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Pharmacogenetics of Leukemia Treatment Response Richard Aplenc May 2 nd , 2008

Pharmacogenetics of Leukemia Treatment Response Richard Aplenc May 2 nd , 2008. Pediatric Leukemia. Most common pediatric malignancy Four types ALL AML CML JMML. Leukemia Treatment. Varies both by disease and treating group Generally curable ~80% in ALL ~60% in AML

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Pharmacogenetics of Leukemia Treatment Response Richard Aplenc May 2 nd , 2008

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  1. Pharmacogenetics of Leukemia Treatment Response Richard Aplenc May 2nd, 2008

  2. Pediatric Leukemia • Most common pediatric malignancy • Four types • ALL • AML • CML • JMML

  3. 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

  4. Leukemia Treatment • Multi-agent • Over time • Substantial impact on patient and family • Accurate response prediction is clinically very important

  5. ALL Therapy Delayed Intensification Interim Maintenance Induction Consolidation Maintenance L-Asp Steroids MTX 6-MP/6-TG Doxorubicin Cyclophosphamide VCR AraC

  6. Predicting Treatment Response • Leukemic blast characteristics • Morphology • Cytogenetics • Molecular alterations (BCR-ABL) • Patient characteristics • Age • Gender • Genetic information?

  7. 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

  8. G GTACGTTCG GGGCGGGAT T 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 Allele Genotype SNP 1 Haplotype SNP 2 ... SNP example:

  9. 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

  10. Our Dream One Genotype Would Explain Treatment Response

  11. Why Did We Have This Dream? • Thiopurine methylatransferase (TPMT) • Low frequency variants have complete loss of thiopurine metabolizing abilities

  12. That Dream Has Ended Why Is That?

  13. Allopurinol TU HGPRT XO TXMP TGMP TPMT Deficiency TPMTOne Gene, One Pathway, One Exposure TX TIMP TPMT Mercaptopurine 6-MMP

  14. Two Remaining Questions

  15. Question 1: Can we utilize data on host genetic variability in a clinically meaningful way?

  16. Question 2: Is Theo Zaoutis really Neo?

  17. This Makes Sense Because… Lisa Z looks like Trinity

  18. And Because… Paul Offit is clearly Morpheus

  19. Now That Everyone is Awake… • Return to Question 1

  20. Moving Towards the Answer • Decide on the question • Understand the complex phenotype issues • Host genetics • Environment • Address the genetic epidemiology issues

  21. 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

  22. 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

  23. 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

  24. 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

  25. 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

  26. 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

  27. MTHFR C677T Cox Model

  28. MTHFR C677T and Infection Risk

  29. 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

  30. MTFHR Issues • Allele versus genotype versus haplotype • Clinically meaningful analysis • Positive predictive value

  31. 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)

  32. 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

  33. TS 28 bp as Example

  34. 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

  35. Other Genetic Epidemiology Issues • Multiple comparisons • Gene-gene and gene-environment interactions

  36. 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%

  37. 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

  38. 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

  39. 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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