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(American Journal of Pathology. 2002;161:1557-1565.)
© 2002 American Society for Investigative Pathology

Technical Advance

Software Tools for High-Throughput Analysis and Archiving of Immunohistochemistry Staining Data Obtained with Tissue Microarrays

Chih Long Liu^*, Wijan Prapong, Yasodha Natkunam, Ash Alizadeh^*, Kelli Montgomery, C. Blake Gilks and Matt van de Rijn

From the Departments of Biochemistry,^* Genetics, and Pathology, Stanford University Medical Center, Stanford, California; and the Department of Pathology and Genetic Pathology Evaluation Centre, Vancouver General Hospital, Vancouver, British Columbia

	Abstract

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 
The creation of tissue microarrays (TMAs) allows for the rapid immunohistochemical analysis of thousands of tissue samples, with numerous different antibodies per sample. This technical development has created a need for tools to aid in the analysis and archival storage of the large amounts of data generated. We have developed a comprehensive system for high-throughput analysis and storage of TMA immunostaining data, using a combination of commercially available systems and novel software applications developed in our laboratory specifically for this purpose. Staining results are recorded directly into an Excel worksheet and are reformatted by a novel program (TMA-Deconvoluter) into a format suitable for hierarchical clustering analysis or other statistical analysis. Hierarchical clustering analysis is a powerful means of assessing relatedness within groups of tumors, based on their immunostaining with a panel of antibodies. Other analyses, such as generation of survival curves, construction of Cox regression models, or assessment of intra- or interobserver variation, can also be done readily on the reformatted data. Finally, the immunoprofile of a specific case can be rapidly retrieved from the archives and reviewed through the use of Stainfinder, a novel web-based program that creates a direct link between the clustered data and a digital image database. An on-line demonstration of this system is available at http://genome-www.stanford.edu/TMA/explore.shtml.

Gene expression studies using cDNA microarrays have necessitated the development and introduction of new analytical and data management tools into molecular biology laboratories.⁵ We have drawn on our experience with gene expression microarray experiments^6-8 to develop a robust system for the comprehensive management of data generated through immunohistochemical staining of TMAs. This includes using pre-existing analytical tools (Cluster and TreeView software), that were specifically created for analysis of complex microarray expression data,⁵ but are here applied to data derived from immunohistochemical staining of TMAs. Development of this system required integration of these software applications with commercially available hardware and software. Additional novel software (TMA-Deconvoluter and Stainfinder, described herein) were designed to link these components to create a fully functional system. To date we have accumulated more than 3000 cases and more than 65,000 different immunostaining results in our archive. Our system allows us to easily add cases or immunostaining results to this data bank, with rapid retrieval of both data sets for further analysis, and of archived digital images of immunostained tissues for review.

	Organization of the Stanford TMA Data System

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 
A flow diagram showing the overall organization of the system we have designed is described in Figure 1 . A description of the components of the system follows, using a lymphoma TMA with 265 cases as an example,⁹ with a more detailed explanation available at the accompanying website: http://genome-www.stanford.edu/TMA.

View larger version (35K): [in this window] [in a new window]   Figure 1. Flow diagram of TMA lab data acquisition, analysis, and archival storage system. Gray boxes indicate components directly relevant to the TMA data management system introduced here.

	TMA Construction

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

View larger version (69K): [in this window] [in a new window]   Figure 2. A user-supplied lookup table of diagnosis information for a given core sample. The unique TMA case numbers are listed in column A.

	Recording Immunohistochemical Staining Data

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 
Each individual array has its own Excel scoring workbook, based on the three-dimensional layout of the array, and consists of multiple worksheets. The first worksheet, or master, contains the two-dimensional layout of the TMA, with each field containing the anonymized numerical code from the Lookup table for a particular case (Figure 3A) . Subsequent worksheets, copies of the first but with blank cells corresponding to each core, are then used to record the staining interpretations for individual antibodies. Hence, the data worksheets, collectively representing the third dimension of the TMA, each correspond to an interpretation of a specific slide taken from the same TMA paraffin block (Figure 3B) . We record data directly onto printed versions of these worksheets, while scoring arrays under the microscope. The use of a worksheet that structurally parallels the layout of the TMA is critical, in our experience, to ensure accurate recording of the data. It is possible to record data by hand in a linear or other format and later transcribe it into a spreadsheet-type database, but this is labor intensive, and the extra step increases the likelihood of introducing recording errors. Additional data recorded on each worksheet include the section number taken from the array, the staining date, the antibody used, the antigen retrieval method, and the name of the interpreter of the stains, together with the date the stain was interpreted. As such, a workbook can contain separate worksheets containing the staining interpretations of multiple pathologists for a large number of antibodies.

View larger version (92K): [in this window] [in a new window]   Figure 3. Worksheets in TMA scoring workbook. A: Master worksheet, containing the unique case numbers from the lookup table. Sectors are padded by one row and one column of blank space. The location of a given sector is defined by the cell address at the top left corner of the worksheet, which indicates the location of the top left data cell in the sector. Each data cell within a sector defines a unique case. B: Worksheet containing immunostaining scoring results. Layout parallels master worksheet layout.

	Reformatting Data with TMA-Deconvoluter Software

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

Operation of the TMA-Deconvoluter is a straightforward process. First, the user specifies the scoring workbook files and the file containing the lookup table of the individual core descriptor information. When the worksheet data are converted into the single text, tab-delimited table format used for hierarchical clustering (Figure 4) , each data worksheet is converted into a column of data corresponding to the staining results for the antibody used in that sheet. The descriptor information (for example "bax" in column E) is placed into the appropriate column, and the UID column contains the filename and URL information that is passed on by TreeView into the Stainfinder program (see below). The EWEIGHT and GWEIGHT columns are used by the Cluster software for hierarchical cluster analysis (see below). The TMA-Deconvoluter software is freely available at http://genome-www.stanford.edu/TMA.

View larger version (97K): [in this window] [in a new window]   Figure 4. Deconvoluter output file. Typical output file from TMA-Deconvoluter, ready for analysis by Cluster software.

	Hierarchical Cluster Analysis

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 
Hierarchal cluster analysis of our TMA data are performed using software tools that were originally designed for analyzing cDNA microarray data.⁵ Hierarchical cluster analysis has been successfully applied to gene expression data, based on the expression of thousands of genes. It has also been remarkably successful in its ability to group tumors according to their primary site, as well as subgrouping tumors that are not reproducibly subclassifiable based on conventional morphology.^6-8,10 Hierarchical clustering can work in two dimensions. For TMA data, the information is used to group tumors together based on the relatedness of their immunostaining with a number of different antibodies, whereas in the second dimension the antibodies are grouped together based on their degree of relatedness in immunoreactivity across the TMA samples examined. Hierarchical clustering can be done using the Cluster program, and the clustered output can be viewed using TreeView, which graphically displays relatedness in both dimensions as dendrograms. Cluster and TreeView are freely available programs that can be obtained at http://rana.lbl.gov/EisenSoftware.htm. The EWEIGHT and GWEIGHT columns in the deconvoluted data, as described previously (Figure 4) , can be modified manually, if it is desired that a given antibody or patient sample should be given a different weight in the clustering process (see also additional information on Cluster at http://rana.lbl.gov/EisenSoftware.html).

The data deconvoluted by TMA-Deconvoluter is in a format suitable for immediate analysis by Cluster, and the resulting clustered file can then be opened in TreeView (Figure 5A) . In this example, a strong positive score is represented as a bright red block and a weak positive score a dark red block, and a negative score appears as a green block. Stains that could not be interpreted because of technical reasons (eg, loss of tissue from the slide) are represented by gray blocks. Dendrograms showing the two dimensions of clustering are seen at the top and left-hand sides of Figure 5 .

View larger version (59K): [in this window] [in a new window]   Figure 5. A: Screenshot of Tree View. TreeView output file, example of deconvoluted, clustered data in TreeView on lymphoma array.9 B: Stainfinder screenshot, with thumbnail representation of selected images.

	Other Data Analyses

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

	Capture and Retrieval of Digital Images Using Stainfinder Software

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 
We use the Bliss system from Bacus Laboratories (Lombard IL; http://bacuslabs.com) for digital image collection. The images were collected using Tracer v0.87h software supplied with the Bliss system. Each tissue core section is digitally recorded in six separate images that are automatically tiled together by the Bliss system. Each tiled image is stored as a JPG compressed image of 400 kb. TMAs are scanned one sector at a time, and the images from each sector are stored in a separate folder using a standardized nomenclature. For a detailed description of the standardized nomenclature, see http://genome-www.stanford.edu/TMA. To date, we have archived digital images on more than 65,000 immunostained tissue cores.

Stainfinder is a novel web-based program that links a selected row in the TreeView graphical output file (corresponding to all immunostaining data for one arrayed core) to the digital images recorded from that core. For TreeView figures showing gene expression microarray data, each row corresponds to a gene or clone, and the link provides access to additional information about this particular gene or clone. In the TMA laboratory, Stainfinder serves a similar but distinct function, and clicking on a row from a TreeView document retrieves a web page that lists all stains scanned in for this core and stored on the server (see screenshot on accompanying website). We currently use a 400GB RAID server to store digital images. Clicking on the display images button rapidly retrieves digital images in a thumbnail format (Figure 5B) that can be enlarged by clicking on the name of the thumbnail. Used in this manner, one can rapidly verify the score given to a particular antibody-stained core sample, by comparing the color block representing the score in the TreeView document to the corresponding digital image. The ability to do this is important and valuable, because the interpretation and determination of scores is subjective and often requires review. This is also valuable for allowing one to compare multiple stains done on the same core, allowing verification of scoring data taken from the same sample. In addition, variables such as nuclear versus membrane staining, the presence of stromal cell reactivity, and so forth, are features that can best be evaluated by re-examining the digital images. In comparison, reanalysis of multiple stains on one core by using the TMA slide sections would be extremely laborious. When immunostains for TMAs are scored by more than one pathologist, or if levels of the same array are stained with the same antibody by more than one laboratory, it is possible to rapidly identify interobserver or interlaboratory differences in results. The Stainfinder software is freely available at http://genome-www.stanford.edu/TMA.

An example of an application of the software is shown in Figure 6 , where a visualization of data obtained from a TMA with 265 lymphomas, stained with 27 antibodies is shown. Immunostaining results were recorded in an Excel workbook and reformatted with TMA-Deconvoluter. Clustered data were visualized in TreeView. The visual representation of the entire dataset is shown on the left panel with magnification of specific areas shown on the right. The dendrogram reveals that two different interpretations (in this case by the same pathologist, YN) of the same section stained with MUM-1 clustered tightly, indicating their high degree of correlation. The TreeView linkage to the image files allows for a rapid inspection and final scoring of the stains in which interpretations differed. Clustering of tumors, based on their immunoreactivity, exhibits a partial grouping together of lesions with the same diagnosis. For example, all 14 T-lineage lymphomas on this TMA clustered together while 5 of 8 Mantle cell lymphomas, and 4 of 8 CLL/SLL were located on unique terminal branches of the dendrogram (Figure 6) .

View larger version (71K): [in this window] [in a new window]   Figure 6. Example of clustering result of lymphoma TMA containing 265 lymphomas and stained with 27 different antibodies. Clustering of the data results in partial grouping together of tumors with the same or similar diagnoses.

	Discussion

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

Initial concerns about technical difficulties in creating and staining of array slides were rapidly put to rest; in our experience, the TMA paraffin blocks can be handled like regular blocks for cutting and staining. A more significant concern centered on how representative small cores (typically 0.6-mm cores in our laboratory) of tissue would be for either morphological or immunohistochemical assessment. The available data indicate that the use of small cores is, perhaps surprisingly, highly representative of the entire tumor, and this has served as an impetus for rapidly increasing the introduction of TMA technology. For example, Nocito and colleagues,³ in a study of more than 2000 cases of bladder cancer, found that four 0.6-mm cores yielded highly concordant information on tumor grade and proliferative activity, when compared to whole sections of tumor. Camp and colleagues² found that analysis of two 0.6-mm cores yielded comparable information to whole section immunohistochemical analysis for estrogen receptor in more than 95% of cases of breast cancer studied. Most significantly, Torhorst and colleagues⁴ found that in a series of 553 cases of breast cancer, a single core of tumor was sufficient to demonstrate the prognostic significance of estrogen receptor, progesterone receptor, and p53 immunostaining, and that immunostaining of a single core was equivalent or superior to staining of whole sections, in demonstration of the prognostic significance of these markers. Thus, intratumoral heterogeneity, although an important theoretical consideration in the use of TMAs, has not proven to be an insurmountable problem in the application of TMA technology.

There are numerous potential applications of TMAs beyond testing of prognostic markers. TMAs are a superior way of testing new markers of potential diagnostic utility against a large panel of different tumors. We anticipate that the phenomenon of newly described antibodies being maximally specific in the first 6 months after they are described, with a progressive decline in specificity as more comprehensive studies are undertaken and more data accumulates, will end with the introduction of TMAs. For example, we were able to test MUM1/IRF4, a marker of plasmacytic differentiation, against 1335 different human tumors and normal tissues, showing that although it is a sensitive marker of plasmacytic differentiation, it lacks specificity, because it also stained other hematolymphoid and melanocytic tumors.⁹ Use of TMAs should become routine in this important application. We have also used TMAs for routine quality assurance; the use of a 351-case TMA block allows us to rapidly determine whether there has been change throughout time in the staining pattern of diagnostic antibodies, and to confirm that new lots of antibody have maintained their sensitivity and specificity.¹¹ In a test of interlaboratory variability in estrogen receptor staining, we were able to rapidly identify both highly concordant staining and interpretation for four of five participating laboratories, as well as a significant trend to weaker staining and resulting discrepant results from one laboratory.¹² TMA experiments are a logical follow-up to gene expression microarray experiments that show expression of groups of genes of prognostic or diagnostic significance, and we have undertaken such experiments in follow-up to our earlier gene expression studies of lymphoma,⁶ breast carcinoma,⁷ and sarcoma.¹³ This may be the most fruitful avenue of study as it allows rapid testing of novel potentially diagnostically relevant immunohistochemical markers identified by genome-wide gene expression studies.

Given the utility of TMAs, it is essential that the tools for data manipulation and storage be sufficiently evolved to meet these emergent needs. As we started to do TMA studies we were immediately struck by the inadequacy of existing approaches for recording immunohistochemical data. TMAs allow a large number of results to be generated, but equally importantly, dramatically increase the number of results that can routinely be generated on a single case. This increased complexity required that we rethink how we manage our data, and move away from project-by-project data management to a larger system in which all data for the TMA laboratory is centrally stored. TMA-Deconvoluter assists us in this goal by allowing rapid conversion of worksheet data to tabular form. This enables a more conventional assessment of the data (eg, Kaplan-Meier survival curves, multivariate analysis) to be done, and also allows hierarchical cluster analysis to be applied to the immunohistochemical staining data. It is not clear yet if hierarchical cluster analysis will be as powerful a tool in analysis of TMA experiments as it has been in gene expression microarray experiments, in which it has been critically important. However, the complexity of data from TMA experiments parallels that of gene expression microarrays and suggests that it will be applicable. Hierarchical cluster analysis may prove useful not just as a research tool, but also in diagnosis. A significant problem in the interpretation of immunohistochemical staining results occurs when there are apparent inconsistencies in the staining profile based on a panel of immunostains (for example a pleural tumor that is calretinin-, CEA-, and B72.3-positive, and negative for WT-1, Ber-EP4, and CK5/6). Although a completely typical immunophenotypic profile is readily interpretable, having extended panels of antibodies applied to difficult cases increases the likelihood that there will be apparent inconsistencies in the staining profiles generated, with one or more antibodies giving aberrant staining results. Currently no systematic approach exists to the interpretation of an extended data set generated by staining a case with a large panel of antibodies. However, application of the system described in this study may be a first step toward more complex analyses of immunostaining results. In a separate experiment, we have shown that with a set of 351 cases stained with 22 antibodies, hierarchical cluster analysis allows a significant degree of clustering of tumors according to tissue of origin.¹¹ In the current study we show that lymphomas of one type tend to cluster together. There are impediments to the diagnostic application of hierarchical cluster analysis, especially the lack of sufficiently tissue-specific markers (although newer markers such as myogenin and thyroid transcription factor are a significant advance). As well, the dynamic range of immunostaining scores is significantly narrower than that obtained for mRNA levels in gene microarray experiments and, as a result, the clustering of tumors based on immunoreactivity can be expected to be less well defined than is seen in gene expression studies. However, new technologies that can quantify the intensity of immunoreactivity may improve the dynamic range of immunostain data.

The main cost of the system we describe is the Bliss system for capture and storage of digital images. Although such a system is desirable, it is possible to set up a data management system for a TMA lab with nothing more than a PC with Excel and a statistical analysis package, as the other software components we describe are freely available. To realize the full potential of TMA technology, however, a digital image collection will allow for revisiting and reanalyzing immunostain interpretations.

The software system described in this article has been used successfully by groups outside our institution. By using the same software, it has made it possible to share the same arrays and array data, facilitating collaborations around areas of unique expertise and further maximizing the important resource that TMAs represent.

	Acknowledgements

 
We thank F. Hsu for comments and suggestions on the use of TMA-Deconvoluter and Drs. Pat Brown and David Botstein for their support. Supported by grant NIH-CA-85129.

	Footnotes

 
Address reprint requests to Matt van de Rijn, M.D., Department of Pathology, Stanford University Medical Center, 300 Pasteur Dr., Stanford, CA 94305. E-mail: mrijn{at}stanford.edu

Supported by the National Institutes of Health–National Cancer Institute (grant 1UO1 CA 85129).

Accepted for publication July 18, 2002.

	References

Top Abstract Organization of the Stanford... TMA Construction Recording Immunohistochemical... Reformatting Data with TMA... Hierarchical Cluster Analysis Other Data Analyses Capture and Retrieval of... Discussion References

 

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				N. A. Makretsov, D. G. Huntsman, T. O. Nielsen, E. Yorida, M. Peacock, M. C. U. Cheang, S. E. Dunn, M. Hayes, M. van de Rijn, C. Bajdik, et al. Hierarchical Clustering Analysis of Tissue Microarray Immunostaining Data Identifies Prognostically Significant Groups of Breast Carcinoma Clin. Cancer Res., September 15, 2004; 10(18): 6143 - 6151. [Abstract] [Full Text] [PDF]

				T. O. Nielsen, F. D. Hsu, K. Jensen, M. Cheang, G. Karaca, Z. Hu, T. Hernandez-Boussard, C. Livasy, D. Cowan, L. Dressler, et al. Immunohistochemical and Clinical Characterization of the Basal-Like Subtype of Invasive Breast Carcinoma Clin. Cancer Res., August 15, 2004; 10(16): 5367 - 5374. [Abstract] [Full Text] [PDF]

				A. Somasiri, J. S. Nielsen, N. Makretsov, M. L. McCoy, L. Prentice, C. B. Gilks, S. K. Chia, K. A. Gelmon, D. B. Kershaw, D. G. Huntsman, et al. Overexpression of the Anti-Adhesin Podocalyxin Is an Independent Predictor of Breast Cancer Progression Cancer Res., August 1, 2004; 64(15): 5068 - 5073. [Abstract] [Full Text] [PDF]

				R. B. West, C. L. Corless, X. Chen, B. P. Rubin, S. Subramanian, K. Montgomery, S. Zhu, C. A. Ball, T. O. Nielsen, R. Patel, et al. The Novel Marker, DOG1, Is Expressed Ubiquitously in Gastrointestinal Stromal Tumors Irrespective of KIT or PDGFRA Mutation Status Am. J. Pathol., July 1, 2004; 165(1): 107 - 113. [Abstract] [Full Text] [PDF]

				T. O. Nielsen, F. D. Hsu, J. X. O'Connell, C. B. Gilks, P. H.B. Sorensen, S. Linn, R. B. West, C. L. Liu, D. Botstein, P. O. Brown, et al. Tissue Microarray Validation of Epidermal Growth Factor Receptor and SALL2 in Synovial Sarcoma with Comparison to Tumors of Similar Histology Am. J. Pathol., October 1, 2003; 163(4): 1449 - 1456. [Abstract] [Full Text] [PDF]

				H. Vrolijk, W. Sloos, W. Mesker, P. Franken, R. Fodde, H. Morreau, and H. Tanke Automated Acquisition of Stained Tissue Microarrays for High-Throughput Evaluation of Molecular Targets J. Mol. Diagn., August 1, 2003; 5(3): 160 - 167. [Abstract] [Full Text] [PDF]

				J Packeisen, E Korsching, H Herbst, W Boecker, and H Buerger Demystified ... Tissue microarray technology Mol. Pathol., August 1, 2003; 56(4): 198 - 204. [Abstract] [Full Text] [PDF]

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