A Primary Xenograft Model of Small Cell Lung Cancer Reveals Irreversible Changes in Gene Expression Imposed by Culture In-Vitro

Abstract

Traditional approaches to the preclinical investigation of cancer therapies rely on the use of established cell lines maintained in serum-based growth media. This is particularly true of small cell lung cancer (SCLC), where surgically resected tissue is rarely available. Recent attention has focused on the need for better models that preserve the integrity of cancer stem cell populations, as well as three-dimensional tumor-stromal interactions. Here we describe a primary xenograft model of SCLC in which endobronchial tumor specimens obtained from chemo-naive patients are serially propagated in vivo in immunodeficient mice. In parallel, cell lines grown in conventional tissue culture conditions were derived from each xenograft line, passaged for 6 months, and then re-implanted to generate secondary xenografts. Using the Affymetrix platform, we analyzed gene expression in primary xenograft, xenograft-derived cell line, and secondary xenograft, and compared these data to similar analyses of unrelated primary SCLC samples and laboratory models. When compared to normal lung, primary tumors, xenografts and cell lines displayed a gene expression signature specific for SCLC. Comparison of gene expression within the xenograft model identified a group of tumor-specific genes expressed in primary SCLC and xenografts that was lost during the transition to tissue culture, and that was not regained when the tumors were re-established as secondary xenografts. Such changes in gene expression may be a common feature of many cancer cell culture systems, with functional implications for the use of such models for preclinical drug development.

Introduction

Lung cancer is the commonest cause of cancer death in the United States (1). Of these, approximately 15–20% of cases are SCLC, a highly aggressive, primitive neuroendocrine tumor that is often widely metastatic at the time of diagnosis. Most cases of SCLC are initially sensitive to cytotoxic chemotherapy, despite the fact that most of these tumors lack both p53 and pRB, and manifest overexpression of BCL2 (2). Regimens based on cis-platinum, usually in combination with etoposide, result in robust and often dramatic clinical responses in SCLC patients (3). Despite the effectiveness of this drug combination, the overwhelming majority of SCLC patients succumb to a chemoresistant recurrence within 2 years of diagnosis (3). Neither the use of novel chemotherapeutics, nor the introduction of dose intensification regimens have improved survival, which has remained essentially unchanged for the last 30 years (3). The clinical imperative in SCLC is the discovery of novel strategies to prevent disease recurrence.

For three decades, the mainstay of preclinical cancer therapeutic research has been the use of human cancer cells lines cultured in vitro, and xenografts derived from these cell lines grown in vivo in immunodeficient mice. Neither of these models consistently predict efficacy in clinical trials, resulting in two major barriers to the successful translation of new cancer therapeutics. First, resources are expended on drug development based on these models that ultimately fail in clinical trials. Secondly, many potentially useful therapies that might be beneficial in humans are discarded because they fail to show efficacy in conventional cell culture and xenograft models. Emerging evidence suggests that the process of establishing conventional cell lines from human cancers results in distinct and irreversible loss of important biological properties, which include (i) gain or loss of gene amplification (4, 5), (ii) the ability to migrate and metastasize (6) (iii) the maintenance of a distinct stem cell population (6) and (iv) the preservation of dependency on embryonic signaling pathways (7, 8). In all cases, these properties are not restored when these conventional cell lines are grown as heterotopic or orthotopic xenografts.

Preclinical modeling of SCLC chemotherapy presents challenges in addition to those outlined above. Since SCLC is usually diagnosed by endobronchial biopsy or fine needle aspiration cytology (FNAC), substantial quantities of fresh or frozen tissues are typically lacking in most tumor banks. For this reason, most SCLC research relies on conventional cell lines, which are often chemoresistant, since they were derived from patients who had received cytotoxic chemotherapy (9). In addition, all of these cell lines suffer from the experimental limitations outlined above, and lack the three dimensional tumor-stromal interactions which appear to significantly affect the response of these cells to chemotherapy (10). As part of our ongoing efforts to develop better models for the study of SCLC, we generated and characterized a series of primary xenograft models derived from chemo-naive patients in order to more accurately model this disease. In our first description of this primary xenograft model, we showed that differential expression of BCL2 in vivo was correlated with growth responses to the BCL2 inhibitor ABT-737 (11). Here, we describe a detailed gene expression analysis of this model that reveals how gene expression is irreversibly altered during the process of establishing conventional cell culture, and how maintenance of SCLC xenografts passaged exclusively in vivo can retain features of the primary tumor of direct relevance to preclinical drug testing.

Materials and Methods

Generation and maintenance of primary xenografts and cell lines

Over an 18 month period, discarded tissue from 3 chemo-naive SCLC patients undergoing therapeutic bronchoscopy for acute bronchial obstruction was obtained fresh and transported to the laboratory in 1X PBS at 4°C. All samples were anonymized, and obtained in accordance with the Johns Hopkins University Institutional Review Board. Due to the small amount of material available, the entire sample was used to generate a xenograft. Under aseptic conditions, tumor samples were finely minced with razor blades, vigorously triturated in 1XPBS, passed through a 60μm filter, centrifuged and then resuspended in 500μl of Matrigel (BD Biosciences) at 4°C. Cells were then injected subcutaneously in the flanks of 5 NOD/SCID mice that were monitored for tumor growth. When the P0 tumors reached 1cm in diameter, the mouse was sacrificed and the tumor divided into sections for snap freezing, frozen section, formalin fixation, conventional cell culture or serial passage. All animal studies were performed in accordance with protocols approved by the Johns Hopkins University Animal Care and Use Committee.

Serial passage in vivo was performed by disaggregating the tumor as described above. Aliquots of cells were then injected into the flanks of athymic nude mice in Matrigel, or cryopreserved in 90% RPMI (Invitrogen)/10% DMSO (Sigma). Conventional cell lines were established by seeding an aliquot of disaggregated cells in culture with Advanced RPMI (Invitrogen)/1% Bovine Calf Serum (Invitrogen). Cell lines were passaged and cryopreserved in standard fashion for SCLC cultures. Publically available SCLC cell lines were obtained from ATCC and cultured in Advanced RPMI (Invitrogen)/1% Bovine Calf Serum. Xenografts derived from these conventional cell lines were grown in the flanks of nude mice as described above. Orthotopic xenografts were generated by dorsoscapular, transcutaneous injection of cells suspended in Matrigel into the right lung of nude mice, essentially as described (12).

Assessment of SCLC phenotype

At each passage in vivo and at every 3 months in vitro, cell suspensions were immunophenotyped by FACS analysis (FACScalibur, BD Biosciences) with antibodies to human CD56, (BD Biosciences). Genomic DNA was purified using DNAzol (Invitrogen). Total RNA was purified using RNAzol (Invitrogen), followed by a secondary purification using the RNEasy system (Qiagen). Standard Affymetrix expression microarray protocols were used for all array studies.

Gene expression annotation and pre-processing

Gene-expression analysis from our xenograft samples were assumed to be human-specific based on (i) quantitative RT-PCR analysis demonstrating that mouse mRNA contributed between 1/8–1/16 of the total mRNA (data not shown), and (ii) probesets developed by Affymetrix that are highly specific for each species⁷. We complemented our gene expression experiments with additional data sets retrieved from the NCBI Gene Expression Omnibus (GEO) database (13). Gene annotation for all the platforms considered was obtained from R-Bioconductor metadata packages (14). For a complete description of platforms and data sets, see Supplementary Information, Table 1. Raw gene expression intensities for all samples were normalized at the probe-level using the RMA algorithm as described by Irizarry et al (15). To compare gene expression across different Affymetrix platforms we first used Entrez Gene identifiers as cross-referencing keys, and then matched all the individual probes at the sequence level to those contained in the hgu133a platform to control laboratory and batch effects using the ‘barcode’ RMA preprocessing algorithm described by Zilliox and Irizarry (16). Standardization across DNA-chips was attained by quantile normalization (17). Details are reported in Supplementary Information Fig 17,18.

Differential gene expression analysis

In all data sets considered in the present study differential gene expression was investigated using functions and methods implemented in the R/Bioconductor (14, 18) package limma (19). Briefly, a fixed effects linear model was fit for each individual feature to estimate expression differences between groups of samples to be compared. When technical replicates or matched samples from the same individual were available, replicates were used as blocks and the average correlation within the blocks was estimated and used in the model (20). An empirical Bayes approach was applied to moderate standard errors of normalized logarithmic fold-change (M-values) (19). Finally, for each analyzed feature, moderated t-statistics, log-odds ratios of differential expression (B-statistics), raw and adjusted p-values (FDR control by the Benjamini and Hochberg method (21)) were obtained. Details are reported in Supplementary Information. Raw expression data and the MIAME required information are currently available at⁹, and will be permanently hosted in the Gene Expression Omnibus database upon publication.

Analysis of functional annotation

To capture biological processes relevant in the investigated sample groups we performed Analysis of Functional Annotation (AFA). Functional Gene Sets (FGS), in the form of lists of genes sharing specific biological properties, were obtained from the Gene Ontology database (22–24), KEGG pathway database (25), and from the Molecular Signature Database (MsigDb)⁸ (26). The AFA was performed by one-sided Wilcoxon test, after ranking the genes by their absolute moderated t-statistics. Multiple testing correction was performed separately for each functional scope (GO, KEGG, and MsigDb) by the Benjamini and Hochberg method (21). Mappings between individual probes on the array to the various functional themes were based on NCBI Entrez Gene identifiers obtained from the R-Bioconductor metadata packages. Overall, this approach is analogous to Gene Set Enrichment Analysis (GSEA) like procedures (26, 27). Details are reported in Supplementary Information.

Relationships among different sample groups

To compare gene expression data among distinct sample groups we compared the lists of differentially expressed genes obtained from the linear model analysis, we computed the squared correlation between normalized gene expression measurements and log2 fold-changes (M-values) across samples, and we used the “correspondence-at-the-top” plot (cat-plot) technique (28). This latter method is specifically designed to assess the agreement and similarity between microarray experiments, where it expected that only a small fraction of genes would be differentially expressed across the large total number of analyzed genes. Principal coordinates analysis and multidimensional scaling was performed using 1-correlation as a measure of distance. Details are reported in Supplementary Information.

Results

Characterization of the primary SCLC xenograft model

An overview of the experimental model is shown in Fig. 1A. With serial passage in vivo, all three primary xenograft lines (LX22, 33, 36) maintained a typical histopathologic appearance ((11) and data not shown). Cell lines derived from each of these xenografts grew as loose aggregates or spheroids typical of SCLC cell lines (9) (data not shown). Immunohistochemical and FACS analysis of freshly isolated cells from each xenograft demonstrated strong expression of human CD56, a marker of neural differentiation in >95% of the cells ((11) and data not shown). Cell lines derived from each xenograft line (LX22CL, 33CL and 36CL) also retained strong immunoreactivity for CD56 (data not shown).

Figure 1.

Generation and characterization of the SCLC primary xenograft model. A, Outline of the experimental approach from primary sample, xenograft, cell line and secondary xenograft from the derived cell line. B, H&E stained section of mouse lungs following orthotopic injection of the LX22 xenograft. The “primary” tumor (T) and metastasis in mediastinal lymph node (LN) are highlighted. Scale bar = 1mm C, H&E stained section of the intrapulmonary tumor shown in B. Scale bar = 20μm.

To further characterize this model, we performed orthotopic tumor implantation using a transcutaneous injection approach. All 3 xenograft lines readily grew in the lungs of nude mice, where they formed discrete “primary tumors”, as well as metastasis to regional mediastinal lymph nodes (Fig. 1B), typical of the pattern seen in human SCLC patients. As shown in Fig 1C, intrapulmonary tumors displayed typical SCLC histology, and aggressively invaded lung parenchyma and vessels. These data demonstrate that this experimental system closely resembles human SCLC in vivo. An additional series of xenografts from well-characterized SCLC cell lines from ATCC were also generated, and all grew as flank xenografts with characteristic SCLC histology.

Organization of gene expression analysis

Samples of RNA derived from the primary xenograft model were grouped into “triplets” obtained from each primary xenograft (XG), its derived cell line (CL) and a secondary xenograft derived from that cell line (CLX). RNA was also purified from 11 well characterized, commonly used SCLC cell lines (H69, H82, H128, H146, H187, H209, H345, H446, H526, H1618, and H1930) and their paired xenografts. De novo expression array analysis was then performed on laboratory model triplets (XG, CL, and CLX) from three distinct patients (LX22, LX33, and LX36), on laboratory model pairs (CL and CLX) from ATCC SCLC cell lines, and on total RNA from normal human lung and universal human reference RNA (Stratagene), using the hgu133plus2 Affymetrix array. These data were complemented with expression data from the public domain, and with 2 de novo data sets, one accounting for 4 additional SCLC cell lines without a paired xenograft, and one for 4 primary SCLC specimens independent from the laboratory models described above

Public domain expression data were obtained from the GEO database (13), which included expression analyses of normal lung specimens, SCLC cell lines and xenografts, and primary tumors (series GSE3526, GSE7307, GSE8920, GSE7097 (29), GSE4127, GSE4824 GSE7670 (30), GSE2361, GSE6044) (31), and one data set was obtained from the Broad Institute website¹⁰ (32). Overall 192 arrays from 13 data sets (5 Affymetrix platforms) were analyzed, accounting for 62 normal lung samples, 19 primary SCLC, 4 primary xenografts (3 patients), 22 secondary xenografts, and 85 SCLC cell lines. Details of the samples and public domain data used in this analysis are shown in Supplementary Information, Table 1. Preprocessing details are reported in Supplementary Information, Fig. 1–4.

Differentially expressed genes in the primary xenograft model

Results of the linear model analysis are shown in Fig. 2. The direct comparisons among the three different type of samples analyzed (XG, CL, and CLX) revealed three sets of differentially expressed genes: 395 significantly different (adjusted P-value < 0.005) when comparing primary xenografts (XG) to their matched derived cell line (CL), 152 were different when comparing XG to their derivative secondary xenografts (CLX), while only 26 genes were differentially expressed when comparing CL to CLX (Fig. 2A and Supplementary Tables A–C). Similar findings were obtained when the triplet samples were compared using gene expression changes with respect to the universal reference RNA, or the normal lung specimen (Supplementary Information Fig. 5,6). The overall squared correlation among the groups of samples analyzed proved to be higher in all comparisons of derived cell lines with their derivative xenografts (Fig. 2B). Similarly, cat-plots show that a higher proportion of differentially expressed genes were in common between cell lines and their derivative xenografts (Fig. 2C; Supplementary Information, Fig 10,11). These data support the notion that gene expression programs change when cell lines are derived in vitro, and that the expression of a significant number of such genes is not restored when the derivative cell line is returned to growth in vivo as a xenograft.

Figure 2.

Differential Expression Analysis in the Primary Xenograft Model. A, Venn Diagram showing the number of differentially expressed genes (adjusted P-value < 0.005) in direct comparisons among primary xenografts (XG), the matched cell lines (CL), the xenografts obtained from these cell lines (CLX). Only 26 genes were differentially expressed between CL and CLX (CLvsCLX), 152 between XG and CLX (XGvsCLX), and 395 between XG and CL (XGvsCL). B, Hierarchical clustering of pair-wise squared correlations between distinct groups of samples. Matched triplets of samples are denoted as XG, CL, and CLX, while pairs of independent samples from established cell lines and from the public domain (CL and CLX) are denoted by the prefix ‘p’. All correlations were computed between mean fold-changes of each sample group compared to the universal reference RNA (Strategene). Comparisons involving CL and CLX groups (CLvsREF, CLXvsREF, CLvsREF, pCLXvsREF) proved to be more correlated than any other comparison involving the primary xenografts group (XGvsREF). C, Correspondence-at-the-top plots (CAT-plots) for all pair-wise comparisons among triplets and pairs of samples. The notation is used is the same as in C. Genes were ranked by the mean fold-change of each sample group compared to the universal reference RNA. On the left is shown the correspondence for the most down regulated genes (4000), and on right for the most up regulated genes (400). The red and the blue lines on the top of the CAT-plot represent the comparisons between CL and CLX for matched triplets and pairs of samples, and show the highest correspondence. For the up-regulated genes, the purple line represents the comparison between cell lines derived from our xenografts (CL) and cell lines from the public domain (pCL). This comparison shows more similarity than when XG are compared to CL.

Analysis of functional annotation in the primary xenograft model

Several functional themes were explored by Analysis of Functional Annotation (AFA) to discover common alterations in biological processes within the model. Gene Ontology (GO), KEGG, and Functional Gene Sets from the Molecular Signature Data Base were used in a Wilcoxon rank-sum test, after ordering the genes according to the moderated t-statistics obtained after fitting the linear models. Overall this analysis showed that both multiple pathways and biological processes are enriched in the comparisons between the model samples groups, and that different genes were responsible for the individual enrichments, including genes coding for transcription factors, adhesion and extra-cellular matrix molecules, apoptosis pathways, RNA metabolism and metabolic enzymes. The results of this analysis using KEGG pathways are shown in Fig. 3. Highly significant enrichment was seen in several pathways relevant to preclinical cancer modeling, including keratan sulfate biosynthesis, aminoacyl-tRNA biosynthesis pathway, MAPK signaling, TGF-beta signaling, apoptosis and proteasomal function. The marked changes in gene expression in the apoptosis pathway are also consistent with the findings of Hann et al (11), who showed that sensitivity to the BCL2 antagonist ABT-737 was a feature of SCLC cell lines and their paired xenografts, and was associated with high level BCL2 expression. Lower levels of BCL2 expression seen in our primary xenograft lines in vivo correlated with resistance to this therapy. All other results and the genes driving the enrichment are reported in Supplementary Information Fig. 13–16, and in Supplementary Tables D–E.

Figure 3.

Analysis of Functional Annotation (AFA). Heat-map showing AFA results for KEGG pathways. Color-coded values correspond to absolute values of base 10 logarithms of raw P-values from the Wilcoxon rank sum test. Vertical blue lines show the same data as a histogram value for each color intensity. All pathways shown proved to be enriched in at least one comparison (adjusted P-value < 0.05). Rows were clustered using Euclidian distance and the average clustering method; columns were not reordered. Several pathways showed significant results changes in all comparisons, although different sets of genes proved to be responsible for the enrichment, while a number of pathways (i.e. Keratan sulfate biosynthesis) proved to be enriched in a subset of the contrasts considered (see Supplementary Tables A–C for genes involved and analysis of additional functional scopes, including Gene Ontology).

Analysis of changes in gene expression induced by cell culture

To characterize gene expression changes induced by cell culture we directly compared the model samples to primary tumors using linear model analysis in a combined data set accounting for primary SCLC specimens from several public sources and from our laboratory. We performed this analysis using all the genes in common among the platforms, before and after removing the inter-laboratory effect using the empirical gene expression distribution described by Zilliox and Irizarry (16) (see Supplementary Information). When we compared the models separately (either XG, CL, or CLX) to the primary tumors, most of the differentially expressed genes were in common. This group of differentially expressed genes results from the comparison of fairly pure samples in terms of cell lineage (SCLC xenograft model samples) with a complex tissue accounting for stromal cells, lymphocytes and cancer cells (the primary tumors). Nevertheless, a number of genes proved to change only in specific contrasts, and also in this case fewer differentially expressed genes were found when primary xenografts were compared to tumors, than in the comparisons involving the cell lines and the secondary xenografts (Fig. 4A). In this aggregated analysis we also compared the laboratory models and the primary SCLC to a large group of normal lung specimens, thus identifying the gene expression signature present in the primary tumors and conserved in all the laboratory models (Supplementary Information Fig 16,19–25 and Supplementary Table G).

Figure 4.

Comparison between laboratory model samples and primary small cell lung cancer (SCLC) specimens. (A) Venn diagram showing the number of differentially expressed genes (adjusted P-value < 0.005) in direct comparisons among primary xenografts (XG), the matched cell lines (CL), the xenografts obtained from these cell lines (CLX) and the group of primary SCLC. A group of 543 genes proved to be differentially expressed between all laboratory model groups (XG, CL, and CLX) and the primary SCLC. This group of genes accounts for genes expressed in cell types present only in primary tumors, such as genes expressed in lymphocytes, stromal cells present in the primary SCLC, and represent the difference in complexity between specimens obtain in vivo with respect to the laboratory models. A number of genes proved different between each group (XG, CL, and CLX) and the primary SCLC: only 91 genes were differentially expressed between XG and SCLC, while 174 were genes differentially expressed between CL and CLX and SCLC. (B) Hierarchical clustering of all pairwise squared correlations between individual samples, based on gene expression levels after barcode-RMA and quantile normalization. Individual primary Small Cell Lung Cancer (SCLC, cyan), primary xenografts (XG, purple), cell lines (CL, green), and xenografts from these cell lines (CLX, blue) are shown. Correlations were computed using all genes (181) that proved to be differentially expressed (adjusted P-value < 0.05) in any comparison between XG, CL, and CLX and that were present in all platforms. Overall the primary xenografts (XG) proved to be more correlated with the primary SCLC than any other sample. Among primary xenografts (XG), the LX22 sample proved to be more correlated to primary SCLC than to its derivative CL and CLX. (C) Multidimensional scaling plot showing the relationships among all individual SCLC, XG, CL, and CLX samples. SCLC are shown in cyan, XG in purple, CL in green, and CLX in blue. The XG samples from LX22 patient proved to be the most similar to primary SCLC samples than to its derivative CL and CLX.

One major problem when comparing expression array data from primary tumor samples with those derived from cell lines is that stromal and lymphoid gene expression in the primary tumor can create marked differences in gene expression that are not tumor-specific. In order to overcome this problem, we searched for highly significant changes in gene expression within the three groups of the xenograft model (XG, CL, CLX), where only human-specific, tumor-specific genes are analyzed. To this end, we selected genes whose expression varied in any comparison between within our laboratory model (XG, CL, CLX), and used this gene set to compare our xenograft model to primary SCLC samples. In this analysis, the primary xenografts more closely resembled primary tumors than derivative cell lines and the secondary xenografts, as shown by all the pair-wise squared correlations and by multidimensional scaling of the first three components. (Fig. 4B and C). This is particularly evident in the LX22 model, in which the differences in gene expression are evident in the primary xenograft, but are lost both in the derivative cell line and secondary xenograft (Fig. 4B and C). Similar findings were also obtained by using different stringency criteria to select the genes differentially expressed in the laboratory models, and both by mapping the genes predicted in our direct comparison of the models (see Figure 2), or by predicting the gene set on the combined data set (see Supplementary Information).

Most significantly, a group of genes was identified across the three models that were differentially expressed when comparing the primary xenograft lines, the xenograft-derived cell lines, and the secondary xenografts derived from these cell lines. This represents an expression signature of the permanent effects of tissue culture on gene expression in SCLC, and includes genes known to be important in tumor growth such as IGFBP4, ITPR3, COX10, NKX2.1, CXCR4, CDH1, DAP3, CXCR7, TLE2 (a detailed list of these genes in shown in Table 1 and Supplementary Table H).

Table 1.

List of genes that are permanently changed by cell culture. The Table shows the genes that are differentially expressed (adjusted P-value < 0.005) between XG and CL and between XG and CLX, but not between CL and CLX. Fold-change (logarithmic scale on base 2) are shown for each comparison.

			Fold Change (Log2)			Adjusted P value
Entrez ID	Symbol	Gene Name	XGvCL	CLvsCLX	XGvsCLX	XGvsCL	CLvsCLX	XGvsCLX
2261	FGFR3	fibroblast growth factor receptor 3	5.65	−0.4	5.25	0	0.91	0
94235	GNG8	guanine nucleotide binding protein (G protein), gamma 8	4.94	−0.04	4.9	0	1	0
3306	HSPA2	heat shock 70kDa protein 2	4.84	−0.7	4.14	0.01	0.9	0.02
283120	H19	H19, imprinted maternally expressed transcript	4.96	−1.54	3.56	0	0.52	0.02
283131	TncRNA	trophoblast-derived noncoding RNA	4.11	−0.17	4.39	0	0.84	0
64065	PERP	PERP, TP53 apoptosis effector	4.43	−0.98	3.9	0.02	0.84	0.03
4821	NKX2-2	NK2 homeobox 2	4.17	−0.27	3.91	0.03	0.98	0.03
999	CDH1	cadherin 1, type 1, E-cadherin (epithelial)	4.09	−0.31	3.79	0.04	0.97	0.05
55466	DNAJA4	DnaJ (Hsp40) homolog, subfamily A, member 4	4.14	−0.31	3.74	0	0.25	0
4070	TACSTD2	tumor-associated calcium signal transducer 2	3.96	−0.22	3.67	0.01	0.4	0.02
7852	CXCR4	chemokine (C-X-C motif) receptor 4	4.22	−1.09	3.38	0	0.62	0.01
1299	COL9A3	collagen, type IX, alpha 3	3.71	0.12	3.83	0	0.98	0
57007	CXCR7	chemokine (C-X-C motif) receptor 7	4.89	−0.25	2.64	0	0.53	0
6662	SOX9	SRY (sex determining region Y)-box 9	3.74	−0.29	3.54	0.03	0.97	0.03
54492	LOC54492	hypothetical LOC54492	3.82	−0.38	3.44	0.02	0.95	0.03
2820	GPD2	glycerol-3-phosphate dehydrogenase 2 (mitochondrial)	5.5	−0.5	1.75	0	0.92	0
164633	CABP7	calcium binding protein 7	3.47	0.17	3.64	0.02	0.98	0.02
219844	HYLS1	hydrolethalus syndrome 1	3.66	−0.24	3.42	0.01	0.97	0.02
3481	IGF2	insulin-like growth factor 2 (somatomedin A)	2.54	−0.26	4.44	0.01	0.45	0.02
5349	FXYD3	FXYD domain containing ion transport regulator 3	3.58	−0.38	3.28	0.02	0.85	0.03
4246	SCGB2A1	secretoglobin, family 2A, member 1	3.55	−0.28	3.28	0.01	0.96	0.01
5617	PRL	prolactin	4.06	−1.32	2.74	0.01	0.59	0.04
3963	LGALS7	lectin, galactoside-binding, soluble, 7 (galectin 7)	3.23	0.28	3.51	0.05	0.97	0.03
25878	MXRA5	matrix-remodelling associated 5	3.3	0.05	3.35	0	0.99	0
10653	SPINT2	serine peptidase inhibitor, Kunitz type, 2	3.46	−0.36	3.1	0.03	0.96	0.04
2977	GUCY1A2	guanylate cyclase 1, soluble, alpha 2	−2.07	0.26	−1.81	0.01	0.92	0.02
255231	MCOLN2	mucolipin 2	−1.6	−0.7	−2.31	0.03	0.72	0.02
8715	NOL4	nucleolar protein 4	−2.19	0.39	−1.8	0.01	0.87	0.03
3709	ITPR2	inositol 1,4,5-triphosphate receptor, type 2	−1.87	−0.84	−2.11	0.01	0.53	0
11001	SLC27A2	solute carrier family 27 (fatty acid transporter), member 2	−1.84	−0.33	−2.17	0.04	0.91	0.02
54530	C1orf218	chromosome 1 open reading frame 218	−2.26	0.42	−1.84	0.01	0.87	0.04
6507	SLC1A3	(glial high affinity glutamate transporter), member 3	−1.89	−0.35	−2.25	0.03	0.62	0.01
5028	P2RY1	purinergic receptor P2Y, G-protein coupled, 1	−1.35	0.85	−2.8	0.05	0.44	0.02
7325	UBE2E2	ubiquitin-conjugating enzyme E2E 2	−2.25	−0.76	−1.91	0.02	0.51	0.03
169200	TMEM64	transmembrane protein 64	−2.22	−0.25	−2.03	0.01	0.38	0.04
5308	PITX2	paired-like homeodomain 2	−2.16	0.06	−2.1	0.02	0.99	0.03
51678	MPP6	membrane protein, palmitoylated 6	−2.18	−0.05	−2.22	0.02	0.99	0.02
1491	CTH	cystathionase (cystathionine gamma-lyase)	−2.54	1.24	−1.86	0	0.25	0.03
79071	ELOVL6	ELOVL family member 6	−2.54	0.49	−2.05	0.01	0.85	0.03
153	ADRB1	adrenergic, beta-1-, receptor	−2.34	0.14	−2.36	0.02	0.74	0.02
401097	LOC401097	Similar to LOC166075	−2.42	−0.29	−2.71	0.04	0.95	0.02
80144	FRAS1	Fraser syndrome 1	−2.33	−0.52	−2.82	0.03	0.57	0.01
594855	CPLX3	complexin 3	−2.89	1.08	−2.26	0.01	0.74	0.03
3049	HBQ1	hemoglobin, theta 1	−2.28	−0.6	−2.88	0.03	0.81	0.01
54941	RNF125	ring finger protein 125	−2.35	−0.47	−2.82	0.01	0.82	0
6096	RORB	RAR-related orphan receptor B	−2.65	0.8	−2.57	0.01	0.45	0.01
285368	PRRT3	proline-rich transmembrane protein 3	−2.91	0.54	−2.37	0.01	0.87	0.04
84864	MINA	MYC induced nuclear antigen	−2.69	0.55	−2.68	0.04	0.85	0.04
29953	TRHDE	thyrotropin-releasing hormone degrading enzyme	−3.05	0.4	−2.64	0	0.86	0
8835	SOCS2	suppressor of cytokine signaling 2	−3.82	1.16	−2.66	0	0.55	0.02

Overall these results demonstrate that tumors cells acclimated to standard cell culture conditions cannot completely regain the gene expression profile characteristic of SCLC in humans.

Validation of gene expression changes

We next sought to validate the gene expression changes observed in our primary xenograft model using quantitative real-time RT-PCR, with particular emphasis on expression changes that were not reversed when cell lines were reimplanted as xenografts. As shown in Fig. 5, irreversible changes in gene expression were quantitatively confirmed for 12 genes that were downregulated (Fig. 5A), and 10 that were upregulated (Fig. 5B) following the transition cell culture in vitro. These data confirm that marked changes in gene expression result during the transition from growth in vivo to culture in vitro, and that these changes are irreversible.

Figure 5.

Validation of gene expression genes in the primary SCLC xenograft model. Expression levels in derivative cell lines (CL) and secondary xenografts (CLX) were compared to those in the corresponding primary xenograft, and are shown as the mean of paired data from all three xenograft lines (n=6). Error bars indicate standard errors. Genes selected from the microarray data (shown on the x axis) were upregulated (A) or downregulated (B) relative to expression in the primary xenograft samples. (C) Heat map expression analysis of BCL2-related genes in SCLC models. The RNA samples are listed as columns, and the genes in rows. The color scale represents the level of expression from low (blue) to high (orange).

A recent study using our xenograft model analyzed the preclinical efficacy of the BCL2 antagonist ABT-737 as a potential targeted therapy for SCLC (11). Four cell lines (NCI-H146, H187, H209, and H345) proved sensitive, while the cell line H82, with markedly lower expression of BCL2 protein, proved resistant. The efficacy of ABT-737 in controlling the tumor growth in vivo using our primary xenografts models (LX22, LX33, and LX36) proved variable, and seemed to correlate with BCL2 protein expression (11). As a further validation of our analysis, we determined the expression of BCL2 and BCL2-related genes in the same models as used in the ABT-737 study (11). As shown in the heat map analysis (Fig. 5C), the expression of BCL2, and of functionally related genes such as BID, BAX and MCL1, varied significantly in the different SCLC models analyzed. In addition, the expression pattern of BCL2 was consistent with the Western blot analysis of BCL2 protein expression in LX22 33 and 36 (11). These data highlight the potential variability in preclinical cell culture models in the investigation of novel targeted agents.

Discussion

Preclinical cancer biology and drug development have traditionally relied on the use of cell lines that are able to grow in serum-containing media, and on artificial surfaces. Considerable debate has focused on the reliability of such cell lines as cancer models, and of their ability to predict the success of novel therapies in humans (33–35). Recent attention has focused on the use of primary xenografts as a way to better model cancer in vivo (36). Although this approach is time consuming and labor-intensive, emerging evidence that these models retain important biological properties that are seen in the primary tumor, including gene amplification (4), genomic architecture (37), characteristic histopathology (38–41), gene expression (42) and cancer stem cell biology (43, 44). By contrast, emerging evidence supports the contention that primary xenografts differ substantially from their parent tumor by acquiring new genomic changes, faster growth rates and nuclear pleomorphism (45, 46).

In 2007, Dangles-Marie et al compared colon cancer primary xenografts to matched cell lines, and demonstrated variable changes in expression of a panel of 66 genes (47). Most importantly, these cells lines differed markedly in their sensitivities to standard chemotherapeutic agents derived from their matched xenografts (47). In our study, we explored the idea that primary xenografts may represent a useful preclinical model in SCLC, a tumor in which little therapeutic progress has been made in the last 30 years. Using a prospective, unbiased, bioinformatics-based approach, we were able to observe marked changes in gene expression as SCLC cells transitioned to standard cell culture conditions. In many cases, these changes were not reversed when cell lines were re-established as xenografts. Even more striking was that these expression changes occurred in a large number of critically important cancer signaling pathways of direct relevance to targeted therapies, stromal interactions, developmental signaling and chemosensitivity.

Interestingly, when analyzing the genes whose expression changed most significantly in the transition to cell culture, expression patterns in our primary xenograft lines were more closely matched to those seen in primary SCLC tissues than in cell lines or cell-line derived models. Moreover, the tendency of tissue culture to reduce the differential gene expression patterns in lines derived from different patients suggests that heterogeneity in tumor cell biology may be under-represented in cell culture systems. The ability of our SCLC models, and of other primary xenograft systems, to predict therapeutic efficacy of cytotoxic and targeted agents in patients will be a more rigorous test of any potential clinical application of this approach.