PubMed · 14635091
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Emmanuel Barranger, Emile Darai. 2003-12-01. Sentinel lymph nodes.. https://doi.org/10.1002/cncr.11811
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Cancer develops through the successive accumulation and selection of genetic and epigenetic alterations, enabling cells to survive, replicate and evade homeostatic control mechanisms such as apoptosis and antiproliferative signals. This transformation process, however, may create vulnerabilities since the accumulation of mutations can expose synthetic lethal gene interactions and oncogene-driven cellular reprogramming ('addiction'), giving rise to new therapeutic avenues. With the completion of the human genome project, it is anticipated that the identification and characterization of genetic networks that regulate cell growth, differentiation, apoptosis and transformation will be fundamental to decoding the complexity of these processes, and ultimately, cancer itself. Genomic methodologies, such as large-scale mRNA profiling using microarrays, have already begun to reveal the molecular basis of cancer heterogeneity and the clinical behavior of tumors. The combination of traditional cell culture techniques with high-throughput screening approaches has given rise to new cellular-genomics methodologies that enable the simultaneous interrogation of thousands of genes in live cells, facilitating true functional profiling of biological processes. Among these, RNA interference (RNAi) has the potential to enable rapid genome-wide loss-of-function (LOF) screens in mammalian systems, which until recently has been the sole domain of lower organisms. Here, we present a broad overview of this maturing technology and explore how, within current technical constraints, large-scale LOF use of RNAi can be exploited to uncover the molecular basis of cancer--from the genetics of synthetic lethality and oncogene-dependent cellular addiction to the acquisition of cancer-associated cellular phenotypes.
A basic problem of microarray data analysis is to identify genes whose expression is affected by the distinction between malignancies with different properties. These genes are said to be differentially expressed. Differential expression can be detected by selecting the genes with P-values (derived using an appropriate hypothesis test) below a certain rejection level. This selection, however, is not possible without accepting some false positives and negatives since the two sets of P-values, associated with the genes whose expression is and is not affected by the distinction between the different malignancies, overlap. We describe a procedure for the study of differential expression in microarray data based on receiver-operating characteristic curves. This approach can be useful to select a rejection level that balances the number of false positives and negatives and to assess the degree of overlap between the two sets of P-values. Since this degree of overlap characterises the balance that can be reached between the number of false positives and negatives, this quantity can be seen as a quality measure of microarray data with respect to the detection of differential expression. As an example, we apply our method to data sets studying acute leukaemia.
Acquired long QT syndrome (LQTS) occurs frequently as a side effect of blockade of cardiac HERG K(+) channels by commonly used medications. A large number of structurally diverse compounds have been shown to inhibit K(+) current through HERG. There is considerable interest in developing in silico tools to filter out potential HERG blockers early in the drug discovery process. We describe a binary classification model that combines a 2D topological similarity filter with a 3D pharmacophore ensemble procedure to discriminate between HERG actives and inactives with an overall accuracy of 82%, with false negative and false positive rates of 29% and 15%, respectively. This model should be generally applicable in virtual library counterscreening against HERG.