Systems toxicology, a branch of toxicology that studies drug effects at the level of biological systems, offers exciting opportunities to discover toxicity-related sub-networks using high-throughput ...technologies. This paper takes a computational approach to systems toxicology and investigates the use of automated signalling path detection for discovery of potential biomarkers of drug-induced non-immune neutropenia. The algorithm utilises a gene expression change measure to mine a large protein interaction network and identify chemical-toxicity signalling paths. Cytoscape-based analysis of detected signalling paths with statistically significant path expression scores reveals 'hub' proteins and a smaller sub-network of path proteins. The importance of 'hub' and drug-toxicity signalling path proteins in haematological and apoptotic signal transduction networks is investigated in order to understand the value of automated signalling path detection approach.
Drug-induced neutropenia can be fatal when severe and therefore requires an improved understanding of its mechanism(s) of toxicity. Systems biology provides an opportunity to understand adverse ...events after drug administration using analysis of biomolecular networks. In this study, a human protein interaction network was analyzed to identify proteins that are most central to topological paths connecting a drug's target proteins to hematopoiesis-related proteins. For a set of non-immune neutropenia inducing drugs, 9 proteins were found to be common to putative signaling paths across all drugs evaluated. All 9 proteins showed relevance to neutrophil biology. Geneset enrichment analysis showed that proteins associated with cancer-related processes such as apoptosis provide topological linkages between drug targets and proteins involved in neutrophil production. The algorithm can be applied towards analysis of any toxicity where the drugs and the physiological processes involved in the toxic mechanism are known.
Background:Bevacizumab with chemotherapy followed by bevacizumab was approved in 2018 for the first-line (1L) treatment of patients with ovarian cancer (OC). However, little is known about the ...real-world experience of OC patients treated with bevacizumab.Methods: This SEER-Medicare retrospective cohort study included females u2265 66 years, OC (stage II-IV) diagnosis between 2009 and 2015, who continued bevacizumab or observation alone after 4 u2013 10 cycles of 1L platinum-based chemotherapy with or without bevacizumab. The primary outcome was overall survival (OS), measured from 1L treatment initiation to death. Safety events of interest included thrombosis, hemorrhage, stroke, and gastrointestinal perforation. Inverse probability of treatment weighting (IPTW) incorporated 32 demographic and prognostic characteristics. Cox proportional hazards models with IPTW were used to compare outcomes.Results:In this cohort (N=2262), 66 (3%) patients received bevacizumab maintenance. Mean age (u00b1SD) was 74.6 (6.2) years, 89% were Stage III or IV, and 26.9% of patients had u22651 co-morbidity. Patients treated (preapproval) with 1L maintenance bevacizumab increased from 0.3% in 2009 to 4.1% in 2015. Compared to observation alone, median OS in patients treated with bevacizumab maintenance was 43.6 vs 37.9 months (adjusted hazard ratio HR=0.83 95% CI: 0.50 u2013 1.36). Among those who received >7 cycles of bevacizumab, OS was longer, though not significantly, compared to observation alone (aHR = 0.62 95% CI: 0.32-1.18). Initiation of nonplatinum chemotherapy within 12 months occurred in 36.4% of observation only and 20.9% of bevacizumab maintenance patients. Compared to observation alone, specified safety events were not higher with bevacizumab maintenance, aHR: 0.94 0.49 u2013 1.82.Conclusions: Survival among this small cohort of elderly OC patients in this real-world study is consistent with that observed in trials of 1L bevacizumab maintenance. First-line maintenance bevacizumab therapy appears to provide modest survival benefit
Systems toxicology, a branch of toxicology that studies chemical effects on biological systems, presents exciting knowledge discovery challenges for the information researcher. The exponential ...increase in availability of genomic and proteomic data in this domain needs to be matched with increasingly sophisticated network analysis approaches. Improved ability to mine complex gene and protein interaction networks may eventually lead to discovery of drugs that target biological sub-networks (‘network medicine’) instead of individual proteins. In this thesis, we have proposed and investigated the use of a maximal edge centrality criterion to discover drug-toxicity signaling paths inside a human protein interaction network. The signaling path detection approach utilizes drug and toxicity information along with two novel edge weighting measures, one based on edge centrality for detected paths and another using differential gene expression between tissues treated with toxicity-inducing drugs and a control set. Drugs known to induce non-immune Neutropenia were analyzed as a test case and common path proteins on discovered signaling paths were evaluated for toxicological significance. In addition to investigating the value of topological connectivity for identification of toxicity biomarkers, the gene expression-based measure led to identification of a proposed biomarker panel for screening new drug candidates. Comparative evaluation of findings from the DTSP approach with standard microarray analysis method showed clear improvements in various performance measures including true positive rate, positive predictive value, negative predictive value and overall accuracy. Comparison of non-immune Neutropenia signaling paths with those discovered for a control set showed increased transcript-level activation of discovered signaling paths for toxicity-inducing drugs. We have demonstrated the scientific value from a systems-based approach for identifying toxicity-related proteins inside complex biological networks. The algorithm should be useful for biomarker identification for any toxicity assuming availability of relevant drug and drug-induced toxicity information.
A major benefit of an electronic medical image database on the World Wide Web is its availability as a tool for education and research. The overall objective of the thesis was to develop an efficient ...technique for accurate delineation of images and to organize the images and its related data in a manner that facilitates extensive information retrieval. An algorithm was developed in order to render images from the RF files. A technique for ROI (Region Of Interest) delineation was arrived at and all the images containing a lesion were delineated. Organization of data for diverse possibilities involved considerations and modifications in design until an optimum database model was reached. The database thus designed and implemented, was then interfaced to the web, using CGI standards.
Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random ...forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.
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