Immunophenotypic Profiling of Nonsmall Cell Lung Cancer Progression Using the Tissue Microarray Approach

Abstract

This study, using tissue microarrays, aimed at the immunomorphologic profiling of nonsmall cell lung cancer (NSCLC) cases to reveal clinically relevant disease groups and biomarkers associated with patients' survival and tumor progression including brain metastatic potential. Donor tissue blocks were form 59 patients, including 33 primary tumors without distant metastasis and 26 brain metastatic primary tumors as well as the brain metastases. Sections were immunostained for 29 markers targeting molecules of cell adhesion, cell growth, cell cycle, and apoptosis regulation. beta-Catenin expression was the only independent prognostic marker associated with better outcome. Elevated expression of collagen XVII, CD44v6, and caspase-9, and the reduced production of beta-catenin and cellular apoptosis susceptibility protein were significantly associated with the metastatic potential of primary NSCLC. Expression of positive cell cycle regulators cyclin D1 and cyclin D3 was also increased in metastatic primary tumors. Metastatic tumor progression into the brain was accompanied by prominent p16, syndecan-1, p53 (DO7), and caspase-3 protein levels. Hierarchical clustering of complex immunoprofiles based on the differentially expressed markers grouped NSCLCs of the poorest outcome with high correlation including 2/3 of brain metastases of mixed histology. The brain metastatic potential of NSCLCs may be linked to the elevated levels of cyclinD1, cyclinD3, p16, p53, caspase-3, caspase-9, CD44v6, and collagen XVII and the down-regulation of beta-catenin and cellular apoptosis susceptibility protein. Unsupervised immunoprofiles based on differentially expressed biomarkers may help selecting lung cancers with aggressive behavior.

Classification using hierarchical clustering of tumor-infiltrating immune cells identifies poor prognostic ovarian cancers with high levels of COX expression

“…It has been frequently used to analyze DNA microarray data, and, more recently, studies of protein expression using immunohistochemical staining have also taken advantage of this method. [6][7][8][9]24,25 As hierarchical clustering uses correlation coefficients to assess similarity, numerical data, such as numbers of tumorinfiltrating immune cells, are suitable for analysis using this method. Therefore, we employed hierarchical clustering to study our data.…”

“…Recently, this method has been used to classify tumors based on the patterns of expression of multiple proteins. [6][7][8][9][10] In this study, we employed hierarchical clustering using the patterns of tumor-infiltrating CD1a þ , CD8 þ , and CD57 þ cells. As far as we know, hierarchical clustering has never been used to analyze the patterns of tumorinfiltrating immune cells.…”

Expression profile of apoptosis-related genes in childhood adrenocortical tumors: low level of expression of BCL2 and TNF genes suggests a poor prognosis

“…All differences were significant by the Mann-Whitney U test. associated higher expression levels of CASP9 with poor survival in colon carcinomas (20) and with the metastatic potential of non-small-cell lung cancer (21). The real significance of this association remains to be determined.…”

Correlation between the protein expression of A-kinase anchor protein 95, cyclin D3 and AKT and pathological indicators in lung cancer tissues

“…Out of 198 cases of non-Hodgkin lymphoma, 43 cases exhibited cyclin D3 overexpression (21.72%), while out of 170 cases of primary invasive breast cancer 38 cases exhibited cyclin D3 overexpression (22.36%) (4,5). A previous study reported that cyclin D3 expression was positive in 23/33 cases of primary lung cancer (69.70%) (16). The current study demonstrated that the positivity ratio of cyclin D3 expression in lung cancer tissue was 68.63%, which was significantly higher compared with that in pericarcinoma tissue (28.57%).…”