A PRIM approach to predictive‐signature development for patient stratification

Patients often respond differently to a treatment because of individual heterogeneity. Failures of clinical trials can be substantially reduced if, prior to an investigational treatment, patients are stratified into responders and nonresponders based on biological or demographic characteristics. These characteristics are captured by a predictive signature. In this paper, we propose a procedure to search for predictive signatures based on the approach of patient rule induction method. Specifically, we discuss selection of a proper objective function for the search, present its algorithm, and describe a resampling scheme that can enhance search performance. Through simulations, we characterize conditions under which the procedure works well. To demonstrate practical uses of the procedure, we apply it to two real‐world data sets. We also compare the results with those obtained from a recent regression‐based approach, Adaptive Index Models, and discuss their respective advantages. In this study, we focus on oncology applications with survival responses. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.     

The suitability of EBC-Pb as a new biomarker to assess occupational exposure to lead

Occupational exposure to lead (Pb) requires continuous surveillance to assure, as much as possible, safe and healthful working conditions. This study addresses the suitability of assessing Pb exposure in relevant workers using their exhaled breath condensate (EBC). This study enrolled workers of two different Pb processing industries characterized by moderate and high Pb exposure levels in the work environment, and a group of non-exposed individuals working in offices who served as baseline for Pb exposure. The EBC-Pb of workers reflected the Pb levels in the work environment of all three settings, although the relationship with B-Pb was not clear. The lack of correlation between EBC-Pb and B-Pb most probably indicates the time lag for Pb to enter in the two body pools. The EBC-Pb seems to reflect immediate exposure, providing a prompt signature of Pb in the environmental that may interact directly with the organ. By delivering short-term evaluation of exposure, EBC-Pb represents a clear advantage in biomonitoring and may become an interesting tool for estimating organ burden.     

LEAP: Biomarker Inference Through Learning and Evaluating Association Patterns

Single nucleotide polymorphism (SNP) high‐dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000‐SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high‐dimensional datasets and determining their probability.     

Circular RNA hsa_circ_0000567 can be used as a promising diagnostic biomarker for human colorectal cancer

Background

Recent studies have revealed that circular RNAs are involved in the biological process of some kinds of human cancers. However, little is known about their diagnostic values and functions in colorectal cancer (CRC).

Methods

The expression levels of hsa_circ_0000567 in 102 paired CRC tissues and adjacent noncancerous tissues, 5 CRC cell lines, and a normal colorectal epithelial cell line were detected by quantitative real‐time polymerase chain reaction (qRT‐PCR). The correlations between hsa_circ_0000567 expression levels and the clinicopathological factors of patients with CRC were analyzed. Furthermore, the loss‐of‐function assay was performed to investigate the functions of hsa_circ_0000567 in vitro. Finally, a receiver operating characteristic (ROC) curve was established to evaluate the diagnostic value of hsa_circ_0000567.

Results

Hsa_circ_0000567 expression was significantly downregulated in CRC tissues and CRC cell lines. In addition, the decreased hsa_circ_0000567 expression in CRC was negatively correlated with tumor size (P = .011), lymph metastasis (P = .003), distal metastasis (P < .0001), and tumor–node–metastasis (TNM) stage (P = .003) in CRC. Moreover, knockdown of hsa_circ_0000567 promoted CRC cells proliferation and migration in vitro. Importantly, the area under the ROC curve (AUC) was 0.8653, which indicates hsa_circ_0000567 can serve as a diagnostic biomarker.

Conclusion

Hsa_circ_0000567 may be a novel suppressor and a potential diagnosis biomarker in CRC.

     

Diagnostic and predictive performance and standardized threshold of traditional biomarkers for drug‐induced liver injury in rats

Traditional biomarkers such as alanine and aspartate aminotransferase (ALT, AST) and total bilirubin (TBIL) have been widely used for detecting drug‐induced liver injury (DILI). Although the Food and Drug Administration (FDA) proposed standardized thresholds for human as Hy's law, those for animals have not been determined, and predictability of these biomarkers for future onset of hepatic lesions remains unclear. In this study, we investigated these diagnostic and predictive performance of 10 traditional biomarkers for liver injury by receiver‐operating characteristic (ROC) curve, using a free‐access database where 142 hepatotoxic or non‐hepatotoxic compounds were administrated to male rats (n = 5253). Standardization of each biomarker value was achieved by calculating the ratio to control mean value, and the thresholds were determined under the condition of permitting 5% false positive. Of these 10 biomarkers, AST showed the best diagnostic performance. Furthermore, ALT and TBIL also showed high performance under the situation of hepatocellular necrosis and bile duct injury, respectively. Additionally, the availability of the diagnostic thresholds in difference testing facility was confirmed by the application of these thresholds to in‐house prepared dataset. Meanwhile, incorrect diagnosis by the thresholds was also observed. Regarding prediction, all 10 biomarkers showed insufficient performance for future onset of hepatic lesions. In conclusion, the standardized diagnostic thresholds enable consistent evaluation of traditional biomarkers among different facilities, whereas it was suggested that novel biomarker is required for more accurate diagnosis and prediction of DILI. Copyright © 2014 John Wiley & Sons, Ltd.     

Biomarkers of Selenium Status

The essential trace element, selenium (Se), has multiple biological activities, which depend on the level of Se intake. Relatively low Se intakes determine the expression of selenoenzymes in which it serves as an essential constituent. Higher intakes have been shown to have anti-tumorigenic potential; and very high Se intakes can produce adverse effects. This hierarchy of biological activities calls for biomarkers informative at different levels of Se exposure. Some Se-biomarkers, such as the selenoproteins and particularly GPX3 and SEPP1, provide information about function directly and are of value in identifying nutritional Se deficiency and tracking responses of deficient individuals to Se-treatment. They are useful under conditions of Se intake within the range of regulated selenoprotein expression, e.g., for humans <55 μg/day and for animals <20 μg/kg diet. Other Se-biomarkers provide information indirectly through inferences based on Se levels of foods, tissues, urine or feces. They can indicate the likelihood of deficiency or adverse effects, but they do not provide direct evidence of either condition. Their value is in providing information about Se status over a wide range of Se intake, particularly from food forms. There is need for additional Se biomarkers particularly for assessing Se status in non-deficient individuals for whom the prospects of cancer risk reduction and adverse effects risk are the primary health considerations. This would include determining whether supranutritional intakes of Se may be required for maximal selenoprotein expression in immune surveillance cells. It would also include developing methods to determine low molecular weight Se-metabolites, i.e., selenoamino acids and methylated Se-metabolites, which to date have not been detectable in biological specimens. Recent analytical advances using tandem liquid chromatography-mass spectrometry suggest prospects for detecting these metabolites.