Immunohistochemical Validation of Rare Tissues and Antigens With Low Frequency of Occurrence: Recommendations From The Anatomic Pathology Patient Interest Association (APPIA)

Laboratories worldwide find it challenging to identify enough tissues and cases for verification and validation studies of low-incidence, rare antigens. These antigens have a low frequency of occurrence in the population, or have little or no expression in normal tissues. Validation studies are essential to assure testing standardization before introducing a new instrument, product, or test into the clinical laboratory. The College of American Pathologists has published comprehensive guidelines for the verification and validation of new immunohistochemical tests introduced into the laboratory menu. Within the guidelines, varied numbers of cases are required for nonpredictive versus predictive markers. However, regarding low-incidence antigens, the laboratory medical director determines the extent of validation required. Recommended practical solutions available to clinical laboratories for low-incidence validation include developing internal resources using the laboratory information system with retrospective and prospective search(s) of archival material and purchase of tissue microarray blocks, slides, or cell lines from external resources. Utilization of homemade multitissue blocks has proved to be extremely valuable in biomarker research and demonstrated great utility in clinical immunohistochemistry laboratories. Participation in External Quality Assessment program(s) may provide insufficient numbers or the ability to calculate concordance rates. However, supplementation with in-house tissues can allow a laboratory to reach the optimal number of cases needed for verification and/or validation schemes. An alternative approach is conducting a thorough literature search and correlating staining patterns of the new test to the expected results. These solutions may be used uniquely or together to assure consistent standardized testing.

Characterization of PD-L1 expression in Chinese non-small cell lung cancer patients with PTEN expression as a means for tissue quality screening

The goal of this study is to evaluate PD-L1 prevalence and its association with major clinical characteristics in Chinese non-small cell lung cancer (NSCLC) patients to inform the clinical development of anti-PD1/PD-L1 agents in this population. We used phosphatase and tensin homolog (PTEN) expression through IHC as a surrogate tissue quality marker to screen surgical NSCLC samples in tissue microarray (TMA; 172 cases) or whole-section (268 cases) format. The samples were then analyzed with a clinically validated PD-L1 IHC assay. The results were correlated with baseline characteristics and clinical outcomes. PTEN IHC showed that 108 TMA samples and 105 whole-section samples qualified for PD-L1 IHC. With a clinically relevant cutoff, 41.7% of the TMA samples were PD-L1 positive. PD-L1 level was much lower in EGFR-mutant patients and seemed to be a favorable prognostic factor for both overall survival (OS) and recurrence-free survival (RFS). These findings were confirmed in the whole-section samples except that their survival data were not mature enough for correlation analysis. In summary, PD-L1 expression was detected in approximately 40% of PTEN-qualified Chinese NSCLC samples, negatively correlated with EGFR mutation and seemed to be a favorable prognostic factor for both OS and RFS. Notably, the different results from PTEN-qualified and PTEN-disqualified samples underscore the importance of tissue quality control prior to biomarker testing.

Laboratory assessment of anti-thrombotic therapy in heart failure, atrial fibrillation and coronary artery disease: insights using thrombelastography and a micro-titre plate assay of thrombogenesis and fibrinolysis

As heart failure, coronary artery disease and atrial fibrillation all bring a risk of thrombosis, anti-thrombotic therapy is recommended. Despite such treatment, major cardiovascular events such as myocardial infarction and stroke still occur, implying inadequate suppression of thrombus formation. Accordingly, identification of patients whose haemostasis remains unimpaired by treatment is valuable. We compared indices for assessing thrombogenesis and fibrinolysis by two different techniques in patients on different anti-thrombotic agents, i.e. aspirin or warfarin. We determined fibrin clot formation and fibrinolysis by a microplate assay and thromboelastography, and platelet marker soluble P selectin in 181 patients with acute or chronic heart failure, coronary artery disease who were taking either aspirin or warfarin. Five thromboelastograph indices and four microplate assay indices were different on aspirin versus warfarin (p < 0.05). In multivariate regression analysis, only microplate assay indices rate of clot formation and rate of clot dissolution were independently related to aspirin or warfarin use (p ≤ 0.001). Five microplate assay indices, but no thrombelastograph index, were different (p < 0.001) in aspirin users. Three microplate assay indices were different (p ≤ 0.002) in warfarin users. The microplate assay indices of lag time and rate of clot formation were abnormal in chronic heart failure patients on aspirin, suggesting increased risk of thrombosis despite anti-platelet use. Soluble P selectin was lower in patients on aspirin (p = 0.0175) but failed to correlate with any other index of haemostasis. The microplate assay shows promise as a tool for dissecting thrombogenesis and fibrinolysis in cardiovascular disease, and the impact of antithrombotic therapy. Prospective studies are required to determine a role in predicting thrombotic risk.

Computational deconvolution of gene expression by individual host cellular subsets from microarray analyses of complex, parasite-infected whole tissues

Analyses of whole organs from parasite-infected animals can reveal the entirety of the host tissue transcriptome, but conventional approaches make it difficult to dissect out the contributions of individual cellular subsets to observed gene expression. Computational deconvolution of gene expression data may be one solution to this problem. We tested this potential solution by deconvoluting whole bladder gene expression microarray data derived from a model of experimental urogenital schistosomiasis. A supervised technique was used to group B-cell and T-cell related genes based on their cell types, with a semi-supervised technique to calculate the proportions of urothelial cells. We demonstrate that the deconvolution technique was able to group genes into their correct cell types with good accuracy. A clustering-based methodology was also used to improve prediction. However, incorrectly predicted genes could not be discriminated using this methodology. The incorrect predictions were primarily IgH- and IgK-related genes. To our knowledge, this is the first application of computational deconvolution to complex, parasite-infected whole tissues. Other computational techniques such as neural networks may need to be used to improve prediction.

Influence of Texture and Colour in Breast TMA Classification

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors.

Validation of 2-mm tissue microarray technology in gastric cancer. Agreement of 2-mm TMAs and full sections for Glut-1 and Hif-1 alpha

BACKGROUND/AIM

Tissue Microarray (TMA) is a widely used method to perform high-throughput immunohistochemical analyses on different tissues by arraying small sample cores from paraffin-fixed tissues into a single paraffin block. TMA-technology has been validated on numerous cancer tissues and also for gastric cancer studies, although it has not been validated for this tumor tissue so far. The objective of this study was to assess, whether the 2-mm TMA-technology is able to provide representative samples of gastric cancer tissue.

MATERIALS AND METHODS

TMA paraffin blocks were constructed by means of 220 formalin-fixed and paraffin-embedded gastric cancer samples with a sample diameter of 2 mm. The agreement of immunohistochemical stainings of Glut-1 and Hif-1 alpha in TMA sections and the original full sections was calculated using kappa statistics and direct adjustment.

RESULTS

The congruence was substantial for Glut-1 (kappa 0.64) and Hif-1 alpha (kappa 0.70), but with an agreement of only 71% and 52% within the marker-positive cases of the full-section slides.

CONCLUSION

Due to tumor heterogeneity primarily, the TMA technology with a 2-mm sample core shows relevant limitations in gastric cancer tissue. Although being helpful for tissue screening purposes, the 2-mm TMA technology cannot be recommended as a method equal to full-section investigations in gastric cancer.

Smooth muscle contamination analysis in clinical oncology gene expression research

Gene expression profiling is one of the most explored methods for studying cancers and microarray data repositories have become a rich and important resource. The most common human cancers develop in organs that are walled by smooth muscles. The only method of sample extraction free of unintentional contamination with surrounding tissue is microdissection. Nevertheless, such an approach is implemented infrequently. In the light of the above, there is a possibility of smooth muscle contamination in a large portion of publicly available data. In this study, 2292 publicly available microarrays were analysed to develop a simple screening method for detecting smooth muscle contamination. Microarray Inspector software was used to perform the tests since it has the unique ability to use many selected genes and probesets in a single group as a tissue definition. Furthermore, the test was dataset-independent. Two strategies of tissue definition were explored and compared. The first one depended on Tissue Specific Genes Database (TiSGeD) and BioGPS web resources, which themselves were based on meta-analysis of thousands of microarrays. The second method was based on a differential gene expression analysis of a few hundred preselected arrays. The comparison of the two methods proved the latter to be superior. Among the tested samples of undefined contamination, nearly half were identified to possibly contain significant smooth muscle traces. The obtained results equip researches with a simple method of examining microarray data for smooth muscle contamination. The presented work serves as an example of how to create definitions when searching for other possible contaminations.

An international Ki67 reproducibility study

BACKGROUND

In breast cancer, immunohistochemical assessment of proliferation using the marker Ki67 has potential use in both research and clinical management. However, lack of consistency across laboratories has limited Ki67's value. A working group was assembled to devise a strategy to harmonize Ki67 analysis and increase scoring concordance. Toward that goal, we conducted a Ki67 reproducibility study.

METHODS

Eight laboratories received 100 breast cancer cases arranged into 1-mm core tissue microarrays-one set stained by the participating laboratory and one set stained by the central laboratory, both using antibody MIB-1. Each laboratory scored Ki67 as percentage of positively stained invasive tumor cells using its own method. Six laboratories repeated scoring of 50 locally stained cases on 3 different days. Sources of variation were analyzed using random effects models with log2-transformed measurements. Reproducibility was quantified by intraclass correlation coefficient (ICC), and the approximate two-sided 95% confidence intervals (CIs) for the true intraclass correlation coefficients in these experiments were provided.

RESULTS

Intralaboratory reproducibility was high (ICC = 0.94; 95% CI = 0.93 to 0.97). Interlaboratory reproducibility was only moderate (central staining: ICC = 0.71, 95% CI = 0.47 to 0.78; local staining: ICC = 0.59, 95% CI = 0.37 to 0.68). Geometric mean of Ki67 values for each laboratory across the 100 cases ranged 7.1% to 23.9% with central staining and 6.1% to 30.1% with local staining. Factors contributing to interlaboratory discordance included tumor region selection, counting method, and subjective assessment of staining positivity. Formal counting methods gave more consistent results than visual estimation.

CONCLUSIONS

Substantial variability in Ki67 scoring was observed among some of the world's most experienced laboratories. Ki67 values and cutoffs for clinical decision-making cannot be transferred between laboratories without standardizing scoring methodology because analytical validity is limited.

Robust automated tumour segmentation on histological and immunohistochemical tissue images

Tissue microarray (TMA) is a high throughput analysis tool to identify new diagnostic and prognostic markers in human cancers. However, standard automated method in tumour detection on both routine histochemical and immunohistochemistry (IHC) images is under developed. This paper presents a robust automated tumour cell segmentation model which can be applied to both routine histochemical tissue slides and IHC slides and deal with finer pixel-based segmentation in comparison with blob or area based segmentation by existing approaches. The presented technique greatly improves the process of TMA construction and plays an important role in automated IHC quantification in biomarker analysis where excluding stroma areas is critical. With the finest pixel-based evaluation (instead of area-based or object-based), the experimental results show that the proposed method is able to achieve 80% accuracy and 78% accuracy in two different types of pathological virtual slides, i.e., routine histochemical H&E and IHC images, respectively. The presented technique greatly reduces labor-intensive workloads for pathologists and highly speeds up the process of TMA construction and provides a possibility for fully automated IHC quantification.

Modified least-variant set normalization for miRNA microarray

MicroRNAs (miRNAs) are short noncoding RNAs that are involved in post-transcriptional regulation of mRNAs. Microarrays have been employed to measure global miRNA expressions; however, because the number of miRNAs is much smaller than the number of mRNAs, it is not clear whether traditional normalization methods developed for mRNA arrays are suitable for miRNA. This is an important question, since normalization affects downstream analyses of the data. In this paper we develop a least-variant set (LVS) normalization method, which was previously shown to outperform other methods in mRNA analysis when standard assumptions are violated. The selection of the LVS miRNAs is based on a robust linear model fit of the probe-level data that takes into account the considerable differences in variances between probes. In a spike-in study, we show that the LVS has similar operating characteristics, in terms of sensitivity and specificity, compared with the ideal normalization, and it is better than no normalization, 75th percentile-shift, quantile, global median, VSN, and lowess normalization methods. We evaluate four expression-summary measures using a tissue data set; summarization from the robust model performs as well as the others. Finally, comparisons using expression data from two dissimilar tissues and two similar ones show that LVS normalization has better operating characteristics than other normalizations.

Assessment of automated image analysis of breast cancer tissue microarrays for epidemiologic studies

BACKGROUND

A major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA).

METHODS

TMAs of 440 breast cancers previously stained for estrogen receptor (ER)-alpha, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-beta, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted kappa statistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels.

RESULTS

Agreement between pathologist and automated negative/positive and categorical scores was excellent for ER-alpha and PR (AUC range = 0.98-0.99; kappa range = 0.86-0.91). Lower levels of agreement were seen for ER-beta categorical scores (AUC = 0.99-1.0; kappa = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; kappa = 0.41-0.67) and HER2 (AUC = 0.94-0.97; kappa = 0.53-0.72). For ER-alpha and PR, there was a strong correlation between mRNA levels and automated (rho = 0.67-0.74) and pathologist immunohistochemical scores (rho = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (rho = 0.63) than automated immunohistochemical scores (rho = 0.41-0.49).

CONCLUSIONS

Automated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches.

Validation of tissue microarray immunohistochemistry staining and interpretation in diffuse large B-cell lymphoma

Tissue microarrays (TMAs) show concordance with whole tissue sections in the immunohistochemical evaluation of tumor cells. However, potential inter-institutional variability among observers and immunohistochemical staining methods has not been fully addressed. We selected 21 cases of diffuse large B-cell lymphoma (DLBCL) to process for TMAs. Immunohistochemical stains were performed in 3 laboratories, and reviewed independently by 3 hematopathologists at the 3 institutions. Stains were scored on a 4-point scale. Statistical analyses of variation in the scoring among observers and among different institutions' stains were performed. Stains for CD3, CD10, CD20, BCL-2, BCL-6, MIB-1, and FOX-P1 revealed little variation among observers, with an average 51-82% complete agreement and 82-100% agreement +/- 1 numerical score. The rate of concordance when evaluating most stains performed in different laboratories was also relatively good, with an average of 55-72% complete agreement and 70-97% agreement +/- 1 score. However, scoring of MUM-1 and p53 stains showed wider variation, with an average of only 37 and 30% complete agreement among observers, and 11 and 45% agreement when stains from different institutions were examined. Further statistical analyses were performed to compare the observers' scoring of their own institution's stains (self-review) vs. observers' scoring of other institutions' stains (non-self). The agreement rate for the p53 stain was significantly higher when based on self-review (average 58% complete agreement) compared with an agreement rate of only 10.5% when based on a review of stains performed in another laboratory, non-self review, P < 0.01. This difference in the self- vs. non-self review was not seen when data for MUM-1 were analysed. In conclusion, most phenotypic markers used in the analysis of DLBCL can be evaluated in TMAs with adequate agreement among observers and laboratories. These include CD3, CD20, CD10, BCL-2, BCL-6, MIB-1, and FOX-P1. However, some markers, such as p53 and MUM-1, are more prone to inter-institutional variation. Variations in interpretation can be partially overcome by self-adjusted/adapt tendency, as seen with p53. Especially with newly developed markers, such as MUM-1, the development of standardized techniques for staining and interpretation is critical to reduce inter-observer variability.

Tissue microarray technology in breast cancer HER2 diagnostics

Tissue microarrays (TMAs) as current medical research tools significantly lower the costs of immunohistochemical examinations (IHC) and fluorescence in situ hybridization (FISH) while enabling high levels of standardization and reliability. Taking HER2 testing of breast cancer into consideration, we assessed the routine applicability of TMAs. A hundred and seventy-four consecutive samples of invasive breast cancer cases were selected. TMAs were constructed in order to conduct double HER2 immunohistochemical analysis and FISH abreast using the conventional slide by slide method. Comparing the immunohistochemical data obtained from TMAs with the routinely processed large sections, we found a 94.5%/92.7%, 85.7%/88.9% and 91.2%/90% concordance at immunohistochemically HER2-negative, HER2 2+ and 3+ cases using the CB11/HercepTest, respectively. FISH performed on TMAs helped to determine Herceptin therapy suitability in all cases, and when discordance was found, we controlled FISH on "large sections". Being able to conduct FISH examinations at a reasonable price with or without prior immunohistochemical analysis, departments confronted with a certain frequency of breast cancer cases might extensively use the type of TMAs applied in our study. This is a relieve not only with regard to diagnostic work using microarrays, but this also allows to take new directions in research by shedding light on certain unusual cases.

Immunohistochemical detection of 3 viral infections in paraffin-embedded tissue from mink (Mustela vison): a tissue-microarray-based study

Immunohistochemical (IHC) assays were developed and tested for the detection of 3 viral infections in archived paraffin-embedded mink tissue. Specimens had been obtained from mink with diagnoses of acute Aleutian disease (AD), mink parvoviral enteritis (MVE), or canine distemper (CD) made by means of routine diagnostic procedures. To improve the efficiency and reduce the costs of IHC analyses, tissue microarray (TMA) technology was used. Representative cores 2 mm in diameter from each tissue specimen and from positive- and negative-control specimens were collected in a TMA block. Immunohistochemical reactions to viral antigens were assessed and graded. Positive reactions were found in 91% of the 32 specimens from mink with AD, 53% to 80% of the 60 specimens from mink with MVE, and all 66 of the specimens from mink with CD. To validate the use of TMAs, the IHC methods were applied to whole-mount paraffin-embedded sections of 10 of the positive specimens for each disease, together with whole-mount sections of small intestine and lung tissue from 2 healthy mink. The IHC grading of the TMA cores and the whole-mount sections from the same animal corresponded completely. These results suggest that IHC demonstration of viral antigen allows rapid and reliable diagnosis of the 3 viral infections in mink and is a potential supplement to histologic diagnostic procedures. The TMA technique proved useful for screening large numbers of samples for expression of specific viral antigens, while reducing overall costs.

Human postmortem brain tissue and 2-mm tissue microarrays

The authors constructed tissue microarray (TMA) blocks from human postmortem brain including numerous core samples measuring 2 mm in diameter from various anatomic regions. These TMA sections were then processed using various stainings and pretreatment techniques to evaluate their properties. The loss of core samples ranged from 2% to 100% and was significantly influenced by the type of glass slide used; it was lowest (2-8%) with SuperFrost Plus slides. The losses were not significantly altered when applying the most demanding pretreatment procedures or using human brain tissue with a long postmortem delay. A slight influence on the quality and the repeatability of some of the IHC stainings was seen by the postmortem delay, by the brain region, or by the glass slide used. One special feature of the constructed brain TMA block including many anatomic brain regions is that persons who lack skills in neuroanatomy can identify various brain structures simply by following the x-y coordinates. Thus, the applications of this brain TMA block in neurologic research by scientists with different skill bases remain to be determined.

Requirements, features, and performance of high content screening platforms

High content screening (HCS) platforms integrate fluorescence microscopy with image analysis algorithms and informatics to automate cell analysis. The initial applications of HCS to secondary screening in drug discovery have spread throughout the discovery pipeline, and now into the expanding research field of systems cell biology, in which new manipulation tools enable the use of large scale screens to understand cellular pathways, and cell functions. In this chapter we discuss the requirements for HCS and the systems that have been designed to meet these application needs. The number of HCS systems available in the market place, and the range of features available, has grown considerably in the past 2 yr. Of the two general optical designs, the confocal systems have dominated the high-throughput HCS market, whereas the more cost effective wide-field systems have dominated all other market segments, and have a much larger market share. The majority of available systems have been optimized for fixed cell applications; however, there is growing interest in live cell kinetic assays, and four systems have successfully penetrated this application area. The breadth of applications for these systems continues to expand, especially with the integration of new technologies. New applications, improved software, better data visualization tools, and new detection methods such as multispectral imaging and fluorescence lifetime are predicted to drive the development of future HCS platforms.

The tissue microarray object model: a data model for storage, analysis, and exchange of tissue microarray experimental data

CONTEXT

Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis.

OBJECTIVE

To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements.

DESIGN

We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation.

RESULT

The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM.

CONCLUSIONS

The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

The CD117 immunohistochemistry tissue microarray survey for quality assurance and interlaboratory comparison: a College of American Pathologists Cell Markers Committee Study

CONTEXT

We have developed tissue microarray-based surveys to allow laboratories to compare their performance in staining predictive immunohistochemical markers, including proto-oncogene CD117 (c-kit), which is characteristically expressed in gastrointestinal stromal tumors (GISTs). GISTs exhibit activating mutations in the c-kit proto-oncogene, which render them amenable to treatment with imatinib mesylate. Consequently, correct identification of c-Kit expression is important for the diagnosis and treatment of GISTs.

OBJECTIVE

To analyze CD117 immunohistochemical staining performance by a large number of clinical laboratories.

DESIGN

A mechanical device was used to construct tissue microarrays consisting of 3 x 1-mm cores of 10 tumor samples, which can be used to generate hundreds of tissue sections from the arrayed cases, suitable for large-scale interlaboratory comparison of immunohistochemical staining.

RESULTS

An initial survey of 63 laboratories and a second survey of 90 laboratories, performed in 2004 and 2005, exhibited >81% concordance for 7 of 10 cores, including all 4 GIST cases, which were immunoreactive for CD117 with >95% staining concordance. Three of the cores achieved less than 81% concordance of results, possibly due to the presence of foci of necrosis in one core and CD117-positive mast cells in 2 cores of CD117-negative neoplasms.

CONCLUSIONS

There was good performance among a large number of laboratories performing CD117 immunohistochemical staining, with consistently higher concordance of results for CD117-positive GIST cases than for nonimmunoreactive cases. Tissue microarrays for CD117 and other predictive markers should be useful for interlaboratory comparisons, quality assurance, and education of participants regarding staining nuances such as the expression of CKIT by nonneoplastic mast cells.

A new approach to the validation of tissue microarrays

Although tissue microarrays (TMA) have been widely used for a number of years, it is still not clear how many core biopsies should be taken to determine a reliable value for percentage positivity or how much heterogeneity in marker expression influences this number. The first aim of this study was to validate the human visual semi-quantitative scoring system for positive staining of tumour tissue with the exact values determined from computer-generated images. The second aim was to determine the minimum number of core biopsies needed to estimate percentage positivity reliably when the immunohistochemical staining pattern is heterogeneous and scored in a non-binary way. Tissue sections from ten colorectal cancer specimens were stained for carbonic anhydrase IX (CA IX). The staining patterns were digitized and 400 artificial computer-generated images were generated to test the accuracy of the human scoring system. To determine the minimal number of core biopsies needed to account for tumour heterogeneity, 50 (artificial) core biopsies per section were taken from the tumoural region of the ten digitally recorded full tissue sections. Based on the semi-quantitative scores from the 50 core biopsies per section, 2500 x n (n = 1-10 core biopsies) experimental core biopsies were then generated and scores recorded. After comparison with field-by-field analysis from the tumoural region of the whole tissue section, the number of core biopsies that need to be taken to minimize the influence of heterogeneity could be determined. In conclusion, visual scoring accurately estimated the percentage positivity and the percentage tumour present in a section, as judged by comparison with the artificial images. The exact number of core biopsies that has to be examined to determine tumour marker positivity using TMAs is affected by the degree of heterogeneity in the expression pattern of the protein, but for most purposes at least four is recommended.