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Pharmacogenetics of Leukemia Treatment

Response

Richard AplencMay 2nd, 2008

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Pediatric Leukemia

Most common pediatric malignancy Four types

ALL AML CML JMML

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Leukemia Treatment

Varies both by disease and treating group Generally curable

~80% in ALL ~60% in AML

Toxicity important Long term effects in ALL Infection and cardiac toxicity in AML

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Leukemia Treatment

Multi-agent Over time Substantial impact on patient and family Accurate response prediction is clinically

very important

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InductionInduction

ALL Therapy

ConsolidationConsolidation MaintenanceMaintenanceInterimInterim

MaintenanceMaintenanceDelayedDelayed

IntensificationIntensification

MTXMTXSteroidsSteroids 6-MP/6-TG6-MP/6-TG DoxorubicinDoxorubicin CyclophosphamideCyclophosphamideL-AspL-Asp VCRVCR AraCAraC

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Predicting Treatment Response

Leukemic blast characteristics Morphology Cytogenetics Molecular alterations (BCR-ABL)

Patient characteristics Age Gender Genetic information?

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Genetic Information

Variation in DNA sequence throughout the genome

Types of variation include Gene deletions (GSTT1) Duplications of DNA regions (TS 28 bp) Changes in single base pairs (SNPs)

Allele, genotype, haplotype

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Allele/Genotype/Haplotype/CNV

SNP: Single Nucleotide Polymorphism

An allele is a single value for a single marker

A genotype is a pair of alleles for a given marker and both chromosomes in a single person

A haplotype is an ordered series of alleles for many markers on a single chromosome

Copy number variation (CNV) of DNA sequences

Chromosome from one parent

Chromosome from other parent

SNP 2

...Allele Genotype

Ha

plo

typ

e

SNP 1

GTGGGCGGGATGTACGTTCG

SNP example:

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Impact of Genetic Variability

Loss of gene = loss of function Duplication of DNA segments and single

base pair changes may have different effects depending on position Gain of function, loss of function, no change

Our Dream

One Genotype Would Explain Treatment Response

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Why Did We Have This Dream?

Thiopurine methylatransferase (TPMT) Low frequency variants have complete loss of

thiopurine metabolizing abilities

That Dream Has Ended

Why Is That?

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TPMTOne Gene, One Pathway, One Exposure

OPO4CH2

SH

N

NN

N

OHHO

O

PO4CH2

SH

N

NN

N

OHHO

HO

N

N N

N

SH

H

N

N N

N

SH

HOH

N

N N

N

SH

HO

OH

H

Mercaptopurine

TIMP

TXMP

TGMPTPMT

6-MMP

TX

TUXO

N

N N

N

SCH3

H

Allopurinol

TPMT Deficiency

HGPRT

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Two Remaining Questions

Can we utilize data on host genetic variability in a clinically

meaningful way?

Question 1:

Question 2:

Is Theo Zaoutis really Neo?

Lisa Z looks like Trinity

This Makes Sense Because…

And Because…

Paul Offit is clearly Morpheus

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Now That Everyone is Awake…

Return to Question 1

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Moving Towards the Answer

Decide on the question Understand the complex phenotype issues

Host genetics Environment

Address the genetic epidemiology issues

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What is the Question?

Does the genotype inform us of the biology underlying a clinical outcome? Etiology

Does the genotype predict a clinical outcome? Prediction

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One Conceptual Approach

Etiology Sensitivity Probability of positive test given disease

Prediction Positive predictive value Probability of disease given positive test

Seems obvious but impacts analysis

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Complex Phenotype: Host Genetics

Common SNPs will have modest effects Potentially large impact for the population

Rare SNPs may have bigger effects Small population impact

SNP frequency and the effect size determine sample size

SNP frequency varies by ethnicity

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Complex Phenotype: Environment

Identify and measure relevant covariates Genotype does not matter if the patient

doesn’t take the medication Concomitant medications

Drug-drug interactions

Alternative medications Folic acid supplimentation

Other environmental exposures

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What are the Genetic Epidemiology Issues?

Population stratification Variation of SNP frequency by ethnicity

High dimensional data Gene-environment interactions

Interaction of host genetics with environment

Gene-gene interactions Interaction of different SNPs

Multiple comparisons

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Some Examples from Our Data

Methotrexate interrupts the folate cycle ALL blasts are sensitive to folate depletion Polymorphisms in genes in the folate cycle

may impact methotrexate efficacy

p = 0.0486

0.50

0.60

0.70

0.80

0.90

1.00

0 5 1015Years

Wildtype (C) Variant (T)

Relapse Free Survival by MTHFR C677T Variant Allele

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MTHFR C677T Cox Model

Covariate HR p 95% CIC677T variant 1.93 0.004 1.229 3.037

Day 7 BM 1.77 0.013 1.125 2.773

Age 1.11 0.016 1.020 1.220

Race 1.71 0.307 0.610 4.798

Gender 1.37 0.238 0.811 2.323

Rx Arm 1.18 0.214 0.908 1.535

WBC 0.99 0.335 0.971 1.010

Phenotype 0.95 0.776 0.661 1.362

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MTHFR C677T and Infection Risk

Num.Infection

C/C 224 46 Sepsis 1

C/T 187 42 Sepsis 1.13 0.700-1.818

T/T 72 16 Sepsis 1.13 0.585-2.188

C/C 224 155 Fever/Neutropenia 1

C/T 187 120 Fever/Neutropenia 0.83 0.546-1.276

T/T 72 53 Fever/Neutropenia 1.32 0.709-2.447

C/C 224 123 Infection - Other 1

C/T 187 113 Infection - Other 1.27 0.850-1.887

T/T 72 43 Infection - Other 1.2 0.690-2.087

0.49

0.86

0.34MTHFR C677T

MTHFR C677T

OR 95% CI P value

MTHFR C677T

Gene Genotype N Infection Type

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MTHFR Conclusions

The MTHFR C677T variant allele seems to impact relapse risk

Dose adjustment of methotrexate for toxicity/infection does not ameliorate this effect

Dose adjustment based on genotype may be clinically useful

Replication in anther sample set is ongoing

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MTFHR Issues

Allele versus genotype versus haplotype Clinically meaningful analysis

Positive predictive value

p = 0.0486

0.50

0.60

0.70

0.80

0.90

1.00

0 5 1015Years

Wildtype (C) Variant (T)

Relapse Free Survival by MTHFR C677T Variant Allele

CC vs TT, p = 0.0477

0.50

0.60

0.70

0.80

0.90

1.00

0 5 1015Years

Wildtype (CC) Heterozygote (CT)Variant (TT)

Relapse Free Survival by MTHFR C677T Genotype

0.00

0.25

0.50

0.75

1.00

0 5 10 15analysis time

CA CA CA CGCG CG TA CA

TA CG TA TATA TG TG CGTG TG

Kaplan-Meier survival estimates, by haplo

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PPV with Time to Relapse Data

This is the metric of interest to oncologists Moscowitz and Pepe defined positive

predictive value in survival time data PPVXk(t) = P(T ≤ t | Xk = 1)

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PPV Conclusions

Although statistically significant, the MTHFR C677T allele has a PPV of 35% This is worse than flipping a coin Important question is the increased predictive

value above baseline

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TS 28 bp as Example

N RFS HR CI p

2R/2R 83 80% 1 --  --

2R/3R 196 79% 1.68 0.863-3.255 0.13

3R/3R 103 73% 1.87 0.942-3.721 0.074

3R/4R 20 60% 3.69 1.436-9.481 0.007

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TS 28 bp Bootstrapping

Does knowledge of TS genotype improve prediction of relapse?

Bootstrap comparison of relapse free survival of all patients with those with particular TS polymorphisms

No additional predictive value from knowing TS genotype Caveat of sample size issues

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Other Genetic Epidemiology Issues Multiple comparisons Gene-gene and gene-environment

interactions

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Multiple Comparisons

Probability of finding a false association by chance = 1 - 0.95n

n = 10, p = 40% n = 100, p = 99.4%

Our data: 19 genotypes, 2 genders, 3 different relapse

sites N = 228, p = 99.99959%

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Methods for Multiple Comparisons

Ignore it Validation sample set Adjust p-values

Bonferroni False discovery rate (FDR) Benjamini et al 2001

Use Bayesian methods False positive report probability (FPRP) Wacholder et al 2004

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High Dimensional Data

The number of cells (N) needed to split R variables into X partitions:

N = XR

A single 2-way combination R = 2, X= 3, N= 9

We have evaluated 19 genotypes All 2-way combinations of our genotypes

R = 19, X = 3, N = 1,162,261,467

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High Dimensional Data Methods

Several methods in current use We have used patterning with recursive

partitioning (CART) Create groups as uniform as possible Use with genotype and other covariates

No p-values Confirmation by cross-validation within the

sample set

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CART Caveats

No p-values Need to validate in a separate sample Often difficult to interpret results,

particularly of higher order interactions i.e. 2 genotypes and 1 environmental factor

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Future Directions

Validate and extend genotyping in another ALL sample set Incorporate drug dose data

Investigate the impact of genetic variability on infection risk in pediatric myeloid leukemia R01 resubmission with Theo Zaoutis

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The End….

Thanks to everyone who makes it safe to swim with the sharks. Bev Lange, Tim Rebbeck,Jinbo Chen, Theo Zaoutis, Tom McWilliams, Peggy Han, Shannon Smith, Michelle Horn, Melanie Doran. Funded by RO1 CA108862-01.