Affiliations of authors: D. Agrawal (Department of Cell Biology), T. Chen, R. Irby, T. J. Yeatman (Department of Surgery), M. Szabo, D. Coppola (Department of Pathology), A. Cantor (Department of Biostatistics), Interdisciplinary Oncology, H. Lee Moffitt Cancer Center, University of South Florida, Tampa; A. F. Chambers, London Regional Cancer Centre, University of Western Ontario, Canada; J. Quackenbush, The Institute for Genomic Research, Rockville, MD.
Correspondence to: Timothy J. Yeatman, M.D., Department of Surgery, H. Lee Moffitt Cancer Center, University of South Florida, 12902 Magnolia Dr., Tampa, FL 33612 (e-mail: yeatman{at}moffitt.usf.edu).
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ABSTRACT |
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INTRODUCTION |
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Genome-wide expression profiling is potentially well suited to addressing the multifaceted problems associated with the discovery of clinically useful tumor markers (2). Microarray-based expression profiling has the capacity to evaluate thousands of genes from many different tumor tissues simultaneously in a single experiment. It has become increasingly clear, however, that the value of gene expression data is enhanced when tumors are first microdissected before analysis (3). This process ensures that the majority of the tumor sample submitted for analysis is composed of viable, non-necrotic tumor with limited incorporation of normal adjacent tissues and stroma that could artifactually influence the resultant gene expression profile. Unfortunately, this process leads to a significant reduction in the volume of tumor available for analysis.
Realizing the practical limitations imposed by restricted access to large quantities of individual tumors and the complexity associated with the analysis of large datasets from numerous tumors, we hypothesized that a sample pooling strategy before gene expression profiling might be effective. For a marker to be clinically relevant, it must be notably overexpressed or underexpressed in the majority of the tumor samples of a given histology. For the marker to have prognostic significance, it should also show expression alterations concordant with tumor stage or clinical outcome. We proposed that signals for useful markers (common to the majority of samples) would be positively reinforced if multiple samples were pooled. Contributions from genes that are altered in only a minority of tumor specimens and are, thus, less useful as markers would be minimized. This pooling design differs significantly from the standard approach of examining gene expression profiles of tumor samples individually and then mathematically analyzing the pooled data to derive gene clusters (4). This latter design carries the advantage of identifying the variation of the expression of each gene from one tumor to another; this is a feature that is lost with sample pooling.
The goal of this study was to determine the feasibility of detecting tumor markers and markers of progression in human colon cancer by using gene expression profiling in combination with a novel analysis algorithm, to assess pooled tumor samples. At present, few tumor markers have been identified that have demonstrated clinical utility. Using 70 human normal and tumor tissue samples of various grouped stages, we examined the capacity of Affymetrix oligonucleotide arrays containing both 6800 and 12 000 elements to uncover tumor markers with potential clinical relevance.
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SUBJECTS AND METHODS |
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Bulk tumors obtained from the tissue procurement facility of the H. Lee Moffitt Cancer Center were histologically confirmed, grossly dissected, and snap frozen in liquid nitrogen within 20 minutes of surgical resection. Tumors for this project were grouped according to stage but selected at random from a large list of banked, frozen tumors.
Tumors that were used to detect candidate tumor markers included 10 modified Astler Collier (AC) stage C microdissected tumors and their paired normal mucosa. An additional 12 AC stage C tumors and their normal mucosal pairs were used to demonstrate that genes identified by sample pooling analyses were also differentially expressed in an independent validation set of tumors.
Sixty tumors of different clinical stage, in addition to 10 normal mucosal specimens, were used for all microarray experiments designed to elucidate tumor progression markers (adenomas without evidence of cancer [n = 10], modified AC stage B1 cancers [partial thickness invasion, lymph node negative; n = 10], AC stage C2 cancers [full thickness invasion, lymph node positive; n = 10], AC stage D cancers [primary tumor metastatic to distant organs; n = 10], and resected liver-metastatic foci [n = 20]). RNA derived from 43 additional tumors was used to perform northern blot analyses using the osteopontin probe. Microdissected (when noted) samples represented bulk frozen samples that were examined using a frozen section technique to identify and eliminate any regions containing normal adjacent tissue, intervening stroma, or necrotic regions. Microdissection was performed with the use of a scalpel tip by a single pathologist (D. Coppola). Although it is not possible to eliminate all intervening stroma and cellular infiltrates without performing laser capture microdissection, these tissues were judged to contain more than 90% tumor cells. For each sample, total RNA was prepared by TRIZOL (Invitrogen, Carlsbad, CA) extraction, quantified, and validated for integrity by gel electrophoresis. Samples were either pooled in equimolar amounts (510 samples/pool) or used independently for GeneChipTM (Affymetrix, Santa Clara, CA) hybridization. All tumors were obtained without personal identifiers under an approved Institutional Review Board protocol (No. 5937).
Oligonucleotide Arrays
All of the experiments described in this study were performed with either the first edition Human HuFl 6800 (6800 elements) or the second edition HuU95A (12 000 elements) GeneChipTM. The HuFL6800 chip contains probes corresponding to 5000 named genes (based on National Center for Biotechnology Information [NCBI] UniGene build 139, as provided by Affymetrix), whereas the HuU95A contains more than 12 000 probe sets corresponding to 8900 named genes (UniGene Build 139). The reduced number of named genes allows for multiply redundant probe sets for individual genes such as osteopontin. Each GeneChipTM was hybridized using targets synthesized from 10 µg of starting material (total RNA); pooled samples were made by combining either 1 µg (for 10-sample pools) or 2 µg (for five-sample pools) of total RNA from each component tissue sample. Target synthesis, hybridization, and posthybridization staining were performed using standard protocols as recommended by the manufacturer (Affymetrix).
Standard Array Analysis
Stained chips were scanned on a GeneArrayTM Scanner (Affymetrix), and data files were processed as summarized below by GeneChipTM software (Affymetrix). Each gene on the chip is assayed by measuring fluorescence intensity resulting from hybridization to 1620 oligonucleotide probe pairs; each pair consists of a perfect match (PM) complement and a one base mismatched (MM) variant to a gene-specific sequence. GeneChipTM software generates a mean intensity for each gene by first calculating the difference between each of the PM and MM probe pairs and then averaging these differences across the gene-specific probe set, yielding an average intensity value for each gene. One problem with this algorithm is that it can generate negative expression measures if there is substantial hybridization to one or more of the MM probes when the MM complement is greater than that of the PM.
A New Algorithm for Analysis
As detailed in the Results section below, the presence of negative expression values prompted us to construct a novel algorithm for assessing expression. Under the assumption that greater hybridization to a MM probe than to its PM partner indicates that the particular probe pair is poorly selected, we eliminated from further consideration any probe pair with a negative difference. In addition, to ensure that the estimate of expression was not based on the biased representation of a few probe pairs, we only included genes that had 16 or more positive probe pairs. Although fewer probe pairs can be used, we chose 16 as a conservative limit. This approach assures positive intensity measures for all genes for which we can accurately assess expression, and it eliminates those genes for which the chip does not provide reliable data. Intensity values were averaged across all good probe sets to provide a single measure for each gene.
Identification of Candidate Tumor Markers and Progression Genes
Gene expression as a function of tumor stage was measured using average intensities generated by the standard and modified methods described above. Genes with a negative average intensity value were excluded from the GeneChipTM results obtained by the standard method. Comparisons of gene expression in tumors relative to normal mucosa were calculated as ratios (fold change) of the mean intensity values for each gene. A ratio of 1 is used to represent no difference in gene expression between a tumor and normal mucosa.
Candidate tumor markers were selected by querying the gene expression database, derived from AC stage C2 tumors, for genes whose expression in tumor tissues versus normal mucosa was increased or decreased more than twofold. Only genes that met this criterion for all tumor stages after applying the modified algorithm described above were selected. These thresholds are determined by the limits of the current technology and represent reproducible and reliable cutoff points for selecting genes whose behavior can be validated by other techniques (e.g., northern blot analysis).
Candidate tumor progression markers were selected by the identification of genes that exhibited an overall pattern of progressively increasing expression concordant with advancing tumor stage (such as normal mucosa < adenoma < AC stage B1 < AC stage C2 < AC stage D < liver metastases), recognizing a twofold difference as a minimally acceptable biologically significant change in expression. A similar approach identified those genes that decreased progressively with advancing tumor stage. The expression of all genes on the HuU95A chip is available on a public Web site (http://cancer.tigr.org/data/pooling.shtml).
Validation by Northern Blot Analyses
Potential tumor markers and progression genes were validated by northern blot analysis using tumors distinct from those used in microarray analysis. For northern blot analysis, 10 µg of total RNA was extracted, submitted to gel electrophoresis, blotted, and then hybridized with radiolabeled, gene-specific probes as described. Ethidium bromide was used to stain the gels and to control for the equivalent loading of lanes; alternatively, northern blots were reassessed with radiolabeled GAPDH probes. Expression levels were quantified by densitometry.
Immunohistochemical Analysis
Using stage-oriented human colon cancer paraffin-embedded tissue microarrays (catalog Nos. CR200 and CR50; Clinomics Laboratories, Inc., Frederick, MD) and tissues from the H. Lee Moffitt Cancer Center, we stained 303 tissue samples (185 human cancers, 67 adenomas, and 51 normal mucosal specimens) with hematoxylin and eosin (H & E) (Richard-Allan Scientific, Kalamazoo, MI) using standard histologic techniques. Tissue sections were also subjected to immunostaining for osteopontin with the murine anti-human osteopontin monoclonal antibody mAb53 (5), using the avidin-biotin peroxidase complex technique (Vectastain Elite ABC Kit; Vector Laboratories, Burlingame, CA), following the manufacturer's instructions. We used antibody at a 1 : 750 dilution, after microwave antigen retrieval (four cycles of 5 minutes each on high in 0.1M citrate buffer). The microwave used is an 1100 W Emerson Model AT 736 (Emerson & Cuming Microwave Products, Randolph, MA). The stain was semiquantitatively examined by two independent pathologists using a scale from 0 to III (none, weak, moderate, strong). Statistical correlations were assessed using Spearman's .
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RESULTS |
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To test whether analysis of pooled samples could accurately reflect gene expression in individual tumor samples and to verify that pooled samples provided reproducible data, we surveyed expression in RNA derived from five individual, microdissected human colon tumor samples of the same stage (AC stage C2) with the use of the HuFL6800 GeneChipTM. In addition, we measured gene expression for two sets of pooled RNA samples (Pools 1 and 2). One set was derived from the five individual tumors (Pool 1), and a second set was constructed from five additional, independent tumors (Pool 2).
Measured RNA expression levels for each of the tumors were compared gene by gene with those measured for Pool 1 and Pool 2, as well as with a "calculated pool" (Pool C) constructed by averaging gene expression levels across each of the five tumors. In addition, both individual tumor samples and pools were compared with measured expression levels from normal mucosa. The results of these comparisons, shown as scatter plots, are summarized in Fig. 1. In general, the measured expression of genes in the five individual tumor samples correlated extremely well with both those for the calculated pool (Pool C) (R2
0.9371) and for the corresponding physical pool (Pool 1) (R2
0.8867). The correlation between the individual samples and the independently derived pool (Pool 2) was nearly as good, and both physical pools correlated extremely well (R2 = 0.9309). Furthermore, all tumor samples correlated much better with each other than with measured expression in normal mucosa. This suggested that pooling samples can, in a single assay, provide results that summarize the expression of individual tumor samples, and that independently derived pools can provide nearly identical measures of gene expression.
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We postulated that we might be able to improve the predictive value of the pool for the most differentially expressed genes by modifying the standard analysis algorithm provided by Affymetrix, which uses all of the probe sets on the array without any assessment of their relative quality. An evaluation of the data across the seven chips used in our pooling analysis revealed that a large number of probe pairs yielded negative values; i.e., the MM probes exhibited greater hybridization intensities than their PM counterparts, suggesting that these probe pairs do not accurately reflect gene expression.
To address this problem, we developed a new algorithm to identify and omit probe pairs that had negative (PM MM) intensities in the normal mucosal samples. To improve the accuracy of this approach, we eliminated genes from our consideration that had fewer than 16 valid oligonucleotide probe pairs. Although fewer probe pairs can be used, we chose 16 as a conservative limit.
Validation of New Algorithm
Application of the new algorithm to the data processed by the standard algorithm generated a reduced set of informative genes (339), for which the pooled sample now predicted the gene expression of the majority of individual tumors (in at least three of five tumors) for 67% (228) of the genes (Table 1). This represents a substantial improvement in performance of the pool in predicting the gene expression of individual samples by the new algorithm. As might be expected, 77 (100%) of the 77 genes that were differentially expressed in all five of the individual tumors were correctly predicted by the pooled sample. Moreover, 75 (91%) of 82 genes that were predicted to be differentially expressed in four of five individual tumors and 78 (63%) of 123 genes predicted in three of five tumors were also correctly predicted by the pool. Of the 52 genes that were differentially expressed in the majority of individual samples and not correctly predicted by the pool, the mean fold change (increase or decrease) across the individual samples was less than twofold in 51 of 52 cases. This suggests that pooling is less effective for genes whose expression is only marginally changed in a subset of the five tumors.
Using the new algorithm, standard statistical analysis using a one-sample, two-sided t test to derive differentially expressed genes from the five individual samples confirms that 77 (100%) of 77 of these genes were also identified in the pool ( coefficient = 0.366) (Table 1
). This represents a considerable improvement over the standard analysis algorithm, in which 366 (18%) of 2058 genes identified as differentially expressed in the individual samples agreed with the pool (
coefficient = 0.056).
Validation of Sample Pooling
To rigorously validate the results derived from the analysis of pooled samples (training set), we performed northern blot analysis to assess the expression of individual genes in a set of up to 12 unrelated AC stage C2 tumors (validation set) not used to derive the pools. We randomly selected 11 of the top 20 genes from this revised data set of 339 candidate tumor markers for further evaluation in this test. These genes appeared to be overexpressed or underexpressed by more than twofold in the tumor pool when compared with the normal mucosal pool and were also present in at least three of the five tumor samples. Of these selected gene probes, nine of 11 identified gene expression changes concordant with those predicted by the pool in the majority of individual samples tested (Fig. 2). Importantly, our data reveal that sample pooling does not exclude the ability to detect genes that show decreased expression. For example, in addition to the experimentally validated genes tested above, we noted that multiple genes, identified in our analysis of a small number of pooled samples with the HuFl 6800 GeneChipTM, were also reported to show decreased expression in an analysis of 18 individual colon adenocarcinomas (6) (e.g., guanylin [M97496], DRA [L02785], and tetranectin [X64559]). Collectively, these data demonstrate that sample pooling accurately identifies genes with altered expression in the majority of tumors, allowing identification of potential markers with clinical utility in defining molecular fingerprints of tumors.
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Our primary goal in this study was the identification of tumor progression markers that might ultimately be used individually or collectively to predict clinical outcome. To that end, we performed two sets of experiments using the pooling strategy with the modified analysis algorithm outlined above. First, five sets (n = 10 tissues/set) of bulk (not microdissected) human colon normal and neoplastic tissues were selected at random from the H. Lee Moffitt Cancer Center Tumor Bank; these tissues represented key stages in tumor progression: normal mucosa, adenomas, AC stage C2 cancers (C2), and two groups of liver metastases (LM1 and LM2, respectively). Total RNA from each set of tissues was extracted and pooled (10 tumors/pool) to derive five pooled RNA samples that were evaluated using the HuFL6800 GeneChipTM. Gene expression data for each pooled tissue set relative to normal mucosal samples were calculated, based on the modified algorithm outlined above, to identify genes whose expression increased or decreased sequentially with tumor stage. This analysis identified osteopontin as a leading candidate marker of tumor progression with (LM1 and LM2) > AC stage C2 tumors > adenomas > normal mucosa (Fig. 3, A).
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Using this approach, we were able to identify 107 genes that displayed both marked tumor progression and a significant differential expression relative to normal mucosacharacteristics favoring a clinically useful tumor marker. Hierarchical clustering analysis (average linkage using a Euclidean distance metric) (7) of these 107 genes shows that multiple copies of the same gene cluster together (two probe sets recognizing osteopontin top the list), as do tumors of related stage, and that invasive tumors cluster away from adenomas (Fig. 4).
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DISCUSSION |
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Microarray analyses are well suited for identifying tumor markers. There is promise that these analyses may be capable of identifying molecular fingerprints that predict outcome, independent of current staging systems. However, because our primary goal was to identify individual tumor markers with potential clinical utility, we chose to discover markers that were correlated with AC stage, the current gold standard for determining colorectal cancer prognosis. Detection of markers reproducing current staging systems, although valuable individually, might also prove integral to molecular signatures derived independently of stage.
Although a detailed analysis of numerous individual tumor specimens is generally ideal because it permits the assessment of gene expression variability from sample to sample, our results strongly suggest that sample pooling is an effective alternative strategy. This is particularly true when the goal is to rapidly identify tumor markers that are expressed by the majority of tumors in a population. The inherent benefits of sample pooling are multiple. By pooling tissue specimens, the RNA requirement per tumor is proportionately reduced, making sample pooling attractive when tissue banks can provide only limited quantities of microdissected sample per patient. The risk of a single specimen contributing bias to the pool is also proportionately reduced with increasing sample size. And finally, because the number of microarray chips necessary for the study is reduced, the computational requirements for analysis are reduced as well.
Although standard Affymetrix algorithms demonstrated that pooled data correlated highly with data derived from individual tumors, these correlations were artificially elevated by the inclusion of many genes whose expression was low (approximately onefold differences) or not substantially different from the normal mucosal specimens. Unfortunately, these genes lacking differential expression are not prime candidates for clinically useful tumor markers. In contrast, genes that are differentially expressed are of great interest. Standard Affymetrix algorithms were relatively ineffective in identifying genes in the pool that predicted similar expression in individual tumors. Because standard Affymetrix algorithms appeared to incorporate all of the microarray chip data, including probe sets that demonstrate greater hybridization for the MM probes than for the PM probes, we postulated that a modified algorithm eliminating negative values for (PM MM) might improve our capacity to identify tumor markers in pooled samples. Because the probes on these chips consist of relatively short oligonucleotides, a significant level of hybridization might occur with closely related sequences that could contribute substantial intensity and, thereby, interfere with measurements for any gene-specific probe. Although other explanations cannot be excluded, we determined that elimination of these probe sets from calculations of gene expression provided an improved dataset with a greater capacity to predict the expression of individual tumors from a pool of the same tumors.
By applying a pooling strategy, we were able to identify a large set (339) of candidate tumor markers. These genes displayed overexpression or underexpression by twofold or more relative to normal colonic mucosa in the majority of tumor samples tested. From the top 20 genes on the list, 11 different genes were selected at random and validated by northern blot analyses of independent tumors (not used to construct the sample pools). This list of genes includes genes associated with tumor invasion (matrilysin), adhesion (prepro-2[1] collagen), and possible tumor growth (human gene for melanoma growth-stimulatory activity).
Of further interest was the derived set of genes linked to tumor progression. These genes were derived from five new pooled sets of tumors from the same pathologic stage. We were able to identify 107 candidate tumor progression markers. These were analyzed using the University of California San Diego HAPI software (9) (http://array.ucsd.edu/hapi/), which links the gene identities to literature citations. Of these identities, 25 (23%) had previously been linked to the digestive system, with 49 (46%) being linked to neoplasms and 14 (13%) showing direct association with digestive disease neoplasia.
Among the progression genes, osteopontin was identified as the leading candidate, validating the results from our initial analysis that identified tumor markers. Osteopontin is a secreted, integrin-binding protein that has already been reported as a marker of tumor progression in breast (5,10), lung (11), and prostate cancer (12). The results presented here provide the first data to suggest that osteopontin is a strong marker of colon cancer progression. Our results indicated that as colon tumors progress from normal mucosa and the adenoma stage, where osteopontin expression is not easily detectable, to invasive cancers (some AC stage B, most AC stage C), to metastatic primary cancers (AC stage D), and to resected liver metastases, they acquire sequentially increased osteopontin expression. In particular, osteopontin induction was most notable in liver metastases where fold increases were as high as 10- to 20-fold over adenomas and normal mucosal samples. These mRNA results were further validated with immunohistochemical studies of 303 tumor specimens. Osteopontin has been shown to bind to cells via integrins (notably v
3,
v
1, and
v
3) as well as CD44 [for review, see (13)]. Although the biologic functions of osteopontin are not fully understood, it has been implicated in malignancy, immune function, and vascular remodeling, as well as in bone remodeling [for reviews, see (1318)]. Osteopontin was identified as a tumor-associated protein in transformed cells in culture (15) and has been shown to be present in some human tumor samples (19). In breast cancer, osteopontin has been shown to contribute functionally to the malignant behavior of the cells (20). Osteopontin has been noted to be present in some colon cancers (19), and a recent report has identified osteopontin overexpression using Affymetrix technology on individually analyzed bulk colon cancers (n = 18) when compared with normal tissues (6). Our study, however, is the first to demonstrate an association of increasing osteopontin expression with tumor progression in colon cancer, making it a candidate tumor marker with potential clinical utility.
Although this study addressed the feasibility of sample pooling, further studies are needed to determine the optimal number of samples needed to construct an informative pool. We have demonstrated that when five to 10 samples are used to construct a pool, informative data can be mined with regard to the identification of both tumor markers and progression markers. Although the identification of individual progression markers is one potential application of sample pooling, it can also be used to rapidly identify larger patterns of tumor markers that may be predictive of clinical outcome. Furthermore, these observations have implications for the design of many microarray-based experiments on both cell lines and tissues.
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NOTES |
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We thank David Boulware (Department of Biostatistics, H. Lee Moffitt Cancer Center) for his statistical evaluation of the data. We are in debt to Marek Wloch and Herman Hernandez (Tissue Procurement Core, H. Lee Moffitt Cancer Center) for their contributions to tissue processing described in this manuscript. We thank Shrikant Mane (Microarray Core, H. Lee Moffitt Cancer Center) for his assistance in processing the RNA samples. We are grateful to Clinomics Laboratories, Inc, Frederick, MD, for contributing human colon cancer tissue microarrays and an associated clinical database for assessment of stage-specific osteopontin protein expression.
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Manuscript received September 21, 2001; revised January 23, 2002; accepted February 5, 2002.
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