Transcript eshg_poster

e-PGA: AN INTEGRATED ELECTRONIC PHARMACOGENOMICS ASSISTANT FOR
PERSONALIZED MEDICINE
K. Lakiotaki1, E. Kartsaki1, A. Kanterakis1, T. Katsila2, G. P. Patrinos2, G. Potamias1
1Institute
of Computer Science, Foundation for Research & Technology – Hellas, Heraklion, Crete
2Department of Pharmacy, University of Patras, Hellas
THE CLINICAL PROBLEM
Pharmacogenomics (PGx) holds promise to personalize medical interventions
by determining genetic influence in drug response and enabling tailor-made
drug prescription according to an individual’s genetic makeup.
Motivations. Drug response varies among individuals, ranging from expected
beneficial effects to adverse reactions and sometimes to even fatal events Different
populations carry different profiles of rare and common genetic variants.
Towards Population Pharmacogenomics (PPGx):
What do SNPs tell us about drug response?
In this work we study how 65 pharmacogenes - genes enabled in the absorption,
distribution, metabolism, excretion and toxicity (ADMET) of drugs - and their variants
(508 SNP biomarkers) vary among 2504 genomes across 26 populations.
eMoDiA: Translation Service
eMoDiA: From Genotype to Phenotype to Recommendations
eMoDiA: electronic Molecular Diagnostic Assistant
I am about to prescribe
fluoropyrimidine to a patient who is
Poor Metabolizer of this drug. Are
there any recommendations?
Patient
Start with at least
a 50% reduction
in starting dose
followed by
titration of dose
based on
toxicity or
pharmacokinetic
test
 Integrates heterogeneous PGx
information from several valid PGx
resources (PharmGKB, Ensembl …)
 Offers automated personalized
PGx translation (genotype-tophenotype) services
 Provides a user friendly interface
for
submitting
newly
discovered PGx related genevariants and alleles
eMoDiA: Explore Service
Methodology. We developed a genotype to
phenotype translation algorithm, which infers
metabolizer phenotypes from individual genetic
(SNP) profiles. For each pharmacogene, and
based
on
available
(PharmGKB)
haplotype/allele tables, an individual’s
genotype-profile is matched against the
available gene-alleles. Next, each inferred allele is
assigned to a metabolizer phenotype, according
to available “look up” tables. [the algorithm was
verified with the Affymetrix© DMET Plus
respective translation results]
PGX VARIATION AMONG 1KG POPULATIONS
European Ancestry (EUR): 20%
Finnish in Finland (FIN)
20%
British in
England and
Scotland
(GBR)
18%
Utah residents with
Northern and Western
European ancestry
(CEU) 20%
Iberian populations in
Spain (IBS) 21%
African Caribbean in
Barbados (ACB) 15%
Colombian in
Medellin, Colombia
(CLM) 27%
Peruvian in
Lima, Peru (PEL)
25%
Indian Telugu in
the UK (ITU) 21%
Gujarati Indian in
Houston,TX (GIH) 21%
Esan in Nigeria
(ESN) 15%
African Ancestry in
Southwest US (ASW) 9%
Americas Ancestry
(AMR): 19%
Bengali in
Bangladesh
(BEB) 17%
Kinh in Ho Chi Minh
City, Vietnam (KHV)
20%

Japanese in Tokyo,
Japan (JPT) 21%

Chinese Dai in
Xishuangbanna,
China(CDX) 18%
Southern
Han
Chinese,
China
(CHS) 21%
1 phSNP
Han Chinese in
Bejing, China
(CHB) 20%
Yoruba in
Ibadan, Nigeria
(YRI) 16%
Mende in Sierra
Leone (MSL)
13%
http://www.1000genomes.org
• The 1000 Genomes (1kG) Project aims to provide a
deep characterization of human genome sequence
variation.
• 2504 individuals from 26 populations
• Total variant sites ~80M
31 phSNPs
35 phSNPs
GWD
ESN
40phSNPs
MSL
YRI
CYP2C19
LWK
CYP2B6
25
ASW
CYP2D6
12 genes
CHS
CDX
20
KHV
15
CHB
10
JPT
5
PJL
0
Chr:
1
2
3
4
5
6
7
8
phGenes
4
12
3
2
2
4
9
2
ABCB1
ABCC2
9
10
11
12
13
14
15
16
17
18
19
20
21
22
X
0
6
1
1
0
0
3
5
1
0
9
0
0
3
2
BEB
STU
phGenes matched to heterozygous or homozygous variant haplotypes
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
AFR
4
11
2
1
2
3
8
2
AMR
4
11
2
1
2
3
9
1
EAS
4
11
2
1
1
2
8
EUR
4
11
2
1
2
3
SAS
4
12
2
1
2
3
PharmacoSNPs
18
16
14
12
10
8
6
4
2
0
0.2
0.3
0.4
0
6
1
1
0
0
3
3
1
0
8
0
0
3
2
0
6
0
1
0
0
3
3
1
0
8
0
0
3
2
1
0
4
0
1
0
0
3
3
0
0
7
0
0
3
2
GBR
8
1
0
6
1
1
0
0
3
3
1
0
7
0
0
3
2
FIN
8
1
0
5
1
1
0
0
3
3
1
0
7
0
0
3
2
GIH
IBS
Potential abnormal metabolism in most phGenes
1kG (~80M SNPs)
0.001 0.005 0.01 0.02 0.03 0.04 0.05 0.1
Allele frequency
ITU
CEU
TSI
0.5
PUR
CLM
PEL
CYP2D6
MXL
ABCB1
ABCC2
ADRB1
ADRB2
APOE
BRCA1
CDA
CFTR
CHRNA5
COMT
CYP1A1
CYP1A2
CYP1B1
CYP2A13
CYP2A6
CYP2B6
CYP2C19
CYP2C8
CYP2C9
CYP2D6
CYP2E1
CYP2F1
CYP2R1
CYP2S1
CYP2W1
CYP3A4
CYP3A43
CYP3A5
CYP3A7
CYP4A22
CYP4B1
CYP4F2
DDC
DPYD
G6PD
HMGCR
HTR2C
IGFBP3
LDLR
NAT1
NAT2
P2RY12
PIK3CA
SCN1A
SCN5A
SCNN1B
SLC22A1
SLC25A27
SLCO1B1
SULT1A2
SULT1C2
SULT2A1
SULT4A1
TPMT
UGT1A1
UGT1A10
UGT1A3
UGT1A4
UGT1A5
UGT1A6
UGT1A7
UGT1A8
UGT1A9
UGT2B15
VKORC1
SNP percentage
11-20 phSNPs
30
African Ancestry (AFR): 26%
Haplotypes
4-10 phSNPs
35
Luhya in
Webuye,
Kenya (LWK)
15%
Gambian in Western Division,
The Gambia (GWD) 17%
2-3 phSNPs
ACB
40
Number of phSNPs
Puerto Rican in
Puerto Rico (PUR)
30%
Mexican Ancestry
in Los Angeles,
California (MXL)
18%
Toscani in
Italia (TSI)
21%
Punjabi in
Lahore,Pakistan
(PJL) 20%
Sri Lankan Tamil in
the UK (STU) 21%
65 pharmacogenes, 508 SNPs, 328 haplotypes found in 1kG samples
22 core PharmADME genes (4 Transporters, 12 Phase I, 6 Phase II), 26 extended
PharmADME genes (1 Transporter, 2 Modifiers, 9 Phase I, 14 Phase II)
15/65 included in FDA’s Pharmacogenomic Biomarkers in Drug Labeling

East Asian
Ancestry (EAS):
20%
South Asian
Ancestry (SAS): 19%
CYP2C19
NAT2
0
5
10
15
CYP2B6
20
25
PharmacoSNPs
30
35
• CYP2C19-a liver enzyme that acts on 10-15% of drugs in current clinical use, including the
antiplatelet clopidogrel (Plavix)-haplotypes, are matched to only 5 individuals assigning a V/V
phenotype (although phSNP coverage is 78%). Same holds for 2 individuals in NAT2, a gene
encoding an enzyme that functions to both activate and deactivate arylamine and hydrazine
drugs and carcinogens (phSNP coverage is 63%).
40
: statistically significant phenotypic difference among populations in 29 genes
R/R: combination of 2 wild type haplotypes =>normal metabolic status
R/V: a combination of 1 wild type and 1 variant haplotype =>intermediate metabolic status
V/V: a combination of 2 variant haplotypes=>poor or ultra-rapid metabolic status
Abnormal metabolism
POPULATION PHARMACOGENOMICS ANALYSIS
Conclusions
EAS
PEL
AFR
SAS
AMR
• We adequately (sample coverage>90%)
phenotypes in 33 out of 65 pharmacogenes.
EUR
assigned
PGx
• We found statistically significant phenotypic difference among
1kG populations in 29 pharmacogenes
• PharmacoSNP allele frequencies reflect population structures
IBS
GBR
IBS
PCA
FIN
Component 2
TSI
PJL
PUR
ACB
CLM
ASW
CHB
GWD
ITU
STU
JPT
LWK
YRI
GIH
CDX
BEB
MXL
MSL
ESN
CHS
KHV
PEL
• The PEL population diverges from its relevant ancestral region
(AMR)
• Population of African Ancestry, South and East Asian Ancestry
can be accurately clustered
• Populations of Americas Ancestry are not easily differentiated
from European Ancestry populations
Component 1
The reported work was funded by the eMoDiA (electronic Molecular Diagnostics Assistant) project (11SYN_10_145)
under the "Competitiveness and Entrepreneurship» (OPCE ΙΙ; Greek-EU) operational program
Contact:
George Potamias ([email protected])