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1.  Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data 
Bioinformatics  2015;31(23):3799-3806.
Motivation: Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret.
Results: We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055.
Availability and implementation: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/.
Contact: aikchoon.tan@ucdenver.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btv427
PMCID: PMC4675831  PMID: 26206305
2.  DSigDB: drug signatures database for gene set analysis 
Bioinformatics  2015;31(18):3069-3071.
Summary: We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22 527 gene sets, consists of 17 389 unique compounds covering 19 531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research.
Availability and implementation: DSigDB is freely available for non-commercial use at http://tanlab.ucdenver.edu/DSigDB.
Supplementary information: Supplementary data are available at Bioinformatics online.
Contact: aikchoon.tan@ucdenver.edu
doi:10.1093/bioinformatics/btv313
PMCID: PMC4668778  PMID: 25990557
3.  An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer 
BMC Genomics  2015;16(Suppl 12):S2.
Background
Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis.
Results
We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. Wevalidated our predictions using published and new experimental data.
Conclusions
In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.
doi:10.1186/1471-2164-16-S12-S2
PMCID: PMC4682411  PMID: 26681397
Kinase dependency; Triple-Negative Breast Cancer; high-throughput screening; bioinformatics
4.  Profile of Differential Promoter Activity by Nucleotide Substitution at GWAS Signals For Multiple Sclerosis 
Medicine  2014;93(28):e281.
Supplemental Digital Content is available in the text
Abstract
This experimental study was conducted with completely randomized design.
Genome-wide association studies (GWAS) have revealed a large number of genetic associations of nucleotide sequence variants with susceptibility to multiple sclerosis (MS). Nevertheless, studies to identify the functional relevance of these variants lag far behind identification of the GWAS signals. Expression quantitative trait loci (eQTLs) analysis and promoter activity analysis with the variants filtered by GWAS were conducted to identify their functional alleles and haplotypes. The promoter activity was assayed with reporter constructs containing variants at 8 MS GWAS signals resulted from 18 GWAS.
The promoter activity differed by alternative sequence variants at upstream regions of the CYP24A1, CYP27B1, SYK, RAD21L1, PVR, ODF3B, and RGS14 genes (P < 0.05). The transcriptional regulations of sequence variants were also found by identifications of eQTLs for their corresponding genes with lymphoblastoid cells in the current study (SYK, ODF3B, RGS14, and PVR, P < 8.33 × 10−3) and with dendritic cells in a previous study (CYP27B1, P = 1.84 × 10−6).
This study identified regulatory nucleotide sequences in the promoters of the CYP24A1, CYP27B1, SYK, RAD21L1, PVR, ODF3B, and RGS14 genes, and their variants differentially affected gene expression. This might result in their associations with MS susceptibility in previous GWAS. Further functional studies are required to understand the process of transcriptional regulation of the variants identified in the current study and the mechanisms underlying susceptibility to MS.
doi:10.1097/MD.0000000000000281
PMCID: PMC4603103  PMID: 25526461
5.  Suppression of STAT3 and HIF-1 Alpha Mediates Anti-Angiogenic Activity of Betulinic Acid in Hypoxic PC-3 Prostate Cancer Cells 
PLoS ONE  2011;6(6):e21492.
Background
Signal transducer and activator of transcription 3 (STAT3) is a transcription factor that regulates various cellular processes such as cell survival, angiogenesis and proliferation. In the present study, we examined that betulinic acid (BA), a triterpene from the bark of white birch, had the inhibitory effects on hypoxia-mediated activation of STAT3 in androgen independent human prostate cancer PC-3 cells.
Methodology/Principal Findings
BA inhibited the protein expression and the transcriptional activities of hypoxia-inducible factor-1α (HIF-1α) under hypoxic condition. Consistently, BA blocked hypoxia-induced phosphorylation, DNA binding activity and nuclear accumulation of STAT3. In addition, BA significantly reduced cellular and secreted levels of vascular endothelial growth factor (VEGF), a critical angiogenic factor and a target gene of STAT3 induced under hypoxia. Furthermore, BA prevented in vitro capillary tube formation in human umbilical vein endothelial cells (HUVECs) maintained in conditioned medium of hypoxic PC-3 cells, implying anti-angiogenic activity of BA under hypoxic condition. Of note, chromatin immunoprecipitation (ChiP) assay revealed that BA inhibited binding of HIF-1α and STAT3 to VEGF promoter. Furthermore, silencing STAT3 using siRNA transfection effectively enhanced the reduced VEGF production induced by BA treatment under hypoxia.
Conclusions/Significance
Taken together, our results suggest that BA has anti-angiogenic activity by disturbing the binding of HIF-1α and STAT3 to the VEGF promoter in hypoxic PC-3 cells.
doi:10.1371/journal.pone.0021492
PMCID: PMC3123343  PMID: 21731766

Results 1-5 (5)