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Tumors are characterized by somatic mutations that drive biological processes ultimately reflected in tumor phenotype. With regard to radiographic phenotypes, generally unconnected through present understanding to the presence of specific mutations, artificial intelligence methods can automatically quantify phenotypic characters by using predefined, engineered algorithms or automatic deep-learning methods, a process also known as radiomics. Here we demonstrate how imaging phenotypes can be connected to somatic mutations through an integrated analysis of independent datasets of 763 lung adenocarcinoma patients with somatic mutation testing and engineered CT image analytics. We developed radiomic signatures capable of distinguishing between tumor genotypes in a discovery cohort (n ¼ 353) and verified them in an independent validation cohort (n ¼ 352). All radiomic signatures significantly outperformed conventional radiographic predictors (tumor volume and maximum diameter).We found a radiomic signature related to radiographic heterogeneity that successfully discriminated between EGFR þ and EGFR Our results argue that somatic mutations drive distinct radiographic phenotypes that can be predicted by radiomics. This work has implications for the use of imaging-based biomarkers in the clinic, as applied noninvasively, repeatedly, and at low cost.

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).q RSNA, 2016 Purpose:To retrospectively identify the relationship between epidermal growth factor receptor (EGFR) mutation status, predominant histologic subtype, and computed tomographic (CT) characteristics in surgically resected lung adenocarcinomas in a cohort of Asian patients. materials andMethods:This study was approved by the institutional review board, with waiver of informed consent. Preoperative chest CT findings were retrospectively evaluated in 385 surgically resected lung adenocarcinomas. A total of 30 CT descriptors were assessed. EGFR mutations at exons 18-21 were determined by using the amplification refractory mutation system. Multiple logistic regression analyses were performed to identify independent factors of harboring EGFR mutation status. The final model was selected by using the backward elimination method, and two areas under the receiver operating characteristic curve (ROC) were compared with the nonparametric approach of DeLong, DeLong, and Clarke-Pearson. Results:EGFR mutations were found in 168 (43.6%) of 385 patients. Mutations were found more frequently in (a) female patients (P , .001); (b)those who had never smoked (P , .001); (c)those with lepidic predominant adenocarcinomas (P = .001) or intermediate pathologic grade (P , .001); (e) smaller tumors (P , .001); (f) tumors with spiculation (P = .019), ground-glass opacity (GGO) or mixed GGO (P , .001), air bronchogram (P = .006), bubblelike lucency (P , .001), vascular convergence (P = .024), thickened adjacent bronchovascular bundles (P = .027), or pleural retraction (P , .001); and (g) tumors without pleural attachment (P = .004), a well-defined margin (P = .010), marked heterogeneous enhancement (P = .001), severe peripheral emphysema (P = .002), severe peripheral fibrosis (P = .013), or lymphadenopathy (P = .028). The most important and significantly independent prognostic factors of harboring EGFR-activating mutation for the model with both clinical variables and CT features were those who had never smoked and those with smaller tumors, bubblelike lucency, homogeneous enhancement, or pleural retraction when adjusting for histologic subtype, pathologic grade, or thickened adjacent bronchovascular bundles. ROC curve analysis showed that use of clinical variables combined with CT features (area under the ROC curve = 0.778) was superior to use of clinical variables alone (area under the ROC curve = 0.690). Conclusion:CT imaging features of lung adenocarcinomas in combination with clinical variables can be used to prognosticate EGFR mutation status better than use of clinical variables alone.q RSNA, 2016

␤2-Chimerin is a member of the "non-protein kinase C" intracellular receptors for the second messenger diacylglycerol and the phorbol esters that is yet poorly characterized, particularly in the context of signaling pathways involved in proliferation and cancer progression. ␤2-Chimerin possesses a C-terminal Rac-GAP (GTPase-activating protein) domain that accelerates the hydrolysis of GTP from the Rac GTPase, leading to its inactivation. We found that ␤2-chimerin messenger levels are significantly down-regulated in human breast cancer cell lines as well as in breast tumors. Adenoviral delivery of ␤2-chimerin into MCF-7 breast cancer cells leads to inhibition of proliferation and G 1 cell cycle arrest. Mechanistic studies show that the effect involves the reduction in Rac-GTP levels, cyclin D1 expression, and retinoblastoma dephosphorylation. Studies using the mutated forms of ␤2-chimerin revealed that these effects were entirely dependent on its C-terminal GAP domain and Rac-GAP activity. Moreover, MCF-7 cells stably expressing active Rac (V12Rac1) but not RhoA (V14RhoA) were insensitive to ␤2-chimerin-induced inhibition of proliferation and cell cycle progression. The modulation of G 1 /S progression by ␤2-chimerin not only implies an essential role for Rac in breast cancer cell proliferation but also raises the intriguing possibility that diacylglycerol-regulated non-protein kinase C pathways can negatively impact proliferation mechanisms controlled by Rho GTPases.Chimerins represent a family of four closely related GAPs 1 (GTPase-activating proteins) for small GTPases that were originally characterized as high affinity intracellular receptors for the second messenger diacylglycerol (DAG) and the phorbol ester tumor promoters (1-4). Structurally, chimerins possess a C1 domain highly homologous to those of PKC isozymes (the DAG/phorbol ester binding site) and a C-terminal GAP domain. The ␣2-and ␤2-chimerins also have a N-terminal Src homology 2 domain of unknown function, which is not present in the splice variants ␣1-(or n-) and ␤1-chimerins (5, 6). Very little information is available regarding the regulation, expression, and function of ␤2-chimerin or the other chimerin isoforms as well as their role in proliferation mechanisms and cancer progression. We have been focusing our attention on ␤2-chimerin, because there is emerging evidence that this isoform is directly regulated by phorbol esters (4, 7) as well as tyrosine-kinase receptors that couple to DAG generation.2 Importantly, early studies in gliomas have suggested a potential role for ␤2-chimerin as a tumor suppressor (8) but its relevance in other cancer models is still unknown.In vitro studies have shown that the C-terminal domain of chimerins is capable of accelerating GTP hydrolysis from the small GTPase Rac1 without affecting the activity of RhoA or Cdc42 GTPases (9, 10). Our recent studies in COS cells revealed that ␤2-chimerin decreases cellular Rac-GTP levels and inhibits the elevation of Rac-GTP levels caused by epidermal growth factor (EGF) (...

The contemporary use of nanomedicines for cancer treatment has been largely limited to serving as carriers for existing therapeutic agents. Here, we provide definitive evidence that, the metallofullerenol nanomaterial Gd@C82(OH)22, while essentially not toxic to normal mammary epithelial cells, possesses intrinsic inhibitory activity against triple-negative breast cancer cells. Gd@C82(OH)22 blocks epithelial-to-mesenchymal transition with resultant efficient elimination of breast cancer stem cells (CSCs) resulting in abrogation of tumour initiation and metastasis. In normoxic conditions, Gd@C82(OH)22 mediates these effects by blocking TGF-β signalling. Moreover, under hypoxic conditions found in the tumour microenvironment, cellular uptake of Gd@C82(OH)22 is facilitated where it functions as a bi-potent inhibitor of HIF-1α and TGF-β activities, enhancing CSC elimination. These studies indicate that nanomaterials can be engineered to directly target CSCs. Thus, Gd-metallofullerenol is identified as a kind of non-toxic CSC specific inhibitors with significant therapeutic potential.

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Integrin β4 (ITGB4) has been reported to be involved in carcinomas. Currently, ITGB4 has been characterized in colon cancer, however, its clinical significance is not very clear. In the present study, we utilized the large public datasets from NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases and collected clinical samples in our center to investigate the transcriptional expressions of ITGB4 in colon cancer, and then explored the associations of ITGB4 with clinicopathological features and overall survival. The statistical analyses suggested that ITGB4 mRNA expressions were up-regulated significantly in colon cancer. High ITGB4 expression was observed to be associated with elder onset age, proximal tumor location, and high microsatellite instability (MSH) status. Further, Kaplan-Meier curves and univariate analysis demonstrated high ITGB4 expression was significantly associated with unfavorable overall survival in colon cancer (HR=1.292, 95%CI=1.084-1.540, P=0.004). And significant association was also found after adjusting the confounding factors including age, gender, and stage (adjusted HR=1.254, 95%CI=1.050-1.497, P=0.012). The annotation of ITGB4 co-expressed genes suggested the pathways including cell growth, positive regulation of cell migration, and apoptotic signaling might be involved in the potential mechanisms of ITGB4 in colon cancer development. The molecular regulation mechanism of ITGB4 ectopic expression in colon cancer was also explored and the results indicated that ITGB4 might be up-regulated by the transcription factor FOSL1 (FOS like 1, AP-1 Transcription Factor Subunit) and its promoter hypomethylation. Our results revealed that ITGB4 might be a therapeutic target and prognosis marker for individual therapy of colon cancer.