Tissue Microarrays Are an Effective Quality Assurance Tool for Diagnostic Immunohistochemistry
Notice bibliographique
Résumé
There has been considerable variability in the reported results of immunohistochemical staining for some diagnostically relevant antigens. Our objectives in this study were to (1) use a multitumor tissue microarray with tissue from 351 cases received in our department, representing 16 normal tissues and 47 different tumor types, to compare immunohistochemical staining results in our laboratory with published data, using a panel of 22 antibodies; (2) assess interlaboratory variability of immunohistochemical staining for S-100 using this microarray; and (3) test the ability of hierarchical clustering analysis to group tumors by primary site, based on their immunostaining profile. Tissue microarrays consisting of duplicate 0.6-mm cores from blocks identified in the hospital archives were constructed and stained according to our usual protocols. Antibodies directed against the following antigens were used: B72.3, bcl-2, carcinoembryonic antigen, c-kit, pankeratin, CD 68, CD 99, CK 5/6, CK 7, CK 8/18, CK19, CK 20, CK 22, epithelial membrane antigen, estrogen receptor, melan-A, p53, placental alkaline phosphatase, S-100, synaptophysin, thyroid transcription factor-1, and vimentin. Staining results on the array cases were compared with published results, and hierarchical clustering analysis was performed based on the immunohistochemical staining results. Unstained slides of the multitumor tissue microarray were sent to five other diagnostic immunohistochemistry laboratories and stained for S-100 protein. The staining results from the different laboratories were compared. Staining results using our current methods and samples from our laboratory were compatible with those described in the literature for most antigens. Placental alkaline phosphatase staining was not specific with our protocol, showing staining of a broad spectrum of different tumors; this finding initiated a review of our recent requests for placental alkaline phosphatase immunostaining and revealed two instances in which placental alkaline phosphatase positivity was incorrectly interpreted as evidence of a germ cell tumor. S-100 staining was less sensitive but more specific for the diagnosis of melanoma or neural tumor in our laboratory, compared to some published reports. Assessment of interlaboratory variability of S-100 immunostaining showed that there was more frequent staining of carcinomas in some laboratories, resulting in decreased specificity of S-100 staining in distinguishing melanoma from carcinoma. Hierarchical clustering analysis showed a strong trend for tumors to cluster by tissue of origin, but there were significant exceptions. We conclude that multiple-tumor microarrays are an efficient method for assessing the sensitivity and specificity of staining with any antibody used diagnostically. As a tool for quality assurance, they offer the advantage of taking into account local differences in tissue fixation, processing, and staining. They also allow cost-effective assessment of interlaboratory variability in immunohistochemical staining. Results of hierarchical clustering analysis show the potential for panels of immunohistochemical stains to identify the primary site of metastatic carcinomas but also confirm the limitations of currently available antibodies in giving unequivocal tissue-specific staining patterns.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».