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Pharmacogenetics of Leukemia Treatment Response Richard Aplenc May 2nd, 2008 CCEB Pediatric Leukemia Most common pediatric malignancy Four types ALL AML CML JMML CCEB 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 CCEB Leukemia Treatment Multi-agent Over time Substantial impact on patient and family Accurate response prediction is clinically very important CCEB ALL Therapy Induction L-Asp Consolidation Steroids MTX Interim Maintenance VCR 6-MP/6-TG Delayed Intensification AraC Maintenance Doxorubicin Cyclophosphamide CCEB Predicting Treatment Response Leukemic blast characteristics Morphology Cytogenetics Molecular alterations (BCR-ABL) Patient characteristics Age Gender Genetic information? CCEB 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 CCEB 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 Copy number variation (CNV) of DNA sequences Genotype SNP 1 SNP 2 Haplotype A haplotype is an ordered series of alleles for many markers on a single chromosome Allele ... Chromosome Chromosome from from one parent other parent SNP example: G GTACGTTCG GGGCGGGAT T CCEB 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 CCEB Our Dream One Genotype Would Explain Treatment Response Why Did We Have This Dream? Thiopurine methylatransferase (TPMT) Low frequency variants have complete loss of thiopurine metabolizing abilities CCEB That Dream Has Ended Why Is That? CCEB CCEB TPMT One Gene, One Pathway, One Exposure SH TX TU HO Allopurinol N N SH OH N N SH N N TPMT SCH 3 N N N HO HGPRT N N PO 4 CH 2 TIMP TXMP TGMP O H N N N XO H N N Mercaptopurine HO OH TPMT Deficiency H 6-MMP CCEB Two Remaining Questions CCEB Question 1: Can we utilize data on host genetic variability in a clinically meaningful way? Question 2: Is Theo Zaoutis really Neo? This Makes Sense Because… Lisa Z looks like Trinity And Because… Paul Offit is clearly Morpheus Now That Everyone is Awake… Return to Question 1 CCEB Moving Towards the Answer Decide on the question Understand the complex phenotype issues Host genetics Environment Address the genetic epidemiology issues CCEB 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 CCEB 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 CCEB 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 CCEB 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 CCEB 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 CCEB 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 CCEB Relapse Free Survival by MTHFR C677T Variant Allele 1.00 0.90 0.80 0.70 0.60 p = 0.0486 0.50 0 5 10 Years Wildtype (C) Variant (T) MTHFR C677T Cox Model Covariate HR p 95% CI C677T 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 CCEB CCEB MTHFR C677T and Infection Risk Gene MTHFR C677T MTHFR C677T MTHFR C677T Genotype N C/C C/T T/T C/C C/T T/T C/C C/T T/T 224 187 72 224 187 72 224 187 72 Num. Infection 46 42 16 155 120 53 123 113 43 Infection Type OR Sepsis Sepsis Sepsis Fever/Neutropenia Fever/Neutropenia Fever/Neutropenia Infection - Other Infection - Other 1 1.13 1.13 1 0.83 1.32 1 1.27 1.2 Infection - Other 95% CI P value 0.700-1.818 0.585-2.188 0.86 0.546-1.276 0.709-2.447 0.34 0.850-1.887 0.690-2.087 0.49 CCEB 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 CCEB MTFHR Issues Allele versus genotype versus haplotype Clinically meaningful analysis Positive predictive value CCEB Relapse Free Survival by MTHFR C677T Variant Allele 1.00 0.90 0.80 0.70 0.60 p = 0.0486 0.50 0 5 10 Years Wildtype (C) Variant (T) Relapse Free Survival by MTHFR C677T Genotype 1.00 0.90 0.80 0.70 0.60 CC vs TT, p = 0.0477 0.50 0 5 10 Years Wildtype (CC) Variant (TT) Heterozygote (CT) Kaplan-Meier survival estimates, by haplo 1.00 0.75 0.50 0.25 0.00 0 5 10 analysis time CA CA CG CG TA CG TA TG TG TG CA CG TA CA TA TA TG CG 15 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) CCEB 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 CCEB 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 CCEB 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 CCEB Other Genetic Epidemiology Issues Multiple comparisons Gene-gene and gene-environment interactions CCEB 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% CCEB 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 CCEB High Dimensional Data The number of cells (N) needed to split R variables into X partitions: R N=X 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 CCEB 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 CCEB CCEB 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 CCEB 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 CCEB 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. CCEB