Clin Cancer Res; 17(12); 4063-70. (C) 2011 AACR.”
“Objectives: Primary carcinomas of the urethra (PCU) are rare and often advanced when diagnosed. Treatment standards are lacking. We studied treatment response and survival in a cohort of patients with PCU, with emphasis on modern platinum-containing chemotherapy regimens plus surgery for advanced disease.\n\nMaterials

and methods: This was a retrospective chart review of consecutive patients with PCU seen by medical oncologists at our institution over a recent 5-year period. Outcome was measured as best response to chemotherapy. Kaplan-Meier estimates were generated SNX-5422 for survival and Cox proportional hazard was used for prognostic factors for survival.\n\nResults: The 44 patients (64% women) included had a median age at diagnosis of 66.5 years. The most prevalent histologic subtypes of PCU were squamous cell carcinoma and adenocarcinoma. At diagnosis, 43% already had lymph node-positive [lymph node (LN)+] disease, and 16% had distant metastases. The entire cohort’s overall survival (OS) was 31.7 months. The response rate to platinum-containing

neoadjuvant chemotherapy was 72%. Twenty-one patients with locally advanced or LN+ PCU underwent chemotherapy plus surgery. Their median OS from chemotherapy initiation was 25.6 months. Four of 9 patients (44%) with LY3039478 inhibitor LN+ PCU at diagnosis were alive at our review, with a minimum follow-up of more than 3 years.\n\nConclusions: Modem platinum-containing

regimens appear to be effective in advanced PCU. Preoperative chemotherapy is associated with prolonged disease-free survival in a subgroup of LN+ cases. (C) 2013 Elsevier Inc. All rights reserved.”
“Background: 4EGI-1 mouse Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.\n\nResults: In this paper recent extensions of R package gcExplorer are presented. gcExplorer is an interactive visualization toolbox for the investigation of the overall cluster structure as well as single clusters. The different visualization options including arbitrary node and panel functions are described in detail. Finally the toolbox can be used to investigate the quality of a given clustering graphically as well as theoretically by testing the association between a partition and a functional group under study.\n\nConclusion: It is shown that gcExplorer is a very helpful tool for a general exploration of microarray experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased.