Synergistic Anti-Cancer Effects of ERB-041 and Genistein through Estrogen Receptor Suppression-Mediated PI3K/AKT Pathway Downregulation in Canine Mammary Gland Tumor Cells.

Canine-mammary-gland tumors (CMTs) are prevalent in female dogs, with approximately 50% of them being malignant and often presenting as inoperable owing to their size or metastasis. Owing to poor outcomes, effective alternatives to conventional chemotherapy for humans are necessary. Two estrogen receptors, estrogen receptor alpha (ERα) and estrogen receptor beta (ERß), which act in opposition to each other, are involved, and CMT growth involves ERα through the phosphoinositide 3-kinases (PI3K)/AKT pathway. In this study, we aimed to identify the synergistic anti-cancer effects of ERB-041, an ERß agonist, and genistein, an isoflavonoid from soybeans known to have ERß-specific pseudo-estrogenic actions, on CMT-U27 and CF41.Mg CMT cell lines. ERB-041 and genistein synergistically inhibited cell proliferation and increased the number of annexin V-positive cells in both cell lines. Furthermore, we observed a synergistic increase in the Bax/Bcl-2 ratio and cleaved caspase-3 expression. Additionally, cell-cycle arrest occurred through the synergistic regulation of cyclin D1 and cyclin-dependent kinase 4 (CDK4). We also found a synergistic decrease in the expression of ERα, and the expression of proteins involved in the PI3K/AKT pathway, including p-PI3K, phosphatase and tensin homolog (PTEN), AKT, and mechanistic target of rapamycin (mTOR). In conclusion, ERB-041 and genistein exhibited a synergistic anticancer effect on CMTs, suggesting that cotreatment with ERB-041 and genistein is a promising treatment for CMTs.

Anti-cancer effect of palmatine through inhibition of the PI3K/AKT pathway in canine mammary gland tumor CMT-U27 cells.

Canine mammary gland tumors (CMTs) are the most common and lethal cancers in female dogs. Dysregulated phosphoinositide 3-kinases (PI3K)/AKT pathway reportedly was involved in the growth and metastasis of CMTs. However, there are few studies on therapeutic strategies for targeting the PI3K pathway in CMTs. In this study, we aimed to determine whether palmatine, a natural isoquinoline alkaloid with anti-cancer properties, could inhibit the growth of CMTs and whether the inhibitory effect was mediated through the PI3K/AKT pathway. Our in vitro experiments on CMT-U27, a CMT cell line, showed that palmatine reduced cell proliferation and induced cell death. Western blotting results revealed that palmatine decreased the protein expression of PI3K, PTEN, AKT, and mechanistic target of rapamycin in the PI3K/AKT pathway, which was supported by the results of immunocytochemistry. Additionally, palmatine suppressed the migration and tube formation of canine aortic endothelial cells as well as the migration of CMT U27 cells. Our in vivo results showed that palmatine inhibited tumor growth in a CMT-U27 mouse xenograft model. We observed a decreased expression of proteins in the PI3K/AKT pathway in tumor tissues, similar to the in vitro results. Furthermore, palmatine significantly disrupted the tumor vasculature and inhibited metastasis to adjacent lymph nodes. In conclusion, our findings demonstrate that palmatine exerts anti-cancer effects against CMTs by inhibiting PI3K/AKT signaling pathway, suggesting that palmatine has potential as a canine-specific PI3K inhibitor for the treatment of CMTs.

Development and clinical evaluation of a quantitative fluorescent immunoassay for detecting canine CRP.

Canine C-reactive protein (cCRP) is one of the major positive acute phase proteins in dogs and is commonly measured to detect and monitor systemic inflammation as well as the efficacy of treatment. Traditional methods for testing cCPR, including enzyme-linked immunosorbent assay (ELISA), have some drawbacks, such as a long time for diagnosis and the requirement of well-equipped laboratories. Therefore, there is a need for a rapid and precise diagnostic test for cCRP at point-of-care. This study assessed the accuracy, precision, and validated clinical effectiveness of a diagnostic test based on fluorescent lateral flow immunoassay to detect cCRP. For the standard cCRP concentration ranging from 0 to 200 µg/mL, the cCRP diagnostic test showed strong linearity with R2 of 0.9977 (p < 0.001), and both inter- and intra-assay CVs were <14%. The limit of detection and limit of quantitation were found to be 4.0 µg/mL and 5.0 µg/mL, respectively. The cCRP serum concentration was evaluated in 21 client-owned dogs and the results were compared to a previously validated ELISA. The Pearson Correlation Coefficient between the diagnostic test kit and ELISA was 0.942 [95% confidence interval: 0.859 to 0.976, p < 0.001], and the Bland-Altman plot indicated a bias of 26.82% [95% limits of agreement: -56.03 to 109.67], indicating a significant correlation and the agreement between the data from the cCRP diagnostic test and ELISA. In conclusion, the fluorescent immunoassay based diagnostic test is a suitable option for rapidly and precisely detecting cCRP in dogs, providing a convenient alternative to traditional methods for diagnosing acute inflammation.

Antitumor Effects of Esculetin, a Natural Coumarin Derivative, against Canine Mammary Gland Tumor Cells by Inducing Cell Cycle Arrest and Apoptosis.

Mammary gland tumors are the most common neoplasms in female dogs, of which 50% are malignant. Esculetin, a coumarin derivative, reportedly induces death in different types of cancer cells. In this study, we explore the anticancer effects of esculetin against CMT-U27 and CF41.mg canine mammary gland tumor cells. Esculetin significantly inhibited the viability and migration of both CMT-U27 and CF41.mg cells in a dose- and time-dependent manner. Flow cytometric analysis and terminal deoxynucleotidyl transferase dUTP nick-end labeling assay revealed increased numbers of annexin-V-positive cells and DNA fragmentation. Furthermore, a cell cycle analysis demonstrated that esculetin blocked the cell progression at the G0/G1 phase and the S phase in CMT-U27 and CF41.mg cells. These results were supported by a Western blot analysis, which revealed upregulated protein expression of cleaved caspase-3, a marker of apoptosis, and downregulated cyclin-dependent kinase 4 and cyclin D1 protein, the cell cycle regulators. In conclusion, this novel study proves that esculetin exerts in vitro antitumor effects by inducing apoptosis and cell cycle arrest in canine mammary gland tumors.

Methyl Gallate Suppresses Tumor Development by Increasing Activation of Caspase3 and Disrupting Tumor Angiogenesis in Melanoma.

Methyl gallate is a phenolic compound mainly found in medicinal plants. It has been reported to its anticancer activity in various tumors. In this study, we aimed to demonstrate the antitumor effect of methyl gallate in the melanoma mouse model and B16F10 cells. Our results showed that methyl gallate decreased cell viability and induced apoptosis by increasing the expression of cleaved caspase3 in B16F10 cells and prevented cell migration and tube formation in human umbilical vein endothelial cells. In B16F10 cell-inoculated mice, methyl gallate not only decreased tumor volume by 30% but also significantly reduced tumor vessel density and pericyte coverage. Moreover, methyl gallate diminished by close to 50% the expression of cytokeratin and LYVE-1 in mouse right inguinal lymph nodes, indicating that methyl gallate could suppress metastasis. In conclusion, this study suggests that methyl gallate inhibits tumor development by inducing apoptosis and blocking tumor angiogenesis and metastasis and might be considered a therapeutic agent for melanoma.

Feasibility study of multi-site split learning for privacy-preserving medical systems under data imbalance constraints in COVID-19, X-ray, and cholesterol dataset.

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide ground-breaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined the term, "multi-site split learning", which enables a secure transfer of medical data between multiple hospitals without fear of exposing personal data contained in patient records. It also explores the effects of varying the number of end-systems and the ratio of data-imbalance on the deep learning performance. A guideline for the most optimal configuration of split learning that ensures privacy of patient data whilst achieving performance is empirically given. We argue the benefits of our multi-site split learning algorithm, especially regarding the privacy preserving factor, using CT scans of COVID-19 patients, X-ray bone scans, and cholesterol level medical data.

Novel Human-Derived RET Fusion NSCLC Cell Lines Have Heterogeneous Responses to RET Inhibitors and Differential Regulation of Downstream Signaling.

Rearranged during transfection (RET) rearrangements occur in 1% to 2% of lung adenocarcinomas as well as other malignancies and are now established targets for tyrosine kinase inhibitors. We developed three novel RET fusion-positive (RET+) patient-derived cancer cell lines, CUTO22 [kinesin 5B (KIF5B)-RET fusion], CUTO32 (KIF5B-RET fusion), and CUTO42 (echinoderm microtubule-associated protein-like 4-RET fusion), to study RET signaling and response to therapy. We confirmed each of our cell lines expresses the RET fusion protein and assessed their sensitivity to RET inhibitors. We found that the CUTO22 and CUTO42 cell lines were sensitive to multiple RET inhibitors, whereas the CUTO32 cell line was >10-fold more resistant to three RET inhibitors. We discovered that our RET+ cell lines had differential regulation of the mitogen-activated protein kinase and phosphoinositide 3-kinase/protein kinase B (AKT) pathways. After inhibition of RET, the CUTO42 cells had robust inhibition of phosphorylated AKT (pAKT), whereas CUTO22 and CUTO32 cells had sustained AKT activation. Next, we performed a drug screen, which revealed that the CUTO32 cells were sensitive (<1 nM IC50) to inhibition of two cell cycle-regulating proteins, polo-like kinase 1 and Aurora kinase A. Finally, we show that two of these cell lines, CUTO32 and CUTO42, successfully establish xenografted tumors in nude mice. We demonstrated that the RET inhibitor BLU-667 was effective at inhibiting tumor growth in CUTO42 tumors but had a much less profound effect in CUTO32 tumors, consistent with our in vitro experiments. These data highlight the utility of new RET+ models to elucidate differences in response to tyrosine kinase inhibitors and downstream signaling regulation. Our RET+ cell lines effectively recapitulate the interpatient heterogeneity observed in response to RET inhibitors and reveal opportunities for alternative or combination therapies. SIGNIFICANCE STATEMENT: We have derived and characterized three novel rearranged during transfection (RET) fusion non-small cell lung cancer cell lines and demonstrated that they have differential responses to RET inhibition as well as regulation of downstream signaling, an area that has previously been limited by a lack of diverse cell line modes with endogenous RET fusions. These data offer important insight into regulation of response to RET tyrosine kinase inhibitors and other potential therapeutic targets.

Unsupervised Deep Contrast Enhancement with Power Constraint for OLED Displays.

Various power-constrained contrast enhance-ment (PCCE) techniques have been applied to an organic light emitting diode (OLED) display for reducing the pow-er demands of the display while preserving the image qual-ity. In this paper, we propose a new deep learning-based PCCE scheme that constrains the power consumption of the OLED displays while enhancing the contrast of the displayed image. In the proposed method, the power con-sumption is constrained by simply reducing the brightness a certain ratio, whereas the perceived visual quality is pre-served as much as possible by enhancing the contrast of the image using a convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE technique without a reference image by unsupervised learning. Ex-perimental results show that the proposed method is supe-rior to conventional ones in terms of image quality assess-ment metrics such as a visual saliency-induced index (VSI) and a measure of enhancement (EME).1.

Resistance to Cleavage of Core⁻Shell Rubber/Epoxy Composite Foam Adhesive under Impact Wedge⁻Peel Condition for Automobile Structural Adhesive.

Epoxy foam adhesives are widely used for weight reduction, watertight property, and mechanical reinforcement effects. However, epoxy foam adhesives have poor impact resistance at higher expansion ratios. Hence, we prepared an epoxy composite foam adhesive with core⁻shell rubber (CSR) particles to improve the impact resistance and applied it to automotive structural adhesives. The curing behavior and pore structure were characterized by differential scanning calorimetry (DSC) and X-ray computed tomography (CT), respectively, and impact wedge⁻peel tests were conducted to quantitatively evaluate the resistance to cleavage of the CSR/epoxy composite foam adhesives under impact. At 5 and 10 phr CSR contents, the pore size and expansion ratio increased sufficiently due to the decrease in curing rate. However, at 20 phr CSR content, the pore size decreased, which might be due to the steric hindrance effect of the CSR particles. Notably, at 0 and 0.1 phr foaming agent contents, the resistance to cleavage of the adhesives under the impact wedge⁻peel condition significantly improved with increasing CSR content. Thus, the CSR/epoxy composite foam adhesive containing 0.1 phr foaming agent and 20 phr CSR particles showed high impact resistance (EC = 34,000 mJ/cm²) and sufficient expansion ratio (~148%).

Exploring the molecular mechanisms of Traditional Chinese Medicine components using gene expression signatures and connectivity map.

BACKGROUND AND OBJECTIVE: Traditional Chinese Medicine (TCM) has been practiced over thousands of years in China and other Asian countries for treating various symptoms and diseases. However, the underlying molecular mechanisms of TCM are poorly understood, partly due to the "multi-component, multi-target" nature of TCM. To uncover the molecular mechanisms of TCM, we perform comprehensive gene expression analysis using connectivity map. METHODS: We interrogated gene expression signatures obtained 102 TCM components using the next generation Connectivity Map (CMap) resource. We performed systematic data mining and analysis on the mechanism of action (MoA) of these TCM components based on the CMap results. RESULTS: We clustered the 102 TCM components into four groups based on their MoAs using next generation CMap resource. We performed gene set enrichment analysis on these components to provide additional supports for explaining these molecular mechanisms. We also provided literature evidence to validate the MoAs identified through this bioinformatics analysis. Finally, we developed the Traditional Chinese Medicine Drug Repurposing Hub (TCM Hub) - a connectivity map resource to facilitate the elucidation of TCM MoA for drug repurposing research. TCMHub is freely available in http://tanlab.ucdenver.edu/TCMHub. CONCLUSIONS: Molecular mechanisms of TCM could be uncovered by using gene expression signatures and connectivity map. Through this analysis, we identified many of the TCM components possess diverse MoAs, this may explain the applications of TCM in treating various symptoms and diseases.

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.

BACKGROUND: With 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. RESULTS: IMPACT 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. CONCLUSION: IMPACT 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 .

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology.

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/.

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.

Big Data Mining and Adverse Event Pattern Analysis in Clinical Drug Trials.

Drug adverse events (AEs) are a major health threat to patients seeking medical treatment and a significant barrier in drug discovery and development. AEs are now required to be submitted during clinical trials and can be extracted from ClinicalTrials.gov ( https://clinicaltrials.gov/ ), a database of clinical studies around the world. By extracting drug and AE information from ClinicalTrials.gov and structuring it into a database, drug-AEs could be established for future drug development and repositioning. To our knowledge, current AE databases contain mainly U.S. Food and Drug Administration (FDA)-approved drugs. However, our database contains both FDA-approved and experimental compounds extracted from ClinicalTrials.gov . Our database contains 8,161 clinical trials of 3,102,675 patients and 713,103 reported AEs. We extracted the information from ClinicalTrials.gov using a set of python scripts, and then used regular expressions and a drug dictionary to process and structure relevant information into a relational database. We performed data mining and pattern analysis of drug-AEs in our database. Our database can serve as a tool to assist researchers to discover drug-AE relationships for developing, repositioning, and repurposing drugs.

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.

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

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. We validated 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.

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.

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.

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.