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Cancer Pharmacogenetics: Lessons Learned

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Title: Cancer Prevention/Control Continuum Author: Aylssa Grauman Last modified by: Daphne Sears Created Date: 12/1/2005 8:37:00 PM Document presentation format – PowerPoint PPT presentation

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Title: Cancer Pharmacogenetics: Lessons Learned


1
Cancer Pharmacogenetics Lessons Learned
  • Geoffrey Liu, MD FRCPC
  • Scientist, OCI

2
Currently Approved Oncology Drugs
3
Cost of Colorectal Cancer Treatment Per 6 Months
()
Meropol NJ, Schulman KA. Cost of Cancer Care
Issues and Implications. J Clin Oncol 2007
25180-186.
4
NY Times, September 2, 2009
5
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

6
"Here's my sequence... New Yorker
7
Personalized or Predictive Medicine
Patients with same diagnosis
8
What Disciplines are Involved?
9
Cancer Pharmacogenomics (PGx)
  • The study of how variation in an individuals
    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

10
Cancer Pharmacogenetics
Cancer Pharmacogenomics
Biomarkers Predictive for Drug Outcomes
Biomarkers Predictive for Treatment Outcomes
11
Cancer Pharmacogenetics
GERMLINE
Cancer Pharmacogenomics
SOMATIC or TUMOUR
Biomarkers Predictive for Drug Outcomes
PROTEINS, IMAGING
Biomarkers Predictive for Treatment Outcomes
RADIATION THERAPY
12
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
    persons life

13
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

14
(inherited) Genetic Variations?
  • Substitutions (or SNPs)
  • Insertions
  • Deletions
  • Duplications
  • Short repeats
  • Gene deletions
  • Copy Number Variation
  • Gene and Protein Expression Levels/Function/Reg
    ulation

15
Polymorphisms can alter function through multiple
mechanisms
Promoter
Exon
Intron
UTRs
Conformational change Binding site change Early
termination
16
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
17
Pharmacology
  • 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
  • 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)

Every aspect may affect the final drug effect
18
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
19
High
Drug Genetic Variation Mechm 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
Level of Evidence
Adapted from Coate et al, JCO, 2010)
20
Candidate Genetic Factors Determining Drug
Response
  • Polymorphisms in
  • Drug Receptors/Targets
  • Beta-2AR
  • Drug Transporters
  • MDR1
  • Drug Metabolizing Enzymes
  • CYP2D6

21
Goal of Pharmacogenetics
Optimize Therapy So Benefits Outweigh the Risks
22
Methodological Approaches
  • Biological Pathway-defined
  • Epidemiological Association Studies
  • In vitro and In vivo
  • Human tissue and Clinical Information

23
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

24
Three Common Genetic and Epidemiological
Approaches
  • Germline
  • Candidate-Gene
  • Genome-Wide Association (GWAS)
  • Candidate-Pathway

25
Candidate-Gene Approach
  • Typically genetic variants are selected based on
    their known physiologic or pharmacologic effect
    on disease or drug response

26
Three Cancer Examples of candidate polymorphism
approaches
  • Irinotecan and UGT1A1 polymorphisms
  • Tamoxifen and CYP2D6 polymorphisms
  • EGFR tyrosine kinase inhibitors and EGFR
    polymorphisms

27
Three Cancer Examples of 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
28
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
Bone Marrow
Intestine
Leukopenia Thrombocytopenia Anemia
Diarrhea
(UGT1A)-- uridine diphospho-glucuronosyltransferas
e 1A subfamily
29
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
Bone Marrow
Intestine
Leukopenia Thrombocytopenia Anemia
Diarrhea
(UGT1A)-- uridine diphospho-glucuronosyltransferas
e 1A subfamily
30
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
Bone Marrow
Intestine
Leukopenia Thrombocytopenia Anemia
Diarrhea
(UGT1A)-- uridine diphospho-glucuronosyltransferas
e 1A subfamily
31
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
Bone Marrow
Intestine
Leukopenia Thrombocytopenia Anemia
Diarrhea
(UGT1A)-- uridine diphospho-glucuronosyltransferas
e 1A subfamily
32
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
Bone Marrow
Intestine
Leukopenia Thrombocytopenia Anemia
Diarrhea
(UGT1A)-- uridine diphospho-glucuronosyltransferas
e 1A subfamily
33
UGT1A1 Genotype
Innocenti et al, JCO, 2004
34
UGT1A1 Genotype
Less functional allele
35
UGT1A1 Genotype
Less functional allele
36
Protein structure of UGT1A family
540 AA, 28 signal AA, 243 common AA in different
isoforms
C
N
37
Protein structure of UGT1A family
540 AA, 28 signal AA, 243 common AA in different
isoforms
TM
38
Protein structure of UGT1A family
540 AA, 28 signal AA, 243 common AA in different
isoforms
39
UGT1A gene family Alternative Splicing Variants
40
Important Genetic Variations for UGT1A1
41
UGT1A7 allele nomenclature and important SNPs
Allele name Protein Promoter Nucleotide change Coding nucleotide change Amino acid change
Allele name Protein Promoter Nucleotide change Coding nucleotide change Amino acid change
UGT1A71a UGT1A7.1 G115, N129, R131, W208
UGT1A71b UGT1A7.1 -70(GgtA)  
UGT1A72 UGT1A7.2 387(TgtG)/391(CgtA)/392(GgtA) ( K129, k131) N129K/R131K  
UGT1A73 UGT1A7.3 387(TgtG)/391(CgtA)/392(GgtA)/ 622(TgtC) (k129, K131,R208) N129K/R131K/W208R  
UGT1A74 UGT1A7.4 622(TgtC) (R208) W208R  
UGT1A75 UGT1A7.5 343(GgtA) G115S  
UGT1A76 UGT1A7.6 417(GgtC) E139D  
UGT1A77 UGT1A7.7 387(TgtG)/391(CgtA)/392(GgtA)/417(GgtC) N129K/R131K/E139D  
UGT1A78 UGT1A7.8 387(TgtG)/391(CgtA)/392(GgtA)/417(GgtC)/622(TgtC) N129K/R131K/E139D/W208R  
UGT1A79 UGT1A7.9 343(GgtA)/387(TgtG)/391(CgtA)/392(GgtA) G115S/N129K/R131K  
UGT1A710 UGT1A7.10 386(AgtG)/387(TgtG)/391(CgtA)/392(GgtA)/622(TgtC) N129R/R131K/W208R  
UGT1A711 UGT1A7.11 392(GgtA) R131Q  
UGT1A712 UGT1A7.12 -57(TgtG) 622(TgtC)/760(CgtT) W208R/R254X  
UGT1A713 UGT1A7.13 828(CgtA) N276K  
UGT1A714 UGT1A7.14 422(GgtC) C141S  
42
UGT1A9 allele nomenclature and important SNPs
43
Variations across UGT1A polymorphisms
Chr234255266-Chr234255944 678bp
Chr2, 234245202
UGT1A7
UGT1A9
-57 TgtG rs7586110
622TgtC W208R rs176832
391CgtA(rs17863778), 392GgtA(rs17868324) R131K
342 GgtA G115S()
387TgtG N129K rs176832
UGT1A922 -118T9/T10 rs3832043
UGT1A7 12345678910111214
44
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.

45
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?
46
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.

47
but dont throw the baby out with the bathwater
48
Training-Test Paradigm in Human Samples
  • Training Set (correct for multiple comparisons)
  • Multiple Validation Sets

49
From Bench to Bedside Complexity of the Human
Being
50
From Bench to Bedside Complexity of the Human
Being
Pharmacogenetics
51
Tamoxifen Metabolism
Clinical Cancer Research January 2009 15 15
52
Tamoxifen Metabolism
Clinical Cancer Research January 2009 15 15
53
Tamoxifen Metabolism
Clinical Cancer Research January 2009 15 15
54
Tamoxifen Metabolism
Clinical Cancer Research January 2009 15 15
55
CYP2D6
Meyer. Nature Review 2004
56
CYP2D6
Meyer. Nature Review 2004
57
CYP2D6 Genotype and Endoxifen
Plt0.001, r20.24
Plasma Endoxifen (nM)
CYP2D64 (most common genetic variant associated
with the CYP2D6 poor metabolizer state)
Jin Y et al. JNCI9730, 2005
58
Relapse-Free Survival
n115
EM
2-year RFS EM 98 IM 92 PM 68 Log Rank P0.009

n40
IM
PM
n16
Years after randomization
CP1229323-16
Goetz et al. Breast Cancer Res Treat. 2007
59
Relapse-Free Survival
Extensive
n115

Decreased
n65
P0.007
Years after randomization
CP1234316-3
Goetz et al. Breast Cancer Res Treat. 2007
60
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

61
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,
    n2800)

62
Tamoxifen complexities
Tamoxifen
CYP2D6 CYP3A
Tamoxifen active metabolites
SULT1A1
Inactive Metabolites
63
Tamoxifen complexities
Tamoxifen
CYP2D6 CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
64
Tamoxifen complexities
CYP inhibitory agents

Tamoxifen
Treatment of Side Effects
CYP2D6 CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
65
Tamoxifen complexities
CYP inhibitory agents

Tamoxifen
Treatment of Side Effects
CYP2D6 CYP3A
Tamoxifen active metabolites
Side Effects
SULT1A1
compliance
Inactive Metabolites
66
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

67
Pharmacogenetic Example 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
68
Pharmacogenetic Example 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
69
CADR and-216G/T combined PFS
RED BLUE
S/ST/- L/-G/G
N () 64 (70) 28 (30)
Med PFS 3.9 mos 2.0 mos
Adj. HR 0.60 reference
95CI (0.36-0.98) (0.36-0.98)
Logrank p0.0006
Phase II Study of Gefitinib In NSCLC
Liu et al, TPJ 2007
70
CADR and-216G/T combined OS
S/ST/- L/-G/G
N () 64 (70) 28 (30)
Med OS 12.0 mos 7.6 mos
Adj. HR 0.60 reference
95CI (0.36-1.00) (0.36-1.00)
Logrank p0.02
Liu et al, TPJ 2007
71
Prospective Validation?
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/gt 15py
Pemetrexed 500mg IV D1
RECIST with re-staging q2 cycles Until PD or
toxicity or withdrawal
72
Schema
X
21 day cycles
Closed due to poor accrual
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/gt 15py
Pemetrexed 500mg IV D1
RECIST with re-staging q2 cycles Until PD or
toxicity or withdrawal
73
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.

74
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 gt93 between normal and corresponding
    tumor tissue DNA.

75
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.

76
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77
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78
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79
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80
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

81
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

82
Three Examples for Discussion
  • Candidate Gene Example

83
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

84
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85
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.

86
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87
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)

88
Where do we go from here?
89
Analytic Framework Key Questions for Evaluating
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
Treatment Decisions
Prediction of Metabolism
Harms of Subsequent Management Options
Analytic Validity
Incorrect Genotype Assignment
Prediction of Adverse Drug Reactions
90
Analytic Framework Key Questions for Evaluating
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
Treatment Decisions
Prediction of Metabolism
Harms of Subsequent Management Options
Analytic Validity
Incorrect Genotype Assignment
Prediction of Adverse Drug Reactions
91
Analytic Framework Key Questions for Evaluating
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
Treatment Decisions
Prediction of Metabolism
Harms of Subsequent Management Options
Analytic Validity
Incorrect Genotype Assignment
Prediction of Adverse Drug Reactions
92
Analytic Framework Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of Drug Efficacy
v
  • Improved Outcomes
  • Enhanced Response
  • Minimize Toxicity

X
v
Germline / Somatic Genotype
Cancer Patients
Treatment Decisions
Prediction of Metabolism
Harms of Subsequent Management Options
Analytic Validity
Incorrect Genotype Assignment
Prediction of Adverse Drug Reactions
UGT1A1 and Irinotecan DPD and 5FU
93
Analytic Framework Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of Drug Efficacy
v
  • Improved Outcomes
  • Enhanced Response
  • Minimize Toxicity

?
v
Germline / Somatic Genotype
Cancer Patients
Treatment Decisions
Prediction of Metabolism
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
94
Analytic Framework Key Questions for Evaluating
Genomic Tests in a Specific Clinical Scenario
Overarching Question
Clinical Validity
Clinical Utility
Prediction of Drug Efficacy
v
  • Improved Outcomes
  • Enhanced Response
  • Minimize Toxicity

?
Germline / Somatic Genotype
Cancer Patients
Treatment Decisions
Prediction of Metabolism
Harms of Subsequent Management Options
Analytic Validity
Incorrect Genotype Assignment
Prediction of Adverse Drug Reactions
FC-gamma-R VEGFR2
95
Clinical Validity
Clinical Utility
Clinical Uptake
Analytical Validity
Epidemiological Research Translational
Research Laboratory Research
Basic Science and Laboratory Medicine Translationa
l Research
Epidemiological Research Health Service
Research Commercialization Regulatory Agencies
96
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

97
Clinical Trials vs. Observational Population
Studies
Randomized Trials Observational Studies
- Designed to measure survival differences, so good resource for secondary analyses of disease outcome related factors (clinico-epidemiologic and biological) - Not a good source for disease risk analyses (due to highly selected case entry criteria, and lack of concurrent healthy controls in study) - Collect detailed treatment toxicity data - Randomization of confounding variables - Accessible specimens for translational research - Can incorporate epidemiologic data collection upfront in study design - Efficient use of resources by tagging epidemiological study onto trial - Either the experimental or standard arm will become obsolete thus, at least one arm may become irrelevant to clinical practice - Standard source for epidemiological studies of disease risk - Evaluate rare and long term toxicities - Large, diverse populations - Follow up can be extensive - Large number of drugs and drug combinations - Includes a wide range of co-morbidities and past medication history - Can be utilized in rarer cancers where randomized trials are not available - Can study standard, approved drugs where no randomized trial data exists
Both are needed to inform practice and policy.
98
Blatant Plug
99
AMP-PEL (Liu lab) Applied Molecular
Profiling-Pharmacogenomic Laboratory
DRY LAB
WET LAB
Biomarker Research Cancer Management Prevention S
creening 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
100
Candidate-Based PG Validation Studies (Secondary
Analyses of Clinical Trials)
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
2011
v
2012
2012
2012
2012
RTOG9704 (Panc) 2013 FFPE III Gemcitabine
ICON7 2013 Blood III Bevacizumab
101
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
102
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
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