New Smoothened ligands based on the purine scaffold as potential agents for treating pancreatic cancer.

Aberrant activation of the Hedgehog (Hh) signalling pathway has been associated with the development and progression of pancreatic cancer. For this reason, blockade of Hh pathway by inhibitors targeting the G protein-coupled receptor Smoothened (SMO) has been considered as a therapeutic target for the treatment of this cancer. In our previous work, we obtained a new SMO ligand based on a purine scaffold (compound I), which showed interesting antitumor activity in several cancer cell lines. In this work, we report the design and synthesis of 17 new purine derivatives, some of which showed high cytotoxic effect on Mia-PaCa-2 (Hh-dependent pancreatic cancer cell lines) and low toxicity on non-neoplastic HEK-293 cells compared with gemcitabine, such as 8f, 8g and 8h (IC50 = 4.56, 4.11 and 3.08 µM, respectively). Two of these purines also showed their ability to bind to SMO through NanoBRET assays (pKi = 5.17 for 8f and 5.01 for 8h), with higher affinities to compound I (pKi = 1.51). In addition, docking studies provided insight the purine substitution pattern is related to the affinity on SMO. Finally, studies of Hh inhibition for selected purines, using a transcriptional functional assay based on luciferase activity in NIH3T3 Shh-Light II cells, demonstrated that 8g reduced GLI activity with a IC50 = 6.4 µM as well as diminished the expression of Hh target genes in two specific Hh-dependent cell models, Med1 cells and Ptch1-/- mouse embryonic fibroblasts. Therefore, our results provide a platform for the design of SMO ligands that could be potential selective cytotoxic agents for the treatment of pancreatic cancer.

Texture analysis as a radiomic marker for differentiating renal tumors.

PURPOSE: To evaluate the utility of texture analysis for the differentiation of renal tumors, including the various renal cell carcinoma subtypes and oncocytoma. MATERIALS AND METHODS: Following IRB approval, a retrospective analysis was performed, including all patients with pathology-proven renal tumors and an abdominal computed tomography (CT) examination. CT images of the tumors were manually segmented, and texture analysis of the segmented tumors was performed. A support vector machine (SVM) method was also applied to classify tumor types. Texture analysis results were compared to the various tumors and areas under the curve (AUC) were calculated. Similar calculations were performed with the SVM data. RESULTS: One hundred nineteen patients were included. Excellent discriminators of tumors were identified among the histogram-based features noting features skewness and kurtosis, which demonstrated AUCs of 0.91 and 0.93 (p < 0.0001), respectively, for differentiating clear cell subtype from oncocytoma. Histogram feature median demonstrated an AUC of 0.99 (p < 0.0001) for differentiating papillary subtype from oncocytoma and an AUC of 0.92 for differentiating oncocytoma from other tumors. Machine learning further improved the results achieving very good to excellent discrimination of tumor subtypes. The ability of machine learning to distinguish clear cell subtype from other tumors and papillary subtype from other tumors was excellent with AUCs of 0.91 and 0.92, respectively. CONCLUSION: Texture analysis is a promising non-invasive tool for distinguishing renal tumors on CT images. These results were further improved upon application of machine learning, and support the further development of texture analysis as a quantitative biomarker for distinguishing various renal tumors.

Parallel imaging enhanced MR colonography using a phantom model.

PURPOSE: To compare various Array Spatial and Sensitivity Encoding Technique (ASSET)-enhanced T2W SSFSE (single shot fast spin echo) and T1-weighted (T1W) 3D SPGR (spoiled gradient recalled echo) sequences for polyp detection and image quality at MR colonography (MRC) in a phantom model. Limitations of MRC using standard 3D SPGR T1W imaging include the long breath-hold required to cover the entire colon within one acquisition and the relatively low spatial resolution due to the long acquisition time. Parallel imaging using ASSET-enhanced T2W SSFSE and 3D T1W SPGR imaging results in much shorter imaging times, which allows for increased spatial resolution. MATERIALS AND METHODS: Using two porcine colon phantoms each with eight simulated 3-10-mm "polyps," baseline reference sequences acquired without ASSET (6-mm slices and readout bandwidth [BW] 62 kHz) were compared with 11 SSFSE and 8 SPGR sequences acquired with 2-fold ASSET acceleration. ASSET-enhanced SSFSE and SPGR sequences comprised BW/matrix combinations ranging from 20-62 kHz/256-352x256, respectively, with slice thicknesses adjusted from 3.0 to 4.5 mm to maintain a 23-26-second acquisition time and 30 cm slab thickness. Two experienced radiologists viewed the datasets in a randomized, blinded fashion. RESULTS: Compared to reference sequences, ASSET-enhanced SSFSE and SPGR sequences facilitated better polyp detection and had similar overall image quality and per-phantom specificity. The two best ASSET-enhanced SSFSE (3 and 4.5 mm slices, each with BW of 62.5 kHz and 352x256 matrices) and three best ASSET-enhanced SPGR BW/slice thickness/matrix combinations of 31 kHz/4.4 msec/192x256; 62/3.4/192x256; and 62/4.0/192x256, respectively, permitted detection of all polyps>or=5 mm. CONCLUSION: Parallel imaging using ASSET-enhanced T2W SSFSE and T1W 3D SPGR improves the ability to detect significant colon polyps in an MRC phantom model.