The diagnostic utility of immunohistochemistry in distinguishing between epithelioid mesotheliomas and squamous carcinomas of the lung: A comparative study

Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Modern Pathology (Impact Factor: 6.19). 04/2006; 19(3):417-28. DOI: 10.1038/modpathol.3800544
Source: PubMed


As both mesotheliomas and squamous carcinomas can present a wide variety of morphological patterns, they can on occasion be confused. Recently, some groups of investigators have called attention to the difficulties that sometimes exist in distinguishing between these malignancies and the need to define a panel of markers that can assist in reaching the correct diagnosis. The aim of the present study is to compare the value of the various immunohistochemical markers currently available for the diagnosis of mesothelioma and squamous carcinoma of the lung. A total of 30 epithelioid pleural mesotheliomas exhibiting a solid or predominantly solid pattern, and 30 nonkeratinizing squamous carcinomas of the lung were investigated for the expression of the following markers: podoplanin, calretinin, mesothelin, WT1, keratin 5/6, keratin 7, p63, carcinoembryonic antigen (CEA), MOC-31, Ber-EP4, B72.3, BG-8 (Lewis(y)), leu-M1 (CD15), and thyroid transcription factor-1 (TTF-1). All 30 (100%) of the mesotheliomas reacted for calretinin, mesothelin and keratin 7, 93% each for podoplanin, WT1 and keratin 5/6, 13% for Ber-EP4, 7% each for p63, MOC-31 and BG-8, and 0% for B72.3, CEA, leu-M1 and TTF-1. All 30 (100%) of the squamous carcinomas were positive for p63 and keratin 5/6, 97% for MOC-31, 87% for Ber-EP4, 80% for BG-8, 77% for CEA, 57% for keratin 7, 40% for calretinin and B72.3, 30% for leu-M1, 27% for mesothelin, 15% for podoplanin, and 0% for WT 1 and TTF-1. After analyzing the results, it is concluded that from a practical point-of-view, a combination of two positive mesothelioma markers (WT1 and calretinin or mesothelin) with two negative mesothelioma markers (p63 and MOC-31) would allow the differential diagnosis to be established between epithelioid mesotheliomas and squamous carcinomas of the lung in nearly all instances.

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    • "It is possible that mesothelium related proteins and/or genes may also be present in other tumors and involved in their tumorigenesis. Indeed, mesothelin and calretinin are found expressed in thymic carcinoma, thymoma, and non-keratinizing squamous cell carcinoma of lung [10,11]. But little is known regarding the expression levels, if any, of other mesothelial markers such as calretinin, CK5/6, D2-40 and WT1. "
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    ABSTRACT: Background Mesothelin, a mesothelial marker, has been found expressed in and as a potential treatment target of cholangioacarcinoma (CC). It is possible that CC may be derived from the cells sharing mesothelial markers. However, the expression of other mesothelial markers in CC is largely unknown. Methods Thirty CC cases (10 extrahepatic and 20 intrahepatic) were retrieved from our institutional archive. The immunohistochemical study of Calretinin (DC8), WT1 (6F-H2), Lymphatic Endothelial Marker (D2-40), CK5/6 (D5/16 B4) and CK19 (b170) was done on formalin fixed paraffin embedded sections for 2–3 blocks of each case. We compared the expression levels between CC and normal bile duct (NBD) on the same block. Results All of the CC and NBD are positive for CK19 (23/23) and negative for WT1 (0/23) and D2-40 (0/23), except one CC positive for D2-40(1/30, 3.3%) and one NBD positive for WT1 (1/23, 4.3%). Calretinin immunoreactivity was detected in 52.2% (12/23) of CC, but none in NBD (0/23). CK5/6 was also detectable in 73.3% (22/30) of CC and all NBD (30/30). Increased expression of calretinin and reduced expression of CK5/6 were more likely associated with CC than NBD (P < 0.001 and P = 0.002, respectively). The sequential staining pattern of positive calretinin and negative CK5/6 in calretinin negative cases has a sensitivity of 69.57% and a specificity of 100% for differentiating CC from NBD. CK5/6 expression was also more likely associated with well-differentiated CC (7/7 versus 12/20 in moderately differentiated, and 9/10 in poorly differentiated, P = 0.019) and extrahepatic CC (10/10 versus 12/20 in intrahepatic, P = 0.029), but there was no association between the calretinin expression and the CC grade or location. Conclusion Calretinin and CK5/6 immunohistochemical stains may be useful for diagnosing a CC. Their immunohistochemical results should be interpreted with caution in the cases with differential diagnoses of mesothelioma and CC. A full mesothelioma panel, including WT1 and/or D2-40, is recommended to better define a mesothelial lineage. The biology of calretinin and CK5/6 expression in CC is unclear, but might shed light on identifying therapeutic targets for CC.
    04/2014; 3(1):12. DOI:10.1186/2162-3619-3-12
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    • "Many publications provide immunohistochemical analysis as percentage of cases and control that are positive and negative for the antigen/protein under study. A recent study on immunohistochemical markers for mesothelioma listed the markers and percentage positive in the cases used but no sensitivity or specificity information [33]. Few studies provide sensitivity and specificity analysis; one recent study on lymphatic markers provided these analyses [34]. "
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    ABSTRACT: Immunohistochemical (IHC) staining of formalin-fixed and paraffin-embedded tissues (FFPE) is widely used in diagnostic surgical pathology. All anatomical and surgical pathologists use IHC to confirm cancer cell type and possible origin of metastatic cancer of unknown primary site. What kinds of improvements in IHC are needed to boost and strengthen the use of IHC in future diagnostic pathology practice? The aim of this perspective is to suggest that continuing reliance on immunohistochemistry in cancer diagnosis, search and validation of biomarkers for predictive and prognostic studies and utility in cancer treatment selection means that minimum IHC data sets including "normalization methods" for IHC scoring, use of relative protein expression levels, use of protein functional pathways and modifications and protein cell type specificity may be needed when markers are proposed for use in diagnostic pathology. Furthermore evidence based methods (EBM), minimum criteria for diagnostic accuracy (STARD), will help in selecting antibodies for use in diagnostic pathology. In the near future, quantitative methods of proteomics, quantitative real-time polymerase chain reaction (qRT-PCR) and the use of high-throughput genomics for diagnosis and predictive decisions may become preferred tools in medicine.
    International journal of clinical and experimental pathology 01/2009; 3(2):169-76. · 1.89 Impact Factor
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    • "An inspection of Fig. 7 reveals that for most of the SQ samples KRT5 is highly expressed while its expression level for the other four groups in the Lung Cancer data set is practically absent. This strong SQ specific signature of KRT5 is also reported in [65,66]. "
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    ABSTRACT: The Signal-to-Noise-Ratio (SNR) is often used for identification of biomarkers for two-class problems and no formal and useful generalization of SNR is available for multiclass problems. We propose innovative generalizations of SNR for multiclass cancer discrimination through introduction of two indices, Gene Dominant Index and Gene Dormant Index (GDIs). These two indices lead to the concepts of dominant and dormant genes with biological significance. We use these indices to develop methodologies for discovery of dominant and dormant biomarkers with interesting biological significance. The dominancy and dormancy of the identified biomarkers and their excellent discriminating power are also demonstrated pictorially using the scatterplot of individual gene and 2-D Sammon's projection of the selected set of genes. Using information from the literature we have shown that the GDI based method can identify dominant and dormant genes that play significant roles in cancer biology. These biomarkers are also used to design diagnostic prediction systems. To evaluate the effectiveness of the GDIs, we have used four multiclass cancer data sets (Small Round Blue Cell Tumors, Leukemia, Central Nervous System Tumors, and Lung Cancer). For each data set we demonstrate that the new indices can find biologically meaningful genes that can act as biomarkers. We then use six machine learning tools, Nearest Neighbor Classifier (NNC), Nearest Mean Classifier (NMC), Support Vector Machine (SVM) classifier with linear kernel, and SVM classifier with Gaussian kernel, where both SVMs are used in conjunction with one-vs-all (OVA) and one-vs-one (OVO) strategies. We found GDIs to be very effective in identifying biomarkers with strong class specific signatures. With all six tools and for all data sets we could achieve better or comparable prediction accuracies usually with fewer marker genes than results reported in the literature using the same computational protocols. The dominant genes are usually easy to find while good dormant genes may not always be available as dormant genes require stronger constraints to be satisfied; but when they are available, they can be used for authentication of diagnosis. Since GDI based schemes can find a small set of dominant/dormant biomarkers that is adequate to design diagnostic prediction systems, it opens up the possibility of using real-time qPCR assays or antibody based methods such as ELISA for an easy and low cost diagnosis of diseases. The dominant and dormant genes found by GDIs can be used in different ways to design more reliable diagnostic prediction systems.
    BMC Bioinformatics 11/2008; 9(1):425. DOI:10.1186/1471-2105-9-425 · 2.58 Impact Factor
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