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1.
Virchows Arch ; 464(1): 61-8, 2014 Jan.
Article in English | MEDLINE | ID: mdl-24221342

ABSTRACT

This study aimed at challenging pulmonary large cell carcinoma (LLC) as tumor entity and defining different subgroups according to immunohistochemical and molecular features. Expression of markers specific for glandular (TTF-1, napsin A, cytokeratin 7), squamous cell (p40, p63, cytokeratins 5/6, desmocollin-3), and neuroendocrine (chromogranin, synaptophysin, CD56) differentiation was studied in 121 LCC across their entire histological spectrum also using direct sequencing for epidermal growth factor receptor (EGFR) and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations and FISH analysis for ALK gene translocation. Survival was not investigated. All 47 large cell neuroendocrine carcinomas demonstrated a true neuroendocrine cell lineage, whereas all 24 basaloid and both 2 lymphoepithelioma-like carcinomas showed squamous cell markers. Eighteen out of 22 clear cell carcinomas had glandular differentiation, with KRAS mutations being present in 39 % of cases, whereas squamous cell differentiation was present in four cases. Eighteen out of 20 large cell carcinomas, not otherwise specified, had glandular differentiation upon immunohistochemistry, with an exon 21 L858R EGFR mutation in one (5 %) tumor, an exon 2 KRAS mutation in eight (40 %) tumors, and an ALK translocation in one (5 %) tumor, whereas two tumors positive for CK7 and CK5/6 and negative for all other markers were considered adenocarcinoma. All six LCC of rhabdoid type expressed TTF-1 and/or CK7, three of which also harbored KRAS mutations. When positive and negative immunohistochemical staining for these markers was combined, three subsets of LCC emerged exhibiting glandular, squamous, and neuroendocrine differentiation. Molecular alterations were restricted to tumors classified as adenocarcinoma. Stratifying LCC into specific categories using immunohistochemistry and molecular analysis may significantly impact on the choice of therapy.


Subject(s)
Carcinoma, Large Cell/classification , Lung Neoplasms/classification , Adult , Aged , Anaplastic Lymphoma Kinase , Carcinoma, Large Cell/genetics , Carcinoma, Large Cell/pathology , Female , Humans , Immunohistochemistry , Lung Neoplasms/genetics , Lung Neoplasms/pathology , Male , Middle Aged , Molecular Biology , Mutation , Neoplasm Staging , Proto-Oncogene Proteins/genetics , Proto-Oncogene Proteins p21(ras) , Receptor Protein-Tyrosine Kinases/genetics , ras Proteins/genetics
2.
Diagn Pathol ; 6 Suppl 1: S7, 2011 Mar 30.
Article in English | MEDLINE | ID: mdl-21489202

ABSTRACT

BACKGROUND: Ki67 labeling index (Ki67 LI), the percentage Ki67 immunoreactive cells, is a measure of tumor proliferation, with important clinical relevance in breast cancer, and it is extremely important to standardize its evaluation. AIM: To test the efficacy of computer assisted image analysis (CAIA) applied to completely digitized slides and to assess its feasibility in routine practice and compare the results obtained using two different Ki67 monoclonal antibodies. MATERIALS AND METHODS: 315 consecutive breast cancer routinely immunostained for Ki-67 (223 with SP6 and 92 with MM1 antibodies previously examined by an experienced pathologist, have been re-evaluated using Aperio Scanscope Xs. RESULTS: Mean human Ki67 LI values were 36%± 14.% and 28% ± 18% respectively for SP6 and MM1 antibodies; mean CAM Ki67 LI values were 31%± 19% and 22% ± 18% respectively for SP6 and MM1. Human and CAIA evaluation are statistically highly correlated (Pearson: 0.859, p<0.0001), although human LI are systematically higher. An interobserver variation study on CAIA performed on 84 cases showed that the correlation between the two evaluations was linear to an excellent degree. DISCUSSION: Our study shows that a) CAIA can be easily adopted in routine practice, b) human and CAIA Ki67 LI are highly correlated, although human LI are systematically higher, c) Ki67 LI using different evaluation methods and different antibodies shows important differences in cut-off values.


Subject(s)
Antibodies, Monoclonal , Breast Neoplasms/pathology , Cell Proliferation , Image Interpretation, Computer-Assisted , Immunohistochemistry/standards , Ki-67 Antigen/analysis , Adult , Aged , Aged, 80 and over , Breast Neoplasms/chemistry , Feasibility Studies , Female , Humans , Middle Aged , Observer Variation
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