Cancer Pharmacogenetics: Lessons Learned

Cancer Pharmacogenetics: Cancer Pharmacogenetics: Lessons LearnedLessons Learned

Geoffrey Liu, MD FRCPC

Scientist, OCI

Currently Approved Oncology DrugsCurrently Approved Oncology Drugs

Cost of Colorectal Cancer Treatment Cost of Colorectal Cancer Treatment Per 6 Months ($)Per 6 Months ($)

Meropol NJ, Schulman KA. Cost of Cancer Care: Issues and Implications. J Clin Oncol 2007 25:180-186.

NY Times, September 2, 2009

Personalized MedicinePersonalized Medicine

Tailoring medical prevention and treatment Tailoring medical prevention and treatment therapies to the characteristics of each patient therapies to the characteristics of each patient improving their quality of life and health improving their quality of life and health outcome.outcome.

"The right medicine to the right person at the "The right medicine to the right person at the right dosage at the right time" right dosage at the right time"

• PharmacoepidemiologyPharmacoepidemiology• Pharmacogenomics Pharmacogenomics

"Here's my

sequence...”

New Yorker

Personalized or Predictive MedicinePersonalized or Predictive Medicine

Patients with same diagnosisRespond to treatment

No response to treatment

Experience adverse events

Personalized/Stratified/

Predictive Medicine

What Disciplines are Involved?

Bioinformatics

Molecularbiology

BioethicsBioStatistics

Cancer Pharmacogenomics (PGx)Cancer Pharmacogenomics (PGx) The study of how variation in an The study of how variation in an

individual’s germline and/or tumor individual’s germline and/or tumor genome are related to their metabolism genome are related to their metabolism and physiological response to drugs and physiological response to drugs used in cancer treatmentused in cancer treatment• Single Nucleotide Polymorphisms (substitutions)Single Nucleotide Polymorphisms (substitutions)• Insertions and deletionsInsertions and deletions• Copy number VariationsCopy number Variations• Methylation patternsMethylation patterns• Molecular biomarkersMolecular biomarkers• Gene expressionGene expression

Cancer Pharmacogenetics

Cancer Pharmacogenomics

Biomarkers Predictive for Drug Outcomes

Biomarkers Predictive for Treatment Outcomes

Cancer Pharmacogenetics

Cancer Pharmacogenomics

Biomarkers Predictive for Drug Outcomes

Biomarkers Predictive for Treatment Outcomes

GERMLINE

SOMATIC or TUMOUR

PROTEINS, IMAGING

RADIATION THERAPY

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?(inherited) Genetic Variations?

Substitutions (or SNPs)Substitutions (or SNPs) InsertionsInsertions DeletionsDeletions DuplicationsDuplications Short repeatsShort repeats Gene deletionsGene deletions Copy Number VariationCopy Number Variation

Gene and Gene and Protein Protein Expression Expression Levels/FunctionLevels/Function/Regulation/Regulation

Polymorphisms can alter function through multiple mechanisms

Promoter Exon UTRsIntron

Conformational changeBinding site changeEarly termination

Polymorphisms can alter function through multiple mechanisms

Promoter Exon

UTRs

UTRsIntron

“junk areas”microRNAsMeta-regulators

Regions that are spliced into non-coding RNAs

mRNATransport guidance

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

Every aspect may affect the final drug effect

Pharmacology

Pharmacogenetics

The Study of the genetics of factors related to PD and PK

Genes involved in PD Drug mechanism of action. targets/downstream effectors

Genes involved in PK Drug Absorption/Transport Activation/Metabolism/Excretion

Drug 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

Leve

l of

Evi

denc

e

Adapted from Coate et al, JCO, 2010)

High

Candidate Genetic Factors Candidate Genetic Factors Determining Drug ResponseDetermining Drug Response

Polymorphisms inPolymorphisms in

• Drug Receptors/Targets Drug Receptors/Targets Beta-2ARBeta-2AR

• Drug Transporters Drug Transporters MDR1MDR1

• Drug Metabolizing Enzymes Drug Metabolizing Enzymes CYP2D6CYP2D6

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 ApproachCandidate-Gene Approach

Typically genetic variants are selected Typically genetic variants are selected based on their known physiologic or based on their known physiologic or pharmacologic effect on disease or drug pharmacologic effect on disease or drug responseresponse

Three Cancer Examplesof candidate polymorphism approaches

Irinotecan and UGT1A1 polymorphisms

Tamoxifen and CYP2D6 polymorphisms

EGFR tyrosine kinase inhibitors and EGFR polymorphisms

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

Irinotecan metabolism and its toxicity

LeukopeniaThrombocytopenia

Anemia

Cytochrome P450 3A family

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

Bone MarrowIntestine

Diarrhea

carboxylesterase 1, 2

ATP-binding cassette transporters (ABC gene family)Help drug transfer into hepatic cell membrane

SN-38+Glucuronide



Anemia




Diarrhea



SN-38+Glucuronide



Anemia




Diarrhea



SN-38+Glucuronide



Anemia




Diarrhea



SN-38+Glucuronide



Anemia




Diarrhea



SN-38+Glucuronide

UGT1A1Genotype

Innocenti et al, JCO, 2004

UGT1A1Genotype

Less functional allele

UGT1A1Genotype

Less functional allele

Protein structure of UGT1A family

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

Signal peptide

Functional part

~269AA~243 AA28AAN C



Signal peptide

Functional part

~269AA~243 AA28AA

TM



Signal peptide

Functional part

~269AA~243 AA28AA

UGT1A gene family: Alternative Splicing Variants

Important Genetic Variations for UGT1A1

Allele name Protein

PromoterNucleotide

change

Coding nucleotide change Amino acid change

UGT1A7*1a UGT1A7.1 G115, N129, R131, W208

UGT1A7*1b UGT1A7.1 -70(G>A)

UGT1A7*2 UGT1A7.2 387(T>G)/391(C>A)/392(G>A) ( K129, k131) N129K/R131K

UGT1A7*3 UGT1A7.3387(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/

W208R

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 -57(T>G) 622(T>C)/760(C>T) W208R/R254X

UGT1A7*13 UGT1A7.13 828(C>A) N276K

UGT1A7*14 UGT1A7.14 422(G>C) C141S

UGT1A7 allele nomenclature and important SNPs

UGT1A9 allele nomenclature and important SNPs

Variations across UGT1A polymorphisms

2 3 4 5A 5BUGT1A1

UGT1A1*6rs4148323

UGT1A1*28rs8175347

Chr234333883-Chr23433633=250bp

UGT1A1*93-3156G>A

rs10929302

UGT1A1*60-3279T>Grs4124874

Chr2:234330521-Chr2:234330398=123bp

Chr2, 234245202

Chr234255266-Chr234255944=678bp

UGT1A7

622T>C W208Rrs176832

387T>G N129Krs176832

-57 T>Grs7586110

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

*11*12*14

UGT1A9

UGT1A9*22-118T9/T10rs3832043

391C>A(rs17863778), 392G>A(rs17868324)

R131K342 G>A G115S()

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.

CLINICAL UTILITY?

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

http://3.bp.blogspot.com/-Ws3fONpIg24/TpJ7rAMYUzI/AAAAAAAAAsM/B6RgZAfIVUM/s1600/babybathwater.png

Training-Test Paradigmin Human Samples

Training Set (correct for multiple comparisons)

Multiple Validation Sets

From Bench to Bedside:Complexity of the Human Being

Biomarkers related to the host

Clinical Outcomes-Hard outcomes (OS/DFS)-Soft outcomes (toxicity/QOL)

Biomarkers of tumor

Environmental Modifying Factors

Treatment Factors

PsychosocialCultural, Economic

Non-causal Prognostic Factors

Causal Prognostic Factors

Adapted from Liu et al, 2006

From Bench to Bedside:Complexity of the Human Being

Biomarkers related to the host

Clinical Outcomes-Hard outcomes (OS/DFS)-Soft outcomes (toxicity/QOL)

Biomarkers of tumor

Environmental Modifying Factors

Treatment Factors

PsychosocialCultural, Economic

Non-causal Prognostic Factors

Causal Prognostic Factors

Adapted from Liu et al, 2006Pharmacogenetics

Tamoxifen Metabolism

Clinical Cancer Research January 2009 15; 15







CYP2D6

Meyer. Nature Review 2004

CYP2D6

Meyer. Nature Review 2004

CYP2D6 Genotype and Endoxifen

0

20

40

60

80

100

120

140

160

180

Wt/Wt Wt/*4 *4/*4

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

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

P<0.001, r2=0.24

Plasma Endoxifen

(nM)

0

20

40

60

80

100

0 2 4 6 8 10 12

Relapse-Free Survival

CP1229323-16

%

Years after randomization

2-year RFSEM 98%IM 92%PM 68%

Log RankP=0.009

EM

IM

PM

n=115

n=40

n=16

Goetz et al. Breast Cancer Res Treat. 2007

Relapse-Free Survival

CP1234316-3

0

20

40

60

80

100

0 2 4 6 8 10 12

%

Years after randomization

P=0.007

Extensive

Decreased

n=115

n=65

Goetz et al. Breast Cancer Res Treat. 2007

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

Tamoxifen active metabolites

Inactive Metabolites

CYP2D6CYP3A

SULT1A1


Tamoxifen



Side Effects

CYP2D6CYP3A

SULT1A1

compliance


Tamoxifen



CYP inhibitory agents

Side Effects

Treatment of Side Effects=

CYP2D6CYP3A

SULT1A1

compliance


Tamoxifen



CYP inhibitory agents

Side Effects

Treatment of Side Effects=

CYP2D6CYP3A

SULT1A1

compliance

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: Pharmacogenetic Example: EGFR polymorphisms and EGFR TKIs (2004-)

Review of existing PK/PD/PG data In silico and bioinformaticdetermination of best targets

Haploview/Tagger

SIFT/PolyPhen/Coddle

SNP - HapMap

I2D/PPI Networks

Proprietary PK data

PGRN and public source PK/PG/PD data

Pharmacogenetic Example: Pharmacogenetic Example: EGFR polymorphisms and EGFR TKIs (2004-)

Functional Assays

LuciferasePromoterAssays

HaplotypeConstructsand functionalBinding andExpression assays

Promoter AnalysisAMPL

Gene Expression/Binding AssaysCollaboration with A. Adjei(Mayo/RPCI)

Identification of key targets to test in patient samples

Liu et al, CR 2005

http://atvb.ahajournals.org/content/vol20/issue5/images/large/hq0501293004.jpeg

http://atvb.ahajournals.org/content/vol20/issue5/images/large/hq0501293004.jpeg

CADR and-216G/T combined: PFS

RED 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)

Logrank p=0.0006

Progression-free Survival (months)

Pro

ba

bili

ty

0 12 24 36 48

0%

20%

40%

60%

80%

100%

Liu et al, TPJ 2007

Phase II Study of Gefitinib In NSCLC

CADR and-216G/T combined: OS

S/S+T/- L/-+G/G

N (%) 64 (70%) 28 (30%)

Med OS 12.0 mos 7.6 mos

Adj. HR 0.60 reference

95%CI (0.36-1.00)

Logrank p=0.02

Overall Survival (months)

Pro

ba

bili

ty

0 12 24 36 48

0%

20%

40%

60%

80%

100%

Liu et al, TPJ 2007

Prospective Validation?

FISH+

FISH-

PRE

REGISTRATION

RANDOMIZATION

Erlotinib 150 mg PO daily

Pemetrexed 500mgIV D1



*21 day cycles

EGFRFISH

status

Stratification

CLINICAL

OUTCOME

Stratification factors:ECOG PS: 0/1/2Cooperative GroupStage: IIIB/IVGender: M/FSmoking Status: Never/≤15py/> 15py RECIST with re-staging q2 cycles

Until PD or toxicity or withdrawal

Schema

FISH+

FISH-

PRE

REGISTRATION

RANDOMIZATION





*21 day cycles

EGFRFISH

status

Stratification

CLINICAL

OUTCOME

Stratification factors:ECOG PS: 0/1/2Cooperative GroupStage: IIIB/IVGender: M/FSmoking Status: Never/≤15py/> 15py RECIST with re-staging q2 cycles

Until PD or toxicity or withdrawal

XClosed due to poor accrual

Mutation Testing First Line

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 non-genotyped 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 Genome-Wide Association Study (GWAS) ApproachStudy (GWAS) Approach

Examines common genetic variations for a Examines common genetic variations for a role in drug response by genotyping large role in drug response by genotyping large sets of genetic variations across genomesets of genetic variations across genome

• ““Discovery-based” vs. “hypothesis-based”Discovery-based” vs. “hypothesis-based”• Relate genetic variations to clinical outcomeRelate genetic variations to clinical outcome• Identify associations in genes not previously Identify associations in genes not previously

suspected suspected

Pathway-based ApproachPathway-based Approach

Examines biologically plausible Examines biologically plausible associations between certain individual associations between certain individual polymorphisms and clinical outcomes polymorphisms and clinical outcomes

Usually combines 2+ related genetic Usually combines 2+ related genetic variants to reveal otherwise undetectable variants to reveal otherwise undetectable effects of individual variants on clinical effects of individual variants on clinical outcome.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?

Cancer Patients

Germline / Somatic Genotype

Prediction of Drug Efficacy

Incorrect Genotype

Assignment

• Improved Outcomes

• Enhanced Response

• Minimize Toxicity Harms of

Subsequent Management

Options

Treatment Decisions

Analytic

Validity

Clinical Validity Clinical Utility

Overarching Question

Prediction of Metabolism

Prediction of Adverse Drug

Reactions

Analytic Framework + Key Questions for Evaluating Genomic Tests in a Specific Clinical Scenario

Cancer Patients



Incorrect Genotype

Assignment





Options

Treatment Decisions

Analytic

Validity





Reactions


Cancer Patients



Incorrect Genotype

Assignment





Options

Treatment Decisions

Analytic

Validity





Reactions


Cancer Patients



Incorrect Genotype

Assignment





Options

Treatment Decisions

Analytic

Validity





Reactions


UGT1A1 and IrinotecanDPD and 5FU

√ X√

Cancer Patients



Incorrect Genotype

Assignment





Options

Treatment Decisions

Analytic

Validity





Reactions


Tamoxifen and CYP2D6Cisplatin and ototoxicity; AIs and MSK toxicity

√ ?√

Cancer Patients



Incorrect Genotype

Assignment





Options

Treatment Decisions

Analytic

Validity





Reactions


FC-gamma-RVEGFR2

?√

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 Profiling-

Pharmacogenomic Laboratory

Clinico-EpidemiologicalResearch: DescriptiveAnd Analytical

EpidemiologicalMethods Research

Biomarker Research:Cancer ManagementPreventionScreening and Early Detection

In vivo and In vitroPharmacogenomic And Radiogenomic Research

Companion ResearchFor Clinical Trials

Health Outcomes and Knowledge Translation Research

DRY LAB WET LAB

Study Name Tissue Sample Phase Drug/Tx

BR.10 (Lung) FFPE III Cisplatin

HN.6 (Head & Neck) Blood III Cisplatin and XRT

Panitumumab

BR.21 (Lung) FFPE/slides or blocks

III Erlotinib

BR.19 (Lung) FFPE III Gefitinib

BR.24 (Lung) Blood III Cediranib

TORCH (Lung) Blood III Erlotinib

MA.31 (Breast) Blood III Her2neu/EGFR

CO.17 (Colon) FFPE III Cetuximab

Candidate-Based PG Validation Studies(Secondary Analyses of Clinical Trials)

√

2012

2012

2011

2012

2012

RTOG9704 (Panc) 2013

FFPE III Gemcitabine

ICON7 2013 Blood III Bevacizumab

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

Candidate-Based PG Validation Studies(Secondary Analyses

of Observational Studies)

AMP-PEL Laboratory (Fall 2011)

Dr. Zhuo ChenDr. Dangxiao ChengDr. Azad KalamDr. Qi WangDr. Prakruthi PalepuDr. Salma MominDr. Ehab FadhelQin KuangKangping CuiMark MacPhersonAnna Sergiou

Devalben PatelMaryam MirshamsKevin BoydAlvina TseDr. Alex ChanDr. Wei XuDr. Manal NakhlaLawson EngAnthony LaDelfaMelody QiuMemori Otsuka

Dr. Marjan EmamiNicole PereraJennifer TeichmanBin SunAndrew FleetLorin DodbibaVincent PangDebbie JohnsonTammy PopperSharon FungDr. Olusola Faluyi

Steven HabbousHenrique HonJenny WangJenny HuiCrystal GagnonTeresa BiancoDr. Sinead CuffeAndrea P-CosioDr. Gord FehringerYonathan Brhane

Thank-you

Documents

Cancer Pharmacogenetics: Lessons Learned