Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing…

By Dusica Vidovic, Amar Koleti, Stephan C. Schürer. 2014

68 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “[35] used gene- and pathway-level drug perturbation transcriptional profiles from the Library of Network-Based Cellular Signatures [288] to train a fully connected deep neural network to predict drug therapeutic uses and indications.”
A survey of current trends in computational drug repositioning (2016, background): “One key source of data behind several repurposing efforts is the Connectivity Map (CMap) [16] project and its extended project Library of Integrated Network-Based Cellular Signatures (LINCS) [17], which produced large-scale gene expression profiles from human cancer cell lines treated with different”
A review of connectivity map and computational approaches in pharmacogenomics (2017, methodology): “The authors highlight how LINCS data set [11] could be used to extend benefits of the group factor analysis-based probabilistic connectivity mapping in drug combination.”