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

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

4. 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

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

6. CCEB Induction ALL Therapy Consolidation Maintenance InterimMaintenance DelayedIntensification MTX Steroids 6-MP/6-TG DoxorubicinCyclophosphamide L-Asp VCRAraC

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

8. 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

9. 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  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 Haplotype SNP 1 G T GGGCGGGATGTACGTTCG SNP example:

10. 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

11. Our Dream One Genotype Would Explain Treatment Response

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

13. That Dream Has Ended Why Is That?

14. CCEB

16. TPMT One Gene, One Pathway, One Exposure Mercaptopurine TIMP TXMPTGMP TPMT 6-MMP TX TU XO Allopurinol TPMT Deficiency HGPRT

17. CCEB Two Remaining Questions

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

19. Question 2: Is Theo Zaoutis really Neo?

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

21. And Because… Paul Offit is clearly Morpheus

22. CCEB Now That Everyone is Awake…  Return to Question 1

23. CCEB Moving Towards the Answer  Decide on the question  Understand the complex phenotype issues  Host genetics  Environment  Address the genetic epidemiology issues

24. 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

25. 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

26. 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

27. 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

28. 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

29. 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

31. CCEB MTHFR C677T Cox Model CovariateHRp95% CI C677T variant1.930.0041.2293.037 Day 7 BM1.770.0131.1252.773 Age1.110.0161.0201.220 Race1.710.3070.6104.798 Gender1.370.2380.8112.323 Rx Arm1.180.2140.9081.535 WBC0.990.3350.9711.010 Phenotype0.950.7760.6611.362

32. CCEB

33. MTHFR C677T and Infection Risk

34. 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

35. CCEB MTFHR Issues  Allele versus genotype versus haplotype  Clinically meaningful analysis  Positive predictive value

39. CCEB 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  PPV Xk (t) = P(T ≤ t | X k = 1)

42. 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

43. CCEB TS 28 bp as Example NRFSHRCIp 2R/2R8380%1-- 2R/3R19679%1.680.863-3.2550.13 3R/3R10373%1.870.942-3.7210.074 3R/4R2060%3.691.436-9.4810.007

45. 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

46. CCEB Other Genetic Epidemiology Issues  Multiple comparisons  Gene-gene and gene-environment interactions

47. CCEB Multiple Comparisons  Probability of finding a false association by chance = 1 - 0.95 n  n = 10, p = 40%  n = 100, p = 99.4%  Our data:  19 genotypes, 2 genders, 3 different relapse sites  N = 228, p = 99.99959%

48. 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

49. CCEB High Dimensional Data  The number of cells (N) needed to split R variables into X partitions: N = X R  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

50. 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

51. CCEB

52. 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

53. 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

54. 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.