% in adenocarcinoma as compared to 5.9% in SCC, and the percentage of poor-differentiation-related genes expressed in the ESC gene set was 44.4% in adenocarcinoma as compared to 3.6% in SCC (Fig. 2b). The variation in expression of these genes in SCC samples (Fig. 2c), despite being statistically significant, was less compared to the variation seen in the adenocarcinoma samples (Fig. 2d). This implies that ESC and Polycomb target gene sets do not correlate with the genes that determine differentiation or survival in SCC of the lung. This is in contrast to other tumor types, including adenocarcinoma of the lung.

Squamous cell carcinoma (SCC) of the lung ESC gene set expression pattern does not correlate with clinical variables. a) Expression pattern of gene sets (rows) in 130 lung SCC samples. Red and green indicate overexpressed or underexpressed gene sets, respectively. Brown bars (bottom) indicate each sample annotation for smoking status, grade, lymph node involvement, TNM classification and stage. No significant correlation with any clinical variables. b) Percentage of survival and differentiation-related genes overlapping with the ESC gene set (Es exp1) in lung adenocarcinoma and SCC. c) Expression of differentiation-related genes in well-differentiated and poorly-differentiated lung SCC. d) Expression of differentiation related genes in a representative 177 samples of well-differentiated and poorly-differentiated lung adenocarcinoma from a single institution, the University of Michigan.

Discussion

Cancer stem/progenitor cells were initially identified in acute myelogenous leukemia (17), and recently have been identified in several solid tumors, including melanoma, and breast, brain, prostate, pancreatic and colon carcinoma (18–24). The capacity for self-renewal, multipotency, and unlimited proliferation is shared between cancer stem cells and embryonic stem cells (ESC). This suggests that pathways controlling such biological processes might be shared between ESC and cancer stem cells. In an effort to establish the gene expression profile of ESC, Ben Porath et al identified 380 genes, designated gene set ES exp1, that were commonly overexpressed in ESC (14). Furthermore, a Polycomb target gene set representing overlapping genes bound to Polycomb repressive complex 2 (PRC2) in human ESCs was designated as PRC2 targets. Overlapping Nanog, Oct4, and Sox2 target genes were designated as the NOS targets gene set, and genes affected by Myc were designated as the Myc targets gene set.

Using these gene sets and Genomica software, Ben-Porath et al, demonstrated an inverse relationship between differentiation and outcome in breast, glioblastoma and bladder carcinoma. The enrichment of an ESC-like gene-set signature was identified by an overexpression of the ESC gene set and a decrease of expression of the PRC2 targets gene set. In this study, we applied the same gene sets and software used by Ben-Porath et al to lung cancer samples, and our results confirm that an ESC-like gene expression profile is preferentially detected in histologically, poorly-differentiated lung adenocarcinoma, independent of cell proliferation. In addition, advanced stage disease, lymph node involvement and current smoker status correlated with the ESC-like gene expression profile, and overall survival was worse in patients who expressed this profile. These findings clearly suggest that ESC genes are involved in both differentiation and prognosis of lung adenocarcinoma. Since the lung cancer stem cell has not yet been definitively identified, a direct correlation between the ESC and lung cancer stem cell expression profile cannot be performed. To confirm the microarray findings, real time quantitative PCR was performed on 47 samples for 109 genes. The Spearman correlation analysis shows that there are 88.1% (96/109) genes having good correlation to microarray data (R>0.5). (Supplement. Fig. 1)

Interestingly, these findings did not apply to lung SCC. No correlation between the expression of these gene sets and any histological or clinical variable assessed was detected in SCC. Specifically, overexpression of ESC genes had no impact on differentiation or survival. This could be explained by the fact that adenocarcinoma had a higher percentage of survival-related and poor-differentiation-related genes expressed in the ESC gene set as compared to SCC. This implies that the ESC and Polycomb gene sets do not correlate with the genes driving differentiation or impacting survival in SCC, a finding that is in direct contrast to adenocarcinoma.

Several studies have used gene signature profiles to predict patient outcome (25–27). Data from these profiles vary and there is a lack of consistency among published studies. Attempts to compare profiles and evaluate whether the results could be integrated were inconsistent, but a common gene profile that is a significant predictor of survival could be identified (28). In addition, similarity in gene sets that are prognostic for both adenocarcinoma and SCC have been identified (16). This manuscript is the first to use ESC profiling in lung cancer with demonstration of differences among subtypes of lung cancer.

In conclusion, these studies suggest that although many poorly-differentiated tumors of different tissue origins exhibit a gene expression profile similar to ESC, it is not a universal phenomenon and other characteristics play a major role in some cancers.

Translational Relevance

Our study shows that overexpression of the embryonic stem cell (ESC) profile correlates with various poor clinical features in adenocarcinoma of the lung including smoking, lymph node involvement and advanced stage. We have also shown that overexpression of this profile is an independent poor prognostic factor in adenocarcinoma which can be used clinically as a prognostic tool. Furthermore, the ESC pathways that control self-renewal, multipotency, and unlimited proliferation ability, represent components that could be targeted with specifically tailored treatments. In addition, this work highlights the difference in the ESC gene expression profile between adenocarcinoma and squamous cell carcinoma of the lung and raises an important issue regarding similar treatment approaches in these lung cancer subtypes.

Supplementary Material

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References

1. Pardal R, Clarke MF, Morrison SJ. Applying the principles of stem-cell biology to cancer. Nat Rev Cancer. 2003;3:895–902. [PubMed] [Google Scholar]

2. Kim CF, Jackson EL, Woolfenden AE, et al. Identification of bronchioalveolar stem cells in normal lung and lung cancer. Cell. 2005;121:823–35. [PubMed] [Google Scholar]

3. Jackson EL, Willis N, Mercer K, et al. Analysis of lung tumor initiation and progression using conditional expression of oncogenic K-ras. Genes Dev. 2001;15:3243–8. [PMC free article] [PubMed] [Google Scholar]

4. Ho MM, Ng AV, Lam S, et al. Side population in human lung cancer cell lines and tumors is enriched with stem-like cancer cells. Cancer Res. 2007;67:4827–33. [PubMed] [Google Scholar]

5. Gutova M, Najbauer J, Gevorgyan A, et al. Identification of uPAR-positive chemoresistant cells in small cell lung cancer. PLoS ONE. 2007;2:243. [PMC free article] [PubMed] [Google Scholar]

6. Eramo A, Lotti F, Sette G, et al. Identification and expansion of the tumorigenic lung cancer stem cell population. Cell Death Differ. 2008;15:504–14. [PubMed] [Google Scholar]

7. Jiang F, Qiu Q, Khanna A, Todd NW, et al. Aldehyde dehydrogenase 1 is a tumor stem cell-associated marker in lung cancer. Mol Cancer Res. 2009;7:330–8. [PMC free article] [PubMed] [Google Scholar]

8. Thomson JA, Itskovitz-Eldor J, Shapiro SS, et al. Embryonic stem cell lines derived from human blastocysts. Science. 1998;282:1145–7. [PubMed] [Google Scholar]

9. Assou S, Le Carrour T, Tondeur S, et al. A meta-analysis of human embryonic stem cells transcriptome integrated into a web-based expression atlas. Stem Cells. 2007;25:961–73. [PMC free article] [PubMed] [Google Scholar]

10. O'Carroll D, Erhardt S, Pagani M, Barton SC, Surani MA, Jenuwein T. The polycomb-group gene Ezh2 is required for early mouse development. Mol Cell Biol. 2001;21:4330–4336. [PMC free article] [PubMed] [Google Scholar]

11. Chambers I, Colby D, Robertson M, et al. Functional expression cloning of Nanog, a pluripotency sustaining factor in embryonic stem cells. Cell. 2003;113:643–655. [PubMed] [Google Scholar]

12. Niwa H, Miyazaki J, Smith AG. Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells. Nat Genet. 2000;24:372–376. [PubMed] [Google Scholar]

13. Graham V, Khudyakov J, Ellis P, et al. Sox2 functions to maintain neural progenitor identity. Neuron. 2003;39:749–765. [PubMed] [Google Scholar]

14. Ben-Porath I, Thomson MW, Carey VJ, et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet. 2008;40:499–507. [PMC free article] [PubMed] [Google Scholar]

15. Shedden K, Taylor JM, Enkemann SA, et al. Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study. Nat Med. 2008;14(8):822–7. [PMC free article] [PubMed] [Google Scholar]

16. Raponi M, Zhang Y, Yu J, et al. Gene expression signatures for predicting prognosis of squamous cell and adenocarcinomas of the lung. Cancer Res. 2006;66:7466–72. [PubMed] [Google Scholar]

17. Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med. 1997;3:730–737. [PubMed] [Google Scholar]

18. Fang D, Nguyen TK, Leishear K, et al. A tumorigenic subpopulation with stem cell properties in melanomas. Cancer Res. 2005;65:9328–9337. [PubMed] [Google Scholar]

19. Al-Hajj M, Wicha MS, Benito-Hernandez A, et al. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA. 2003;100:3983–3988. [PMC free article] [PubMed] [Google Scholar]

20. Patrawala L, Calhoun T, Schneider-Broussard R, et al. Highly purified CD44+ prostate cancer cells from xenograft human tumors are enriched in tumorigenic and metastatic progenitor cells. Oncogene. 2006;25:1696–1708. [PubMed] [Google Scholar]

21. Singh SK, Clarke ID, Terasaki M, et al. Identification of a cancer stem cell in human brain tumors. Cancer Res. 2003;63:5821–5828. [PubMed] [Google Scholar]

22. Li C, Heidt DG, Dalerba P, et al. Identification of pancreatic cancer stem cells. Cancer Res. 2007;67:1030–1037. [PubMed] [Google Scholar]

23. Ricci-Vitiani L, Lombardi DG, Pilozzi E, et al. Identification and expansion of human colon-cancer-initiating cells. Nature. 2006;445:111–115. [PubMed] [Google Scholar]

24. O'Brien CA, Pollett A, Gallinger S, et al. A human colon cancer cell capable of initiating tumour growth in immunodeficient mice. Nature. 2006;445:106–110. [PubMed] [Google Scholar]

25. Beer DG, Kardia SL, Huang CC, et al. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med. 2002;8:816–824. [PubMed] [Google Scholar]

26. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A. 2001;98:13790–13795. [PMC free article] [PubMed] [Google Scholar]

27. Guo L, Ma Y, Ward R, et al. Constructing molecular classifiers for the accurate prognosis of lung adenocarcinoma. Clin Cancer Res. 2006;12:3344–3354. [PubMed] [Google Scholar]

28. Parmigiani G, Garrett-Mayer ES, Anbazhagan R, et al. A cross-study comparison of gene expression studies for the molecular classification of lung cancer. Clin Cancer Res. 2004;10:2922–2927. [PubMed] [Google Scholar]