Presentation at JSBi2013 Excellent Poster Awards Full paper http://www.ncbi.nlm.nih.gov/pubmed/24565165

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)