Frequent genomic copy number gain and overexpression of GATA-6 in pancreatic carcinoma

Multiple genetic alterations are well recognized as contributing to pancreatic carcinogenesis, although the finding of recurrent copy number changes indicates additional targets remain to be found. The objective of this study was to identify novel targets of genetic alteration that contribute to pancreatic cancer development or progression. We used Representational Oligonucleotide Microarray Analysis (ROMA) to identify copy number changes in pancreatic cancer xenografts, and validated these findings using FISH, quantitative PCR, Western blotting and immunohistochemical labeling. With this approach, we identified a 0.36-Mb amplification at 18q11.2 containing two known genes, GATA-6 and cTAGE1. Using a cutoff value of 3.0 fold compared to haploid controls, copy number gain or amplification was confirmed in 4 of 42 (9.5%) pancreatic carcinomas analyzed. Combined genetic and transcriptional analyses showed consistent overexpression of GATA-6 in all carcinomas with 18q11.2 gain, as well as in the majority of pancreatic cancers examined (17 of 30 cancers, 56.7%) that did not have gain of this region. By contrast, overexpression of cTAGE1 was rare in these same cancers suggesting GATA-6 is the true target of this copy number increase. GATA-6 mRNA overexpression corresponded to robust nuclear protein expression in cancer cell lines and resected tissues consistent with its role as a transcription factor. Intense nuclear labeling was significantly increased in PanIN-3 lesions and infiltrating carcinomas compared to normal duct epithelium (p < 0.000001 and p < 0.003, respectively). Forced overexpression of GATA6 in MiaPaca2 cells resulted in increased proliferation and growth in soft-agar. Gain and overexpression of the development-related transcription factor GATA-6 may play an important and hitherto unrecognized role in pancreatic carcinogenesis

The Management of Bilateral Ureteric Injury following Radical Hysterectomy

Iatrogenic ureteric injury is a well-recognised complication of radical hysterectomy. Bilateral ureteric injuries are rare, but do pose a considerable reconstructive challenge. We searched a prospectively acquired departmental database of ureteric injuries to identify patients with bilateral ureteric injury following radical hysterectomy. Five patients suffered bilateral ureteric injury over a 6-year period. Initial placement of ureteric stents was attempted in all patients. Stents were placed retrogradely into 6 ureters and antegradely into 2 ureters. In 1 patient ureteric stents could not be placed and they underwent primary ureteric reimplantation. In the 4 patients in which stents were placed, 2 were managed with stents alone, 1 required ureteric reimplantation for a persistent ureterovaginal fistula, and 1 developed a recurrent stricture. No patient managed by ureteric stenting suffered deterioration in serum creatinine. We feel that ureteric stenting, when possible, offers a safe primary management of bilateral ureteric injury at radical hysterectomy

Context matters: using reinforcement learning to develop human-readable, state-dependent outbreak response policies

The number of all possible epidemics of a given infectious disease that could occur on a given landscape is large for systems of real-world complexity. Furthermore, there is no guarantee that the control actions that are optimal, on average, over all possible epidemics are also best for each possible epidemic. Reinforcement learning (RL) and Monte Carlo control have been used to develop machine-readable context-dependent solutions for complex problems with many possible realizations ranging from video-games to the game of Go. RL could be a valuable tool to generate context-dependent policies for outbreak response, though translating the resulting policies into simple rules that can be read and interpreted by human decision-makers remains a challenge. Here we illustrate the application of RL to the development of context-dependent outbreak response policies to minimize outbreaks of foot-and-mouth disease. We show that control based on the resulting context-dependent policies, which adapt interventions to the specific outbreak, result in smaller outbreaks than static policies. We further illustrate two approaches for translating the complex machine-readable policies into simple heuristics that can be evaluated by human decision-makers