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Anticancer drug clustering in lung cancer based on gene expression profiles and sensitivity database

ABSTRACT: Anticancer drug clustering in lung cancer based on gene expression profiles. We performed gene expression analysis in lung cancer cell lines. (used: Affymetrix GeneChip Human Genome U133 Array Set HG-U133A). We also examines the sensitivity of these cell lines to commonly used anti-cancer agents (docetaxel, paclitaxel, gemcitabine, vinorelbine, 5-FU, SN38, cisplatin, and carboplatin) via MTT assay. We related the cytoxic activity of each of these agents to corresponding expression pattern in each of the cell lines using modified NCI program. Keywords: cytotoxicity, lung cancer, anticancer drugs

ORGANISM(S): Homo sapiens

PROVIDER: GSE4127 | GEO | 2006/02/10

SECONDARY ACCESSION(S): PRJNA95007

REPOSITORIES: GEO

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Project description:Bordel2018 - GSMM for Human Metabolic Reactions (HMR database) This model is described in the article: Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows Sergio Bordel Oncotarget. 2018; 9:19716-19729 Abstract: In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments. This model is hosted on BioModels Database and identified by: MODEL1707250000. To cite BioModels Database, please use: Chelliah V et al. BioModels: ten-year anniversary. Nucl. Acids Res. 2015, 43(Database issue):D542-8. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Purpose: Irinotecan (SN38) and oxaliplatin are chemotherapeutic agents used in the treatment of colorectal cancer. However, the frequent development of resistance to these drugs represents a considerable challenge in the clinic. Alus as retrotransposons comprise 11% of the human genome. Genomic toxicity induced by carcinogens or drugs can reactivate Alus by altering DNA methylation. Whether or not reactivation of Alus occurs in SN38 and oxaliplatin resistance remains unknown. Methods: We applied reduced representation bisulfite sequencing (RRBS) to investigate the DNA methylome in SN38- or oxaliplatin-resistant colorectal cancer cell line models. Moreover, we extended the RRBS analysis to tumor tissue from 14 patients with colorectal cancer who either did or did not benefit from capecitabine + oxaliplatin treatment. For the clinical samples, we applied a concept of DNA methylation entropy to estimate the diversity of DNA methylation states of the identified resistance phenotype-associated methylation loci observed in the cell line models. Results: We identified different loci being characteristic for the different resistant cell lines. Interestingly, 53% of the identified loci were Alu sequences -- especially the Alu Y subfamily. Furthermore, we identified an enrichment of Alu Y sequences that likely results from increased integration of new copies of Alu Y sequence in the drug-resistant cell lines. In the clinical samples, SOX1 and other SOX gene family members were shown to display variable DNA methylation states in their gene regions. The Alu Y sequences showed remarkable variation in DNA methylation states across the clinical samples. Our findings imply a crucial role of Alu Y in colorectal cancer drug resistance. Our study underscores the complexity of colorectal cancer aggravated by mobility of Alu elements and stresses the importance of personalized strategies, using a systematic and dynamic view, for effective cancer therapy. Investigation of the representive methylome of well-established SN38 and Oxaliplatin resistant cell line models and 14 clinical colorectal metastatic samples that have developed resistance to XELOX to review the epigenetic mechnism of the drug resistance.

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Anticancer drug clustering in lung cancer based on gene expression profiles and sensitivity database

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