Prevalence of germ-line mutations in p16, p19ARF, and CDK4 in familial melanoma:analysis of a clinic-based population

Five to ten percent of individuals with melanoma have another affected family member, suggesting familial predisposition. Germ-line mutations in the cyclin-dependent kinase (CDK) inhibitor p16 have been reported in a subset of melanoma pedigrees, but their prevalence is unknown in more common cases of familial melanoma that do not involve large families with multiple affected members. We screened for germ-line mutations in p16 and in two other candidate melanoma genes, p19ARF and CDK4, in 33 consecutive patients treated for melanoma; these patients had at least one affected first or second degree relative (28 independent families). Five independent, definitive p16 mutations were detected (18%, 95% confidence interval: 6%, 37%), including one nonsense, one disease-associated missense, and three small deletions. No mutations were detected in CDK4. Disease-associated mutations in p19ARF, whose transcript is derived in part from an alternative codon reading frame of p16, were only detected in patients who also had mutations inactivating p16. We conclude that germ-line p16 mutations are present in a significant fraction of individuals who have melanoma and a positive family history.

An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models

Clean and renewable energy generation and supply has drawn much attention worldwide in recent years, the proton exchange membrane (PEM) fuel cells and solar cells are among the most popular technologies. Accurately modeling the PEM fuel cells as well as solar cells is critical in their applications, and this involves the identification and optimization of model parameters. This is however challenging due to the highly nonlinear and complex nature of the models. In particular for PEM fuel cells, the model has to be optimized under different operation conditions, thus making the solution space extremely complex. In this paper, an improved and simplified teaching-learning based optimization algorithm (STLBO) is proposed to identify and optimize parameters for these two types of cell models. This is achieved by introducing an elite strategy to improve the quality of population and a local search is employed to further enhance the performance of the global best solution. To improve the diversity of the local search a chaotic map is also introduced. Compared with the basic TLBO, the structure of the proposed algorithm is much simplified and the searching ability is significantly enhanced. The performance of the proposed STLBO is firstly tested and verified on two low dimension decomposable problems and twelve large scale benchmark functions, then on the parameter identification of PEM fuel cell as well as solar cell models. Intensive experimental simulations show that the proposed STLBO exhibits excellent performance in terms of the accuracy and speed, in comparison with those reported in the literature.

High-dose therapy and autologous stem cell transplantation in first relapse for diffuse large B cell lymphoma in the rituximab era:an analysis based on data from the European Blood and Marrow Transplantation Registry

Autologous stem cell transplantation (ASCT) consolidation remains the treatment of choice for patients with relapsed diffuse large B cell lymphoma. The impact of rituximab combined with chemotherapy in either first- or second-line therapy on the ultimate results of ASCT remains to be determined, however. This study was designed to evaluate the benefit of ASCT in patients achieving a second complete remission after salvage chemotherapy by retrospectively comparing the disease-free survival (DFS) after ASCT for each patient with the duration of the first complete remission (CR1). Between 1990 and 2005, a total of 470 patients who had undergone ASCT and reported to the European Blood and Bone Transplantation Registry with Medical Essential Data Form B information were evaluated. Of these 470 patients, 351 (74%) had not received rituximab before ASCT, and 119 (25%) had received rituximab before ASCT. The median duration of CR1 was 11 months. The median time from diagnosis to ASCT was 24 months. The BEAM protocol was the most frequently used conditioning regimen (67%). After ASCT, the 5-year overall survival was 63% (95% confidence interval, 58%-67%) and 5-year DFS was 48% (95% confidence interval, 43%-53%) for the entire patient population. Statistical analysis showed a significant increase in DFS after ASCT compared with duration of CR1 (median, 51 months versus 11 months; P < .001). This difference was also highly significant for patients with previous exposure to rituximab (median, 10 months versus not reached; P < .001) and for patients who had experienced relapse before 1 year (median, 6 months versus 47 months; P < .001). Our data indicate that ASCT can significantly increase DFS compared with the duration of CR1 in relapsed diffuse large B cell lymphoma and can alter the disease course even in patients with high-risk disease previously treated with rituximab.

HPV prevalence and type-distribution in cervical cancer and premalignant lesions of the cervix: A population-based study from Northern Ireland

Assessment of Human papillomavirus (HPV) prevalence and genotype distribution is important for monitoring the impact of prophylactic HPV vaccination. This study aimed to demonstrate the HPV genotypes predominating in pre-malignant and cervical cancers in Northern Ireland (NI) before the vaccination campaign has effect. Formalin fixed paraffin embedded tissue blocks from 2,303 women aged 16-93 years throughout NI were collated between April 2011 and February 2013. HPV DNA was amplified by PCR and HPV genotyping undertaken using the Roche^® linear array detection kit. In total, 1,241 out of 1,830 eligible samples (68.0%) tested positive for HPV, with the majority of these [1,181/1,830 (64.5%)] having high-risk (HR) HPV infection; 37.4% were positive for HPV-16 (n=684) and 5.1% for HPV-18 (n=93). HPV type-specific prevalence was 48.1%, 65.9%, 81.3%, 92.2%, and 64.3% among cervical intraepithelial neoplasias (CIN) Grades I-III, squamous cell carcinomas (SCC) and adenocarcinoma (AC) cases, respectively. Most SCC cases (81.3%) had only one HPV genotype detected and almost a third (32.0%) of all cervical pathologies were HPV negative including 51.9% of CIN I (n=283), 34.1% CIN II (n=145), 18.7% of CIN III (n=146), 7.8% of SCC (n=5), and 35.7% of AC (n=5) cases. This study provides important baseline data for monitoring the effect of HPV vaccination in NI and for comparison with other UK regions. The coverage of other HR-HPV genotypes apart from 16 and 18, including HPV-45, 31, 39, and 52, and the potential for cross protection, should be considered when considering future polyvalent vaccines.

HDAC1 and HDAC2 independently predict mortality in hepatocellular carcinoma by a competing risk regression model in a Southeast Asian population

Histone deacetylases (HDACs) are enzymes involved in transcriptional repression. We aimed to examine the significance of HDAC1 and HDAC2 gene expression in the prediction of recurrence and survival in 156 patients with hepatocellular carcinoma (HCC) among a South East Asian population who underwent curative surgical resection in Singapore. We found that HDAC1 and HDAC2 were upregulated in the majority of HCC tissues. The presence of HDAC1 in tumor tissues was correlated with poor tumor differentiation. Notably, HDAC1 expression in adjacent non-tumor hepatic tissues was correlated with the presence of satellite nodules and multiple lesions, suggesting that HDAC1 upregulation within the field of HCC may contribute to tumor spread. Using competing risk regression analysis, we found that increased cancer-specific mortality was significantly associated with HDAC2 expression. Mortality was also increased with high HDAC1 expression. In the liver cancer cell lines, HEP3B, HEPG2, PLC5, and a colorectal cancer cell line, HCT116, the combined knockdown of HDAC1 and HDAC2 increased cell death and reduced cell proliferation as well as colony formation. In contrast, knockdown of either HDAC1 or HDAC2 alone had minimal effects on cell death and proliferation. Taken together, our study suggests that both HDAC1 and HDAC2 exert pro-survival effects in HCC cells, and the combination of isoform-specific HDAC inhibitors against both HDACs may be effective in targeting HCC to reduce mortality.

Statin use after diagnosis of breast cancer and survival: a population-based cohort study

BACKGROUND: Preclinical studies have shown that statins, particularly simvastatin, can prevent growth in breast cancer cell lines and animal models. We investigated whether statins used after breast cancer diagnosis reduced the risk of breast cancer-specific, or all-cause, mortality in a large cohort of breast cancer patients.

METHODS: A cohort of 17,880 breast cancer patients, newly diagnosed between 1998 and 2009, was identified from English cancer registries (from the National Cancer Data Repository). This cohort was linked to the UK Clinical Practice Research Datalink, providing prescription records, and to the Office of National Statistics mortality data (up to 2013), identifying 3694 deaths, including 1469 deaths attributable to breast cancer. Unadjusted and adjusted hazard ratios (HRs) for breast cancer-specific, and all-cause, mortality in statin users after breast cancer diagnosis were calculated using time-dependent Cox regression models. Sensitivity analyses were conducted using multiple imputation methods, propensity score methods and a case-control approach.

RESULTS: There was some evidence that statin use after a diagnosis of breast cancer had reduced mortality due to breast cancer and all causes (fully adjusted HR = 0.84 [95% confidence interval = 0.68-1.04] and 0.84 [0.72-0.97], respectively). These associations were more marked for simvastatin 0.79 (0.63-1.00) and 0.81 (0.70-0.95), respectively.

CONCLUSIONS: In this large population-based breast cancer cohort, there was some evidence of reduced mortality in statin users after breast cancer diagnosis. However, these associations were weak in magnitude and were attenuated in some sensitivity analyses.

Digoxin use after diagnosis of prostate cancer and survival: a population-based cohort study

PURPOSE:
Preclinical studies have shown that digoxin exerts anticancer effects on different cancer cell lines including prostate cancer. A recent observational study has shown that digoxin use was associated with a 25% reduction in prostate cancer risk. The aim of this study was to investigate whether digoxin use after diagnosis of prostate cancer was associated with decreased prostate cancer-specific mortality.
METHODS:
A cohort of 13 134 patients with prostate cancer newly diagnosed from 1998 to 2009 was identified from English cancer registries and linked to the UK Clinical Practice Research Datalink (to provide digoxin and other prescription records) and to the Office of National Statistics mortality data (to identify 2010 prostate cancer-specific deaths). Using time-dependent Cox regression models, unadjusted and adjusted hazard ratios (HR) and 95% confidence intervals (CIs) were calculated for the association between post-diagnostic exposure to digoxin and prostate cancer-specific mortality.
RESULTS:
Overall, 701 (5%) patients with prostate cancer used digoxin after diagnosis. Digoxin use was associated with an increase in prostate cancer-specific mortality before adjustment (HR = 1.59; 95% CI 1.32-1.91), but after adjustment for confounders, the association was attenuated (adjusted HR = 1.13; 95% CI 0.93-1.37) and there was no evidence of a dose response.
CONCLUSIONS:
In this large population-based prostate cancer cohort, there was no evidence of a reduction in prostate cancer-specific mortality with digoxin use after diagnosis.