Identifying Differentially Expressed Genes and Screening Small Molecule Drugs for Lapatinib-resistance of Breast Cancer by a Bioinformatics Strategy

Wu, Zhuo; Zhang, Liang; Xie, Qi-Chao; Zhu, Bo; Chen, Zhengtang

doi:10.7314/apjcp.2014.15.24.10847

Cited by 4 publications

(3 citation statements)

References 35 publications

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“…One of these is Lapatinib, a dual tyrosine kinase inhibitor that interferes with epidermal growth factor receptor and HER2/neu pathways by treating HER2-positive breast cancer. A study presented a novel strategy for researching the mechanism of laptinib-resistance breast cancers 31. The oncogenic isoform of HER2, HER2Δ16, is expressed with HER2 in nearly 50% of HER2 positive breast tumors, and HER2Δ16 drives metastasis and resistance to drugs including tamoxifen and trastuzumab 32.…”

Section: Gene Expression Omnibusmentioning

confidence: 99%

Identification of Biomarkers for Breast Cancer Using Databases

Lee

Moon

2016

J Cancer Prev

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Breast cancer is one of the major causes of cancer death in women. Many studies have sought to identify specific molecules involved in breast cancer and understand their characteristics. Many biomarkers which are easily measurable, dependable, and inexpensive, with a high sensitivity and specificity have been identified. The rapidly increasing technology development and availability of epigenetic informations play critical roles in cancer. The accumulated data have been collected, stored, and analyzed in various types of databases. It is important to acknowledge useful and available data and retrieve them from databases. Nowadays, many researches utilize the databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Surveillance, Epidemiology and End Results (SEER), and Embase, to find useful informations on biomarkers for breast cancer. This review summarizes the current databases which have been utilized for identification of biomarkers for breast cancer. The information provided by this review would be beneficial to seeking appropriate strategies for diagnosis and treatment of breast cancer.

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Section: Gene Expression Omnibusmentioning

confidence: 99%

Identification of Biomarkers for Breast Cancer Using Databases

Lee

Moon

2016

J Cancer Prev

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“…CMap was first developed as a database of genome-wide transcriptional expression profiles of bioactive small molecules from cultured human cell lines and utilizes a pattern-matching algorithm to detect similarities among genomic signatures. It has been widely used to predict novel drug indications, such as discovering repurposed drug activities against common diseases, including diabetes, , Alzheimer̀s disease, solid tumors such as breast cancer , and inflammatory bowel diseases . Based on CMap, LTMap was focused on safety drug assessment especially liver drug toxicity.…”

Section: Introductionmentioning

confidence: 99%

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis

Liu

Tsompana

Wang

et al. 2016

J. Chem. Inf. Model.

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Drug discovery and development is a costly and time-consuming process with a high risk for failure resulting primarily from a drug's associated clinical safety and efficacy potential. Identifying and eliminating inapt candidate drugs as early as possible is an effective way for reducing unnecessary costs, but limited analytical tools are currently available for this purpose. Recent growth in the area of toxicogenomics and pharmacogenomics has provided with a vast amount of drug expression microarray data. Web servers such as CMap and LTMap have used this information to evaluate drug toxicity and mechanisms of action independently; however, their wider applicability has been limited by the lack of a combinatorial drug-safety type of analysis. Using available genome-wide drug transcriptional expression profiles, we developed the first web server for combinatorial evaluation of toxicity and efficacy of candidate drugs named "Connection Map for Compounds" (CMC). Using CMC, researchers can initially compare their query drug gene signatures with prebuilt gene profiles generated from two large-scale toxicogenomics databases, and subsequently perform a drug efficacy analysis for identification of known mechanisms of drug action or generation of new predictions. CMC provides a novel approach for drug repositioning and early evaluation in drug discovery with its unique combination of toxicity and efficacy analyses, expansibility of data and algorithms, and customization of reference gene profiles. CMC can be freely accessed at http://cadd.tongji.edu.cn/webserver/CMCbp.jsp .

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“…• Lapatinib (associated with CPS1 and MAGI2 amplification): Lapatinib is a HER2-positive breast cancer medicine. It is a dual tyrosine kinase inhibitor that interrupts the epidermal growth factor receptor (EGFR) and HER2/neu pathways [35].…”

Section: Association Of Biomarker Sensitivity With Approved Drugsmentioning

confidence: 99%

Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer

Ullah¹,

Shama

et al. 2017

Preprint

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Personal medicine has been associated with different patient responses to different anti-cancer therapies. Recently, scientists are looking not only for new biomarkers associated with a disease such as cancer but also identifying biomarkers that predict patients who are most likely to respond to a particular cancer treatment. Orderly endeavors to relate cancer mutational information with biological conditions may encourage the interpretation of somatic mutation indexes into significant biomarkers for patient stratification.We have screened and incorporated a board of cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) database to recognize genomic highlights related with drug sensitivity. We used mutation, DNA copy number variation, and gene expression information from Catalogue of Somatic Mutations in Cancer (COSMIC) and The Cancer Genome ATLAS (TCGA) for cell lines with their reactions to associate focused and cytotoxic treatments with approved drugs and drugs under clinical and preclinical examination.We discovered mutated cancer genes were related with cell reaction to, mostly accessible, cancer medications and some mutated genes were related with sensitivity to an expansive scope of therapeutic agents. By connecting drug activity to the useful many-sided quality of cancer genomes, efficient pharmacogenomic profiling in tumor cell lines gives an intense biomarker revelation stage to guide balanced malignancy remedial systems.Our study highlights that gene ANK2 amplification, and gene CELSER1 amplification and deletion are highly associated with anti-leukemic drug candidate LFM-A13. It also highlights that gene NUP214 and ROS1 copy number and gene NSD1 amplification are as a group highly associated with the parkinson drug Nilotinib. Finally, our study confirms that gene BRAF mutation is interacting with the BRAF-selective inhibitors drugs PLX4720 and SB590885. On the other hand, our study provides two open source analysis packages: bastah for the multitask-association analysis, and UNGeneAnno for automatic annotation of the variants.

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Identifying Differentially Expressed Genes and Screening Small Molecule Drugs for Lapatinib-resistance of Breast Cancer by a Bioinformatics Strategy

Cited by 4 publications

References 35 publications

Identification of Biomarkers for Breast Cancer Using Databases

Identification of Biomarkers for Breast Cancer Using Databases

Connection Map for Compounds (CMC): A Server for Combinatorial Drug Toxicity and Efficacy Analysis

Integrative Statistical Inferences for Drug Sensitivity Biomarkers in Cancer

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