The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell and its disruption is one of the 'Hallmarks of Cancer'. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer by radiation therapies or by genotoxic chemotherapies. More recently, protein components of the DDR systems are being identified as promising avenues for targeted cancer therapeutics. Here we present an in-depth analysis of the function, disease role and therapeutic potential of ~450 expert-curated human DDR genes. We discuss the current state of DDR drugs both FDA approved or under clinical investigation. We examine large-scale genomic and expression data in 15 cancers to identify deregulated components of the DDR in these tumours, and we apply systematic computational analysis to identify DDR proteins amenable to modulation by small molecules, highlighting potential novel therapeutic targets. 3The DNA Damage Response (DDR) evolved in response to the exposure of the genome to exogenous and endogenous genotoxins. Unless repaired in an error-free process, DNA damage can result in mutations and altered cellular behavior. Consequently, cells deploy a diverse repertoire of mechanisms to maintain genetic integrity 1 (see TABLE 1). These mechanisms involve the DNA repair processes themselves, the systems that regulate and organize them, and the systems that integrate DNA damage repair with the cell cycle 2 .Disruption of the DDR is observed in many cancers [3][4][5] , and underlies the genomic instability that accompanies tumourigenesis and progression. However, in the majority of cases, the specific underlying defects are poorly characterised 6,7 . Conversely, there are well-described cancers where disruption of a DDR mechanism is directly causal. In addition to these licensed drugs, there are a number of compounds currently under clinical evaluation that target DDR pathways directly. These targets include the protein kinases involved in cell cycle DNA checkpoint for DNA damage and/or replicative stress (eg CHEK1, WEE1), and individual enzymes involved in base excision repair (BER; APEX1), direct repair (MGMT), non-homologous DNA double strand break repair (NHEJ; PRKDC / DNA-PK) and telomere maintenance (TM; TERT).The initial rationale for development of DDR enzyme-targeted drugs focused on their use as potentiators, inhibiting repair of damage caused by radiotherapy and/or conventional genotoxins 11 .However, this approach has been extended to stand-alone use, targeting DNA repair pathways critical to tumour survival by exploiting synthetic sensitivity/lethality 16 (SSL). SSL arises when a combination of loss-of-function in two or more genes leads to cell death, while loss-of-function in only one of them does not. The therapeutic aim is to exploit genetic defects essential to a tumour's survival by combining the defect in an affected pathway with a pharmacologically induced defect in a compensating pathway 17 . 4The best example to date is the pharmaceutical inhibition...
BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.
canSAR (http://cansar.icr.ac.uk) is a publicly available, multidisciplinary, cancer-focused knowledgebase developed to support cancer translational research and drug discovery. canSAR integrates genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and druggability data. canSAR is widely used to rapidly access information and help interpret experimental data in a translational and drug discovery context. Here we describe major enhancements to canSAR including new data, improved search and browsing capabilities, new disease and cancer cell line summaries and new and enhanced batch analysis tools.
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