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Cluster of differentiation (CD) 166 or activated leukocyte cell adhesion molecule (ALCAM) is a transmembrane molecule known to be an intercellular adhesion factor. The expression and function of ALCAM in medulloblastoma (MB), a pediatric brain tumor with highly advanced molecular genetics, remains unclear. Therefore, this study aimed to clarify the significance and functional role of ALCAM expression in MB. ALCAM expression in 45 patients with MB was evaluated by immunohistochemical analysis of formalin-fixed paraffin-embedded clinical specimens and the relationship between ALCAM expression and pathological type/molecular subgroup, such as WNT, SHH, Group 3, and Group 4, was examined. Eight ALCAM positive (18%), seven partially positive (16%), and 30 negative (67%) cases were detected. All seven cases of the WNT molecular subgroup were ALCAM positive and ALCAM expression strongly correlated with this subgroup (P < 0.0001). In addition, functional studies using MB cell lines revealed ALCAM expression affected proliferation and migration as a positive regulator in vitro. However, ALCAM silencing did not affect survival or the formation of leptomeningeal dissemination in an orthotopic mouse model, but did induce a malignant phenotype with increased tumor cell invasion at the dissemination sites (P = 0.0029). In conclusion, our results revealed that ALCAM exhibited highly specific expression in the WNT subgroup of MB. Furthermore, we demonstrated that the cell kinetics of MB cell lines can be altered by the expression of ALCAM. [ABSTRACT FROM AUTHOR]

Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma. [ABSTRACT FROM AUTHOR]