Dataset Information

In Search of Outliers. Mining for Protein Kinase Inhibitors Based on Their Anti-Proliferative NCI-60 Cell Lines Profile.

ABSTRACT: Protein kinases play a pivotal role in signal transduction, protein synthesis, cell growth and proliferation. Their deregulation represents the basis of pathogenesis for numerous diseases such as cancer and pathologies with cardiovascular, nervous and inflammatory components. Protein kinases are an important target in the pharmaceutical industry, with 48 protein kinase inhibitors (PKI) already approved on the market as treatments for different afflictions including several types of cancer. The present work focuses on facilitating the identification of new PKIs with antitumoral potential through the use of data-mining and basic statistics. The National Cancer Institute (NCI) granted access to the results of numerous previously tested compounds on 60 tumoral cell lines (NCI-60 panel). Our approach involved analyzing the NCI database to identify compounds that presented similar growth inhibition (GI) profiles to that of existing PKIs, but different from approved oncologic drugs with other mechanisms of action, using descriptive statistics and statistical outliers. Starting from 34,000 compounds present in the database, we filtered 400 which displayed selective inhibition on certain cancer cell lines similar to that of several already-approved PKIs.

SUBMITTER: Ion GND

PROVIDER: S-EPMC7221881 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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In Search of Outliers. Mining for Protein Kinase Inhibitors Based on Their Anti-Proliferative NCI-60 Cell Lines Profile.

Ion George Nicolae Daniel GND Nitulescu George Mihai GM

Molecules (Basel, Switzerland) 20200411 8

Protein kinases play a pivotal role in signal transduction, protein synthesis, cell growth and proliferation. Their deregulation represents the basis of pathogenesis for numerous diseases such as cancer and pathologies with cardiovascular, nervous and inflammatory components. Protein kinases are an important target in the pharmaceutical industry, with 48 protein kinase inhibitors (PKI) already approved on the market as treatments for different afflictions including several types of cancer. The p ...[more]

PMID: 32290461

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Project description:BackgroundCancer is a complex disease where various types of molecular aberrations drive the development and progression of malignancies. Large-scale screenings of multiple types of molecular aberrations (e.g., mutations, copy number variations, DNA methylations, gene expressions) become increasingly important in the prognosis and study of cancer. Consequently, a computational model integrating multiple types of information is essential for the analysis of the comprehensive data.ResultsWe propose an integrated modeling framework to identify the statistical and putative causal relations of various molecular aberrations and gene expressions in cancer. To reduce spurious associations among the massive number of probed features, we sequentially applied three layers of logistic regression models with increasing complexity and uncertainty regarding the possible mechanisms connecting molecular aberrations and gene expressions. Layer 1 models associate gene expressions with the molecular aberrations on the same loci. Layer 2 models associate expressions with the aberrations on different loci but have known mechanistic links. Layer 3 models associate expressions with nonlocal aberrations which have unknown mechanistic links. We applied the layered models to the integrated datasets of NCI-60 cancer cell lines and validated the results with large-scale statistical analysis. Furthermore, we discovered/reaffirmed the following prominent links: (1) Protein expressions are generally consistent with mRNA expressions. (2) Several gene expressions are modulated by composite local aberrations. For instance, CDKN2A expressions are repressed by either frame-shift mutations or DNA methylations. (3) Amplification of chromosome 6q in leukemia elevates the expression of MYB, and the downstream targets of MYB on other chromosomes are up-regulated accordingly. (4) Amplification of chromosome 3p and hypo-methylation of PAX3 together elevate MITF expression in melanoma, which up-regulates the downstream targets of MITF. (5)Mutations of TP53 are negatively associated with its direct target genes.ConclusionsThe analysis results on NCI-60 data justify the utility of the layered models for the incoming flow of cancer genomic data. Experimental validations on selected prominent links and application of the layered modeling framework to other integrated datasets will be carried out subsequently.

Dataset Information

In Search of Outliers. Mining for Protein Kinase Inhibitors Based on Their Anti-Proliferative NCI-60 Cell Lines Profile.

Publications

In Search of Outliers. Mining for Protein Kinase Inhibitors Based on Their Anti-Proliferative NCI-60 Cell Lines Profile.

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