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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.

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Cancer pharmacogenetics lessons learned

Cancer Pharmacogenetics: Lessons Learned

Geoffrey Liu, MD FRCPC

Scientist, OCI



Cost of colorectal cancer treatment per 6 months
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.



Personalized medicine
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
Personalized or Predictive Medicine

Respondto treatment

Patients with same diagnosis

No responseto treatment

Experienceadverse events


What disciplines are involved
What Disciplines are Involved?

Personalized/Stratified/Predictive Medicine

Pharmaco- epidemiology

Molecularbiology

Bioethics

Bioinformatics

BioStatistics

Genomics

Pharmacology


Cancer pharmacogenomics pgx
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 Drug Outcomes

PROTEINS, IMAGING

Biomarkers Predictive for Treatment Outcomes

RADIATION THERAPY


Gene mutations inherited or acquired
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
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
(inherited) Genetic Variations?

Gene and Protein Expression Levels/Function/Regulation

Substitutions (or SNPs)

Insertions

Deletions

Duplications

Short repeats

Gene deletions

Copy Number Variation


Polymorphisms can alter function through multiple mechanisms
Polymorphisms can alter function through multiple mechanisms

Promoter

Exon

Intron

UTRs

Conformational change

Binding site change

Early termination


Polymorphisms can alter function through multiple mechanisms1
Polymorphisms can alter function through multiple mechanisms

mRNA

Transport guidance

UTRs

Promoter

Exon

Intron

UTRs

Regions that are

spliced into

non-coding RNAs

“junk areas”

microRNAs

Meta-regulators


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)

Pharmacokinetics (PK): the study of the time course of substances and their relationship with an organism or system (Journey of drugs)

Absorption

Distribution

Metabolism

Excretion

Pharmacology

Every aspect may affect the final drug effect


Pharmacogenetics
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


High

Level of Evidence

Adapted from Coate et al, JCO, 2010)


Candidate genetic factors determining drug response
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
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
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
Three Common Genetic and Epidemiological Approaches

  • Germline

    • Candidate-Gene

    • Genome-Wide Association (GWAS)

    • Candidate-Pathway


Candidate gene approach
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
Three Cancer Examplesof candidate polymorphism approaches

  • Irinotecan and UGT1A1 polymorphisms

  • Tamoxifen and CYP2D6 polymorphisms

  • EGFR tyrosine kinase inhibitors and EGFR polymorphisms


Three cancer examples of candidate polymorphism approaches1
Three Cancer Examplesof candidate polymorphism approaches

  • Irinotecan and UGT1A1 polymorphisms

  • Tamoxifen and CYP2D6 polymorphisms

  • EGFR tyrosine kinase inhibitors and EGFR polymorphisms

EACH TO ILLUSTRATE SPECIFIC ISSUES

WITH ASSOCIATION STUDIES


SN-38+Glucuronide

Irinotecan metabolism and its toxicity

ATP-binding cassette transporters (ABC gene family)

Help drug transfer into hepatic cell membrane

carboxylesterase 1, 2

Cytochrome P450 3Afamily

Bone Marrow

Intestine

Leukopenia

Thrombocytopenia

Anemia

Diarrhea

(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily


SN-38+Glucuronide

Irinotecan metabolism and its toxicity

ATP-binding cassette transporters (ABC gene family)

Help drug transfer into hepatic cell membrane

carboxylesterase 1, 2

Cytochrome P450 3Afamily

Bone Marrow

Intestine

Leukopenia

Thrombocytopenia

Anemia

Diarrhea

(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily


SN-38+Glucuronide

Irinotecan metabolism and its toxicity

ATP-binding cassette transporters (ABC gene family)

Help drug transfer into hepatic cell membrane

carboxylesterase 1, 2

Cytochrome P450 3Afamily

Bone Marrow

Intestine

Leukopenia

Thrombocytopenia

Anemia

Diarrhea

(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily


SN-38+Glucuronide

Irinotecan metabolism and its toxicity

ATP-binding cassette transporters (ABC gene family)

Help drug transfer into hepatic cell membrane

carboxylesterase 1, 2

Cytochrome P450 3Afamily

Bone Marrow

Intestine

Leukopenia

Thrombocytopenia

Anemia

Diarrhea

(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily


SN-38+Glucuronide

Irinotecan metabolism and its toxicity

ATP-binding cassette transporters (ABC gene family)

Help drug transfer into hepatic cell membrane

carboxylesterase 1, 2

Cytochrome P450 3Afamily

Bone Marrow

Intestine

Leukopenia

Thrombocytopenia

Anemia

Diarrhea

(UGT1A)-- uridine diphospho-glucuronosyltransferase 1A subfamily


UGT1A1

Genotype

Innocenti et al, JCO, 2004


UGT1A1

Genotype

Less functional allele


UGT1A1

Genotype

Less functional allele


Protein structure of ugt1a family

28AA

~243 AA

~269AA

Signal peptide

Functional part

Protein structure of UGT1A family

540 AA, 28 signal AA, ~243 common AA in different isoforms

C

N


Protein structure of ugt1a family1

28AA

~243 AA

~269AA

Signal peptide

Functional part

Protein structure of UGT1A family

540 AA, 28 signal AA, ~243 common AA in different isoforms

TM


Protein structure of ugt1a family2

28AA

~243 AA

~269AA

Signal peptide

Functional part

Protein structure of UGT1A family

540 AA, 28 signal AA, ~243 common AA in different isoforms






Chr2:234330521-Chr2:234330398

=123bp

Chr234333883-Chr23433633

=250bp

UGT1A1

2 3 4 5A 5B

UGT1A1*6

rs4148323

UGT1A1*28

rs8175347

UGT1A1*93

-3156G>A

rs10929302

UGT1A1*60

-3279T>G

rs4124874

Variations across UGT1A polymorphisms

Chr234255266-Chr234255944

=678bp

Chr2, 234245202

UGT1A7

UGT1A9

-57 T>G

rs7586110

622T>C

W208R

rs176832

391C>A(rs17863778),

392G>A(rs17868324)

R131K

342 G>A

G115S()

387T>G

N129K

rs176832

UGT1A9*22

-118T9/T10

rs3832043

UGT1A7 *1*2*3*4*5*6*7*8*9*10*11*12*14


Current situation
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 situation1
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.

CLINICAL UTILITY?


Take home message heterogeneity and complexity of associations affect results

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….



Training test paradigm in human samples
Training-Test Paradigmin 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

Treatment Factors

Biomarkers of tumor

  • Clinical Outcomes

  • Hard outcomes (OS/DFS)

  • Soft outcomes (toxicity/QOL)

Non-causal Prognostic Factors

Adapted from Liu et al, 2006

From Bench to Bedside:Complexity of the Human Being


From bench to bedside complexity of the human being1

Causal Prognostic Factors

Biomarkers related to the host

Environmental Modifying Factors

Psychosocial

Cultural, Economic

Treatment Factors

Biomarkers of tumor

  • Clinical Outcomes

  • Hard outcomes (OS/DFS)

  • Soft outcomes (toxicity/QOL)

Non-causal Prognostic Factors

Adapted from Liu et al, 2006

From Bench to Bedside:Complexity of the Human Being

Pharmacogenetics


Tamoxifen metabolism
Tamoxifen Metabolism

Clinical Cancer Research January 2009 15; 15


Tamoxifen metabolism1
Tamoxifen Metabolism

Clinical Cancer Research January 2009 15; 15


Tamoxifen metabolism2
Tamoxifen Metabolism

Clinical Cancer Research January 2009 15; 15


Tamoxifen metabolism3
Tamoxifen Metabolism

Clinical Cancer Research January 2009 15; 15


Cyp2d6
CYP2D6

Meyer. Nature Review 2004


Cyp2d61
CYP2D6

Meyer. Nature Review 2004


Cyp2d6 genotype and endoxifen
CYP2D6 Genotype and Endoxifen

P<0.001, r2=0.24

Plasma Endoxifen

(nM)

CYP2D6*4 (most common genetic variant associated with the CYP2D6 poor metabolizer state)

Jin Y et al. JNCI;97:30, 2005


Relapse free survival
Relapse-Free Survival

n=115

EM

2-year RFS

EM 98%

IM 92%

PM 68%

Log Rank

P=0.009

%

n=40

IM

n=16

PM

Years after randomization

CP1229323-16

Goetz et al. Breast Cancer Res Treat. 2007


Relapse free survival1
Relapse-Free Survival

Extensive

n=115

%

Decreased

n=65

P=0.007

Years after randomization

CP1234316-3

Goetz et al. Breast Cancer Res Treat. 2007


Validation
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
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 complexities

Tamoxifen

CYP2D6

CYP3A

Tamoxifen active metabolites

SULT1A1

Inactive Metabolites


Tamoxifen complexities1
Tamoxifen complexities

Tamoxifen

CYP2D6

CYP3A

Tamoxifen active metabolites

Side Effects

SULT1A1

compliance

Inactive Metabolites


Tamoxifen complexities2
Tamoxifen complexities

CYP inhibitory agents

=

Tamoxifen

Treatment of Side Effects

CYP2D6

CYP3A

Tamoxifen active metabolites

Side Effects

SULT1A1

compliance

Inactive Metabolites


Tamoxifen complexities3
Tamoxifen complexities

CYP inhibitory agents

=

Tamoxifen

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

Take-Home Messages: Confounders Play Key Roles in Association StudiesProper Phenotyping Critical

Importance of accounting for variables and of choosing reliable and accurate clinical endpoints


Pharmacogenetic Example: Association Studies

EGFR polymorphisms and EGFR TKIs (2004-)

In silico and bioinformatic

determination of best targets

Review of existing PK/PD/PG data

SNP - HapMap

Haploview/Tagger

I2D/PPI Networks

Proprietary PK data

PGRN and public source

PK/PG/PD data

SIFT/PolyPhen/Coddle


Pharmacogenetic Example: Association Studies

EGFR polymorphisms and EGFR TKIs (2004-)

Identification of

key targets to test in patient samples

Functional Assays

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
CADR Association Studies and-216G/T combined: PFS

Logrank p=0.0006

Phase II Study of

Gefitinib

In NSCLC

Liu et al, TPJ 2007


Cadr and 216g t combined os
CADR Association Studies and-216G/T combined: OS

Logrank p=0.02

Liu et al, TPJ 2007


Prospective validation
Prospective Validation? Association Studies

*21 day cycles

C

L

I

N

I

C

A

L

O

U

T

C

O

M

E

P

R

E

R

E

G

I

S

T

R

A

T

I

O

N

Erlotinib 150 mg

PO daily

R

A

N

D

O

M

I

Z

A

T

I

O

N

Stratification

FISH+

Pemetrexed 500mg

IV D1

EGFR

FISH

status

Erlotinib 150 mg

PO daily

FISH-

Stratification factors:

ECOG PS: 0/1/2

Cooperative Group

Stage: IIIB/IV

Gender: M/F

Smoking Status: Never/≤15py/> 15py

Pemetrexed 500mg

IV D1

RECIST with re-staging q2 cycles

Until PD or toxicity or withdrawal


Schema
Schema Association Studies

X

Closed due to poor accrual

*21 day cycles

C

L

I

N

I

C

A

L

O

U

T

C

O

M

E

P

R

E

R

E

G

I

S

T

R

A

T

I

O

N

Erlotinib 150 mg

PO daily

R

A

N

D

O

M

I

Z

A

T

I

O

N

Stratification

FISH+

Pemetrexed 500mg

IV D1

EGFR

FISH

status

Erlotinib 150 mg

PO daily

FISH-

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

RECIST with re-staging q2 cycles

Until PD or toxicity or withdrawal


Retrospective validation
Retrospective Validation? Association Studies

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
Results Association Studies

  • 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.


Results1
Results Association Studies

  • 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 non-genotyped patients had OS/PFS and benefited similarly from study treatment.


Issues
Issues Association Studies

  • 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

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
Three Examples for Discussion Study Results;

  • Candidate Gene Example


Genome wide association study gwas approach
Genome-Wide Association Study (GWAS) Approach Study Results;

  • 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
Pathway-based Approach Study Results;

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
What have we learned? Study Results;

  • 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)



Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions


Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions


Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions


Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

X

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions

UGT1A1 and Irinotecan

DPD and 5FU


Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

?

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions

Tamoxifen and CYP2D6

Cisplatin and ototoxicity; AIs and MSK toxicity


Analytic Framework + Key Questions for Evaluating Study Results;

Genomic Tests in a Specific Clinical Scenario

Overarching Question

Clinical Validity

Clinical Utility

Prediction of Drug Efficacy

  • Improved Outcomes

  • Enhanced Response

  • Minimize Toxicity

?

Germline / Somatic Genotype

Cancer Patients

Prediction of Metabolism

Treatment Decisions

Harms of Subsequent Management Options

Analytic

Validity

Incorrect Genotype Assignment

Prediction of Adverse Drug Reactions

FC-gamma-R

VEGFR2


Summary
Summary Study Results;

  • 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

Blatant Plug Study Results;


Amp pel liu lab applied molecular profiling pharmacogenomic laboratory
AMP-PEL (Liu lab) Study Results;Applied Molecular Profiling-Pharmacogenomic Laboratory

DRY LAB

WET LAB

Biomarker Research:

Cancer Management

Prevention

Screening and Early Detection

Clinico-Epidemiological

Research: Descriptive

And Analytical

Epidemiological

Methods Research

In vivo and In vitro

Pharmacogenomic

And Radiogenomic Research

Health Outcomes and Knowledge Translation Research

Companion Research

For Clinical Trials


Candidate-Based PG Validation Studies Study Results;

(Secondary Analyses of Clinical Trials)

2011

2012

2012

2012

2012


Candidate-Based PG Validation Studies Study Results;

(Secondary Analyses

of Observational Studies)


AMP-PEL Laboratory (Fall 2011) Study Results;

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 WangJenny Hui

Crystal Gagnon

Teresa Bianco

Dr. Sinead Cuffe

Andrea P-Cosio

Dr. Gord Fehringer

Yonathan Brhane


Thank you

Thank-you Study Results;


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