Article Figures & Data
Figures
- Figure 1.
The promise of pharmacogenetics. By making associations between genotype and characteristic phenotypes (e.g., toxicity and nonresponse), physicians can use pharmacogenetics to choose medication doses that are appropriate for a patient's expected response. This approach has the potential to minimize toxicity and maximize efficacy by reducing dose interruptions and severe toxicities.
- Figure 2.
Methods to identify genetic variants in pharmacogenetics/pharmacogenomics. Candidate gene studies involve the analysis of one or more variants within a single gene that is known to be important in the pharmacokinetic or pharmacodynamic pathway of a drug. Such studies require limited resources and often yield results with the highest impact (i.e., variation within TPMT and 6-MP toxicity). Probes for thousands or even hundreds of thousands of genetic variants can be combined on microarray chips, and investigators can simultaneously investigate all of these variants against a phenotype of interest. These chips can be custom designed by investigators or can contain variants within DMEs or throughout the entire genome. These analyses can be considerably more expensive than candidate gene studies, and interpretation of results requires bioinformatics expertise. However, genome-wide studies can provide novel insights into the biology of drug response, and equal weight is given to all variants represented on the chip without bias to known genes.
- Figure 3.
Advantages of cell-based pharmacogenomic models. Data are publically available for lymphoblastoid cell lines with up to 10 million SNPs per cell line from the International HapMap Project and 1000 Genomes Project. Several laboratories have made mRNA and promoter methylation data for subsets of the HapMap cell lines publically available. Both miRNA and protein expression data can also be used for integrated analyses to assess how genotype affects sensitivity to pharmacologic phenotypes through effects on expression, epigenetics, or protein. These datasets (genotype, expression, and drug sensitivity) can be integrated to evaluate how genotype influences expression, and how genotype and expression influence sensitivity to a drug.
Tables
- Table 1.
Pharmacogenetic studies relevant to pediatric oncology
Study Population Gene and selected variants Important findings References Relling et al. (2006) Pharmacogenetic dosing of 6-MP in 246 patients (231 homozygous WT and 15 heterozygous variant) with ALL treated prospectively on St. Jude Protocol Total XIIIB TPMT*2 • Variant alleles have decreased ability to inactivate TGNs, leading to increased adverse events. Reduced dosing strategy for heterozygous variant patients showed no difference in risk of relapse or acute toxicity. (25, 26) Stocco et al. (2009) TPMT*3A • Reduced dosing strategy for heterozygous variant patients showed no difference in risk of relapse or acute toxicity. TPMT*3B TPMT*3C Marcuello et al. (2011) Ninety-four Spanish adults with metastatic colorectal cancer being treated prospectively with FOLFIRI UGT1A1*28 • A 7-TA repeat in the promoter region leads to decreased enzyme activity. (33) • Genotype-directed dosing strategies allowed for dose escalations in WT and heterozygous patients. Ross et al. (2009) DME variant microarray study of 166 children (54 in test cohort, 112 in validation) with various malignancies treated with a median of 360 mg/m2 cumulative cisplatin TPMT:rs12201199 A/T • Variant alleles in TPMT and COMT identified in this analysis in linkage disequilibrium with nonfunctional alleles. (38) COMT:rs9332377 A/G • Unique carriers of either variant allele had a 12-fold increase in ototoxicity. Visscher et al. (2011) DME variant microarray study of 440 children (156 in test cohort, 284 in 2 validation cohorts) with various malignancies treated with various cumulative anthracycline doses SLC28A3:rs7853758 C/T • Patients with variant alleles were protected from anthracycline cardiotoxicity (OR 0.31; 95% CI, 0.16–0.6). (46) • Combination with 8 additional variants able to construct a model predictive for development of cardiotoxicity. Chen et al. (2010) GWAS of asparaginase hypersensitivity in 485 children with ALL (322 in discovery and 163 in validation cohorts) GRIA1 • Excess of associations at 5q33 in intronic regions of GRIA1 locus. (10) • Unclear significance of GRIA1 in immune function and hypersensitivity. Stanulla et al. (2005) Sixty-eight children (34 cases, 34 controls) with ALL and 97 patients with Hodgkin lymphoma GSTP1: rs1695 A/G • Missense variant leading to decreased enzyme activity.• Homozygous variants with ALL at decreased risk of CNS relapse (6, 7) Hohaus et al. (2005) • In patients with Hodgkin lymphoma, a variant allele was found to predict OS in a dose-dependent manner. Yang et al. (2009) GWAS of treatment response in 487 children with ALL (318 in test cohort, 169 in validation) IL15 • IL15 is a proliferation-enhancing cytokine that was previously linked to glucocorticoid resistance. (8) Yang et al. (2010) GWAS of relapse in 2,534 children with ALL, including 405 children of Native American ancestry. PDE4B:rs6683977 C/G • Top associated SNP with relapse risk in PDE4B. (9) • Leukemic blasts with higher PDE4B expression were more resistant to prednisolone. • SNP was associated with Native American ancestry and may partially explain ethnic disparities in relapse risk.
Volume 18, Issue 10
