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

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

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

Patterns of EphA2 protein expression in primary and metastatic pancreatic carcinoma and correlation with genetic status

EphA2 is a transmembrane receptor tyrosine kinase that functions in the regulation of cell growth, survival, angiogenesis, and migration and EphA2 targeting has been proposed as a novel therapeutic strategy for neoplasms that overexpress this protein. EphA2 overexpression has been correlated with increased invasive and metastatic ability in pancreatic cancer cell lines. However, the patterns of EphA2 expression in human pancreatic cancers and associated metastases is unknown, as are the genetics of EphA2 in this tumor type. We collected clinicopathologic data and paraffin-embedded materials from 98 patients with primary and/or metastatic pancreatic cancer and performed immunohistochemical labeling for EphA2 protein. EphA2 protein immunolabeling was found in 207 of 219 samples (95%). The expression was predominantly cytoplasmic, although predominant membranous staining was observed in a minority of cases. When evaluated specifically for labeling intensity, primary and metastatic carcinomas were more strongly positive compared to benign ducts and PanIN lesions (P < 0.00001 and P < 0.01, respectively) and poorly differentiated carcinomas were more strongly positive for EphA2 than well and moderately differentiated tumors (P < 0.005). When primary carcinomas without metastatic disease were specifically compared to carcinomas with associated metastatic disease, the advanced carcinomas showed relatively less strong positive labeling for EphA2 (P < 0.008). Moreover, decreased EphA2 labeling was more commonly found in liver (P < 0.002), lung (P < 0.004) or peritoneal metastases (P < 0.01) as compared to distant lymph node metastases (P < 0.01). Genetic sequencing of the tyrosine kinase domain of EPHA2 in 22 samples of xenograft enriched pancreatic cancer did not reveal any inactivating mutations. However, EPHA2 amplification was found in 1 of 33 pancreatic cancers corresponding to a lymph node metastasis, indicating EPHA2 genomic amplification may underlie EphA2 overexpression in a minority of patients. Our data confirms that EphA2 is overexpressed in pancreatic cancer, but suggests a relative loss of EphA2 in co-existent pancreatic cancer metastases as well as a role for EPHA2 in organ specific metastasis

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.This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.</jats:p