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Background: The decision environment for cancer care is becoming increasingly complex due to the discovery and development of novel genomic tests that offer information regarding therapy response, prognosis and monitoring, in addition to traditional histopathology. There is, therefore, a need for translational clinical tools based on molecular bioinformatics, particularly in current cancer care, that can acquire, analyze the data, and interpret and present information from multiple diagnostic modalities to help the clinician make effective decisions., Results: We present a platform for molecular signature discovery and clinical decision support that relies on genomic and epigenomic measurement modalities as well as clinical parameters such as histopathological results and survival information. Our Physician Accessible Preclinical Analytics Application (PAPAyA) integrates a powerful set of statistical and machine learning tools that leverage the connections among the different modalities. It is easily extendable and reconfigurable to support integration of existing research methods and tools into powerful data analysis and interpretation pipelines. A current configuration of PAPAyA with examples of its performance on breast cancer molecular profiles is used to present the platform in action., Conclusion: PAPAyA enables analysis of data from (pre)clinical studies, formulation of new clinical hypotheses, and facilitates clinical decision support by abstracting molecular profiles for clinicians.

Background: MicroRNAs (miRNAs) are known to play an important role in cancer development by post-transcriptionally affecting the expression of critical genes. The aims of this study were two-fold: (i) to develop a robust method to isolate miRNAs from small volumes of saliva and (ii) to develop a panel of saliva-based diagnostic biomarkers for the detection of head and neck squamous cell carcinoma (HNSCC). Methods: Five differentially expressed miRNAs were selected from miScript™ miRNA microarray data generated using saliva from five HNSCC patients and five healthy controls. Their differential expression was subsequently confirmed by RT-qPCR using saliva samples from healthy controls ( n = 56) and HNSCC patients ( n = 56). These samples were divided into two different cohorts, i.e., a first confirmatory cohort ( n = 21) and a second independent validation cohort ( n = 35), to narrow down the miRNA diagnostic panel to three miRNAs: miR-9, miR-134 and miR-191. This diagnostic panel was independently validated using HNSCC miRNA expression data from The Cancer Genome Atlas (TCGA), encompassing 334 tumours and 39 adjacent normal tissues. Receiver operating characteristic (ROC) curve analysis was performed to assess the diagnostic capacity of the panel. Results: On average 60 ng/μL miRNA was isolated from 200 μL of saliva. Overall a good correlation was observed between the microarray data and the RT-qPCR data. We found that miR-9 ( P <0.0001), miR-134 ( P <0.0001) and miR-191 ( P <0.001) were differentially expressed between saliva from HNSCC patients and healthy controls, and that these miRNAs provided a good discriminative capacity with area under the curve (AUC) values of 0.85 ( P <0.0001), 0.74 ( P < 0.001) and 0.98 ( P < 0.0001), respectively. In addition, we found that the salivary miRNA data showed a good correlation with the TCGA miRNA data, thereby providing an independent validation. Conclusions: We show that we have developed a reliable method to isolate miRNAs from small volumes of saliva, and that the saliva-derived miRNAs miR-9, miR-134 and miR-191 may serve as novel biomarkers to reliably detect HNSCC. [ABSTRACT FROM AUTHOR]

Abstract: Chronic arsenic exposure through contaminated drinking water is a major environmental health issue. Chronic arsenic exposure is known to exert its toxic effects by a variety of mechanisms, of which generation of reactive oxygen species (ROS) is one of the most important. A high level of ROS, in turn, leads to DNA damage that might ultimately culminate in cancer. In order to keep the level of ROS in balance, an array of enzymes is present, of which catalase (CAT) and myeloperoxidase (MPO) are important members. Hence, in this study, we determined the activities of these two enzymes in the sera and chromosomal aberrations (CA) in peripheral blood lymphocytes in individuals exposed and unexposed to arsenic in drinking water. Arsenic in drinking water and in urine was used as a measure of exposure. Our results show that individuals chronically exposed to arsenic have significantly higher CAT and MPO activities and higher incidence of CA. We found moderate positive correlations between CAT and MPO activities, induction of CA and arsenic in urine and water. These results indicate that chronic arsenic exposure causes higher CAT and MPO activities in serum that correlates with induction of genetic damage. We conclude that the serum levels of these enzymes might be used as biomarkers of early arsenic exposure induced disease much before the classical dermatological symptoms of arsenicosis begin to appear. [ABSTRACT FROM AUTHOR]