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Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates
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Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates

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Presentation at JSBi2013 …

Presentation at JSBi2013
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  • 1. Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as drug target candidates 1, Mitsuo Iwadate2, Ryoichi Kinoshita 2 and Y-H. Taguchi1 Hideaki Umeyama 1Department of Physics 2Department of Biological Science JSBi Poster Chuo University #24
  • 2. 1. Background Gene (protein coding) → Mutation → Cancer → does not explain all cancer related gene anomaly → anomaly in unmutated (protein coding) gene expression may also be important e.g. cancer ↔ aberrant promoter methylation
  • 3. Cancer Drug → efficiency is always < 100% → possibly because of genomic divergence → Need of tailor-made medicine genomic divergence ↔ SNPs ? Purpose of this study: SNP ↔ DNA methlylation → gene anomaly → Drug discovery
  • 4. 2 Materials assumed to be unmutated in between mutated GEO:GSE20123 Taken from 30 patients blood 30 samples adjacent tumor normal 30 samples tissue 30 samples Genotype microarray measurement Nsp:262339 probes (SNPs) names of Restriction enzyme DNA methylation microarray measurement Sty:238379 probes (SNPs) → SNP arrays
  • 5. 3. Methods DNA methylation Genotype normal tumor blood tissue blood normal tumor tissue Selection by PCA Nsp 300 SNPs Sty1 300 SNPs Sty2 300 SNPs Nsp 300 SNPs Sty1 300 SNPs Sty2 300 SNPs Intersection between genotype and DNA methylation These SNPs are …, abundant and aberrantly methylated in cancer Nsp 68 SNPs Sty1 81 SNPs Sty2 50 SNPs
  • 6. Selection by PCA DNA methylation PC2 PC1 PC2 ☓ Cancer △ adjacent normal tissue ◯ blood Genotype 2D embeddings of SNPs PC1
  • 7. Comparison of commonly selcted SNPs between genotype and methylation ☓ Cancer △ adjacent normal tissue ◯ blood These SNPs are …, abundant and aberrantly methylated in cancer hypomethylated an nd bu a in t ca er nc
  • 8. Screened by t test and detection of associated genes Nsp 68 SNPs Sty1 81 SNPs Sty2 50 SNPs Screened by pairwise t test (blood, normal tissue, tumor) Nsp 59 SNPs Sty1 22 SNPs Sty2 37 SNPs Genes associated with SNPs 155 Genes
  • 9. Screened by cancer association and prediction of protein 3D structure 155 Genes Literature based disease association data base Screened by cancer association (by gendoo server) 86 Genes 3D structure Prediction by FAMS & phyre2 3D Structures Profile based structure prediction server
  • 10. Drug discovery phase for selected three genes ALK1 4FOD_0UV_n1 3LCS_STU_n2 3AOX_EMH_n3 4FOC_0UU_n4 4FOB_0US_n5 2YFX_VGH_n6 2XB7_GUI_n7 2XBA_571_n9 EGLN3 3HQR_OGA_n1 3OUJ_AKG_n2 [2HBT_FE2_n3] 2HBT_UN9_n4 2G19_4HG_n5 3OUH_014_n6 3OUI_42Z_n7 NUAK1 ligands binding to templates PDBs 3I7C_BK2_n1 3V5T_UW9_n2 3I7B_BK1_n3 3V51_I76_n4 3UPZ_B5A_n5 3NYV_DTQ_n6 3SX9_BK7_n7 3T3V_BK4_n8 3SXF_BK5_n9 3T3U_BK6_n10 3V5P_C88_n11 3N51_BK3_n12 3UPX_B6A_n13 DrugBank (6583 compounds) Ligands 3D structure (6510 compounds) Tanimoto Index >0.25 905 905 compounds compounds >0.20 1001 compounds >0.20 1090 compounds Babel Candidate compounds
  • 11. Virtual screening 905 compounds 4FOC_A 1001 compounds 2HBT_A ChooseLD Ranked 905 compounds for ALK Ranked 1001 compounds for ELGN3 (with/without Fe) Candidate 1090 compounds compounds 3I7C_A Templates (PDB ID) ligand protein docking simulator Ranked 1090 compounds for NUAK1 Ranked drug candidates
  • 12. 10 top ranked drug candidate compounds for ALK
  • 13. 7-Hydroxystaurosporine binding to ALK (Top ranked drug candidate) ALK 7-Hydroxystaurosporine
  • 14. Conclusion JSBi Poster #24 We have successfully constructed virtual screening pipe line for drug discovery from genotype specific DNA methylation information. This pipeline is expected to be applied to other diseases than cancers
  • 15. This study was accepted for the publication in BMC Systems Biology in a supplementary issue as a proceedings of APBC2014 (17th-19th Jan. 2014, Shanghai)
  • 16. Supported by 中央大学共同研究プロジェクト 「FAMSを用いたタンパク質機能予測に基 づくDrug Discovery」2013年ー201 5年 (Chuo University Joint Research Grant, “Drug Discovery based on protein function inference by FAMS” 2013-2015)

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