A Perspective on DNA Microarrays in Pathology Research and Practice

【摘要】  DNA microarray technology matured in the mid-1990s, and the past decade has witnessed a tremendous growth in its application. DNA microarrays have provided powerful tools for pathology researchers seeking to describe, classify, and understand human disease. There has also been great expectation that the technology would advance the practice of pathology. This review highlights some of the key contributions of DNA microarrays to experimental pathology, focusing in the area of cancer research. Also discussed are some of the current challenges in translating utility to clinical practice.
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Over the past decade, DNA microarray technology has fundamentally transformed the way much of modern pathology research is performed. Although the resultant advances have so far made only modest impact in clinical pathology practice, there remain expectations of much more to follow. This review highlights some of the key contributions of DNA microarrays to pathology research, focusing in the area of oncology, and also discusses some of the remaining challenges in translating its utility to pathology practice. The review is not meant to be comprehensive but rather aims to provide insight and perspective from a clinical pathology-trained investigative pathologist working in the field of cancer genomics and with interests in translating microarray technology and its derived discoveries to improve the practice of pathology. Comprehensive reviews of DNA microarray methods and data analysis can be found elsewhere.1-5

【关键词】  perspective microarrays pathology research practice

DNA Microarrays

DNA microarray technology emerged in the early 1990s, made possible by the convergence of two advances. First, large-scale DNA sequencing efforts, preceding the full-scale Human Genome Project and focused on the expressed component of the genome, provided DNA sequence information and physical clones for thousands of human genes. Second, technical advances provided methods to manufacture slides or chips containing thousands of DNA probes arrayed within a small surface area, for example, less than 1 cm2. Dr. Patrick Brown and colleagues6 at Stanford University developed cDNA microarrays based on spotting polymerase chain reaction (PCR)-amplified gene fragments onto glass microscope slides, whereas at Affymetrix (Santa Clara, CA), scientists used light masks to direct the in situ synthesis of DNA oligonucleotide probes on silicon wafers to produce GeneChips.7,8 The number of studies using DNA microarrays has risen markedly over the past few years (Figure 1A) , and at present, many different types of "homemade" and commercial DNA microarrays are in use.

Figure 1. Microarray analysis. A: Growth of microarray studies over the past decade, as evidenced by the number of publications in PubMed and in seven top-ranked (by impact factor) general pathology journals, using the search term "microarray." The graph is meant to depict a trend; numbers likely represent substantial underestimates because of limitations of the search. B: Schematic depiction of a two-color microarray-based expression profiling method. mRNA isolated from test and reference samples are differentially labeled using two different fluors (shown as red and green, respectively) and then co-hybridized to a DNA microarray comprising an ordered array of gene-specific DNA probes (left). Labeled mRNAs bind their cognate probes on the microarray by Watson-Crick base pairing. Following hybridization and imaging (center), the ratio of red to green fluorescence for each gene spot on the array reflects that gene??s relative expression level in the test compared with reference sample. The ERBB2 gene, shown as a red spot in the scanned image, is more highly expressed in the test sample. Analysis of many samples produces a colorimetric table ("heat map") of gene expression ratios (right), where each column represents a different sample, and each row represents a different gene on the array. The columns and rows here have been ordered by unsupervised hierarchical cluster analysis10 to reveal patterns in the data, where the dendrogram (tree) branches indicate relationships among samples and among genes.

Although there were many envisioned uses for DNA microarrays, profiling gene expression became the predominant application in the early years. The power of the technology derives from the ability to measure, in the case of gene expression, mRNA levels across thousands of genes simultaneously (Figure 1B) . The resultant expression profiles, likened to "molecular portraits,"9 provide the researcher and pathologist a new tool to observe, describe, and understand the molecular variation within tissue specimens. Microarray data analysis methods can be generally divided into supervised and unsupervised approaches. Supervised analyses make up-front use of specimen annotations, for example, to identify genes differentially expressed between tumor and normal, whereas unsupervised analyses (eg, hierarchical clustering10 ) seek to organize data agnostic to sample annotation, which is useful in discovering previously unrecognized sample classes.

In profiling across many genes and specimens, the resultant high-dimensional data sets have also brought new statistical challenges.11,12 In large data sets, the particular expression patterns sought are often found but might not be statistically meaningful. Another concern has been "over-fitting" of data, where data models are developed and tested on the same patient cohort. An additional common shortcoming has been the use of insufficiently large cohorts in both discovery and validation phases. Indeed, in the early years some inappropriate statistical analyses likely contributed to inflated expectations of DNA microarray technology.

Cancer Classification

Due in part to the ready availability of human tumor specimens, often excised as part of standard patient treatment, as well as the impact of the disease, many early microarray studies focused on human cancer. Leukemia specimens, devoid of many of the stromal components present in solid tumors, further simplified analysis. A major goal of many DNA microarray studies has been cancer classification. The pathologist classifies cancer, for example, based on its anatomical site of origin and histopathology, sometimes using ancillary tests like immunohistochemistry or cytogenetics. Classification systems provide important information for prognostication and selection of therapies. By defining hitherto unrecognized molecular variation in gene expression, DNA microarrays might provide a means for improved cancer classification.

An early landmark study by Golub et al13 laid the computational foundations for applying DNA microarrays to the problem of cancer classification, both to predict known tumor classes and to validate new classes. Using supervised methods, the investigators identified genes differentially expressed between two classes of leukemia, acute myelogenous leukemia (AML) and acute lymphoblastic leukemia. Statistically significant differences could be defined as those occurring above what was expected by chance, estimated by comparison to differences observed in the same data set but after first randomly permuting class labels (AML versus acute lymphoblastic leukemia). Further, the expression of genes distinguishing AML and acute lymphoblastic leukemia could be used to classify new cases with high accuracy and quantifiable predictive strengths. In a proof-of-principle unsupervised analysis, the investigators could also "rediscover" the known leukemic classes from the expression data. Although AML and acute lymphoblastic leukemia are of course readily distinguishable by existing cytochemical staining and flow cytometry techniques, the concepts developed, and many variations on the original computational methods, are applicable to more difficult classification problems.

The first microarray-based discovery of novel tumor classes was reported by Alizadeh et al.14 By unsupervised hierarchical cluster analysis of variably expressed genes in diffuse large B-cell lymphoma, the investigators identified two subclasses with distinct expression patterns. One pattern was similar to that of normal germinal center B cells, whereas the other was to that of activated B cells. The latter diffuse large B-cell lymphoma subtype was also associated with constitutive nuclear factor-B activity and a less favorable prognosis.14-15 Therefore, although indistinguishable by histology, expression profiling nonetheless suggested a refined classification of diffuse large B-cell lymphoma, which might improve outcome prediction and possibly selection of therapies. Indeed, BCL6 gene expression, a surrogate indicator of the germinal center B cell-like subtype, has since been shown to predict survival independent of the currently used International Prognostic Index score.16

Soon thereafter, microarray analysis of breast cancer also identified multiple tumor subclasses, refining the existing classification.9,17 Estrogen receptor (ER)-negative tumors included those with ERBB2 amplification as well as a previously underappreciated subclass with basal epithelial markers and poor prognosis. ER-positive breast tumors could be subdivided into luminal A and B subtypes, with the former associated with more favorable outcome. Likewise, in prostate cancer, our own microarray studies have defined three clinically relevant tumor subtypes indistinguishable by histology (Figure 2A) .18 Surrogate immunohistochemical markers (AZGP1 and MUC1) for these subtypes are predictive of tumor recurrence, independent of tumor stage, Gleason grade, and serum prostate-specific antigen levels (Figure 2B) . Similar microarray studies have identified tumor subclasses within other tumor types as well.19

Figure 2. Microarray analysis identifies clinically relevant prostate cancer subtypes. A: Hierarchical cluster analysis of gene expression patterns of normal and cancerous prostate specimens. The dendrogram indicates relationships among samples based on gene expression profiles; only a select subset of gene clusters is shown. Cluster analysis is seen to distinguish malignant from normal prostate (pink branches). Note that AMACR (open arrow) is among the genes more highly expressed in prostate cancer. Cluster analysis also defines three subtypes of prostate cancer (numbered above) not distinguishable histologically. AZGP1 and MUC1 (closed arrows) are located within gene expression patterns that characterize subtype-1 and subtypes-2/3, respectively. B: Prostate cancer gene expression subtypes are prognostically relevant. IHC staining (tissue microarray, left) of AZGP1 and MUC1, surrogate markers for subtype-1 and subtype-2/3 expression patterns, predict lower and higher tumor recurrence rates, respectively, independent of tumor stage, Gleason grade, and preoperative serum prostate-specific antigen.18 Findings identify subtype-1 as a clinically favorable prostate cancer subclass.

Outcome Prediction

Microarray analysis has also been applied directly to define gene signatures for prognostication and for prediction of response to therapies. In a landmark study, van??t Veer et al20 compared tumor gene expression profiles between two groups of patients with surgically excised lymph node-negative breast cancer, those who did or did not develop distant metastases within 5 years of follow-up. Supervised analysis based on the group distinction defined a 70-gene signature that could predict disease-free and overall survival in two independent cohorts of breast cancer patients,20,21 outperforming current prognostic indices based on clinical and histological parameters such as the St. Galen and National Institutes of Health consensus criteria (but see Ref. 22 ). The poor-prognosis signature might therefore improve the selection of patients who would benefit from adjuvant therapy.

Our own microarray studies of AML have defined a 133-gene signature that predicts overall survival independent of cytogenetics, itself a strong prognosticator.23 This signature, recently validated in another AML patient cohort,24 may prove particularly applicable for selecting appropriate risk-adapted therapy within the large subset of AML cases with no karyotypic abnormality. Gene signatures have also been reported that define responses to specific therapies, for example, to rituximab (an anti-CD20 antibody) treatment for patients with follicular lymphoma.25

Insights into Cancer Pathogenesis

In addition to the discovery of previously unrecognized tumor subclasses, expression profiling has made many other significant contributions to our understanding of cancer biology. For example, Ramaswamy et al26 explored genes differentially expressed between primary and metastatic tumors across a spectrum of solid tumor types. The investigators defined a 17-gene signature of metastasis that, surprisingly, was also expressed in a subset of primary tumors. The signature, which included genes with expected expression mainly in the stromal compartment, was predictive of patient outcome across several tumor types. It is noteworthy that this study challenged the existing paradigm that metastases arise from rare cells in the primary tumor that have acquired additional genetic alterations, suggesting rather that the propensity to metastasize is determined early in tumor progression and characterizes the bulk population of tumor cells. The findings also underscored the contribution of tumor stroma to cancer progression.

Another compelling contribution of microarray analysis was the recent discovery of recurrent gene fusions in prostate cancer. By analyzing "outlier" values of gene expression in prostate tumor microarray data sets, Tomlins et al27 identified the ETS family of oncogenic transcriptional factors ERG and ETV1 to be highly expressed in a subset of prostate tumors. Further characterization revealed chromosome rearrangement and gene fusion, resulting in the promoter of the prostate-expressed gene TMPRSS2 driving androgen-regulated overexpression of ERG or ETV1. This finding not only provides novel insight into prostate tumorigenesis but also challenges the long-standing assumption that recurrent chromosomal alterations, frequent in hematogenous and mesenchymal malignancies, are rare in common epithelial tumor types.

In both of the aforementioned examples, it is worth noting that these discoveries resulted from exploratory rather than "hypothesis-driven" investigations. Such nonhypothesis-driven research, sometimes derogatorily labeled as "fishing expeditions," had initially received less favorable enthusiasm among grant-funding agencies, although many agencies now recognize its value. In addition, both of the above studies benefited from the public availability of clinically annotated microarray data, increasingly but not universally a requirement for peer-reviewed publication.

Array CGH and Integrative Genomics Analysis

Whereas DNA microarrays were first used widely to profile gene expression, other applications soon emerged. For example, in array-based comparative genomic hybridization (array CGH),28-30 tumor and normal genomic DNA are differentially labeled and compared by hybridization to microarrays comprising DNA probes of defined human genome map position, such as large genomic clones (eg, bacterial artificial chromosomes), gene fragments (cDNAs), or oligonucleotides (Figure 3A) . The resultant tumor/normal ratios can be mapped onto the genome sequence to reveal in tumor genomes DNA amplifications and deletions, which drive the altered expression of cancer genes. Because genomic DNA comprises a more complex mixture of DNA sequences compared with the subset of expressed genes, array CGH presents additional technical challenges compared with expression profiling. Nevertheless, robust protocols have been developed, and array CGH analyses have pinpointed new cancer genes, for example, PPM1D in breast cancer31 and MITF in melanoma.32 In addition, patterns of DNA copy number alteration, analogous to signatures of gene expression, have been proposed for cancer classification and outcome prediction.33,34

Figure 3. Array CGH and integrative microarray analysis. A: Schematic depiction of array-based comparative genomic hybridization (array CGH) method. Genomic DNA (gDNA) isolated from tumor and normal samples is differentially labeled (shown as red and green fluor, respectively) and then co-hybridized to a microarray comprising DNA probes of known chromosome location. Following hybridization and imaging, the ratio of red to green fluorescence for each DNA spot on the array reflects that gene??s relative copy number in the tumor genome. The MYC gene, shown as a red spot in the scanned image, is amplified in the tumor genome. Plotting fluorescence ratios by genome map position is useful in defining DNA amplifications and deletions. Illustrative data are shown (right) for chromosome 8 of the breast cancer cell line SKBR3. Peaks reflect DNA amplification (including an amplicon harboring MYC, arrow), and valleys DNA deletion. B: Integrative analysis of array CGH and gene expression data. Shown are heat map representations of DNA copy number (left) and gene expression (right) for the subset of genes identified by microarray to be both highly amplified and overexpressed in breast cancer cell lines or tumors. Samples are ordered identically in both panels; genes are ordered by chromosome location. This subset of amplified overexpressed genes is a rich source for breast cancer gene discovery; previously known bona fide and putative oncogenes are highlighted by red text and include MYC, CCND1, and ERBB2. Data abstracted from Ref. 35 . C: Integrative analysis reveals distinct classes of DNA copy number alteration associated with the different gene expression subtypes of breast cancer. Box plots indicate 25th, 50th (median), and 75th percentiles of genome fraction exhibiting chromosome segment gain/loss (above) or high-level DNA amplification (below), for tumors stratified by expression subtype. Basal-like tumors exhibit significantly higher numbers of gain/loss, whereas luminal-B tumors display more high-level DNA amplification. Findings suggest that subtypes arise by different underlying mechanisms of genomic instability. Data abstracted from Ref. 36 .

Although expression profiling and array CGH each provides important information, integrating data from both of these methods can reveal additional insight. For example, although many genes exhibit elevated expression in cancer, the subset that is also highly amplified is enriched for key genes driving tumorigenesis (Figure 3B) ; such an integrative analysis is therefore valuable for cancer gene discovery. Our own integrative analysis of breast cancers has also uncovered a significant impact of aneuploidy (chromosome copy number imbalances) on gene expression patterns.35 This finding, since observed in many tumor types, suggests the possibility that aneuploidy contributes to tumor progression through the altered expression of many genes (perhaps even hundreds), in turn affecting cancer phenotypes like metastatic potential or drug resistance. Integrative analysis has also revealed distinct patterns of DNA copy number alteration underlying the above-mentioned gene expression subtypes of breast cancer (Figure 3C) .36,37 This finding suggests that breast cancer subtypes arise via different genetic pathways and with distinct underlying mechanisms of genomic instability.

Other Microarray Applications

The DNA microarray platform has been adapted to other applications as well (Table 1) , such as measuring germline genetic variation (the originally envisioned application), occurring as single nucleotide polymorphisms (SNPs) or copy number variations. Genome-wide association studies using SNP arrays have identified genetic loci conferring cancer risk.38 In tumor genomes, SNP arrays have been used to map somatic alterations in gene dosage (like array CGH), as well as loss of heterozygosity, where genetic information might be lost in a copy-number neutral state.39,40 DNA microarrays have also been used to identify altered patterns of DNA methylation and chromatin in cancers,41 and have even been applied to characterize gene function by "reverse transfection" of spotted full-length genes (cloned into expression cassettes) into overlaying cultured cells.42

Table 1. Diverse Applications of Microarrays

Beyond arraying DNA probes, arrayed antibodies and antigens have been used to respectively quantify levels of tumor proteins43 and the antitumor humoral response.44 Cell lysates and even tissue specimens have also been arrayed. Tissue microarrays comprise cylindrical tissue cores from several hundred different tumor specimens sectioned onto a single glass slide and permit the measurement of a single gene??s expression across many samples (rather than a single sample??s expression across many genes, as for DNA microarrays) simultaneously by immunohistochemistry (IHC) or RNA in situ hybridization.45 tissue microarrays, which also provide information on cellular localization of expression, have markedly sped the evaluation and validation of initial DNA microarray discoveries across larger patient cohorts (Figure 2B) .

Applications in Pathology Practice

There are many challenges in using DNA microarrays as a platform for clinical diagnosis (discussed below). Not surprisingly then, most current diagnostic applications resulting from microarray discoveries rely on methods already in common use in histopathology and molecular pathology laboratories, such as IHC or reverse transcription (RT)-PCR. For example, AMACR (-methylacyl-CoA racemase) was identified by microarray analysis to be more highly expressed in prostate cancer compared with normal prostate46-48 (Figure 2A) . IHC analysis of AMACR expression is now in routine use in some pathology centers to evaluate difficult biopsy cases for the presence of prostate cancer. Many other promising single-gene biomarkers discovered using DNA microarrays are under evaluation but not yet in routine use (Table 2) .

Table 2. Selected Candidate Biomarkers Identified by DNA Microarray Analysis

Multigene tests derived from microarray data are also being evaluated, although few are in routine use. A 70-gene breast cancer prognostic signature, described above and assayed by DNA microarray analysis of freshly frozen specimens (Agendia??s MammaPrint; Amsterdam, The Netherlands), recently received Food and Drug Administration clearance for the prediction of breast cancer recurrence for node-negative tumors.49 Likewise, a 21-gene signature (16 cancer-related and five reference genes), derived in part from microarray studies and assayed by quantitative-RT-PCR using formalin-fixed, paraffin-embedded specimens (Genomic Health??s Oncotype DX; Redwood City, CA), predicts the risk of tumor recurrence in ER-positive, node-negative breast cancers.50 Such tests, in conjunction with other clinical and laboratory information, might be used to select patients who are likely to benefit from adjuvant chemotherapy. Both of these multigene tests are currently being evaluated in prospective clinical trials. Interestingly, although these two tests share few genes in common, a recent study indicates a high concordance in predictions, suggesting that they are tracking similar tumor biology.51 Other multigene signatures have been described, several reporting on key biological features of tumors and with prognostic value across multiple tumor types (Table 3) .

Table 3. Selected Signatures, Many with Potential Clinical Utility across Multiple Tumor Types

Current Challenges

Many challenges remain in adopting DNA microarrays as a commonplace platform for diagnostic testing. Early concerns with expression profiling centered on discordances of microarray findings among different laboratories. Gene expression signatures ostensibly reporting on the same biological or clinical parameter often shared few genes in common. Such discrepancies are likely attributable in large part to differences in specimen cohorts, array platforms (and probes), protocols, and analysis methods. More recently, investigators have shown high reproducibility of findings when standard operating procedures are followed, and improved interplatform concordance with careful matching of probe annotations between platforms.52,53 In addition, further scrutiny reveals that the disparate gene signatures reported by different laboratories might nonetheless reflect the same underlying biology54 or provide comparable clinical utility.51 Many of the early claims attributable to overfitting of data or to insufficiently large sample sets remain unsubstantiated, but more rigorous statistical analysis has led to an increased likelihood of validation.

Evaluating and validating microarray testing in the clinical laboratory is far from straightforward. Foremost, DNA microarrays are multianalyte tests, where tens, hundreds, or even thousands of individual probes each report on the expression of a different gene with differing performance. Individual gene probe performance characteristics include analytic sensitivity (limit of detection), dynamic range and linearity, specificity (minimizing cross-hybridization to other genes), precision, and accuracy. Together, multiple gene probes comprise a diagnostic gene signature with its own set of performance characteristics, like sensitivity (minimizing false negatives), specificity (minimizing false positives), reproducibility, and robustness. Much effort in testing laboratories is currently being directed to standardize operating procedures for specimen collection and processing, RNA isolation and labeling, and microarray hybridization, imaging, and data analysis. The development of appropriate hybridization controls and standards is in progress,55 as well as appropriate quality-control metrics to assess specimen characteristics and hybridization quality.

Complicating matters, microarray technology has been rapidly evolving, with changing microarray platform/version releases, protocols, and even gene annotations themselves. There is also still no consensus on the optimal specimen type, freshly frozen (for high-quality RNA) versus paraffin (for the convenience of standard pathology processing), and whole specimen versus microdissected (to enrich for tumor cells). Nor is there agreement as to whether microarray tests should assay only a focused set of diagnostic genes or alternatively a wider set of genes, the latter of which might provide additional information but perhaps also additional risks, like unanticipated diagnoses. Although the above discussion has focused on expression profiling, many of the same considerations are relevant to other microarray applications.

Future Directions

Recent technical and informatic advances promise new opportunities for DNA microarrays in pathology research and practice. Currently available microarrays permit expression analysis of transcript variants with alternative exons and of microRNAs, a recently discovered and expanding class of small RNAs that regulate gene expression. Microarray platforms also now support the typing of several hundred thousand SNPs for whole-genome dosage, loss of heterozygosity, and linkage/association studies. On the informatics side, and although not required for diagnostic utility, the biological interpretation of gene expression patterns has long been a rate-limiting step, typically requiring painstaking literature searches. Recent advances in interpreting gene signatures include Gene Ontology vocabularies,56 pathway analysis,57 and gene set enrichment analysis.54 Additional insights will derive from integrating data across diverse microarray applications, such as measurements of DNA copy number, gene expression and protein levels, and from integrating data across species, for example, leveraging data from genetically tractable mouse models of human cancer. Informatics analysis will also be increasingly used to discover new connections between genes, disease states, and candidate therapies.58

Both technical and informatic advances in microarray analysis are expected to continue. However, whereas state-of-the-art microarray technology continues to be costly, informatics is by comparison the great equalizer. Anyone anywhere in the world with a computer, an Internet connection, and some basic knowledge can access and mine large publicly available microarray data sets to obtain new biological and clinical insight. We can expect an increasing number of such studies in the years to follow, as well as many more meta-analyses combining the results of multiple studies. The availability of infrastructure to support microarray data access and analysis, for example, the Stanford Microarray Database59 and Oncomine,60 will further facilitate such studies.

In regard to clinical testing, as performance concerns are adequately addressed, we can expect microarrays to be increasingly used as a platform for clinical diagnosis. Microarray testing will emerge for indications where i) microarrays provide additional information or outperform standard histopathological markers, ii) many genes provide more information than one or a few, iii) adequate performance characteristics are demonstrated, iv) testing impacts a patient management decision, v) there has been appropriate validation of clinical utility (ideally including prospective clinical trials), and vi) testing is cost-worthy. Although the path to clinical acceptance, Food and Drug Administration approval and reimbursement remains largely untrodden, regulatory authorities are aware of the need to meet the many challenges.61 Also uncertain are which microarray platforms will become preferred for testing (where high performance, ease of use, automation, and adaptability are desirable) and whether testing will be performed predominantly at many sites, eg, using kits, or alternatively at central labs.

Microarray-based applications likely to have clinical utility in the future include cancer classification and subtyping. For example, approximately 5% of tumors present as metastatic cancers of unknown primary.62 Gene expression signatures have potential utility in classifying a tumor??s anatomical site of origin,63,64 which would be useful in selecting the optimal treatment regimen. Likewise, such an assay could be applicable to other diagnostically challenging cases, eg, primary lung cancer versus metastatic cancer to lung.

Microarray analysis should also prove useful in prognostication where improved outcome prediction impacts patient management, for example identifying the subset of prostate cancer patients who can be safely followed without therapeutic intervention (ie, "watchful waiting"). Microarray analysis will also define germline DNA sequence variants, as well as somatic changes and deregulated pathways in individual patient tumors, thereby informing the selection of new molecularly directed therapies to realize "personalized" medicine. Other possible applications of microarray analysis include monitoring treatment response and toxicity. Outside of oncology, microarrays should have significant impact in microbiology in the identification of pathogens65 and in cytogenetics, where array CGH can reveal microdeletions associated with mental retardation and other developmental disabilities.66 Analysis of SNPs, copy number variations, and soon whole-genome DNA sequences, should also improve assessments of an individual??s disease risks.

However, although we can expect that microarrays will become useful ancillary tests for many specific applications, microarrays are unlikely in the foreseeable future to replace most existing tests or to render obsolete the trained pathologist. Histopathological analysis by the trained eye can render quick, accurate, and cost-effective diagnoses. Nevertheless, the future looks bright for microarray technology in both pathology research and practice.

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作者单位：From the Department of Pathology, Stanford University, Stanford, California