HIPEC for Ovarian Cancer: A Controversial Discussion

Peritoneal carcinomatosis is a sign of advanced disease of ovarian cancer. The prognosis of ovarian cancer is significantly improved after cytoreductive surgery with complete tumor debulking followed by platin based chemotherapy. If cytoreductive surgery results in a tumor free situation with remaining tumor less than 0.25 cm, HIPEC may further improve prognosis. Materials and methods: The results of the Krefeld study are presented and the literature is reviewed according to overall survival and progression free survival with or without HIPEC. In the Krefeld study, patients with ovarian cancer and peritoneal carcinomatosis underwent cytoreductive surgery. In patients with optimal tumor debulking, HIPEC was performed. The peri- and postoperative course was observed. Adverse events were recorded after the Clavien-Dindo classification. Results: 43 patients were treated with cytoreductive surgery and HIPEC. In all patients an optimal cytoreductive situation with remaining tumor less than 0.25 cm was achieved. HIPEC was performed with a cisplatin solution (50 mg/m2) at 41°C. The median age of the patients was 56 years (range: 32–74 years), the median peritoneal cancer index (PCI) was 13 (range: 4–21), the median operation time was 356 minutes (range: 192–507 minutes). The median time to postoperative systemic treatment with chemotherapy was 29 days (range 21–70). There was no postoperative surgically associated death. No adverse events were recorded in 16 (37.2%) of 43 patients, no grade III or IV adverse events were reported for 33 (76.7%) patients, and no grade IV adverse events were reported for 41 (95.3%) patients. Grade III adverse events occured in 19 (44.2%) of the 43 patients; a total of 29 grade III adverse events were reported in these 19 patients. Grade IV adverse events occured in 3 (7.0%) of the 43 patients; a total of 3 grade IV adverse events were reported. Two of them resulted in return to the operating room. This was a fistula of the distal small bowel caused by drainage and a revision of wound infection. Conclusion: In ovarian cancer multiple surgical procedures may be necessary in order to have macroscopically eradicated tumor tissue. Combined with HIPEC, this seems to have positive effects on the survival of patients with peritoneal carcinomatosis. Since we have no marked additional adverse events caused by HIPEC in our case series, HIPEC seems to be an additional treatment option of peritoneal carcinomatosis in ovarian cancer. This statement is strengthened by the literature review in that metaanalysis show significant improved OAS and PFS

Structural Characterization of Acidic M17 Leucine Aminopeptidases from the TriTryps and Evaluation of Their Role in Nutrient Starvation in Trypanosoma brucei

Leucine aminopeptidase (LAP) is found in all kingdoms of life and catalyzes the metal-dependent hydrolysis of the N-terminal amino acid residue of peptide or amino acyl substrates. LAPs have been shown to participate in the N-terminal processing of certain proteins in mammalian cells and in homologous recombination and transcription regulation in bacteria, while in parasites, they are involved in host cell invasion and provision of essential amino acids for growth. The enzyme is essential for survival in Plasmodium falciparum, where its drug target potential has been suggested. We report here the X-ray structures of three kinetoplastid acidic LAPs (LAP-As from Trypanosoma brucei, Trypanosoma cruzi, and Leishmania major) which were solved in the metal-free and unliganded forms, as well as in a number of ligand complexes, providing insight into ligand binding, metal ion requirements, and oligomeric state. In addition, we analyzed mutant cells defective in LAP-A in Trypanosoma brucei, strongly suggesting that the enzyme is not required for the growth of this parasite either in vitro or in vivo. In procyclic cells, LAP-A was equally distributed throughout the cytoplasm, yet upon starvation, it relocalizes in particles that concentrate in the perinuclear region. Overexpression of the enzyme conferred a growth advantage when parasites were grown in leucine-deficient medium. Overall, the results suggest that in T. brucei, LAP-A may participate in protein degradation associated with nutrient depletion. IMPORTANCE Leucine aminopeptidases (LAPs) catalyze the hydrolysis of the N-terminal amino acid of peptides and are considered potential drug targets. They are involved in multiple functions ranging from host cell invasion and provision of essential amino acids to site-specific homologous recombination and transcription regulation. In kinetoplastid parasites, there are at least three distinct LAPs. The availability of the crystal structures provides important information for drug design. Here we report the structure of the acidic LAPs from three kinetoplastids in complex with different inhibitors and explore their role in Trypanosoma brucei survival under various nutrient conditions. Importantly, the acidic LAP is dispensable for growth both in vitro and in vivo, an observation that questions its use as a specific drug target. While LAP-A is not essential, leucine depletion and subcellular localization studies performed under starvation conditions suggest a possible function of LAP-A in the response to nutrient restriction

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

Improved Statistics for Genome-Wide Interaction Analysis

Recently, Wu and colleagues [1] proposed two novel statistics for genome-wide interaction analysis using case/control or case-only data. In computer simulations, their proposed case/control statistic outperformed competing approaches, including the fast-epistasis option in PLINK and logistic regression analysis under the correct model; however, reasons for its superior performance were not fully explored. Here we investigate the theoretical properties and performance of Wu et al.'s proposed statistics and explain why, in some circumstances, they outperform competing approaches. Unfortunately, we find minor errors in the formulae for their statistics, resulting in tests that have higher than nominal type 1 error. We also find minor errors in PLINK's fast-epistasis and case-only statistics, although theory and simulations suggest that these errors have only negligible effect on type 1 error. We propose adjusted versions of all four statistics that, both theoretically and in computer simulations, maintain correct type 1 error rates under the null hypothesis. We also investigate statistics based on correlation coefficients that maintain similar control of type 1 error. Although designed to test specifically for interaction, we show that some of these previously-proposed statistics can, in fact, be sensitive to main effects at one or both loci, particularly in the presence of linkage disequilibrium. We propose two new “joint effects” statistics that, provided the disease is rare, are sensitive only to genuine interaction effects. In computer simulations we find, in most situations considered, that highest power is achieved by analysis under the correct genetic model. Such an analysis is unachievable in practice, as we do not know this model. However, generally high power over a wide range of scenarios is exhibited by our joint effects and adjusted Wu statistics. We recommend use of these alternative or adjusted statistics and urge caution when using Wu et al.'s originally-proposed statistics, on account of the inflated error rate that can result

The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator

A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies

Genome-wide association studies (GWAS) are a valuable approach to understanding the genetic basis of complex traits. One of the challenges of GWAS is the translation of genetic association results into biological hypotheses suitable for further investigation in the laboratory. To address this challenge, we introduce Network Interface Miner for Multigenic Interactions (NIMMI), a network-based method that combines GWAS data with human protein-protein interaction data (PPI). NIMMI builds biological networks weighted by connectivity, which is estimated by use of a modification of the Google PageRank algorithm. These weights are then combined with genetic association p-values derived from GWAS, producing what we call ‘trait prioritized sub-networks.’ As a proof of principle, NIMMI was tested on three GWAS datasets previously analyzed for height, a classical polygenic trait. Despite differences in sample size and ancestry, NIMMI captured 95% of the known height associated genes within the top 20% of ranked sub-networks, far better than what could be achieved by a single-locus approach. The top 2% of NIMMI height-prioritized sub-networks were significantly enriched for genes involved in transcription, signal transduction, transport, and gene expression, as well as nucleic acid, phosphate, protein, and zinc metabolism. All of these sub-networks were ranked near the top across all three height GWAS datasets we tested. We also tested NIMMI on a categorical phenotype, Crohn’s disease. NIMMI prioritized sub-networks involved in B- and T-cell receptor, chemokine, interleukin, and other pathways consistent with the known autoimmune nature of Crohn’s disease. NIMMI is a simple, user-friendly, open-source software tool that efficiently combines genetic association data with biological networks, translating GWAS findings into biological hypotheses

Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci

Evaluation and Characterization of Bacterial Metabolic Dynamics with a Novel Profiling Technique, Real-Time Metabolotyping

BACKGROUND: Environmental processes in ecosystems are dynamically altered by several metabolic responses in microorganisms, including intracellular sensing and pumping, battle for survival, and supply of or competition for nutrients. Notably, intestinal bacteria maintain homeostatic balance in mammals via multiple dynamic biochemical reactions to produce several metabolites from undigested food, and those metabolites exert various effects on mammalian cells in a time-dependent manner. We have established a method for the analysis of bacterial metabolic dynamics in real time and used it in combination with statistical NMR procedures. METHODOLOGY/PRINCIPAL FINDINGS: We developed a novel method called real-time metabolotyping (RT-MT), which performs sequential (1)H-NMR profiling and two-dimensional (2D) (1)H, (13)C-HSQC (heteronuclear single quantum coherence) profiling during bacterial growth in an NMR tube. The profiles were evaluated with such statistical methods as Z-score analysis, principal components analysis, and time series of statistical TOtal Correlation SpectroScopY (TOCSY). In addition, using 2D (1)H, (13)C-HSQC with the stable isotope labeling technique, we observed the metabolic kinetics of specific biochemical reactions based on time-dependent 2D kinetic profiles. Using these methods, we clarified the pathway for linolenic acid hydrogenation by a gastrointestinal bacterium, Butyrivibrio fibrisolvens. We identified trans11, cis13 conjugated linoleic acid as the intermediate of linolenic acid hydrogenation by B. fibrisolvens, based on the results of (13)C-labeling RT-MT experiments. In addition, we showed that the biohydrogenation of polyunsaturated fatty acids serves as a defense mechanism against their toxic effects. CONCLUSIONS: RT-MT is useful for the characterization of beneficial bacterium that shows potential for use as probiotic by producing bioactive compounds

Binary systems and their nuclear explosions

Peer ReviewedPreprin