Cancer Pharmacogenetics:
Lessons Learned
Geoffrey Liu, MD FRCPC
Scientist, OCI
Currently Approved Oncology Drugs
Cost of Colorectal Cancer Treatment
Per 6 Months ($)
Meropol NJ, Schulman KA. Cost of Cancer Care: Issues and Implications. J Clin Oncol 2007
25:180-186.
NY Times, September 2, 2009
Personalized Medicine

Tailoring medical prevention and treatment
therapies to the characteristics of each patient
improving their quality of life and health
outcome.
"The right medicine to the right person at the
right dosage at the right time"
• Pharmacoepidemiology
• Pharmacogenomics
"Here's my
sequence...”
New Yorker
Personalized or Predictive Medicine
Patients with same diagnosis
Respond to treatment
No response to treatment
Experience adverse events
What Disciplines are Involved?
Pharmacology
Genomics
Molecular
biology
Pharmacoepidemiology
Personalized/
Stratified/
Predictive Medicine
Bioinformatics
BioStatistics
Bioethics
Cancer Pharmacogenomics (PGx)

The study of how variation in an
individual’s germline and/or tumor
genome are related to their metabolism
and physiological response to drugs
used in cancer treatment
•
•
•
•
•
•
Single Nucleotide Polymorphisms (substitutions)
Insertions and deletions
Copy number Variations
Methylation patterns
Molecular biomarkers
Gene expression
Cancer Pharmacogenetics
Cancer Pharmacogenomics
Biomarkers Predictive for
Drug Outcomes
Biomarkers Predictive for
Treatment Outcomes
Cancer Pharmacogenetics
GERMLINE
Cancer Pharmacogenomics
SOMATIC or TUMOUR
Biomarkers Predictive for
PROTEINS, IMAGING
Drug Outcomes
RADIATION THERAPY
Biomarkers Predictive for
Treatment Outcomes
Gene Mutations — Inherited or Acquired

Hereditary (germline) mutations
• alterations in DNA inherited from a
parent and are found in the DNA of
virtually all of your cells.

Acquired (somatic) mutations
• alterations in DNA that develop
throughout a person’s life
Somatic Examples





Her2neu and Herceptin in breast ca
KRAS and EGFR MoAbs in colorectal ca
EGFR activating mutations and EGFR TKIs in
NSCLC
?ALK-EML4 translocation and ALK-targeting
?BRAF mutations and BRAF inhibitor in
melanoma
(inherited) Genetic Variations?







Substitutions (or SNPs)
Insertions
Deletions
Duplications
Short repeats
Gene deletions
Copy Number Variation
Gene and
Protein
Expression
Levels/Function
/Regulation
Polymorphisms can alter function
through multiple mechanisms
Promoter
Exon
Intron
Conformational change
Binding site change
Early termination
UTRs
Polymorphisms can alter function
through multiple mechanisms
mRNA
Transport guidance
UTRs
Promoter
Exon
Intron
Regions that are
spliced into
non-coding RNAs
UTRs
“junk areas”
microRNAs
Meta-regulators
Pharmacology

Pharmacokinetics (PK): the study
of the time course of substances
and their relationship with an
organism or system (Journey of
drugs)
•
•
•
•

Every aspect may affect the
final drug effect
Absorption
Distribution
Metabolism
Excretion
Pharmacodynamics (PD): the
study of the biochemical and
physiological effects of drugs and
the mechanisms of drug action and
the relationship between drug
concentration and effect (Drug
effect on the body)
Pharmacogenetics

The Study of the genetics of factors
related to PD and PK
Genes involved in PK
Drug Absorption/Transport
Activation/Metabolism/Excretion
Genes involved in PD
Drug mechanism of action.
targets/downstream effectors
Genetic Variation
Mech’m
Outcome
5FU/analogue
DPD
PK
Toxicity
6MP and AZA
TPMT
PK
Toxicity
Irinotecan
UGT1A1
PK
Toxicity
Aromatase
Inhibitors
TCL1
PD?
Toxicity
Warfarin
CYP2C9 & VKORC1
PK & PD
Toxicity
Cisplatin
TPMT and COMT
Unclear
Toxicity
Tamoxifen
CYP2D6
PK
Efficacy
5FU/analogue
TS
PK
Toxicity
5FU/analogue
MTHFR
PK
Toxicity
Cyclophosphamide
CYPs
PK
Eff & Tox
MoAbs
Fc-gamma-RII & III
PD
Efficacy
EGFR TKIs
EGFR, ABCG2
PD
Eff & Tox
Cisplatin
DNA repair SNPs
PD
Eff & Tox
Dasatinib
CYP3A4/3A5
PK
Eff & Tox
Adapted from Coate et al, JCO, 2010)
High
Level of Evidence
Drug
Candidate Genetic Factors
Determining Drug Response

Polymorphisms in
• Drug Receptors/Targets

Beta-2AR
• Drug Transporters

MDR1
• Drug Metabolizing Enzymes

CYP2D6
Goal of Pharmacogenetics
Optimize Therapy So Benefits Outweigh the Risks
Methodological Approaches




Biological Pathway-defined
Epidemiological Association Studies
In vitro and In vivo
Human tissue and Clinical Information
Issues to consider with Epidemiological
Association Studies


Tumour vs Blood = which is your target tissue?
When do you believe an association study
biomarker result?
• Multiple comparisons?
• Heterogeneity (of disease, of patients, of clinical
scenario) = humans are not mice; how are these
things controlled?
• Biological Grounding/Functional Data?
• Study Design and Study Population issues = if I
choose the “right” controls, I will always be able to
find a statistically significant result
Three Common Genetic and
Epidemiological Approaches

Germline
• Candidate-Gene
• Genome-Wide Association (GWAS)
• Candidate-Pathway
Candidate-Gene Approach

Typically genetic variants are selected
based on their known physiologic or
pharmacologic effect on disease or drug
response
Three Cancer Examples
of candidate polymorphism approaches

Irinotecan and UGT1A1 polymorphisms

Tamoxifen and CYP2D6 polymorphisms

EGFR tyrosine kinase inhibitors and
EGFR polymorphisms
Three Cancer Examples
of candidate polymorphism approaches

Irinotecan and UGT1A1 polymorphisms

Tamoxifen and CYP2D6 polymorphisms

EGFR tyrosine kinase inhibitors and
EGFR polymorphisms
Irinotecan metabolism and its toxicity
ATP-binding cassette
transporters (ABC gene
family)
Help drug transfer into
hepatic cell membrane
carboxylesterase 1, 2
Cytochrome P450 3A family
SN-38+Glucuronide
Bone Marrow
Intestine
Leukopenia
Thrombocytopenia
Anemia
(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily
Diarrhea
Irinotecan metabolism and its toxicity
ATP-binding cassette
transporters (ABC gene
family)
Help drug transfer into
hepatic cell membrane
carboxylesterase 1, 2
Cytochrome P450 3A family
SN-38+Glucuronide
Bone Marrow
Intestine
Leukopenia
Thrombocytopenia
Anemia
(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily
Diarrhea
Irinotecan metabolism and its toxicity
ATP-binding cassette
transporters (ABC gene
family)
Help drug transfer into
hepatic cell membrane
carboxylesterase 1, 2
Cytochrome P450 3A family
SN-38+Glucuronide
Bone Marrow
Intestine
Leukopenia
Thrombocytopenia
Anemia
(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily
Diarrhea
Irinotecan metabolism and its toxicity
ATP-binding cassette
transporters (ABC gene
family)
Help drug transfer into
hepatic cell membrane
carboxylesterase 1, 2
Cytochrome P450 3A family
SN-38+Glucuronide
Bone Marrow
Intestine
Leukopenia
Thrombocytopenia
Anemia
(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily
Diarrhea
Irinotecan metabolism and its toxicity
ATP-binding cassette
transporters (ABC gene
family)
Help drug transfer into
hepatic cell membrane
carboxylesterase 1, 2
Cytochrome P450 3A family
SN-38+Glucuronide
Bone Marrow
Intestine
Leukopenia
Thrombocytopenia
Anemia
(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily
Diarrhea
UGT1A1
Genotype
Innocenti et al, JCO, 2004
UGT1A1
Genotype
Less functional allele
UGT1A1
Genotype
Less functional allele
Protein structure of UGT1A family
540 AA, 28 signal AA, ~243 common AA in different isoforms
N
Signal peptide
Functional part
28AA
~243 AA
~269AA
C
Protein structure of UGT1A family
540 AA, 28 signal AA, ~243 common AA in different isoforms
28AA
~243 AA
~269AA
Signal peptide
Functional part
TM
Protein structure of UGT1A family
540 AA, 28 signal AA, ~243 common AA in different isoforms
28AA
Signal peptide
Functional part
~243 AA
~269AA
UGT1A gene family: Alternative Splicing Variants
Important Genetic Variations for UGT1A1
UGT1A7 allele nomenclature and important SNPs
Promoter
Nucleotide
change
Allele name
Protein
Coding nucleotide change
Amino acid change
UGT1A7*1a
UGT1A7.1
UGT1A7*1b
UGT1A7.1
UGT1A7*2
UGT1A7.2
387(T>G)/391(C>A)/392(G>A) ( K129, k131)
N129K/R131K
UGT1A7*3
UGT1A7.3
387(T>G)/391(C>A)/392(G>A)/ 622(T>C); (k129,
K131,R208)
N129K/R131K/W208R
UGT1A7*4
UGT1A7.4
622(T>C) (R208)
W208R
UGT1A7*5
UGT1A7.5
343(G>A)
G115S
UGT1A7*6
UGT1A7.6
417(G>C)
E139D
UGT1A7*7
UGT1A7.7
387(T>G)/391(C>A)/392(G>A)/417(G>C)
N129K/R131K/E139D
UGT1A7*8
UGT1A7.8
387(T>G)/391(C>A)/392(G>A)/417(G>C)/622(T>C)
N129K/R131K/E139D/W208
R
UGT1A7*9
UGT1A7.9
343(G>A)/387(T>G)/391(C>A)/392(G>A)
G115S/N129K/R131K
UGT1A7*10
UGT1A7.10
386(A>G)/387(T>G)/391(C>A)/392(G>A)/622(T>C)
N129R/R131K/W208R
UGT1A7*11
UGT1A7.11
392(G>A)
R131Q
UGT1A7*12
UGT1A7.12
622(T>C)/760(C>T)
W208R/R254X
UGT1A7*13
UGT1A7.13
828(C>A)
N276K
UGT1A7*14
UGT1A7.14
422(G>C)
C141S
G115, N129, R131, W208
-70(G>A)
-57(T>G)
UGT1A9 allele nomenclature and important SNPs
Variations across UGT1A polymorphisms
Chr2:234330521-Chr2:234330398
=123bp
Chr2, 234245202
Chr234255266-Chr234255944
=678bp
Chr234333883-Chr23433633
=250bp
UGT1A7
UGT1A9
UGT1A1
-57 T>G
2 3 4
5A
5B
622T>C
W208R
rs7586110
rs176832
UGT1A1*6
rs4148323
391C>A(rs17863778),
392G>A(rs17868324)
R131K
342 G>A
G115S()
UGT1A1*28
rs8175347
387T>G
N129K
UGT1A1*93
rs176832
UGT1A9*22
-118T9/T10
rs3832043
-3156G>A
rs10929302
UGT1A7
*1*2*3*4*5*6*7*8*9*10
*11*12*14
UGT1A1*60
-3279T>G
rs4124874
Current Situation

UGT1As much more complex than initially
thought

Additional polymorphisms involved in
determining metabolism of irinotecan

Despite FDA labeling change, UGT testing
is currently not being used widespread.
Current Situation

UGT1As much more complex than initially
thought

Additional polymorphisms involved in
determining metabolism of irinotecan

Despite FDA labeling change, UGT testing
is currently not being used widespread.
Take-Home Message:
Heterogeneity and Complexity
of Associations affect Results
That is why you get difference
association studies that state that red
meat is good, neutral or bad for you….
…but don’t throw the baby out
with the bathwater
Training-Test Paradigm
in Human Samples

Training Set (correct for multiple comparisons)

Multiple Validation Sets
From Bench to Bedside:
Complexity of the Human Being
Causal Prognostic Factors
Biomarkers related to the host
Environmental Modifying Factors
Psychosocial
Cultural, Economic
Biomarkers of tumor
Treatment Factors
Clinical Outcomes
Non-causal
Prognostic Factors
-Hard outcomes (OS/DFS)
-Soft outcomes (toxicity/QOL)
Adapted from Liu et al, 2006
From Bench to Bedside:
Complexity of the Human Being
Causal Prognostic Factors
Biomarkers related to the host
Environmental Modifying Factors
Psychosocial
Cultural, Economic
Biomarkers of tumor
Treatment Factors
Clinical Outcomes
-Hard outcomes (OS/DFS)
-Soft outcomes (toxicity/QOL)
Non-causal
Prognostic Factors
Pharmacogenetics
Adapted from Liu et al, 2006
Tamoxifen Metabolism
Clinical Cancer
Research January
2009 15; 15
Tamoxifen Metabolism
Clinical Cancer
Research January
2009 15; 15
Tamoxifen Metabolism
Clinical Cancer
Research January
2009 15; 15
Tamoxifen Metabolism
Clinical Cancer
Research January
2009 15; 15
CYP2D6
Meyer.
Nature
Review 2004
CYP2D6
Meyer.
Nature
Review 2004
CYP2D6 Genotype and Endoxifen
P<0.001, r2=0.24
180
160
140
120
Plasma 100
Endoxifen 80
(nM)
60
40
20
0
Wt/Wt
Wt/*4
*4/*4
CYP2D6*4 (most common genetic variant associated
with the CYP2D6 poor metabolizer state)
Jin Y et al. JNCI;97:30, 2005
Relapse-Free Survival
100
80
EM n=115
60
2-year RFS
EM 98%
IM 92%
PM 68%
%
40
IM
n=40
PM
n=16
20
Log Rank
P=0.009
0
0
2
4
6
8
10
12
Years after randomization
Goetz et al. Breast Cancer Res Treat. 2007
CP1229323-16
Relapse-Free Survival
100
Extensive n=115
80
60
%
40
Decreased n=65
20
P=0.007
0
0
2
4
6
8
10
12
Years after randomization
Goetz et al. Breast Cancer Res Treat. 2007
CP1234316-3
Validation?

Follow-up studies have had variable results
• Not as clear cut

CYP2D6 is inducible and inhibited by many
drugs
• including anti-depressants and SSRIs

Many of these drugs have been used to
ameliorate peri-menopausal symptoms induced
by Tamoxifen
Tamoxifen and CYP2D6


CYP2D6 associated with BC outcome
• Goetz et al. 2005, 2007 (USA)
• Schroth et al. 2007 (Germany)
• Kiyotani et al. 2008 (Japan)
• Newman et al. 2008 (UK)
• Xu et al. 2008 (China)
• Okishiro et al. 2009 (Japan)
• Ramon et al. 2009 (Spain)
• Bijl et al. 2009 (Netherlands)
CYP2D6 not associated with BC outcome
• Wegman et al. 2005, 2007 (Sweden)
• Nowell et al. 2005 (USA)
• Goetz et al. 2009 (international consortia, n=2800)
Tamoxifen complexities
Tamoxifen
CYP2D6
CYP3A
Tamoxifen active metabolites
SULT1A1
Inactive Metabolites
Tamoxifen complexities
Tamoxifen
CYP2D6
CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
Tamoxifen complexities
Tamoxifen
CYP inhibitory agents
=
Treatment of Side Effects
CYP2D6
CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
Tamoxifen complexities
Tamoxifen
CYP inhibitory agents
=
Treatment of Side Effects
CYP2D6
CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
Take-Home Messages:
Confounders Play Key Roles in
Association Studies
Proper Phenotyping Critical
Importance of accounting for
variables and of choosing reliable
and accurate clinical endpoints
Pharmacogenetic Example:
EGFR polymorphisms and EGFR TKIs (2004-)
Review of existing PK/PD/PG data
In silico and bioinformatic
determination of best targets
SNP - HapMap
Haploview/Tagger
I2D/PPI Networks
Proprietary PK data
PGRN and public source
PK/PG/PD data
SIFT/PolyPhen/Coddle
Pharmacogenetic Example:
EGFR polymorphisms and EGFR TKIs (2004-)
Functional Assays
Identification of
key targets to test in patient samples
Promoter Analysis
AMPL
Gene Expression/Binding Assays
Collaboration with A. Adjei
(Mayo/RPCI)
Luciferase
Promoter
Assays
Haplotype
Constructs
and functional
Binding and
Expression assays
Liu et al, CR 2005
CADR and-216G/T combined:
PFS
BLUE
S/S+T/-
L/-+G/G
N (%)
64 (70%)
28 (30%)
Med PFS
3.9 mos
2.0 mos
Adj. HR
0.60
reference
95%CI
(0.36-0.98)
100%
Logrank p=0.0006
80%
Probability
RED
60%
40%
20%
0%
Phase II Study of
Gefitinib
In NSCLC
0
12
24
36
48
Progression-free Survival (months)
Liu et al, TPJ 2007
CADR and-216G/T combined: OS
L/-+G/G
N (%)
64 (70%)
Med OS
12.0 mos 7.6 mos
Adj. HR
0.60
95%CI
(0.36-1.00)
100%
28 (30%)
reference
Logrank p=0.02
80%
Probability
S/S+T/-
60%
40%
20%
0%
0
12
24
36
48
Overall Survival (months)
Liu et al, TPJ 2007
Prospective Validation?
*21 day cycles
P
R
E
Stratification
R
E
G
I
S
T
R
A
T
I
O
N
FISH+
EGFR
FISH
status
FISH-
R
A
N
D
O
M
I
Z
A
T
I
O
N
Stratification factors:
ECOG PS: 0/1/2
Cooperative Group
Stage: IIIB/IV
Gender: M/F
Smoking Status: Never/≤15py/> 15py
Erlotinib 150 mg
PO daily
Pemetrexed 500mg
IV D1
Erlotinib 150 mg
PO daily
Pemetrexed 500mg
IV D1
C
L
I
N
I
C
A
L
O
U
T
C
O
M
E
RECIST with re-staging q2 cycles
Until PD or toxicity or withdrawal
Schema
P
R
E
*21 day cycles
Closed due to poor accrual
Stratification
R
E
G
I
S
T
R
A
T
I
O
N
FISH+
EGFR
FISH
status
FISH-
R
A
N
D
O
M
I
Z
A
T
I
O
N
Erlotinib 150 mg
PO daily
Pemetrexed 500mg
IV D1
Erlotinib 150 mg
PO daily
Mutation Testing First Line
Stratification factors:
ECOG PS: 0/1/2
Cooperative Group
Stage: IIIB/IV
Gender: M/F
Smoking Status: Never/≤15py/> 15py
Pemetrexed 500mg
IV D1
C
L
I
N
I
C
A
L
O
U
T
C
O
M
E
RECIST with re-staging q2 cycles
Until PD or toxicity or withdrawal
Retrospective Validation?
The NCIC CTG study, BR.21

double-blind randomized trial of erlotinib
versus placebo as second/third line
treatment in Stage IIIB/IV NSCLC.

No blood collected = tiny small biopsies
collected.
Results

Normal tissue (± tumor) DNA was
extracted from 242/731 enrolled patients.

Genotyping success rates exceeded 92%.

In a 30 patient subset, genotyping
concordance rates were >93% between
normal and corresponding tumor tissue
DNA.
Results

Individuals without tissue for genotyping:
• were more likely to be Asian
• had greater PR/CR rates
• were more likely to have 2+ prior treatment
regimens
• and had longer time to randomization

Subgroups of genotyped and nongenotyped patients had OS/PFS and
benefited similarly from study treatment.
Issues


Too small a sample?
Skewed non-representative population?

Perhaps differences between erlotinib and
gefitinib

BR.19 analysis (also underpowered)
RTOG 0436 – years away
BIBW2772 – pending, but different drug


Take-Home Message:
Validation Key to Accepting
Association Study Results;
Validation not so easy…
1. Training Set  Validation/Test Sets
2. Biological or Functional Validation
Three Examples for Discussion

Candidate Gene Example
Genome-Wide Association
Study (GWAS) Approach

Examines common genetic variations for a
role in drug response by genotyping large
sets of genetic variations across genome
• “Discovery-based” vs. “hypothesis-based”
• Relate genetic variations to clinical outcome
• Identify associations in genes not previously
suspected
Pathway-based Approach

Examines biologically plausible
associations between certain individual
polymorphisms and clinical outcomes

Usually combines 2+ related genetic
variants to reveal otherwise undetectable
effects of individual variants on clinical
outcome.
What have we learned?




Training and Validation Sets important
Control sample important (Prognostic vs
Predictive)
GWAS and Pathway analyses may
improve chances of finding important and
novel associations
If Phenotype is carefully measured,
chances improve in finding association
(e.g. clinical trial data)
Where do we go from here?
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of
Drug Efficacy
Cancer
Patients
Germline
/ Somatic
Genotype
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
• Improved
Outcomes
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of
Drug Efficacy
Cancer
Patients
Germline
/ Somatic
Genotype
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
• Improved
Outcomes
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of
Drug Efficacy
Cancer
Patients
Germline
/ Somatic
Genotype
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
• Improved
Outcomes
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
√
Cancer
Patients
Clinical Utility
Prediction of
Drug Efficacy
√
Germline
/ Somatic
Genotype
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
UGT1A1 and Irinotecan
DPD and 5FU
• Improved
Outcomes
X
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
√
Cancer
Patients
Clinical Utility
Prediction of
Drug Efficacy
√
Germline
/ Somatic
Genotype
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
• Improved
Outcomes
?
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Tamoxifen and CYP2D6
Cisplatin and ototoxicity; AIs and MSK toxicity
Analytic Framework + Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
√
Cancer
Patients
Clinical Utility
Prediction of
Drug Efficacy
Germline
/ Somatic
Genotype
?
Incorrect
Genotype
Assignment
Prediction of
Metabolism
Prediction of
Adverse Drug
Reactions
FC-gamma-R
VEGFR2
• Improved
Outcomes
Treatment
Decisions
Harms of
Subsequent
Management
Options
• Enhanced
Response
• Minimize
Toxicity
Summary

Germline pharmacogenetic studies have changed
patient management in several diseases
• Cancer included

In cancer, effects can be related to efficacy or toxicity,
related to either PK or PD relationships

Studies in patient populations require consideration of
confounders (e.g. enzyme induction/inhibition) and
interactions (drug-drug)

Current research involves candidate gene, candidate
pathway, or agnostic genome-wide evaluations
• Next Gen Sequencing coming soon

Validation, validation, validation
Blatant Plug
AMP-PEL (Liu lab)
Applied Molecular ProfilingPharmacogenomic Laboratory
DRY LAB
Clinico-Epidemiological
Research: Descriptive
And Analytical
Epidemiological
Methods Research
Health Outcomes and
Knowledge Translation
Research
WET LAB
Biomarker Research:
Cancer Management
Prevention
Screening and Early
Detection
In vivo and In vitro
Pharmacogenomic
And Radiogenomic Research
Companion Research
For Clinical Trials
Candidate-Based PG Validation Studies
(Secondary Analyses of Clinical Trials)
Study Name
BR.10 (Lung)
Tissue Sample
2011 FFPE
Phase
Drug/Tx
III
Cisplatin
HN.6 (Head & Neck)
Blood
III
Cisplatin and XRT
Panitumumab
BR.21 (Lung)
FFPE/slides or
blocks
III
Erlotinib
√
BR.19 (Lung)
2012 FFPE
III
Gefitinib
BR.24 (Lung)
2012 Blood
III
Cediranib
TORCH (Lung)
2012 Blood
III
Erlotinib
MA.31 (Breast)
Blood
III
Her2neu/EGFR
2012 FFPE
III
Cetuximab
FFPE
III
Gemcitabine
2013 Blood
III
Bevacizumab
CO.17 (Colon)
RTOG9704 (Panc)
2013
ICON7
Candidate-Based PG Validation Studies
(Secondary Analyses
of Observational Studies)
Study Name
Approach
Sample
Size
Drug/Tx
Harvard-Toronto
Lung Cancer
Pathway
Candidate
3000+
Cisplatin
Carboplatin
Radiation
Harvard-Toronto
Pancreatic Cancer
Pathway
Candidate
GWAS
1000+
Gemcitabine
Harvard-Toronto
Esophageal Cancer
Pathway
Candidate
1000+
Cisplatin
5FU
Radiation
Toronto-Quebec Head
and Neck Cancer*
Pathway
Candidate
GWAS
1400+
Radiation
Cisplatin
AMP-PEL Laboratory (Fall 2011)
Dr. Zhuo Chen
Dr. Dangxiao Cheng
Dr. Azad Kalam
Dr. Qi Wang
Dr. Prakruthi Palepu
Dr. Salma Momin
Dr. Ehab Fadhel
Qin Kuang
Kangping Cui
Mark MacPherson
Anna Sergiou
Devalben Patel
Maryam Mirshams
Kevin Boyd
Alvina Tse
Dr. Alex Chan
Dr. Wei Xu
Dr. Manal Nakhla
Lawson Eng
Anthony LaDelfa
Melody Qiu
Memori Otsuka
Dr. Marjan Emami
Nicole Perera
Jennifer Teichman
Bin Sun
Andrew Fleet
Lorin Dodbiba
Vincent Pang
Debbie Johnson
Tammy Popper
Sharon Fung
Dr. Olusola Faluyi
Steven Habbous
Henrique Hon
Jenny Wang
Jenny Hui
Crystal Gagnon
Teresa Bianco
Dr. Sinead Cuffe
Andrea P-Cosio
Dr. Gord Fehringer
Yonathan Brhane
Thank-you