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Original Contribution |

Genomic Instability Within Tumor Stroma and Clinicopathological Characteristics of Sporadic Primary Invasive Breast Carcinoma FREE

Koichi Fukino, MD, PhD; Lei Shen, PhD; Attila Patocs, MD, PhD; George L. Mutter, MD; Charis Eng, MD, PhD
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Author Affiliations: Genomic Medicine Institute, Lerner Research Institute and Taussig Cancer Center, Cleveland Clinic Foundation, Cleveland, Ohio (Drs Fukino, Shen, Patocs, and Eng); Department of Neurosurgery, Nippon Medical School, Tokyo, Japan (Dr Fukino); Division of Biostatistics, The Ohio State University School of Public Health, Columbus (Dr Shen); Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass (Dr Mutter); and Department of Genetics and CASE Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio (Dr Eng).

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JAMA. 2007;297(19):2103-2111. doi:10.1001/jama.297.19.2103.
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Context That genomic alterations occur in both the epithelium and stroma of sporadic breast cancers has been documented by several groups. However, whether these microenvironmental alterations relate to clinicopathological features is unknown.

Objective To analyze the relationship between stromal genomic alterations and presenting clinicopathological features in sporadic breast cancer.

Design, Setting, and Participants A retrospective cross-sectional analysis of DNA from the epithelium and stroma of 220 primary sporadic invasive breast carcinomas for global genomic alterations manifested by loss of heterozygosity/allelic imbalance with 386 microsatellite markers. Data were collected from October 2003 through June 2006 from samples at Brigham and Women's Hospital, Boston, Mass.

Main Outcome Measures Association of the loss of heterozygosity/allelic imbalance, in both the stroma and epithelium, with presenting clinicopathological features, such as tumor grade, expression status of estrogen receptor and progesterone receptor, human epidermal growth factor receptor 2, clinical stage, and regional lymph node metastasis status. Associations were assessed in regression models and tested with Fisher exact test. Bonferroni correction was applied to P values, with significance set at P<.0022.

Results We found significant associations between loss of heterozygosity/allelic imbalance on chromosome 11 in the stroma and tumor grade (P = .0013), on chromosomes 1, 2, 5, 18, 20, and 22 in the stroma and regional lymph node metastasis (P = .0002-.0016), and on chromosome 14 in the epithelium and progesterone-receptor expression status (P = .002). Specific markers contributing to the loss of heterozygosity/allelic imbalance on chromosome 11 in the stroma associated with tumor grade were D11S1999 (P = .00055) and D11S1986 (P = .042). The loss of heterozygosity/allelic imbalance at various markers in the stroma was significantly associated with regional lymph node metastasis: ATA42G12 (chromosome 1, P = .00095), D5S1457 (P = .00095), D5S1501 (P = .0011), D5S816 (P = .0008), D18S858 (P = .0026), D20S103 (P = .0027), D20S851 (P = .0045), D22S683 (P = .00033), and D22S1045 (P = .0013).

Conclusions There were more correlations between clinicopathological features and the loss of heterozygosity/allelic imbalance in the stroma than in the epithelium, suggesting that stromal genomic alterations may help account for clinical diversity. Future research is necessary to validate these results and investigate their significance for prognosis and outcome.

Figures in this Article

A high degree of variability is observed in both biological be havior and clinical outcome in sporadic breast cancer, and this interpatient diversity in breast cancer biology and behavior may confound clinical management based on averages. Breast conserving surgery has become the standard of care for early stage breast cancer. In a recently published study, 2929 patients with early stage breast cancer were examined for the relative impact of the patient, the surgeon, hospital factors, or all 3 on surgical treatment outcome variation in patients with breast cancer. The study by Gort et al1 showed that 91.2% of the total variance was attributable to the patient level (there was large interpatient variability). These data suggested that interpatient variation accounts for the high degree of clinical variability.1

Indeed, the demand for personalized medicine illustrates the medical community's and public's recognition of interpatient variability. It has been recognized for decades that identical chemotherapeutic regimens for similar stage and grade in patients with, for example, breast cancer (or virtually any malignancy) respond differently.1,2 The complexities of genetic alterations in breast cancer may provide a primary basis for these consequent (secondary) clinicopathological features, an idea supported by prior positive correlations between certain breast cancer genotype and phenotype.3 For example, well-differentiated (grade 1) breast cancers show a low number of genetic alterations with highly recurrent losses of 16q, although poorly differentiated (grade 3) cancers show complex genetic changes containing DNA losses as well as DNA amplifications.3

However, many previous studies focused only on restricted regions of the genome-harboring known tumor-associated genes, such as TP53, or were limited to a small sample of patients. High throughput genome-wide scanning for genetic alterations can now be performed on larger series of clinical samples to discover genotypic-phenotypic correlations unbiased by prior work. Moreover, virtually all previous studies exploring these somatic genotype-phenotype correlations fail to separately analyze malignant epithelium and reactive host elements. Tumor microenvironment, incorporating both invasive epithelium and reactive host elements, dynamically determines cancer behavior.4,5 The contribution of cancer-associated stromal cell genetic changes to this interaction have been variously ascribed to epigenetic changes (DNA methylation6,7), or mutation, which has been shown by tumor-associated stroma from breast, colon, bladder, and ovarian cancers.813 Our previous work with breast cancer revealed that tumor-associated stroma may contain a higher density of genetic alterations than the malignant epithelium itself.12

In our study of sporadic breast carcinomas, we test the hypothesis that stromal cell genomic alterations significantly alter tumor behavior, as reflected in clinicopathological features at the time of diagnosis.

Breast Carcinoma Samples and Laser Capture Microdissection

A total of 220 unrelated samples of primary sporadic invasive carcinomas of the female breast annotated by basic clinicopathological features were obtained under the approval of the respective institutional review boards (Brigham and Women's Hospital, Boston, Mass; and Cleveland Clinic, Cleveland, Ohio). Samples from men with breast cancer, women with a personal history of ovarian cancer, and women with 1 or more first-degree relatives with breast or ovarian cancer were excluded. Widely metastatic disease (TX NX M1) was also an exclusion criterion. Anonymized sections from archived blocks were linked only to their respective clinicopathological features (Table 1). No personal identifiers or linking files were maintained.

Table Graphic Jump LocationTable 1. Demographic and Clinicopathological Characteristics of the Invasive Breast Carcinomas

Laser capture microdissection was performed using the Arcturus PixCell II microscope (Arcturus Engineering Inc, Mountain View, Calif) to isolate neoplastic epithelium and tumor stroma separately.9,11,12,18,19 Tumor-associated stromal fibroblasts were collected from locations proximate to epithelial tumor cells, being within 5 mm of an epithelial tumor nodule. Corresponding germline reference DNA for each case was procured from normal tissue, either within the breast, but at least 1 cm distant from malignant epithelial cells, or from histologically normal tissues outside the breast. The different origins of the corresponding germline DNA had no effect on the frequency or pattern of loss of heterozygosity/allelic imbalance (LOH/AI).

Whole-Genome Genotyping for LOH/AI

Genomic DNA was extracted as previously described,9,11 with incubation in proteinase K at 65°C for 2 days.12 The primer sets for multiplex polymerase chain reaction (PCR) defined 386 microsatellite markers in 72 multiplex panels (ResGen, Invitrogen, Carlsbad, Calif). Genotyping was performed with the ABI 3730 DNA analyzer (Applied Biosystems, Foster City, Calif). The genotyping results were analyzed by automated fluorescence detection using the ABI Genemapper version 3.5 (Applied Biosystems). Scoring of LOH/AI and retention of heterozygosity was performed by inspection of the Genemapper outputs (Figure 1). A ratio of peak heights of alleles between germline and epithelial carcinoma or surrounding stromal DNA of at least 1.5 was used to define LOH/AI.1922 The methodological accuracy of LOH/AI using multiplex PCR on archived templates was validated as published.12 Three samples were excluded from statistical analyses because none of the tested loci were informative (all loci homozygous in germline). Data were collected from October 2003 through June 2006.

Figure 1. Examples of Multiplex PCR Plots Performed for Total Genome Loss of Heterozygosity/ Allelic Imbalance (LOH/AI) Scanning
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PCR indicates polymerase chain reaction. A multiplex PCR genotyping for LOH/AI analysis with a primer panel composed of 3 microsatellite markers (D20S851, D4S3243, and D10S212) labeled with tetrachloro-6-carboxy-fluorescein. By comparing the heights of the matched genotypes of normal tissue and tumor stroma or epithelium, LOH/AI was detected in the stroma at D20S851 and D10S212, and in the epithelium at D20S851 (asterisks).

Statistical analyses were performed on the remaining 217 samples, each of which was informative for at least 79 chromosomal loci. The total number of 386 microsatellite markers were used for total genome LOH/AI scan, and each chromosome contained from 7 (chromosome 21) to 31 (chromosome 1) markers. Standard quality control measures for both laser capture microdissection procurement and replicability of compartment-specific LOH/AI calls are detailed in our previous publications, including the comparisons between the results of PCR on the DNA extracted from laser capture microdissection−captured tissues and those on the DNA from the corresponding frozen tissues, and between the results from multiplex PCR genotyping and those of quantitative PCR and the lack of cross contamination between compartments.9,12,18,19,23,24

Mutation Analysis of

Mutation analysis was performed on the 112 breast cancer samples, which had informative LOH/AI data at D17S796. Genomic DNA from the epithelium and stroma from each breast carcinoma was subjected to mutation analysis for TP53. The classic mutation cluster region of this gene, exons 4 through 9, exon-intron boundaries, and flanking intronic regions of TP53 were analyzed by PCR-based direct sequence analysis using the ABI3730xl, as previously described.12 When a mutation was found in the epithelium, stroma, or both, the corresponding germline was examined. No germline TP53 mutations were found; therefore, all mutations found in the breast cancer samples were somatic.

Clinicopathological Features at Time of Diagnosis

Presenting demographic and clinicopathological features included age, tumor grade (modified Scarff-Bloom-Richardson grades 1-3),1416 tumor size, estrogen-receptor and progesterone-receptor expression status, and human epidermal growth factor receptor 2 (HER2/neu) expression status, as well as primary tumor status, regional lymph node metastasis status (pN), and clinical stage grouping, which was based on the 6th edition of the American Joint Committee on Cancer Cancer Staging Manual.17 For hormone receptor analysis, the percentage of immunoreactive nuclei was assessed visually and the results were categorized as (+) equals more than 10% of nuclei, (±) equals more than 0% but less than 10% of nuclei, and (−) equals 0% of nuclei immunoreactive. In HER2/neu analysis, the results were scored as 0 indicates no immunoreactivity or immunoreactivity in less than 10% of tumor cells, 1+ indicates faint weak immunoreactivity in more than 10% of tumor cells but only a portion of the membrane is positive, 2+ indicates weak to moderate complete membrane immunoreactivity in more than 10% of tumor cells, 3+ indicates moderate to strong complete membrane immunoreactivity in more than 10% of tumor cells. Scores of 0 and 1+ were regarded as negative (−), and scores of 2+ and 3+ as positive (+), respectively. Cytoplasmic immunoreactivity alone was scored as a negative result.

Compartment-Specific LOH/AI Profile and Clinicopathological Features Analysis

McNemar tests were performed to compare the LOH/AI between each compartment-pair (epithelium and stroma) from each of the tumors with the pooled samples to test whether LOH/AI is more frequent in one compartment vs the other compartment. Dissimilarities between each compartment-pair (epithelium and stroma) from each of the tumors can be measured by the percentage of discordant pairs of LOH/AI (the proportion of markers showing loss of heterozygosity in one compartment and retention of heterozygosity in the other compartment among all the markers, which were informative in both compartments). Multidimensional scaling using principal coordinate analysis measures the distance between a pair of samples and approximates the dissimilarity between the 2 as measured by the percentage of discordant LOH/AI. Based on the results for multidimensional scaling, 2 of 217 tumors appeared to have different LOH/AI patterns from those of the rest of the samples. This was most likely due to the small number of informative markers for these 2 tumors (39 and 46 informative markers in the epithelium and stroma combined). Therefore, these 2 samples were excluded from multidimensional scaling and hierarchical clustering analyses.

We first performed hierarchical clustering with average linkage and multidimensional scaling for 430 samples, epithelium and stroma separately, derived from the 215 tumors. The clustering was performed by using a function in the statistical package R (which was used for all statistical analyses and detailed by Venables and Ripley25,26), and the standard option of average linkage was used. As an unsupervised (unbiased) method, we correlated genotype with the presenting clinicopathological features by repeating the same analysis using 1 clinicopathological variable at a time. The same analysis was then performed by combining the epithelium and stroma samples from the same tumor to study the overall LOH/AI profile of the tumor.

Associations Between LOH/AI and Clinicopathological Features

We applied statistical models to study the relationships between compartment-specific LOH/AI and clinicopathological data. Logistic regression models were used for clinicopathological features with binary features, and proportional odds regression models were used for clinicopathological features with more than 2 ordered classes. The covariates in these models are chromosome-wise LOH/AI frequencies for either compartment (stroma or epithelium) from each tumor. From these analyses, we obtained a P value across each chromosome in each compartment and each clinicopathological feature, representing the strength of evidence for the correlation between LOH/AI on that particular chromosome in that compartment and the clinicopathological feature. For the group of tests for a specific compartment and clinicopathological feature, Bonferroni adjustment was applied to correct for multiple testing, resulting in a significance level of P<.05 divided by 23 (P<.0022). For any association that was statistically significant, 2-tailed Fisher exact tests were used to associate the clinicopathological feature with LOH/AI at individual markers on that chromosome in that compartment.

Comparisons of LOH/AI in the Epithelium vs in the Stroma

Overall, LOH/AI was more frequent in the epithelium than in the stroma. In the epithelium across all tumors, 43 598 PCR reactions were informative for evaluation of LOH/AI, and 22 288 PCR reactions (51.1%) showed LOH/AI compared with an overall 18 644 (47.6%) of 39 192 PCR reactions in the stroma (χ2 test, P = 2.2×10−16). At the chromosomal level, model-based estimates for the LOH/AI frequency12 were significantly higher in the epithelium than in the stroma for 5 chromosomes (chromosomes 7, 8, 13, 16, and 17) at the P<.05 level (Table 2), and remained so for 3 of the 5 chromosomes (chromosomes 8, 13, and 17) after Bonferroni adjustment for multiple testing (P<.0022).

Table Graphic Jump LocationTable 2. Comparisons Between the Loss of Heterozygosity/Allelic Imbalance in the Neoplastic Epithelium and in the Stroma at the Chromosome Level

As proof of concept that regions with significantly high LOH/AI often have relevant genes, we looked at the p13 region of chromosome 17, which contains the TP53 tumor suppressor gene. One of the major regions of loss of heterozygosity is within 17p13, where loss of heterozygosity at D17S796 (17p13.2) in the epithelium occurs in 72 (64%) of 112 informative (germline heterozygous at this marker) breast cancer samples from our series and, in the stroma, occurs in 56 (53%) of 106 informative breast cancer samples from our series (7 stromal samples failed to amplify). D17S796 is a proximal marker for the TP53 tumor suppressor gene.

We therefore performed direct mutation analysis by sequencing of the classic mutation cluster region, exons 4 through 9, and flanking intronic sequences of TP53 of all epithelial and stromal samples from the 113 breast cancer samples with informative loss of heterozygosity data at this locus. We found that 29 (27%) of 112 tumors had somatic intragenic TP53 mutations in the epithelium and 28 (26%) of 106 had somatic TP53 mutations in the stroma. Only 8 tumors had somatic TP53 mutations in both the epithelium and stroma, but for each of these 8 samples, the mutation found in the epithelium was different from that in the stroma. Thus, 21 tumors had TP53 mutations only in the epithelium and another 20 tumors had somatic mutations only in the stroma. Of the 30 tumors with TP53 mutations in the epithelium, 80% had loss of heterozygosity at D17S796. Among the 28 tumors with TP53 mutations in the stroma, 65% had loss of heterozygosity at this marker.

Comparison of LOH/AI Profiles Between the Epithelium and the Stroma Derived From the Same Samples

The results of the McNemar test comparing the LOH/AI between the epithelium and stroma samples derived from the 217 participants revealed that for a larger number of participants, LOH/AI was observed more frequently in the epithelium, represented by the positive P values. This result is consistent with the overall test, which indicated strong evidence for more frequent LOH/AI in the epithelium (P<.001). Neither multidimensional scaling nor hierarchical clustering revealed any strong similarity between LOH/AI profiles for the epithelial or the stromal samples from the same participant, providing a good control for noncontamination between compartments (data not shown). The hierarchical clustering did result in the samples clustering progressively, with the most similar samples clustered together first.

Model of the Association Between Clinicopathological Features and LOH/AI

We took a 2-stage approach to look for associations between compartment-specific LOH/AI and clinicopathological features. First, these associations were screened at the chromosome level. The chromosomes that yielded significant correlations were then subjected to analysis at the individual marker level to determine associations between LOH/AI at the specific markers or loci and the clinicopathological features. For the first stage, we applied formal model-based methods to examine the correlations between LOH/AI and the presenting clinicopathological features. We used compartment-specific LOH/AI data to classify the clinicopathological features using logistic and ordinal regression models, with chromosome-wise LOH/AI as the independent variable, for each chromosome in turn, and obtained P values for each presenting clinicopathological feature. The obtained P values (Table 3) represent the strength of evidence for the correlation between LOH/AI on a particular chromosome and the particular presenting clinicopathological feature.

Table Graphic Jump LocationTable 3. Logistic Regression and Ordinal Regression Models of the Association Between Clinicopathological Features and Compartment-Specific Loss of Heterozygosity/Allelic Imbalance at Specific Markers at the Chromosome Level

More statistically significant associations (after Bonferroni adjustment) with clinicopathological features were found for LOH/AI in the stroma (7 associations) than in the epithelium (1 association). Specifically, we found significant associations between tumor grade and LOH/AI on chromosome 11 in the stroma (P = .0013), between tumor grade and LOH/AI on chromosome 14 in the epithelium and progesterone receptor (P = .002), and between tumor grade and LOH/AI on chromosomes 1 (P = .0006), 2 (P = .0016), 5 (P = .0009), 18 (P = .0009), 20 (P = .001), and 22 (P = .0002) in the stroma and pathological pN status (Table 3 and Figure 2).

Figure 2. Associations Between Loss of Heterozygosity/Allelic Imbalance (LOH/AI) and Presenting Clinicopathological Features
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For each chromosome and compartment for which the LOH/AI frequency (y-axis) was found to be related to a clinicopathological feature (x-axis), the summary statistics of LOH/AI frequency for each level of the clinicopathological features (tumor grade [1-3], progesterone receptor [(+) equals >10% of nuclei, (±) equals >0% but <10% of nuclei, and (−) equals 0% of nuclei immunoreactive], and regional lymph node metastasis status [pN: 0, 1, ≥2]) are shown in a box plot. The characteristics depicted include the mean (line in the middle of each box), the interquartile range (height of the box), and outlying observations (error bars above and below each box). From the pattern of boxes in each plot, it is evident that each of these chromosomes shows a consistently increasing or consistently decreasing trend over the levels of the corresponding clinicopathological features. For example, for the plot labeled chromosome 1 stromal LOH/AI, the frequencies of LOH/AI start at an average of slightly under 40% with no regional lymph node metastases (pN0) to 45% at pN1 and increase to an average of 80% for pN2 and higher.

Once promising chromosomes were identified, we proceeded with the second stage to associate LOH/AI at specific loci and the clinicopathological features. To determine if specific markers were responsible for the LOH/AI along the chromosomes noted above that were significantly associated with tumor grade, pN, and progesterone-receptor status, 2-tailed Fisher exact tests were used to test the association of the corresponding clinicopathological feature with LOH/AI at each marker in the corresponding compartment (Table 4). Markers along chromosome 14 in the epithelium associated with progesterone-receptor status were D14S588 (P = .029) and D14S1426 (P = .027). Specific markers contributing to the LOH/AI on chromosome 11 in the stroma associated with tumor grade were D11S1999 (P = .00055) and D11S1986 (P = .042). Importantly, LOH/AI at various markers in the stroma was significantly associated with pN: ATA42G12 (chromosome 1, P = .00095), D5S1457 (P = .00095), D5S1501 (P = .0011), D5S816 (P = .0008), D18S858 (P = .0026), D20S103 (P = .0027), D20S851 (P = .0045), D22S683 (P = .00033), and D22S1045 (P = .0013).

Table Graphic Jump LocationTable 4. Specific Markers Significantly Associated With Clinicopathological Features

Eight significant associations were found between compartment-specific, chromosome-specific LOH/AI, and clinicopathological features. Although only 2 markers on chromosome 14 in the epithelium were significantly associated with any clinicopathological feature at all (in this case, progesterone receptor), genomic instability within 7 chromosomes in the stroma of primary invasive breast carcinomas were significantly associated with tumor grade (chromosome 11) and the presence of regional lymph node metastases (chromosomes 1, 2, 5, 18, 20, and 22). Our previous observational studies of total genome LOH/AI in breast cancer compartments have also revealed specific regions of chromosome 11 as an important target of genomic alteration.9,12 Furthermore, because this 11q region is also a hot spot (a significantly higher frequency of LOH at a marker or markers compared with other markers along the same chromosome) in the stroma of head and neck cancers, the role of this region in the stroma might be more universal.27

The ataxia-telangiectasia (ATM) gene is mapped to chromosomal region 11q23.1, the locus associated with tumor grade. Because the ATM gene is responsible for maintaining genomic integrity,28 it may be postulated that LOH/AI at the ATM locus in the stroma might initiate general genomic instability in that compartment. Lack of the ATM gene has also been shown to be associated with increased neoangiogenesis and with increased tumor grade and poor clinical outcome in non-Hodgkin lymphoma.29 At least 1 previous study has shown that LOH/AI at the ATM locus correlated with increased tumor grade in whole (without compartment-specific analysis) primary breast carcinomas.30 However, sometimes genotype–clinicopathological feature associations may not be as straightforward as merely reflecting a gene or genes within an identified hot spot. For example, we used our model-based statistic to look for potential associations between compartment-specific presence or absence of somatic TP53 mutations and specific hot spot LOH/AI. Interestingly, the presence of somatic TP53 mutations in the stroma, but not in the epithelium, were associated with the presence of LOH/AI at our 2 stroma-specific hot spot markers on chromosome 11 associated with tumor grade (A.P. and C.E., unpublished data, 2007). Thus, it is possible that somatic mutation of TP53 in the stroma results in genomic instability leading to LOH/AI, including LOH/AI at 11q23, and affecting the ATM locus that sets up a perpetuating cycle of increasing genomic instability and, therefore, high-grade tumors.

The association of LOH/AI at 9 specific loci residing on 6 chromosomes in the stroma of primary breast carcinomas with pathological pN status is worth mentioning. In the process of lymph node metastases, there would be at least 2 rate-limiting steps: gaining access to the lymphatics at the site of the primary lesion and tumor formation at the regional lymph node.31 For successful metastasis, it would seem straightforward that the primary tumor stroma should have many important roles, providing a permissive microenvironment that permits invasion. Our observation that genetic alterations at the 9 loci (on 6 chromosomes) in the stroma associating with pN should reflect the genetically altered microenvironment favorable to metastasis. There are 2 broad categories of metastasis-associated genes (metastasis activators and metastasis suppressors).32 As an example, at least 4 of these are located at those chromosomal loci significantly associated with pN in this study, such as Maspin31 at 18q21.3 (D18S858), EP30033 at 22q13 (D22S1045), and PLCB134 at D20S851, as well as MYH935 on D22S683, which are known to be associated with metastasis, invasion, or both. In particular, EP300 encodes p300, which is a transcriptional cofactor and prototype histone acetyltransferase that plays a role in multiple cellular processes. In vitro, p300-deficient cells appeared to have an aggressive phenotype with loss of cell-cell adhesion and defects in cell-matrix adhesion.33 In vivo, embryos lacking p300 were shown to arrest development and die between E8.5 and E11, suggesting that p300 would be necessary for healthy organ development.36 Our observation might also explain why epigenetic phenomena are more prominent in tumor stroma.6,7 However, our current data suggest that structural loss of such genes as EP300 occur first, with consequent epigenetic alterations important in tumor stroma occurring thereafter. Within or close to 7 of the 9 pN-associated markers lie genes or loci associated with immune modulation (eg, IL2RB, IBD537), and several quantitative trait loci for rheumatoid arthritis.38,39 Overall, our observations lend evidence that genetic alterations in the tumor stroma activates or promotes genomic instability and neovascularization (ATM locus LOH/AI and tumor grade) followed by further dysfunction in such genes as EP300 and Maspin whose consequences interact with inflammation and immune suppressive responses (IL2RB, IBD5, and quantitative trait loci for rheumatoid arthritis), which promote cell migration and invasion.40

These results support a model in which genetic changes in both stromal and epithelial compartments occur during tumorigenesis, and progression is codetermined by local interaction between these cell populations within the primary tumor.12 We previously found that the stroma had a greater multiplicity of genetic alterations than the epithelium and the targets of genetic alterations in the stroma were more numerous and widely distributed than those in the epithelium. This suggests that epithelium only requires a small number of LOH/AI events to undergo malignant transformation, but local behavior of the resultant epithelial neoplasm is substantially modified by a broader repertoire of genetic changes in adjacent stroma.

Our data suggest that clinical tumor progression, reflected in the measured clinicopathological features, may be more influenced by locally acquired changes in the stromal environment than carcinoma cell genotype per se.12 Stromal genetic changes that contribute to clinically relevant outcomes can be mapped to particular chromosomal loci, including 2 markers on chromosome 11 that correlate with tumor grade and 9 markers on 6 chromosomes associated with regional lymph node metastasis. Genetic changes acquired in the stroma adjacent to transformed epithelial cells contribute an additional dimension of progression modulation beyond that contributed by the carcinoma cells themselves. The combination of stromal and epithelial genetic changes produces a greater range of outcome scenarios than can otherwise be explained by carcinoma cell genotype alone.

The genetics and genomics of tumor stroma from human patients is a relatively new field of exploration compared with the cell biology of epithelial-stromal interactions in in vitro and nonhuman solid tumor models, which may date back to 20 or more years ago. Given the technology of the day, albeit modern, there are always caveats to studies such as this. For example, despite our best intention to avoid cross contamination between compartments, there might have been a few stray cells from each compartment that crossed over. When this occurs, very low level LOH/AI (eg, in subpopulations) will be missed, and so subtle clinicopathological feature−associations may be missed. Furthermore, we used a 385-marker total genome coverage (10-Mb mean intermarker distance); therefore, it is possible that a few important regions or genes that are relatively distant from each marker were missed. For example, the 17q markers closest to the HER2/neu gene showed a relatively low allelic imbalance (genomic amplification) frequency compared with HER2/neu protein expression by immunohistochemistry. It is possible that the 386-marker whole genome coverage still did not have enough resolution to capture the 1-Mb HER2/neu amplicon, as this marker set did not include a marker within this gene. Added confidence is provided by similar findings of reproducible genomic, epigenomic, and expressional changes found by different technologies, such as chromosome genomic hybridization and expression profiling in breast and other carcinomas published by several other articles.6,7,10 Nonetheless, as with any patient-oriented study, our data should be validated, perhaps with emerging novel technologies, in larger series especially those with event-free survival data and therapeutic trials with longer follow-up.

Corresponding Author: Charis Eng, MD, PhD, Cleveland Clinic Genomic Medicine Institute, 9500 Euclid Ave, NE-50, Cleveland, OH 44195 (engc@ccf.org).

Author Contributions: Dr Eng had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Eng.

Acquisition of data: Fukino, Patocs, Mutter.

Analysis and interpretation of data: Fukino, Shen, Patocs, Eng.

Drafting of the manuscript: Fukino, Shen, Eng.

Critical revision of the manuscript for important intellectual content: Fukino, Shen, Patocs, Mutter, Eng.

Statistical analysis: Shen.

Obtained funding: Eng.

Administrative, technical, or material support: Fukino, Shen, Patocs, Mutter, Eng.

Study supervision: Eng.

Financial Disclosures: None reported.

Funding/Support: This work was partially funded by grants P01CA97189-01A2 and P50CA113001-01 from the National Cancer Institute, Bethesda, Md (Dr Eng). Dr Eng is a recipient of the Doris Duke Distinguished Clinical Scientist Award.

Role of the Sponsor: The funding organization and sponsor had no role in the design and conduct of the study, in the collection, management, analysis, and interpretation of the data, or in the preparation, review, or approval of the manuscript. All analytical and interpretative work and drafting of the manuscript occurred under the direction of Dr Eng at the Cleveland Clinic Genomic Medicine Institute.

Acknowledgment: We thank Petra Platzer, PhD, Manager of Integrative Genomics Analysis, Cleveland Clinic Genomic Medicine Institute, for her critical review of drafts of the revised manuscript and for helpful discussions. Dr Platzer did not receive any compensation for her contribution.

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Fukino K, Shen L, Matsumoto S, Morrison C, Mutter G, Eng C. Combined total genome loss of heterozygosity scan of breast cancer stroma and epithelium reveals multiplicity of stromal targets.  Cancer Res. 2004;64:7231-7236
PubMed   |  Link to Article
Tuhkanen H, Anttila M, Kosma VM.  et al.  Genetic alterations in the peritumoral stromal cells of malignant and borderline epithelial ovarian tumors as indicated by allelic imbalance on chromosome 3p.  Int J Cancer. 2004;109:247-252
PubMed   |  Link to Article
Bloom HJ, Richardson WW. Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years.  Br J Cancer. 1957;11:359-377
PubMed   |  Link to Article
Le Doussal V, Tubiana-Hulin M, Friedman S, Hacene K, Spyratos F, Brunet M. Prognostic value of histologic grade nuclear components of Scarff-Bloom-Richardson (SBR): an improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas.  Cancer. 1989;64:1914-1921
PubMed   |  Link to Article
Elston CW, Ellis IO. Pathological prognostic factors in breast cancer, I: the value of histological grade in breast cancer: experience from a large study with long-term follow-up.  Histopathology. 1991;19:403-410
PubMed   |  Link to Article
Greene F, Page D, Fleming I.  et al.  AJCC Cancer Staging Manual. 6th ed. New York, NY: Springer-Verlag; 2002
Weber F, Fukino K, Sawada T.  et al.  Variability in organ-specific EGFR mutational spectra in tumour epithelium and stroma may be the biological basis for differential responses to tyrosine kinase inhibitors.  Br J Cancer. 2005;92:1922-1926
PubMed   |  Link to Article
Weber F, Shen L, Fukino K.  et al.  Total-genome analysis of BRCA1/2-related invasive carcinomas of the breast identifies tumor stroma as potential landscaper for neoplastic initiation.  Am J Hum Genet. 2006;78:961-972
PubMed   |  Link to Article
Marsh DJ, Zheng Z, Zedenius J.  et al.  Differential loss of heterozygosity in the region of the Cowden locus within 10q22-23 in follicular thyroid adenomas and carcinomas.  Cancer Res. 1997;57:500-503
PubMed
Dacic S, Ionescu D, Finkelstein S, Yousem S. Patterns of allelic loss of synchronous adenocarcinomas of the lung.  Am J Surg Pathol. 2005;29:897-902
PubMed   |  Link to Article
Nelson HH, Wilkojmen M, Marsit CJ, Kelsey KT. TP53 mutation, allelism and survival in non-small cell lung cancer.  Carcinogenesis. 2005;26:1770-1773
PubMed   |  Link to Article
Ginzinger DG, Godfrey TE, Nigro J.  et al.  Measurement of DNA copy number at microsatellite loci using quantitative PCR analysis.  Cancer Res. 2000;60:5405-5409
PubMed
Nigro JM, Takahashi MA, Ginzinger DG.  et al.  Detection of 1p and 19q loss in oligodendroglioma by quantitative microsatellite analysis, a real-time quantitative polymerase chain reaction assay.  Am J Pathol. 2001;158:1253-1262
PubMed   |  Link to Article
Venables WN, Ripley BD. Modern Applied Statistics With S-Plus. New York, NY: Springer; 1994
Venables WN, Ripley BD. S Programming. New York, NY: Springer; 2000
Weber F, Xu Y, Zhang L.  et al.  Microenvironmental genomic alterations and clinico-pathologic behavior in head and neck squamous cell carcinomas.  JAMA. 2007;297:187-195
PubMed   |  Link to Article
Khanna KK, Chenevix-Trench G. ATM and genome maintenance: defining its role in breast cancer susceptibility.  J Mammary Gland Biol Neoplasia. 2004;9:247-262
PubMed   |  Link to Article
Cuneo A, Bigoni R, Rigolin G.  et al.  Acquired chromosome 11q deletion involving the ataxia teleangiectasia locus in B-cell non-Hodgkin's lymphoma: correlation with clinicobiologic features.  J Clin Oncol. 2000;18:2607-2614
PubMed
Rio PG, Pernin D, Bay JO.  et al.  Loss of heterozygosity of BRCA1, BRCA2 and ATM genes in sporadic invasive ductal breast carcinoma.  Int J Oncol. 1998;13:849-853
PubMed
Schedin P, Elias A. Multistep tumorigenesis and the microenvironment.  Breast Cancer Res. 2004;6:93-101
PubMed   |  Link to Article
Debies MT, Welch DR. Genetic basis of human breast cancer metastasis.  J Mammary Gland Biol Neoplasia. 2001;6:441-451
PubMed   |  Link to Article
Krubasik D, Iyer N, English W.  et al.  Absence of p300 induces cellular phenotypic changes characteristic of epithelial to mesenchyme transition.  Br J Cancer. 2006;94:1326-1332
PubMed   |  Link to Article
Cocco L, Manzoli L, Palka G, Martelli A. Nuclear phospholipase C beta1, regulation of the cell cycle and progression of acute myeloid leukemia.  Adv Enzyme Regul. 2005;45:126-135
PubMed   |  Link to Article
Canobbio I, Noris P, Pecci A, Balduini A, Balduini C, Torti M. Altered cytoskeleton organization in platelets from patients with MYH9-related disease.  J Thromb Haemost. 2005;3:1026-1035
PubMed   |  Link to Article
Yao TP, Oh SP, Fuchs M.  et al.  Gene dosage-dependent embryonic development and proliferation defects in mice lacking the transcriptional integrator p300.  Cell. 1998;93:361-372
PubMed   |  Link to Article
 The AutoImmune Disease Database. http://www.uni-rostock.de/aidb/home.php. Accessibility verified April 19, 2007
Cornelis F, Faure S, Martinez M.  et al.  New susceptibility locus for rheumatoid arthritis suggested by a genome-wide linkage study.  Proc Natl Acad Sci U S A. 1998;95:10746-10750
PubMed   |  Link to Article
Shiozawa S, Hayashi S, Tsukamoto Y.  et al.  Identification of the gene loci that predispose to rheumatoid arthritis.  Int Immunol. 1998;10:1891-1895
PubMed   |  Link to Article
Chen EI, Yates JR. Maspin and tumor metastasis.  IUBMB Life. 2006;58:25-29
PubMed   |  Link to Article

Figures

Figure 1. Examples of Multiplex PCR Plots Performed for Total Genome Loss of Heterozygosity/ Allelic Imbalance (LOH/AI) Scanning
Graphic Jump Location

PCR indicates polymerase chain reaction. A multiplex PCR genotyping for LOH/AI analysis with a primer panel composed of 3 microsatellite markers (D20S851, D4S3243, and D10S212) labeled with tetrachloro-6-carboxy-fluorescein. By comparing the heights of the matched genotypes of normal tissue and tumor stroma or epithelium, LOH/AI was detected in the stroma at D20S851 and D10S212, and in the epithelium at D20S851 (asterisks).

Figure 2. Associations Between Loss of Heterozygosity/Allelic Imbalance (LOH/AI) and Presenting Clinicopathological Features
Graphic Jump Location

For each chromosome and compartment for which the LOH/AI frequency (y-axis) was found to be related to a clinicopathological feature (x-axis), the summary statistics of LOH/AI frequency for each level of the clinicopathological features (tumor grade [1-3], progesterone receptor [(+) equals >10% of nuclei, (±) equals >0% but <10% of nuclei, and (−) equals 0% of nuclei immunoreactive], and regional lymph node metastasis status [pN: 0, 1, ≥2]) are shown in a box plot. The characteristics depicted include the mean (line in the middle of each box), the interquartile range (height of the box), and outlying observations (error bars above and below each box). From the pattern of boxes in each plot, it is evident that each of these chromosomes shows a consistently increasing or consistently decreasing trend over the levels of the corresponding clinicopathological features. For example, for the plot labeled chromosome 1 stromal LOH/AI, the frequencies of LOH/AI start at an average of slightly under 40% with no regional lymph node metastases (pN0) to 45% at pN1 and increase to an average of 80% for pN2 and higher.

Tables

Table Graphic Jump LocationTable 1. Demographic and Clinicopathological Characteristics of the Invasive Breast Carcinomas
Table Graphic Jump LocationTable 2. Comparisons Between the Loss of Heterozygosity/Allelic Imbalance in the Neoplastic Epithelium and in the Stroma at the Chromosome Level
Table Graphic Jump LocationTable 3. Logistic Regression and Ordinal Regression Models of the Association Between Clinicopathological Features and Compartment-Specific Loss of Heterozygosity/Allelic Imbalance at Specific Markers at the Chromosome Level
Table Graphic Jump LocationTable 4. Specific Markers Significantly Associated With Clinicopathological Features

References

Gort M, Broekhuis M, Otter R, Klatzinga NS. Improvement of best practice in early breast cancer: actionable surgeon and hospital factors [published online ahead of print October 7, 2006].  Breast Cancer Res Treat. 2007;102:219-226
PubMed   |  Link to Article
Weigelt B, Wessels LF, Bosma AJ.  et al.  No common denominator for breast cancer lymph node metastasis.  Br J Cancer. 2005;93:924-932
PubMed   |  Link to Article
Simpson PT, Reis-Filho JS, Gale T, Lakhani SR. Molecular evolution of breast cancer.  J Pathol. 2005;205:248-254
PubMed   |  Link to Article
Bissell MJ, Barcellos-Hoff MH. The influence of extracellular matrix on gene expression: is structure the message?  J Cell Sci Suppl. 1987;8:327-343
PubMed   |  Link to Article
Shekhar MP, Werdell J, Santner SJ, Pauley RJ, Tait L. Breast stroma plays a dominant regulatory role in breast epithelial growth and differentiation: implications for tumor development and progression.  Cancer Res. 2001;61:1320-1326
PubMed
Allinen M, Beroukhim R, Cai L.  et al.  Molecular characterization of the tumor microenvironment in breast cancer.  Cancer Cell. 2004;6:17-32
PubMed   |  Link to Article
Hu M, Yao J, Cai L.  et al.  Distinct epigenetic changes in the stromal cells of breast cancers.  Nat Genet. 2005;37:899-905
PubMed   |  Link to Article
Moinfar F, Man YG, Arnould L, Bratthauer GL, Ratschek M, Tavassoli FA. Concurrent and independent genetic alterations in the stromal and epithelial cells of mammary carcinoma: implications for tumorigenesis.  Cancer Res. 2000;60:2562-2566
PubMed
Kurose K, Hoshaw-Woodard S, Adeyinka A, Lemeshow S, Watson P, Eng C. Genetic model of multi-step breast carcinogenesis involving the epithelium and stroma: clues to tumour-microenvironment interactions.  Hum Mol Genet. 2001;10:1907-1913
PubMed   |  Link to Article
Wernert N, Locherbach C, Wellmann A, Behrens P, Hugel A. Presence of genetic alterations in microdissected stroma of human colon and breast cancers.  Anticancer Res. 2001;21:(4A)  2259-2264
PubMed
Kurose K, Gilley K, Matsumoto S, Watson P, Zhou X, Eng C. Frequent somatic mutations in PTEN and TP53 are mutually exclusive in the stroma of breast carcinomas.  Nat Genet. 2002;32:355-357
PubMed   |  Link to Article
Fukino K, Shen L, Matsumoto S, Morrison C, Mutter G, Eng C. Combined total genome loss of heterozygosity scan of breast cancer stroma and epithelium reveals multiplicity of stromal targets.  Cancer Res. 2004;64:7231-7236
PubMed   |  Link to Article
Tuhkanen H, Anttila M, Kosma VM.  et al.  Genetic alterations in the peritumoral stromal cells of malignant and borderline epithelial ovarian tumors as indicated by allelic imbalance on chromosome 3p.  Int J Cancer. 2004;109:247-252
PubMed   |  Link to Article
Bloom HJ, Richardson WW. Histological grading and prognosis in breast cancer: a study of 1409 cases of which 359 have been followed for 15 years.  Br J Cancer. 1957;11:359-377
PubMed   |  Link to Article
Le Doussal V, Tubiana-Hulin M, Friedman S, Hacene K, Spyratos F, Brunet M. Prognostic value of histologic grade nuclear components of Scarff-Bloom-Richardson (SBR): an improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas.  Cancer. 1989;64:1914-1921
PubMed   |  Link to Article
Elston CW, Ellis IO. Pathological prognostic factors in breast cancer, I: the value of histological grade in breast cancer: experience from a large study with long-term follow-up.  Histopathology. 1991;19:403-410
PubMed   |  Link to Article
Greene F, Page D, Fleming I.  et al.  AJCC Cancer Staging Manual. 6th ed. New York, NY: Springer-Verlag; 2002
Weber F, Fukino K, Sawada T.  et al.  Variability in organ-specific EGFR mutational spectra in tumour epithelium and stroma may be the biological basis for differential responses to tyrosine kinase inhibitors.  Br J Cancer. 2005;92:1922-1926
PubMed   |  Link to Article
Weber F, Shen L, Fukino K.  et al.  Total-genome analysis of BRCA1/2-related invasive carcinomas of the breast identifies tumor stroma as potential landscaper for neoplastic initiation.  Am J Hum Genet. 2006;78:961-972
PubMed   |  Link to Article
Marsh DJ, Zheng Z, Zedenius J.  et al.  Differential loss of heterozygosity in the region of the Cowden locus within 10q22-23 in follicular thyroid adenomas and carcinomas.  Cancer Res. 1997;57:500-503
PubMed
Dacic S, Ionescu D, Finkelstein S, Yousem S. Patterns of allelic loss of synchronous adenocarcinomas of the lung.  Am J Surg Pathol. 2005;29:897-902
PubMed   |  Link to Article
Nelson HH, Wilkojmen M, Marsit CJ, Kelsey KT. TP53 mutation, allelism and survival in non-small cell lung cancer.  Carcinogenesis. 2005;26:1770-1773
PubMed   |  Link to Article
Ginzinger DG, Godfrey TE, Nigro J.  et al.  Measurement of DNA copy number at microsatellite loci using quantitative PCR analysis.  Cancer Res. 2000;60:5405-5409
PubMed
Nigro JM, Takahashi MA, Ginzinger DG.  et al.  Detection of 1p and 19q loss in oligodendroglioma by quantitative microsatellite analysis, a real-time quantitative polymerase chain reaction assay.  Am J Pathol. 2001;158:1253-1262
PubMed   |  Link to Article
Venables WN, Ripley BD. Modern Applied Statistics With S-Plus. New York, NY: Springer; 1994
Venables WN, Ripley BD. S Programming. New York, NY: Springer; 2000
Weber F, Xu Y, Zhang L.  et al.  Microenvironmental genomic alterations and clinico-pathologic behavior in head and neck squamous cell carcinomas.  JAMA. 2007;297:187-195
PubMed   |  Link to Article
Khanna KK, Chenevix-Trench G. ATM and genome maintenance: defining its role in breast cancer susceptibility.  J Mammary Gland Biol Neoplasia. 2004;9:247-262
PubMed   |  Link to Article
Cuneo A, Bigoni R, Rigolin G.  et al.  Acquired chromosome 11q deletion involving the ataxia teleangiectasia locus in B-cell non-Hodgkin's lymphoma: correlation with clinicobiologic features.  J Clin Oncol. 2000;18:2607-2614
PubMed
Rio PG, Pernin D, Bay JO.  et al.  Loss of heterozygosity of BRCA1, BRCA2 and ATM genes in sporadic invasive ductal breast carcinoma.  Int J Oncol. 1998;13:849-853
PubMed
Schedin P, Elias A. Multistep tumorigenesis and the microenvironment.  Breast Cancer Res. 2004;6:93-101
PubMed   |  Link to Article
Debies MT, Welch DR. Genetic basis of human breast cancer metastasis.  J Mammary Gland Biol Neoplasia. 2001;6:441-451
PubMed   |  Link to Article
Krubasik D, Iyer N, English W.  et al.  Absence of p300 induces cellular phenotypic changes characteristic of epithelial to mesenchyme transition.  Br J Cancer. 2006;94:1326-1332
PubMed   |  Link to Article
Cocco L, Manzoli L, Palka G, Martelli A. Nuclear phospholipase C beta1, regulation of the cell cycle and progression of acute myeloid leukemia.  Adv Enzyme Regul. 2005;45:126-135
PubMed   |  Link to Article
Canobbio I, Noris P, Pecci A, Balduini A, Balduini C, Torti M. Altered cytoskeleton organization in platelets from patients with MYH9-related disease.  J Thromb Haemost. 2005;3:1026-1035
PubMed   |  Link to Article
Yao TP, Oh SP, Fuchs M.  et al.  Gene dosage-dependent embryonic development and proliferation defects in mice lacking the transcriptional integrator p300.  Cell. 1998;93:361-372
PubMed   |  Link to Article
 The AutoImmune Disease Database. http://www.uni-rostock.de/aidb/home.php. Accessibility verified April 19, 2007
Cornelis F, Faure S, Martinez M.  et al.  New susceptibility locus for rheumatoid arthritis suggested by a genome-wide linkage study.  Proc Natl Acad Sci U S A. 1998;95:10746-10750
PubMed   |  Link to Article
Shiozawa S, Hayashi S, Tsukamoto Y.  et al.  Identification of the gene loci that predispose to rheumatoid arthritis.  Int Immunol. 1998;10:1891-1895
PubMed   |  Link to Article
Chen EI, Yates JR. Maspin and tumor metastasis.  IUBMB Life. 2006;58:25-29
PubMed   |  Link to Article
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