Minjae Yoo

DSigDB: drug signatures database for gene set analysis

UNLABELLED 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.

K-Map: connecting kinases with therapeutics for drug repurposing and development

Protein kinases play important roles in regulating signal transduction in eukaryoticcells. Due to evolutionary conserved binding sites in the catalytic domain of thekinases, most inhibitors that target these sites promiscuously inhibit multiplekinases. Quantitative analysis can reveal complex and unexpected interactions betweenprotein kinases and kinase inhibitors, providing opportunities for identifyingmulti-targeted inhibitors of specific diverse kinases for drug repurposing anddevelopment. We have developed K-Map—a novel and user-friendly web-basedprogram that systematically connects a set of query kinases to kinase inhibitorsbased on quantitative profiles of the kinase inhibitor activities. Users can useK-Map to find kinase inhibitors for a set of query kinases (obtained fromhigh-throughput ‘omics’ experiments) or to reveal new interactionsbetween kinases and kinase inhibitors for rational drug combination studies.Availability and implementationK-Map has been implemented in python scripting language and the website is freelyavailable at: http://tanlab.ucdenver.edu/kMap.

Bioinformatics-driven discovery of rational combination for overcoming EGFR-mutant lung cancer resistance to EGFR therapy

MOTIVATION Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death in the United States. Targeted tyrosine kinase inhibitors (TKIs) directed against the epidermal growth factor receptor (EGFR) have been widely and successfully used in treating NSCLC patients with activating EGFR mutations. Unfortunately, the duration of response is short-lived, and all patients eventually relapse by acquiring resistance mechanisms. RESULT We performed an integrative systems biology approach to determine essential kinases that drive EGFR-TKI resistance in cancer cell lines. We used a series of bioinformatics methods to analyze and integrate the functional genetics screen and RNA-seq data to identify a set of kinases that are critical in survival and proliferation in these TKI-resistant lines. By connecting the essential kinases to compounds using a novel kinase connectivity map (K-Map), we identified and validated bosutinib as an effective compound that could inhibit proliferation and induce apoptosis in TKI-resistant lines. A rational combination of bosutinib and gefitinib showed additive and synergistic effects in cancer cell lines resistant to EGFR TKI alone. CONCLUSIONS We have demonstrated a bioinformatics-driven discovery roadmap for drug repurposing and development in overcoming resistance in EGFR-mutant NSCLC, which could be generalized to other cancer types in the era of personalized medicine. AVAILABILITY AND IMPLEMENTATION K-Map can be accessible at: http://tanlab.ucdenver.edu/kMap. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data

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.

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer

BackgroundTriple-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.ResultsWe 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.ConclusionsIn 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.

Resistance to RET-Inhibition in RET-Rearranged NSCLC Is Mediated By Reactivation of RAS/MAPK Signaling

Oncogenic rearrangements in RET are present in 1%–2% of lung adenocarcinoma patients. Ponatinib is a multi-kinase inhibitor with low-nanomolar potency against the RET kinase domain. Here, we demonstrate that ponatinib exhibits potent antiproliferative activity in RET fusion–positive LC-2/ad lung adenocarcinoma cells and inhibits phosphorylation of the RET fusion protein and signaling through ERK1/2 and AKT. Using distinct dose escalation strategies, two ponatinib-resistant LC-2/ad cell lines, PR1 and PR2, were derived. PR1 and PR2 cell lines retained expression, but not phosphorylation of the RET fusion and lacked evidence of a resistance mutation in the RET kinase domain. Both resistant lines retained activation of the MAPK pathway. Next-generation RNA sequencing revealed an oncogenic NRAS p.Q61K mutation in the PR1 cell. PR1 cell proliferation was preferentially sensitive to siRNA knockdown of NRAS compared with knockdown of RET, more sensitive to MEK inhibition than the parental line, and NRAS dependence was maintained in the absence of chronic RET inhibition. Expression of NRAS p.Q61K in RET fusion expressing TPC1 cells conferred resistance to ponatinib. PR2 cells exhibited increased expression of EGFR and AXL. EGFR inhibition decreased cell proliferation and phosphorylation of ERK1/2 and AKT in PR2 cells, but not LC-2/ad cells. Although AXL inhibition enhanced PR2 sensitivity to afatinib, it was unable to decrease cell proliferation by itself. Thus, EGFR and AXL cooperatively rescued signaling from RET inhibition in the PR2 cells. Collectively, these findings demonstrate that resistance to ponatinib in RET-rearranged lung adenocarcinoma is mediated by bypass signaling mechanisms that result in restored RAS/MAPK activation. Mol Cancer Ther; 16(8); 1623–33. ©2017 AACR.

Integrating heterogeneous drug sensitivity data from cancer pharmacogenomic studies

The consistency of in vitro drug sensitivity data is of key importance for cancer pharmacogenomics. Previous attempts to correlate drug sensitivities from the large pharmacogenomics databases, such as the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC), have produced discordant results. We developed a new drug sensitivity metric, the area under the dose response curve adjusted for the range of tested drug concentrations, which allows integration of heterogeneous drug sensitivity data from the CCLE, the GDSC, and the Cancer Therapeutics Response Portal (CTRP). We show that there is moderate to good agreement of drug sensitivity data for many targeted therapies, particularly kinase inhibitors. The results of this largest cancer cell line drug sensitivity data analysis to date are accessible through the online portal, which serves as a platform for high power pharmacogenomics analysis.

Systems Pharmacology-Based Approach of Connecting Disease Genes in Genome-Wide Association Studies with Traditional Chinese Medicine

Traditional Chinese medicine (TCM) originated in ancient China has been practiced over thousands of years for treating various symptoms and diseases. However, the molecular mechanisms of TCM in treating these diseases remain unknown. In this study, we employ a systems pharmacology-based approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. We studied 102 TCM components and their target genes by analyzing microarray gene expression experiments. We constructed disease-gene networks from 2558 GWAS studies. We applied a systems pharmacology approach to prioritize disease-target genes. Using this bioinformatics approach, we analyzed 14,713 GWAS disease-TCM-target gene pairs and identified 115 disease-gene pairs with q value < 0.2. We validated several of these GWAS disease-TCM-target gene pairs with literature evidence, demonstrating that this computational approach could reveal novel indications for TCM. We also develop TCM-Disease web application to facilitate the traditional Chinese medicine drug repurposing efforts. Systems pharmacology is a promising approach for connecting GWAS diseases with TCM for potential drug repurposing and repositioning. The computational approaches described in this study could be easily expandable to other disease-gene network analysis.

IMPACT web portal: Oncology database integrating molecular profiles with actionable therapeutics

BackgroundWith the advancement of next generation sequencing technology, researchers are now able to identify important variants and structural changes in DNA and RNA in cancer patient samples. With this information, we can now correlate specific variants and/or structural changes with actionable therapeutics known to inhibit these variants. We introduce the creation of the IMPACT Web Portal, a new online resource that connects molecular profiles of tumors to approved drugs, investigational therapeutics and pharmacogenetics associated drugs.ResultsIMPACT Web Portal contains a total of 776 drugs connected to 1326 target genes and 435 target variants, fusion, and copy number alterations. The online IMPACT Web Portal allows users to search for various genetic alterations and connects them to three levels of actionable therapeutics. The results are categorized into 3 levels: Level 1 contains approved drugs separated into two groups; Level 1A contains approved drugs with variant specific information while Level 1B contains approved drugs with gene level information. Level 2 contains drugs currently in oncology clinical trials. Level 3 provides pharmacogenetic associations between approved drugs and genes.ConclusionIMPACT Web Portal allows for sequencing data to be linked to actionable therapeutics for translational and drug repurposing research. The IMPACT Web Portal online resource allows users to query genes and variants to approved and investigational drugs. We envision that this resource will be a valuable database for personalized medicine and drug repurposing. IMPACT Web Portal is freely available for non-commercial use at http://tanlab.ucdenver.edu/IMPACT.