Background— A significant number of patients treated with anthracyclines develop cardiotoxicity (anthracycline-induced cardiotoxicity [ACT]), mainly presenting as arrhythmias (acute ACT) or congestive heart failure (chronic ACT). There are no data on pharmacogenomic predictors of ACT. Methods and Results— We genotyped participants of the German non-Hodgkin lymphoma study (NHL-B) who were followed up for the development of heart failure for a median of >3 years. Single-nucleotide polymorphisms (SNPs) were selected from 82 genes with conceivable relevance to ACT. Of 1697 patients, 55 developed acute and 54 developed chronic ACT (cumulative incidence of either form, 3.2%). We detected 5 significant associations with polymorphisms of the NAD(P)H oxidase and doxorubicin efflux transporters. Chronic ACT was associated with a variant of the NAD(P)H oxidase subunit NCF4 (rs1883112, −212A→G; symbols with right-pointing arrows, as edited?‘ odds ratio [OR], 2.5; 95% CI, 1.3 to 5.0). Acute ACT was associated with the His72Tyr polymorphism in the p22phox subunit (rs4673; OR, 2.0; 95% CI, 1.0 to 3.9) and with the variant 7508T→A (rs13058338; OR, 2.6; 95% CI, 1.3 to 5.1) of the RAC2 subunit of the same enzyme. In agreement with these results, mice deficient in NAD(P)H oxidase activity, unlike wild-type mice, were resistant to chronic doxorubicin treatment. In addition, acute ACT was associated with the Gly671Val variant of the doxorubicin efflux transporter multidrug resistance protein 1 (MRP1) (OR, 3.6; 95% CI, 1.6 to 8.4) and with the Val1188Glu-Cys1515Tyr (rs8187694-rs8187710) haplotype of the functionally similar MRP2 (OR, 2.3; 95% CI, 1.0 to 5.4). Polymorphisms in adrenergic receptors previously demonstrated to be predictive of heart failure were not associated with ACT. Conclusions— Genetic variants in doxorubicin transport and free radical metabolism may modulate the individual risk to develop ACT.

Prognostic predictions in B-cell chronic lymphocytic leukemia (B-CLL) at early clinical stage are based on biological disease parameters, such as ZAP-70 and CD38 protein levels, genomic aberrations as well as immunoglobulin variable heavy chain gene (IgV H ) mutation status. In the current study, ZAP-70 and CD38 expressions were examined by flow cytometry in 252 patients with B-CLL. Cytoplasmic ZAP-70 expression in more than 20% (ZAP-70 þ ) and surface CD38 expression on more than 30% (CD38 þ ) of B-CLL cells were associated with an unfavorable clinical course. The levels of ZAP-70 and CD38 did not change over time in the majority of patients where sequential samples were available for analysis. Combined analysis of ZAP-70 and CD38 yielded discordant results in 73 patients (29.0%), whereas 120 patients (47.6%) were concordantly negative and 59 patients (23.4%) were concordantly positive for ZAP-70 and CD38 expression. Median treatment-free survival times in patients whose leukemic cells were ZAP-70 þ CD38 þ was 30 months as compared to 130 months in patients with a ZAP-70À status. In patients with discordant ZAP-70/CD38 results, the median treatment-free survival time was 43 months. Thus, ZAP-70 and CD38 expression analyses provided complementary prognostic information identifying three patient subgroups with good, intermediate and poor prognosis. Over-representation of high-risk genomic aberrations such as 17p deletion or 11q deletion and distribution of the IgV H mutation status in B-CLL discordant for ZAP-70/ CD38 pointed toward a distinct biologic background of the observed disease subgroups. This finding was also supported by microarray-based gene expression profiling in a subset of 35 patients. The expression of 37 genes differed significantly between the three groups defined by their expression of ZAP-70 and CD38, including genes that are involved in regulation of cell survival and chemotherapy resistance.

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The power of genome-wide SNP association studies is limited, among others, by the large number of false positive test results. To provide a remedy, we combined SNP association analysis with the pathway-driven gene set enrichment analysis (GSEA), recently developed to facilitate handling of genome-wide gene expression data. The resulting GSEA-SNP method rests on the assumption that SNPs underlying a disease phenotype are enriched in genes constituting a signaling pathway or those with a common regulation. Besides improving power for association mapping, GSEA-SNP may facilitate the identification of disease-associated SNPs and pathways, as well as the understanding of the underlying biological mechanisms. GSEA-SNP may also help to identify markers with weak effects, undetectable in association studies without pathway consideration. The program is freely available and can be downloaded from our website.