Genetics of ancestry-specific risk for relapse in acute lymphoblastic leukemia.

The causes of individual relapses in children with acute lymphoblastic leukemia (ALL) remain incompletely understood. We evaluated the contribution of germline genetic factors to relapse in 2225 children treated on Children's Oncology Group trial AALL0232. We identified 302 germline single-nucleotide polymorphisms (SNPs) associated with relapse after adjusting for treatment and ancestry and 715 additional SNPs associated with relapse in an ancestry-specific manner. We tested for replication of these relapse-associated SNPs in external data sets of antileukemic drug pharmacokinetics and pharmacodynamics and an independent clinical cohort. 224 SNPs were associated with rapid drug clearance or drug resistance, and 32 were replicated in the independent cohort. The adverse risk associated with black and Hispanic ancestries was attenuated by addition of the 4 SNPs most strongly associated with relapse in these populations (for blacks: model without SNPs hazard ratio (HR)=2.32, P=2.27 × 10-4, model with SNPs HR=1.07, P=0.79; for Hispanics: model without SNPs HR=1.7, P=8.23 × 10-5, model with SNPs HR=1.31, P=0.065). Relapse SNPs associated with asparaginase resistance or allergy were overrepresented among SNPs associated with relapse in the more asparaginase intensive treatment arm (20/54 in Capizzi-methorexate arm vs 8/54 in high-dose methotrexate arm, P=0.015). Inherited genetic variation contributes to race-specific and treatment-specific relapse risk.

Genome-Wide Study Links PNPLA3 Variant With Elevated Hepatic Transaminase After Acute Lymphoblastic Leukemia Therapy.

Remission induction therapy for acute lymphoblastic leukemia (ALL) includes medications that may cause hepatotoxicity, including asparaginase. We used a genome-wide association study to identify loci associated with elevated alanine transaminase (ALT) levels after induction therapy in children with ALL enrolled on St. Jude Children's Research Hospital (SJCRH) protocols. Germline DNA was genotyped using arrays and exome sequencing. Adjusting for age, body mass index, ancestry, asparaginase preparation, and dosage, the PNPLA3 rs738409 (C>G) I148M variant, previously associated with fatty liver disease risk, had the strongest genetic association with ALT (P = 2.5 × 10-8 ). The PNPLA3 rs738409 variant explained 3.8% of the variability in ALT, and partly explained race-related differences in ALT. The PNPLA3 rs738409 association was replicated in an independent cohort of 2,285 patients treated on Children's Oncology Group protocol AALL0232 (P = 0.024). This is an example of a pharmacogenetic variant overlapping with a disease risk variant.

Genomewide Approach Validates Thiopurine Methyltransferase Activity Is a Monogenic Pharmacogenomic Trait.

We performed a genomewide association study (GWAS) of primary erythrocyte thiopurine S-methyltransferase (TPMT) activity in children with leukemia (n = 1,026). Adjusting for age and ancestry, TPMT was the only gene that reached genomewide significance (top hit rs1142345 or 719A>G; P = 8.6 × 10-61 ). Additional genetic variants (in addition to the three single-nucleotide polymorphisms [SNPs], rs1800462, rs1800460, and rs1142345, defining TPMT clinical genotype) did not significantly improve classification accuracy for TPMT phenotype. Clinical mercaptopurine tolerability in 839 patients was related to TPMT clinical genotype (P = 2.4 × 10-11 ). Using 177 lymphoblastoid cell lines (LCLs), there were 251 SNPs ranked higher than the top TPMT SNP (rs1142345; P = 6.8 × 10-5 ), revealing a limitation of LCLs for pharmacogenomic discovery. In a GWAS, TPMT activity in patients behaves as a monogenic trait, further bolstering the utility of TPMT genetic testing in the clinic.

Comparison of genome sequencing and clinical genotyping for pharmacogenes.

We compared whole exome sequencing (WES, n = 176 patients) and whole genome sequencing (WGS, n = 68) and clinical genotyping (DMET array-based approach) for interrogating 13 genes with Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. We focused on 127 CPIC important variants: 103 single nucleotide variations (SNV), 21 insertion/deletions (Indel), HLA-B alleles, and two CYP2D6 structural variations. WES and WGS provided interrogation of nonoverlapping sets of 115 SNV/Indels with call rate >98%. Among 68 loci interrogated by both WES and DMET, 64 loci (94.1%, confidence interval [CI]: 85.6-98.4%) showed no discrepant genotyping calls. Among 66 loci interrogated by both WGS and DMET, 63 loci (95.5%, CI: 87.2-99.0%) showed no discrepant genotyping calls. In conclusion, even without optimization to interrogate pharmacogenetic variants, WES and WGS displayed potential to provide reliable interrogation of most pharmacogenes and further validation of genome sequencing in a clinical lab setting is warranted.