Diagnosis of Prostate Cancer Using Differentially Expressed Genes in Stroma

There are more than 1 million prostate biopsy procedures carried out in the United States every year (1). More than 60% of the results are negative (2–4). However even the best current methods, including transrectal ultrasound (TRUS) procedures, may miss up to 30% of clinically significant prostate cancers (5). Indeed, about 20% to 30% of patients who give negative results on initial biopsy are rebiopsied in about 3 to 12 months [∼190,000 patients owing to the presence of prostatic intraepithelial neoplasia (PIN), high-grade PIN (HGPIN), atypical small acinar proliferation (ASAP), or other grounds for clinical suspicion of the presence of tumor (2–4, 6, 7)]. Many repeat biopsies give the diagnosis of adenocarcinoma. For example, 16% to 23% of HGPIN and up to 59% of ASAP cases give the diagnosis of adenocarcinoma upon repeat biopsy (2–4, 8). Patients deferred to repeat biopsy, receive little treatment or guidance during the interim—a period when tumors may continue to progress. Therefore, there is a need for methods that resolve false-negative and equivocal cases.

Equivocal and negative biopsies are, by definition, deficient in diagnostic tumor but contain ample stroma. Moreover, stroma near tumor may contain changes in gene expression that are not found in nontumor samples, which could be the basis for a clinical test. Epithelial cells of prostate cancer infiltrate and propagate in a microenvironment consisting largely of myofibroblast cells as well as inflammatory cells and other supporting cells and structures. It has long been appreciated that this mesenchymal component is not passive but responds to signals from the tumor component and, in turn, alters tumor properties, some of which are essential for tumor growth and progression (9, 10). Indeed, studies of prostate cancer were among the first to demonstrate an important role of the stroma in cancer progression. Mouse model studies showed that survival and growth of immortalized nontumorigenic human prostate epithelial cells as renal subcapsular xenografts required stroma from tumor-bearing prostate (10). Numerous studies have subsequently demonstrated large numbers of gene expression changes at the RNA level specific to the tumor microenvironment of prostate cancer (e.g., refs. 11–18). Similarly, a variety of protein expression changes have been associated with the microenvironment of prostate cancer. For example, reactive stroma, which is believed to occur in a subset of aggressive tumors, has been shown to correlate with changes in a variety of proteins including FGF2 (fibroblast growth factor); connective tissue growth factor; vimentin; actin, alpha, skeletal muscle; collagen, type I, alpha; and tenascin; some of which have been attributed to epithelial-derived TGF-β (12, 19).

Here, we investigate whether RNA expression changes may be identified that are sufficiently reliable to distinguish normal stroma from stroma near tumor. We have previously developed linear regression method for the identification of cell-type–specific expression of RNA from array data of prostate tumor samples (20). The method was validated using immunohistochemistry and using quantitative PCR applied to laser capture microdissection samples of tumor, stroma, and epithelia of benign prostate hyperplasia for 28 genes involving more than 400 measurements (20). Here, we have extended this approach to identify differentially expressed genes between normal volunteer prostate biopsy samples versus stroma from near tumors. More than a thousand gene expression changes were observed. A subset of stroma-specific genes was used to derive a classifier of 114 genes which accurately identifies tumor or nontumor status of a large number of independent testcases. The classifier may be useful in the diagnosis of stroma-rich biopsies from patients with equivocal pathology.

Data sets 1 and 2 (Table 1) are based on postprostatectomy frozen tissue samples obtained by informed consent using Institutional Review Board (IRB)-approved and HIPPA-compliant protocols. All tissues, except where noted, were collected at surgery and escorted to pathology for expedited review, dissection, and snap freezing in liquid nitrogen. In addition, data set 1 contains 27 prostate biopsy specimens obtained as fresh snap-frozen biopsy cores from 18 normal prostates. These samples were obtained from the control untreated subjects of a clinical trial to evaluate the role of difluoromethylornithine (DFMO) to decrease the prostate size of normal men. Eighteen of these were collected before the treatment period and 9 were collected after the treatment period had ended (21). Finally, 13 samples of normal prostate tissue were obtained from the rapid autopsy program of the Sun Health Research Institute (Sun City, AZ) and were frozen within 6 hours of demise.

RNA for expression analysis was prepared directly from frozen tissue following dissection of OCT (optimum cutting temperature compound) blocks with the aid of a cryostat. For expression analysis, 50 μg (10 μg for biopsy tissue) of total RNA samples were processed for hybridization to Affymetrix GeneChips. Expression analysis for all samples for data set 1 were assessed using the U133 Plus 2.0 platform, whereas for data set 2, the U133A platform was used. The data have been deposited in the Gene Expression Omnibus (GEO) database with accession numbers GSE17951 (data set 1) and GSE8218 (data set 2). For data sets 1 and 2, the distributions for the 4 principal cell types (tumor epithelial cells, stroma cells, epithelial cells of benign prostatic hyperplasia, and epithelial cells of dilated cystic glands) were estimated by 3 (data set 1) or 4 pathologists (data set 2), whose estimates were averaged as described (20).

Data sets 3 and 4 were independently developed and used as test sets (Table 1). Data set 3 consists of a series of 79 samples (22, 23), whereas data set 4 (24) is composed of 57 samples from 44 patients including 13 samples of stroma near tumor and 44 tumor-bearing samples. Expression analysis of the data sets was determined using the U133A platform.

Seventy-one of the tumor-bearing samples of data set 2 were manually microdissected to obtain tumor-adjacent stroma which was used for validation of the diagnostic classifier. For manual microdissection, the tumor-bearing tissue was embedded in an OCT block then mounted in a cryostat. Frozen sections were stained using hematoxylin and eosin (H&E) to visualize the location of the tumor. A border between tumor and adjacent stroma was marked on the glass slide using a Pilot Ultrafine Point Pen which was used as a guide to locate the border on the OCT block surface. Then the OCT-embedded block was etched with a single straight cut with a scalpel (∼1-mm deep) to divide the embedded tissue into a tumor zone and tumor-adjacent stroma. Subsequent cryosections produced 2 halves at the site of the etched cut and were separately used for H&E staining and examined to confirm their composition. Multiple subsequent frozen sections of the tumor-adjacent stroma half were then pooled and used for RNA preparation and microarray hybridization. A final frozen section was used for H&E staining and examined to confirm that the tumor-adjacent stroma remained free of tumor cells.

The U133 Plus 2.0 platform used for data set 1 has about 55,000 probe sets whereas the U133A used for data sets 2, 3, and 4, contains 22,000 probe sets. Normalization was carried out across multiple data sets using the ∼22,000 probe sets in common to all data sets. First, data set 1 was quantile-normalized using the function “normalizeQuantiles” of LIMMA (Linear Models for Microarray Data) routine (25). Data sets 2 to 4 were then quantile-normalized by referencing normalized data set 1 using a modified function “REFnormalizeQuantiles” which was coded by Z.J. and is available at the SPECS Web site (26).

The LIMMA package from Bioconductor was used to detect differentially expressed genes.

Prediction Analysis of Microarray (PAM; ref. 27), implemented in R, was used to develop an expression-based classifier from the training sets and then applied to the test sets without further change.

A multiple linear regression (MLR) model was used to fit gene expression data and known percent cell-type composition for 4 cell types to estimate expression coefficients for each cell component (see Supplement for details). Percent cell-type distributions were estimated by 3 (data set 1) or 4 (data set 2) pathologists and exhibited an overall agreement of 4.3% SD for the 4 estimated cell types. The resulting significantly differentially expressed genes for the comparison of normal prostate biopsies to tumor-bearing prostate tissue were used for development of the diagnostic classifier.

We hypothesized that stroma within and directly adjacent to prostate cancer epithelial cells exhibits significant RNA expression changes compared with normal prostate stroma. To generate candidate biomarkers, we developed a 3-step strategy. First, we identified genes that are differentially expressed between tumor-adjacent stroma and normal stroma. Second, these differences were filtered by removing the age-related genes and removing the genes that are also expressed in tumor cells to create a stroma-specific set of differentially expressed genes. Finally, owing to the limited number of normal biopsies, we repeated steps 1 and 2 using a permutation procedure which greatly enhanced the extraction of information in the normal biopsies.

In step 1, Affymetrix gene expression data were acquired from normal frozen biopsies from each of the 15 subjects that were judged to be free of cancer by histologic examination of the 6 cores of the volunteer biopsies (21). Data from 13 of these samples (with 2 held in reserve as explained later) were compared with the gene expression data for 13 tumor-bearing patient cases from data set 1 selected with tumor cell content (T) greater than 0% but less than 10% (the average stroma content is ∼80%). These criteria ensured that the majority of stroma tissues included from the cancer-positive patients was close to tumor, whereas T less than 10% ensured that the impact from tumor cells is minimal to allow capture of altered expression signals from stroma cells rather than tumor cells. Using a moderated t test implemented in the LIMMA package of R (25), this comparison yielded 3,888 significant expression changes between these 2 groups with a value of P < 0.05. We used a relatively relaxed P value cutoff for the first step of feature selection to allow more genes to enter subsequent screening steps. The 3,888 probe sets were composed of a nearly equal number of up- and downregulated genes.

There was a substantial difference in age between the normal stroma group (average age = 51.9 years) and the near-tumor stroma group (average age = 60.6 years). In step 2, we compared the overall gene expression of the 13 normal stroma samples used for training versus 13 normal prostate specimens obtained by rapid autopsy (see “Materials and Methods”) with an average age of 82 years. Prostate glands from the rapid autopsy series with an average age of 84 years exhibited a markedly increased heterogeneity of gland shapes with stroma containing increased fibroblast and myofibroblast-like cells. The comparison revealed 8,898 significant expression changes (P < 0.05). Of these probe sets, 1,678 were also detected in the comparison of normal stroma samples to stroma near tumor. After eliminating all of these potential aging-related genes, the remaining 2,210 probe sets consisted of nearly equal numbers of up- and downregulated genes.

It remained likely that some differential expression in this comparison included expression changes specific to the residual tumor cells or epithelium cells in some samples, rather than changes between more than one type of stromal cell. To reduce the possibility that epithelial cell–derived expression changes might influence subsequent results, we removed genes that appeared to be expressed in tumor at 10% or more of the expression in stroma. However, even “pure” tumor samples are contaminated with stroma thereby risking the elimination of genes expressed only in stroma. So, identification of genes expressed in tumor was achieved using MLR analysis (described in “Materials and Methods” and Supplement). The percent cell composition of 108 samples from 87 patients in data set 1 intentionally encompassing a wide range of tissue percentages was determined by a panel of 3 pathologists (20). The distribution is shown in Figure 1A. Model diagnostics showed that the fitted model for genes significantly expressed in tumor or stroma accounted for more than 70% of the total variation (i.e., the variation of error was less than 30% of the total variation), indicating a plausible modeling scheme.

Of the 2,210 probe sets, derived above, we obtained 160 probe sets that were predominantly expressed in stroma cells and also show differential expression between near-tumor stroma and normal stroma. The average expression of these 160 probe sets was estimated to be more than 2-fold greater than the average of all genes expressed in stroma, which is a consequence for the filtering steps for robustness, and also favors good sensitivity.

Finally in step 3, a permutation analysis was performed. The above procedure for the generation of differentially expressed genes between 13 of the 15 normal stroma biopsies and the 13 biopsies of stroma near tumor was repeated using a different selection of 13 biopsy samples from 15, until all 105 possible combinations of 13 normal biopsy samples drawn from 15 samples (⁠|$C_{15}^{13} = 105$|⁠, where |$C_n^m$| is the number of combinations of m elements chosen from a total of n elements) was complete. After filtering for genes associated with aging (discussed earlier), a total of 339 probe sets that were differentially expressed between stroma near tumor and normal stroma were generated by the 105-fold gene selection procedure (the frequency of selection is summarized in Supplementary Fig. S1). Thus, the permutation increased the basis set by 339/160 or more than 2-fold. One hundred forty-six probe sets with at least 50 occurrences in the 105-fold permutation were selected for classifier construction (listed in Table 3)

PAM (28) was used to build a diagnostic classifier. The training set (Table 2, line 1) included all the 15 normal biopsies and the initial 13 samples of stroma near tumor. Of the 146 PAM input probe sets, 131 probe sets–corresponding to 114 genes—were retained following the 10-fold cross-validation procedure of PAM (28) leading to a prediction accuracy of 96% (Table 2). Supplementary Figure S2 presents a “heatmap” of the relative expression of the 131 probe sets among all training samples. The separation of normal and near-tumor stroma samples of the training set by the classifier is illustrated by the 2 distinct populations shown in Figure 2.

The 131 probe set classifier was then tested on 243 samples that had not been used for training, and that all contained tumor, though usually very little tumor (Table 2, lines 2–5). Almost all the 243 samples were recognized as being from cancer patients with high average accuracy of about 99% (see Supplementary Table S1 for derived operating characteristics). Only 2 cases were misclassified. In Figure 1A, the 2 misclassified test are marked with asterisks. Although these samples are ostensibly given tumor percentages of 20% and 25% by pathologists, they are predicted to possibly contain little or no tumor using the CellPred program which estimates the tissue components using an in silico multiple-variate linear regression model (29). It is possible that these 2 exceptions were archived incorrectly and are not from patients with cancer or are from a very distant location relative to the tumor.

We examined whether the PAM classification results correlated with cell composition (Fig. 1). For the test cases of data sets 1 and 2, these values are known from the pathologists estimates, whereas for data sets 3 and 4 (Fig. 1C and D, respectively), these tumor cell contents were estimated using the CellPRed program (29). Examining the tumor cell percentages in all the samples in Figure 1, it is clear that the PAM classification is successful on independent test samples with a broad range of tumor epithelial cells including samples with just a few percent of epithelial cells. These observations argue that the classifier is accurate in the categorization of prostate cancer cases independent of the presence or amount of the tumor epithelial component.

The classifier was then tested using specimens composed of normal prostate stroma and epithelium. Twelve biopsies from the DFMO study, all of them different from the 15 samples used earlier for training, were separated into 2 groups. In group 1, there were 7 second biopsies from the same participants whose first biopsy samples were included in the training set, taken 12 months later. These were accurately (100%) identified as nontumor (Table 2, line 6). In group 2, there were 5 biopsy samples not from subjects previously used for training. Two of these 5 biopsy samples were categorized as being from cancer patients (Table 2, line 7). When the histories for these volunteers were investigated, it was found that both donors had consistently exhibited elevated prostate-specific antigen (PSA) levels of 6.1 and 8 ng/mL (normal values < 3 ng/mL), respectively, although no tumor was observed in either of 2 sets of sextant biopsies obtained from these volunteers. The volunteers also had a history of prostate cancer in the family. All other donors of the normal biopsy volunteers exhibited normal PSA values. The IRB-approved protocol precluded following up further to establish that these patients had cancer that had been missed in the biopsies.

The classifier was then tested on 13 specimens obtained by rapid autopsy of individuals dying of unrelated causes (Table 2, line 8). Twelve of 13 of these samples, 92% accuracy, were classified as nontumor. Histologic examination of all embedded tissue of the one “misclassified” case revealed multiple foci of small “latent” tumors.

In summary, 25 nominally normal samples were classified as being from donors without prostate cancer or were classified in accordance with abnormal features that were subsequently uncovered. These results provide further support for the ability of the classifier to discriminate among normal and abnormal prostate tissue in the absence of histologically recognizable tumor cells in the samples studied.

We sought to validate the classifier by developing histologic confirmed samples of stroma adjacent to tumor. An etching procedure was used to prepare 71 samples of tumor-adjacent stroma from patient tissues of data set 2, and 13 samples from data set 4. An additional 12 samples from data set 1 were obtained from OCT blocks entirely by manual microdissection, that is, without etching but leaving a margin of tissue between tumor and stroma, followed by histologically examining frozen section analysis of the OCT surface and bottom side of the pieces, to ensure the absence of tumor. These 12 manually excised pieces are termed “close stroma” (∼3 mm). The expression values for all 96 samples were used to test the 131 probe set classifier using the PAM procedure. The accuracy in classifying that the samples were from patients with tumor was 97% for the 71 adjacent stroma samples from data set 2, 100% for 13 adjacent stroma samples from data set 4, and 75% for the 12 “close” stroma samples from data set 1 (Table 2, lines 9–11). This is an overall accuracy of 95% for the 96 independent samples.

Five of the 96 samples appeared “misclassified” as normal. Three of these misclassifications were among the 12 “close” stroma samples in data set 1. These 12 samples were obtained by manual excision and therefore some of the samples may not have been as near to tumor as the samples obtained by the etching method. Therefore, we examined how far the expression changes characteristic of tumor stroma may extend away from the tumor; we obtained 28 samples greater than 15 mm from any known tumor and generally from the contralateral lobe (Table 2, line 12). Only 10 of the 28 samples (36%) were categorized as tumor-associated stroma. Using the Fisher's exact test, the distribution for the 28 “remote” samples was significantly different from the 12 stroma samples from “close” to tumor of the same patient tissues (P = 0.038). This result, as well as the observation of a gradient of classification frequency values from 98%, 75%, and 36% for samples adjacent, close, and greater than 15 mm from tumor, suggests that the expression changes recognized by the classifier decline with increasing distance of stroma from tumor. Such observation bears on the likely mechanism for the production of differential gene expression in tumor-adjacent stroma which is generally believed to involve the influence of “paracrine” factors emanating from tumor foci (10, 30, 31).

We found that the normal samples and rapid autopsy samples can be easily distinguished from samples containing tumor using many of the individual genes (e.g., heatmap, Supplementary Fig. S3). However, the differences that allow near stroma to be distinguished from control stroma are more subtle and vary between patients, requiring a classifier based on a number of genes.

Further validation included a comparison with 100 random classifiers generated by arbitrarily sampling 131 probe sets for each classifier. The results (Supplementary Table S1 and Supplement) showed that these random classifiers had no diagnostic value, further indicating that the results obtained with the 131 probe set classifier cannot be attributed to chance.

Finally, we sought to validate that representative genes were in fact preferentially expressed in stroma by PCR. In addition, to test the translational relevance, we utilized independent cases from a formalin-fixed and paraffin-embedded (FFPE) clinical collection. Gene expression was assessed by a modified quantitative PCR procedure (see “Materials and Methods”). In a limited survey, 4 genes were found to have reliably preserved short amplicons. Blocks of 63 tumor cases were examined and tumor and stroma regions in H&E sections were demarcated by a pathologist (D.A.M.). Punches were removed from adjacent unstained sections and used for PCR for 63 tumor portions and 38 stroma portions. For all 4 genes, highly significant preferential expression in stroma was observed (Supplementary Table S4). These results for independent cases and by an independent method further support the preferential expression of these genes in tumor stroma and further argue that the classifier may be adapted to clinical biopsies preserved in FFPE, the standard method of archiving patient biopsies.

Finally, we also reviewed 2 recent studies describing expression analysis results for subclasses of the stroma of prostate cancer (16, 17), which showed consistent findings (see Supplement). In particular, the 339 probe sets (Affymetrix arrays) we identified map to 557 genes on Agilent arrays which have been used for deriving profiles for “reactive” stroma, a special case of adjacent stroma associated with poor outcome disease (17). A total of 31 genes or probe sets appeared to be concordant (in terms of gene identity and the direction of expression alteration) between the 339 probe sets (Affymetrix arrays) we identified in this study and the 557 mapped genes (Agilent arrays) in the “reactive” stroma study (17) with P value of 0.0001 (Supplementary Table S2). The formation of reactive stroma has been associated with poor prognosis (32). Thus, it is possible that some diagnostic markers in stroma could also be of prognostic interest.

We compared the expression profiles of 15 normal biopsy samples and 13 tumor-adjacent stroma samples from prostatectomies using a permutation strategy to enhance detection of significant differences. About 3,800 significant gene expression changes were observed, which were then filtered to exclude genes known to be expressed at similar levels in epithelial tumor cells and to remove genes that change with age. The top-ranked 146 probe sets remaining after applying these filters were used for the 10-fold cross-validation procedure of PAM using the same 28 samples used for the initial training. The PAM procedure led to a 131 probe set classifier, which had a training accuracy of 96%. We then tested the classifier on a number of independent expression microarray data sets of tumor-bearing tissue including data from 110 samples generated by us (Table 2, data sets 1 and 2) and data from 123 samples generated elsewhere (Table 2, data sets 3 and 4). These samples were classified as being from cancer patients with an overall accuracy of 98%, a value compares favorably with the diagnostic accuracy of PSA-based methods of about 70% (33). Only 2 samples recorded as containing tumor cells were misclassified. Upon further investigation of these 2 samples using CellPred, a method to determine the tumor percentage of samples based solely on their expression profile (29), these samples were predicted to have little or no tumor, although they had been recorded as having more than 20% tumor, indicating their assignment as tumor may have been a bookkeeping error. Similarly, we generated data from 25 samples of normal prostate, which were recognized as nontumor with an accuracy of 92%. Only 3 samples were “misclassified”. Two of these samples were biopsies donated by men with abnormally high PSA levels and a family history of prostate cancer, although no tumor was recognized in any of the sextant biopsies taken at the beginning and end of the study period for which these volunteers were controls. In addition, one sample derived from the rapid autopsy donors was potentially “misclassified” as cancerous. Examination of multiple blocks of the glands taken from both lobes and all zones revealed tumor foci in the misclassified case. Thus, the “misclassifications” correlate well with the unusual clinical and pathologic features of the cases. In summary, the handful of misclassifications of tumor and normal each had evidence that they had been mislabeled before the test, potentially raising the actual sensitivity and specificity for classifying these samples to 100%.

Finally, for validation, we used 153 samples from data sets 1 and 4 to prepare “pure” stroma adjacent, close, and far (>15 mm) from known tumor foci. These data sets were able to detect the presence of tumor in the prostate with a decreasing accuracy of 98%, 75%, and 36%, respectively. The observation of a gradual reduction in the sensitivity of the classifier as the distance increases bears on the likely mechanism for the production of differential gene expression in tumor-adjacent stroma which is generally believed to involve the influence of “paracrine” factors emanating from tumor foci (10, 30, 31). Indeed, the tumor microenvironment is likely the source of factors that are required for tumor formation by the epithelial component (10). The amount of diffusible paracrine factors of this complex interaction mechanism likely declines with separation of target cells from the secreting cells. Indeed, a simple radial dilution model would predict a decline of effects of tumor-derived factors by at least the square of the distance of target stroma cells from a tumor focus. On the basis of this simple model, the decrease in the frequency of categorization stroma taken from more than 15 mm from a known tumor focus to 36% suggests a 50% recognition distance of about 13 mm in fresh frozen tissue. In view of the modest average fold-change of the 131 probe sets of the classifier (Table 3), the distance at which “presence of tumor” is recognized suggests a surprisingly large range of “influence” of tumor more than steady-state gene expression changes in nearby stroma. Systematic studies of differential expression as a function of known distances will be required to confirm and refine this inference.

The classifier developed here used highly selective methods to enrich for mesodermal and ectodermal derivatives compared with endoderm/epithelial derivatives. Computer-assisted gene enrichment analysis classification using DAVID (34) identified a number of statistically significant gene enrichment categories. The 10 most significant categories are summarized in Supplementary Table S3. Numerous genes associated with expression in nerve and muscle are apparent, such as the nine genes of the actin cytoskeleton enrichment category, and in the disease mutation category including MPZ (Charcot-Maire-Tooth neuropathy 1b), optic atrophy 1, EPM2a (Lafora disease), BDGF, PLN (phospholamban), SGCA (dystophin-associated glycoprotein), and EFEMP. Biochemical associations include genes related to the TGF-β pathway (SMAD3, TGFIT, ID4, and CKDN1C/p57), the Wnt pathway (FZD7, SMAD3, DAAM1, and WISP2) and interacting genes (PCH12, PCDH7, and CDH19). These pathways are associated with tumor–stroma paracrine interactions (16, 17, 32, 35, 36). Given that reactive stroma has been associated with poor prognosis (32), it is possible that some of the 131 diagnostic markers identified in stroma could also be of prognostic interest. Nevertheless, we have not ruled that classifier developed here can distinguish other prostate conditions such as acute and chronic inflammation of the prostate and, therefore, stroma near these lesions may conceivably be misdiagnosed. Additional work with samples containing such lesions could identify genes that distinguish inflammation from cancer.

Our preclinical results suggest practical applications. Assessment of suspicious initial biopsies for expression of the classifier genes was identified here by microarray but could also potentially by any number of other biomarker methods, including those available for assessment of RNA, protein, or epigenetic markers in FFPE samples. Such quantitation may have use in defining “presence of tumor” based solely on the detection of changes in the microenvironment near a focus of tumor by quantitative criteria. Such a method would be applicable to cases with an initial negative biopsy results that would otherwise be referred for rebiopsy owing to the presence of ASAP or PIN. The determination of “presence of tumor” may strengthen guidance for neoadjuvant therapy or prevention therapy or an accelerated scheduling of rebiopsy. Finally, because stroma facilitates tumor growth (10), the expression changes that occur in stroma indicating the presence of tumor might be targets for therapeutic intervention that could leave normal stroma relatively unaffected.

M. McClelland and D. Mercola are cofounders and W. Lernhardt is CEO of Proveri Inc., which is engaged in translational development of aspects of the subject matter. The other authors disclosed no potential conflicts of interest.

Average cell distribution for samples of data set 1 deposited in GEO (GSE17951) were based in part on readings by David Tarin, MD, and Linda Wasserman, MD, PhD. The authors thank Dr. Eileen Adamson for her effort in proofreading the manuscript.

This research was supported by the NIH SPECS Consortium grant U01 CA1148102 and National Cancer Institute Early Detection Research Network (EDRN) Consortium grant U01 CA152738 and the UCI Faculty Career Development Award to Z. Jia. M. McClelland was supported in part by DOD 08-1-0720.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.