Abstract
The Library of Integrated Network-based Cellular Signatures (LINCS) project is a large-scale coordinated effort to build a comprehensive systems biology reference resource. The goals of the program include the generation of a very large multidimensional data matrix and informatics and computational tools to integrate, analyze, and make the data readily accessible. LINCS data include genome-wide transcriptional signatures, biochemical protein binding profiles, cellular phenotypic response profiles and various other datasets for a wide range of cell model systems and molecular and genetic perturbations. Here we present a partial survey of this data facilitated by data standards and in particular a robust compound standardization workflow; we integrated several types of LINCS signatures and analyzed the results with a focus on mechanism of action (MoA) and chemical compounds. We illustrate how kinase targets can be related to disease models and relevant drugs. We identified some fundamental trends that appear to link Kinome binding profiles and transcriptional signatures to chemical information and biochemical binding profiles to transcriptional responses independent of chemical similarity. To fill gaps in the datasets we developed and applied predictive models. The results can be interpreted at the systems level as demonstrated based on a large number of signaling pathways. We can identify clear global relationships, suggesting robustness of cellular responses to chemical perturbation. Overall, the results suggest that chemical similarity is a useful measure at the systems level, which would support phenotypic drug optimization efforts. With this study we demonstrate the potential of such integrated analysis approaches and suggest prioritizing further experiments to fill the gaps in the current data.
Highlights
Modern molecular biomedical science relies to a great extent on understanding gene function, and significant progress was made in understanding the roles of numerous individual genes (Silverman and Loscalzo, 2012)
Cancer cell lines and primary cancer cells have recently been established as powerful model systems to study cancer biology and the pharmacology of drug responses in cancer subtypes
CHARACTERIZATION OF Library of Integrated Network-based Cellular Signatures (LINCS) SMALL MOLECULE PERTURBAGENS Small molecules tested in different LINCS datasets were compiled, and after removing salts and addends, were submitted to the PubChem web services first for the compound standardization and for retrieving the PubChem CID identifiers
Summary
Modern molecular biomedical science relies to a great extent on understanding gene function, and significant progress was made in understanding the roles of numerous individual genes (Silverman and Loscalzo, 2012). Further advances in the prevention, diagnosis and treatment of cancer require a more comprehensive knowledge of the molecular mechanisms that lead to the malignant state. Understanding cancer pathogenesis requires knowledge of the specific contributory genetic mutations and the cellular framework in which they arise and function (Hong et al, 2008). Model, and understand drug sensitivity relies on systems-wide approaches to integrate large-scale biological responses in diseased and healthy cell states, involving various molecular entities such as drugs, proteins, genes, transcripts, cellular, and molecular processes, characteristics (e.g., genetic) of the cell model systems, etc. MoA describes biochemical interaction through which a drug modulates the corresponding target resulting in a phenotypic response (or pharmacological effect of the drug). There are studies linking drug pharmacology to transcriptional www.frontiersin.org
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