Abstract
Daunorubicin is an anthracycline antibiotic agent used in the treatment of hematopoietic malignancies. Toxicities associated with this agent include myelosuppression and cardiotoxicity; however, the genes or genetic determinants that contribute to these toxicities are unknown. We present an unbiased genome-wide approach that incorporates heritability, whole-genome linkage analysis, and linkage-directed association to uncover genetic variants contributing to the sensitivity to daunorubicin-induced cytotoxicity. Cell growth inhibition in 324 Centre d' Etude du Polymorphisme Humain lymphoblastoid cell lines (24 pedigrees) was evaluated following treatment with daunorubicin for 72 h. Heritability analysis showed a significant genetic component contributing to the cytotoxic phenotypes (h2 = 0.18–0.63 at 0.0125, 0.025, 0.05, 0.1, 0.2, and 1.0 μmol/L daunorubicin and at the IC50, the dose required to inhibit 50% cell growth). Whole-genome linkage scans at all drug concentrations and IC50 uncovered 11 regions with moderate peak LOD scores (>1.5), including 4q28.2 to 4q32.3 with a maximum LOD score of 3.18. The quantitative transmission disequilibrium tests were done using 31,312 high-frequency single-nucleotide polymorphisms (SNP) located in the 1 LOD confidence interval of these 11 regions. Thirty genes were identified as significantly associated with daunorubicin-induced cytotoxicity (P ≤ 2.0 × 10−4, false discovery rate ≤ 0.1). Pathway and functional gene ontology analysis showed that these genes were overrepresented in the phosphatidylinositol signaling system, axon guidance pathway, and GPI-anchored proteins family. Our findings suggest that a proportion of susceptibility to daunorubicin-induced cytotoxicity may be controlled by genetic determinants and that analysis using linkage-directed association studies with dense SNP markers can be used to identify the genetic variants contributing to cytotoxicity. [Cancer Res 2007;67(11):5425–33]
Introduction
Daunorubicin is a chemotherapeutic agent used in the treatment of hematopoietic malignancies, such as acute lymphocytic and acute myelogenous leukemia, as well as some lymphomas and breast cancer (1). There are several proposed mechanisms of daunorubicin, most notably DNA intercalation, with a preference for dGdC-rich regions flanked by A/T basepairs (2). Daunorubicin has also been shown to inhibit DNA topoisomersase II by trapping DNA strand passage intermediates, eventually resulting in DNA single-strand and double-strand breaks. Recent studies have shown that the formation of DNA-anthracycline complexes can significantly modify the ability of the helicases to separate DNA into single strands in an ATP-dependent fashion, thereby hindering the process of strand separation and limiting replication (3). There are also reports that this drug can inhibit protein kinase C pathways (4). Patients often initially respond but then relapse due to resistance mechanisms, such as P-170 glycoprotein–mediated drug efflux, altered topoisomerase II activity, or overexpression of bcl-2 (5).
The cumulative and dose-dependent toxicities that have limited the usage of daunorubicin are myelosuppression, mucositis, and cardiotoxicity (6–8). The risk of anthracycline-induced cardiotoxicity is 10% to 26% and is dependent primarily on dose (9, 10). Anthracycline-induced cardiotoxicity is thought to be mediated through reactive oxygen species production. Experiments conducted on rat cardiomyocytes have shown that corticosterone can inhibit apoptosis induced by doxorubicin, a structural analogue of daunorubicin. This effect was mediated by the regulation of multiple genes, including antioxidant/detoxification enzymes, receptors, signaling molecules, and amino acid and protein synthesis (11). Furthermore, Yi et al. have identified significant gene expression changes in mice after doxorubicin treatment, including a series of genes that encode oxidative stress-related proteins, signal transduction, and apoptotic proteins (12). Matrix metalloproteinases 2 and 9 expression levels were enhanced in mice after acute doxorubicin treatment (13). In humans, tumor necrosis factor α and phospholipase C-δ1 have been shown to be critical in doxorubicin-induced cardiotoxicity (14). The genes important in daunorubicin-induced cardiotoxicity have not been well studied.
In this report, we used classic and modern genetic approaches to identify genes that contribute to daunorubicin-induced cytotoxicity. To this end, lymphoblastoid cell lines (LCLs) derived from large Centre d' Etude du Polymorphisme Humain (CEPH) reference pedigrees of Northern and Western European descent were used to identify the extent to which heritable factors contribute to drug cytotoxicity. There have been candidate gene approaches to study the cellular sensitivity of daunorubicin in multiple tumor cell lines (15–19). However, our approach uses whole-genome linkage analysis and linkage-directed association studies to facilitate identifying regions within the genome that harbor genes contributing to daunorubicin-induced cytotoxicity.
Materials and Methods
Cell lines. LCLs derived from 24 Caucasian Utah CEPH families (1331, 1333, 1334, 1340, 1341, 1344, 1345, 1346, 1347, 1349, 1350, 1358, 1362, 1375, 1408, 1413, 1416, 1420, 1423, 1444, 1447, 1454, 1459, and 1463) were purchased from the Coriell Institute for Medical Research4
Coriell Institute for Medical Research; http://www.locus.umdnj.edu/ccr/.
Drug. Daunorubicin (NSC-82151) was kindly provided by the Drug Synthesis and Chemistry Branch, Division of Cancer Treatment, National Cancer Institute (NCI), Bethesda, MD.
Cell cytotoxicity assay. Cell growth inhibition was evaluated at concentrations of 0, 0.0125, 0.025, 0.05, 0.1, 0.2, and 1.0 μmol/L daunorubicin. These concentrations were selected through assay optimization over a large range of daunorubicin treatment concentrations. We chose the daunorubicin concentrations within the limits of our assay that best characterized the sigmoid shape of cell growth inhibition. Daunorubicin was prepared in PBS (pH 7.4; Invitrogen) immediately before use. The cytotoxic effect of daunorubicin on these CEPH cell lines was determined using the nontoxic colorimetric-based assay, alamarBlue (Biosource). Cell viability was assessed on exponentially growing LCLs by trypan blue dye exclusion using the Vi-Cell XR viability analyzer (Beckman Coulter). Cells (100 μL) with viabilities of >85% were plated at a density of 1 × 105 cells/mL (1 × 104 cells per well), in triplicate, in 96-well round-bottomed plates (Corning). After 24 h incubation, cells were treated with either vehicle (media contains 0.1% PBS) or increasing concentrations of daunorubicin for 72 h. At 72-h incubation time, untreated cells were in exponential growth. AlamarBlue was added 24 h before absorbance reading at wavelengths 570 and 600 nm using the Synergy-HT multidetection plate reader (BioTek). Percentage survival was quantified using manufacturer's protocol.5
Cell percentage survival calculation; http://www.invitrogen.com/content/sfs/manuals/BioSource%20DAL1100.pdf.
Heritability analysis. Heritability analysis was done using Sequential Oligogenic Linkage Analysis Routines (SOLAR)6
SOLAR; http://www.sfbr.org/solar/.
Error checking. Error checking for Mendelian incompatibility, misspecified relationships, and unlikely recombinations has been done as described previously (21); however, this study used a much denser map. The web-based platform integrates and formats data (pedigree, genotype, phenotype) and executes error checking using PedCheck (22) to detect genotypic incompatibilities, PREST (23) to detect relationship misspecifications and multipoint engine for rapid likelihood inference (MERLIN)7
(24) to detect unlikely recombinants before linkage analysis and is enabled to run linkage analysis on multiple platforms including MERLIN, GENEHUNTER, and SOLAR. From the combined pool of genotyped markers, 7,209 single-nucleotide polymorphisms (SNPs) and microsatellite nonredundant markers yielding a very dense genetic map with highly heterozygous markers (heterozygosity: 1% at <0.7, 7% at 0.7–0.8, 28% at 0.8–0.9, 64% at 0.9–1) were used for linkage mapping studies.Linkage analysis. The genotypic data and map distances were downloaded from the CEPH Version 9 database and the Marshfield map database8
Marshfield map database; http://www.research.marshfieldclinic.org/genetics.
The score for each inheritance vector S(v) is calculated by summing squared scores for each founder allele. The score for each founder allele is calculated by summing the mean deviates (yi − μ) for all individuals who carry the founder allele, in which yi is the phenotype for individual i, μ is the population mean, and v is the list of individuals who carry a particular founder allele. Inheritance vectors are used to construct a likelihood ratio test for linkage.
Single-nucleotide polymorphisms. From the online CEU dataset in the HapMap project (release 21)9
International HapMap Project; http://www.hapmap.org.
Association analysis. Eighty-six HapMap CEU samples (of 90) were phenotyped for daunorubicin sensitivity. Three samples (GM11839, GM12716, and GM12717) were not phenotyped due to the inability to grow the cells above 85% viability. Additionally, another sample (GM12236) was not available from Coriell at the time of the experiment. The cytotoxicity values of HapMap CEU cell lines, as part of the CEPH pedigrees, were also transformed using the inverse normalization of the percentile rank function in Microsoft Excel software. Population stratification and total association between the selected 31,312 SNPs and percentage cell survival at 0.0125, 0.025, 0.05, 0.1, 0.2, and 1.0 μmol/L daunorubicin and the IC50 was done using the QTDT program. Gender was used as a covariate to adjust for the normalized cytotoxicity values. False discovery rate (FDR) procedure was used to control for multiple testing within each cytotoxic phenotype using R statistics software10
R statistics software; http://www.r-project.org.
Gene ontology classification and pathway analysis. Gene ontology categories and KEGG pathways11
KEGG knowledge database; http://www.genome.jp/kegg/pathway.html.
DAVID; http://niaid.abcc.ncifcrf.gov/.
Results
Cell cytotoxicity and heritability analysis. Using a short-term cytotoxicity assay, 324 CEPH LCLs derived from 24 three-generation CEPH Utah pedigrees were exposed to increasing concentrations of daunorubicin (0.0125, 0.025, 0.05, 0.1, 0.2, and 1.0 μmol/L). These families also contained a subset of 86 HapMap CEU which were used for the association analyses. The mean (±SD) percentage of survival decreased from 82.7 ± 13.4 to 11.4 ± 4.8 after 72 h after exposure to 0.0125 to 1 μmol/L daunorubicin (Table 1). The mean and median concentration required to inhibit 50% cell growth (IC50) for these 324 cell lines were 0.051 and 0.046 μmol/L, respectively. These values were within the range of the IC50's determined for a panel of NCI60 human tumor cell lines13
NCI60; http://dtp.nci.nih.gov/.
Drug concentration (μmol/L) . | Mean percentage survival in CEPH pedigrees (mean percentage survival in HapMap CEU trios)* . | Heritability (h2) . | P value for h2 . |
---|---|---|---|
0.0125 | 82.7 ± 13.4 (82.4 ± 13.7) | 0.63 | <1.0 × 10−7 |
0.025 | 62.6 ± 12.8 (62.3 ± 13.2) | 0.41 | <1.0 × 10−7 |
0.05 | 45.3 ± 10.8 (45.3 ± 11.7) | 0.18 | 3.6 × 10−3 |
0.1 | 34.3 ± 10.1 (33.8 ± 10.6) | 0.19 | 1.7 × 10−3 |
0.2 | 24.6 ± 9.1 (23.9 ± 9.2) | 0.18 | 2.1 × 10−3 |
1 | 11.4 ± 4.8 (10.9 ± 4.8) | 0.33 | <1.0 × 10−7 |
Drug concentration (μmol/L) . | Mean percentage survival in CEPH pedigrees (mean percentage survival in HapMap CEU trios)* . | Heritability (h2) . | P value for h2 . |
---|---|---|---|
0.0125 | 82.7 ± 13.4 (82.4 ± 13.7) | 0.63 | <1.0 × 10−7 |
0.025 | 62.6 ± 12.8 (62.3 ± 13.2) | 0.41 | <1.0 × 10−7 |
0.05 | 45.3 ± 10.8 (45.3 ± 11.7) | 0.18 | 3.6 × 10−3 |
0.1 | 34.3 ± 10.1 (33.8 ± 10.6) | 0.19 | 1.7 × 10−3 |
0.2 | 24.6 ± 9.1 (23.9 ± 9.2) | 0.18 | 2.1 × 10−3 |
1 | 11.4 ± 4.8 (10.9 ± 4.8) | 0.33 | <1.0 × 10−7 |
Data are presented as mean ± SD from either 324 LCLs in 24 three-generation CEPH families or 30 HapMap CEU trios.
Linkage analysis. Nonparametric linkage analysis was done on seven daunorubicin phenotypes using 7,209 high heterozygous SNPs and microsatellite markers. Drug cytotoxicity is a multigenic trait; therefore, LOD of >1.5 was chosen in an attempt to be inclusive of genes that may contribute to a small extent. The findings from MERLIN multipoint analyses, in which LOD scores exceeded 1.5, are summarized in Table 2. Among the seven phenotypes, there were 11 linkage peaks with a maximum LOD score of larger than 1.5 (Supplementary Fig. S3). The highest LOD score (3.18) that reached genome-wide significance was located on 4q28.2 to 4q32.3 for the 0.05 μmol/L daunorubicin treatment phenotype (Supplementary Fig. S3, peak D and Fig. 2). It is worth noting that this region also contained suggestive LOD scores for other phenotypes corresponding to 0.025, 0.1, and 0.2 μmol/L and IC50 daunorubicin phenotypes with LOD scores of 1.72, 2.53, 1.91, and 2.05, respectively (Fig. 2). This implies that the same linkage region confer sensitivity to daunorubicin-induced cytotoxicity in all concentrations except the highest and lowest concentration. Examination of the linkage peaks associated with the lowest daunorubicin treatment concentration revealed a peak with a maximum LOD score of 2.11 at 16q23.1 to 16q24.1 (Supplementary Fig. S3, peak K and Fig. 3), extending from 104 to 118 Mb of chromosome 16. This concentration-dependent phenomenon is further supported by a hierarchical cluster view of the seven daunorubicin phenotypes (Supplementary Fig. S4). Two major distinguishable groups are formed between the two lowest daunorubicin concentrations (0.0125 and 0.025 μmol/L) and higher concentrations including IC50. The middle daunorubicin concentrations (IC50, 0.05, 0.1, and 0.2 μmol/L) were more closely clustered together within the subgroup. Not surprisingly, unique genes are associated with the lowest concentration of daunorubicin, suggesting that genetic contribution to drug cytotoxicity varies with drug concentration.
Peak* . | 1 LOD confidence interval . | Maximum LOD . | Retrieved HapMap SNPs† . | Number of genes covered by SNPs . | Daunorubicin concentration (μmol/L) . |
---|---|---|---|---|---|
A | 1q31–32 | 1.81 | 2842 | 183 | 1 |
B | 2q36.3–37.3 | 1.8 | 2719 | 140 | 0.025 |
C | 3q13.12–21.2 | 1.78 | 3795 | 146 | 1 |
D | 4q28.2–32.3 | 1.72 | 3067 | 174 | 0.025 |
3.18 | 3067 | 174 | 0.05 | ||
2.53 | 3067 | 174 | 0.1 | ||
1.91 | 3067 | 174 | 0.2 | ||
2.05 | 3067 | 174 | IC50 | ||
E | 5p13.1–5q13.3 | 1.87 | 3905 | 173 | 0.05 |
F | 5q34–35.2 | 1.57 | 2370 | 88 | 0.025 |
1.62 | 2370 | 88 | 0.1 | ||
G | 6p12.3–6q14 | 1.58 | 4121 | 159 | 0.05 |
H | 8q24–24.2 | 1.68 | 1931 | 45 | 0.0125 |
I | 11p14.3–13 | 1.71 | 2449 | 102 | 0.1 |
J | 13q31.3–32 | 1.72 | 2209 | 38 | 0.1 |
1.85 | 2209 | 38 | 0.2 | ||
K | 16q23.1–24.1 | 2.11 | 1901 | 33 | 0.0125 |
Peak* . | 1 LOD confidence interval . | Maximum LOD . | Retrieved HapMap SNPs† . | Number of genes covered by SNPs . | Daunorubicin concentration (μmol/L) . |
---|---|---|---|---|---|
A | 1q31–32 | 1.81 | 2842 | 183 | 1 |
B | 2q36.3–37.3 | 1.8 | 2719 | 140 | 0.025 |
C | 3q13.12–21.2 | 1.78 | 3795 | 146 | 1 |
D | 4q28.2–32.3 | 1.72 | 3067 | 174 | 0.025 |
3.18 | 3067 | 174 | 0.05 | ||
2.53 | 3067 | 174 | 0.1 | ||
1.91 | 3067 | 174 | 0.2 | ||
2.05 | 3067 | 174 | IC50 | ||
E | 5p13.1–5q13.3 | 1.87 | 3905 | 173 | 0.05 |
F | 5q34–35.2 | 1.57 | 2370 | 88 | 0.025 |
1.62 | 2370 | 88 | 0.1 | ||
G | 6p12.3–6q14 | 1.58 | 4121 | 159 | 0.05 |
H | 8q24–24.2 | 1.68 | 1931 | 45 | 0.0125 |
I | 11p14.3–13 | 1.71 | 2449 | 102 | 0.1 |
J | 13q31.3–32 | 1.72 | 2209 | 38 | 0.1 |
1.85 | 2209 | 38 | 0.2 | ||
K | 16q23.1–24.1 | 2.11 | 1901 | 33 | 0.0125 |
NOTE: LOD score of >1.5 was considered as supportive evidence of linkage.
Letter corresponds to peak labeled on Supplemental Fig. S3.
Genotypes are retrieved from HapMap CEU genotype database. All SNPs meet the requirements of no Mendelian errors, three genotypes, and two counts per genotype in the 60 unrelated parents of the HapMap trios.
Association results. The association studies were done using the percentage survival data from seven daunorubicin phenotypes and 31,312 SNPs in the 1 LOD confidence interval of the 11 linkage regions (Table 2). These SNPs are located throughout 1,278 genes. Using an FDR threshold of 10%, a total of 137 SNPs from 30 genes were shown to be significantly associated with daunorubicin cytotoxic phenotypes (P ≤ 2 × 10−4, FDR ≤ 0.1; see Table 3 and Supplementary Table S1). An intronic SNP (rs978752) of INPP4B in the chromosome 4 linkage peak is associated with multiple concentrations of daunorubicin (0.1 μmol/L, P = 2 × 10−4, FDR = 0.1; 0.2 μmol/L, P = 4 × 10−5, FDR = 0.22; 1 μmol/L, P = 3 × 10−5, FDR = 0.66; Fig. 2). The genotype CC of SNP rs978752 (INPP4B) is correlated with greater cell sensitivity to 1 μmol/L daunorubicin (Fig. 2) and with other daunorubicin concentrations (0.1 and 0.2 μmol/L; Supplementary Table S1). As shown in Fig. 3, multiple intronic SNPs of CDH13 in the chromosome 16 linkage peak are significantly associated with 0.0125 μmol/L daunorubicin (P ≤ 2 × 10−4, FDR≤ 0.1). SNP rs1862831 AA genotype is associated with greater cell sensitivity to 0.0125 μmol/L daunorubicin–induced cytotoxicity. The linkage-directed association study in HapMap CEU resulted in significant associations in 9 of 11 linkage regions (Table 3). The two linkage regions that did not result in significant association signals were at 6p12.3 to 6q14 (LOD, 1.58) and 8q24 to 8q24.2 (LOD, 1.68) for 0.05 and 0.0125 μmol/L daunorubicin, respectively.
Gene symbol . | Cytoband . | Description . | 0.0125 μmol/L . | 0.025 μmol/L . | 0.05 μmol/L . | 0.1 μmol/L . | 0.2 μmol/L . | 1 μmol/L . | IC50 . | Functional pathway or categories or evidence* . |
---|---|---|---|---|---|---|---|---|---|---|
CACNA1S | 1q32 | Calcium channel, voltage-dependent, L type, α 1S subunit | ++ | Cardiovascular disease | ||||||
SKIP† | 2q36 | Sphingosine kinase type 1 interacting protein | ++ | + | ++ | Regulation of cell proliferation | ||||
LOC646794 | 2q36.3 | Similar to EAP30 subunit of ELL complex | ++ | Unknown | ||||||
TM4SF20† | 2q36.3 | Transmembrane 4 L six family member 20 | + | ++ | + | ++ | Integral to membrane | |||
NGEF† | 2q37 | Neuronal guanine nucleotide exchange factor | ++ | + | Axon guidance | |||||
SP110† | 2q37.1 | SP110 nuclear body protein | ++ | + | Pulmonary tuberculosis | |||||
ILKAP† | 2q37.3 | Integrin-linked kinase-associated serine/threonine phosphatase 2C | ++ | Cell cycle | ||||||
KIF1A† | 2q37.3 | Kinesin family member 1A | ++ | Membrane transport | ||||||
TAGLN3† | 3q13.2 | Transgelin 3 | ++ | Central nervous system development | ||||||
ZBTB20† | 3q13.2 | Zinc finger and BTB domain containing 20 | ++ | DNA binding | ||||||
LSAMP† | 3q13.2–q21 | Limbic system–associated membrane protein | ++ | GPI anchor | ||||||
LOC653584 | 3q13.31 | Similar to zinc finger and BTB domain containing 20 | ++ | Unknown | ||||||
LOC391698 | 4q28.2 | Similar to elongation factor 1-γ (EF-1γ; eEF-1Bγ) | ++ | Unknown | ||||||
INPP4B† | 4q31.21 | Inositol polyphosphate-4-phosphatase, type II, 105 kDa | ++ | + | + | Phosphatidylinositol signaling system | ||||
LOC338095 | 4q32 | Proteasome activator subunit 2 pseudogene | ++ | Unknown | ||||||
FLJ25371 | 4q32.1 | Hypothetical protein FLJ25371 | ++ | Unknown | ||||||
FLJ33641 | 5q11.2 | Hypothetical protein FLJ33641 | + | ++ | Unknown | |||||
LOC441074 | 5q11.2 | Hypothetical LOC441074 | ++ | Unknown | ||||||
SDCCAG10† | 5q12.3 | Serologically defined colon cancer antigen 10 | + | ++ | + | ++ | Protein folding | |||
MAP1B† | 5q13 | Microtubule-associated protein 1B | ++ | Neuronal survival | ||||||
PIK3R1† | 5q13.1 | PI3K, regulatory subunit 1 (p85 α) | ++ | Phosphatidylinositol signaling system | ||||||
RANBP17† | 5q34 | RAN binding protein 17 | ++ | Nuclear export | ||||||
SLIT3 | 5q35 | Slit homologue 3 (Drosophila) | ++ | + | ++ | Axon guidance | ||||
TMEM16C | 11p14.2 | Transmembrane protein 16C | ++ | ++ | Integral to membrane | |||||
LUZP2† | 11p14.3 | Leucine zipper protein 2 | ++ | Unknown | ||||||
CLYBL† | 13q32 | Citrate lyase β like | ++ | Up-regulation of Clybl in murine metastatic tumor cells | ||||||
GPC5 | 13q32 | Glypican 5 | ++ | + | GPI anchor | |||||
CMIP† | 16q23 | c-Maf–inducing protein | ++ | ++ | Cellular growth and proliferation | |||||
DC13† | 16q23.2 | Chromosome 16 open reading frame 61 | ++ | Unknown | ||||||
CDH13† | 16q24.2–q24.3 | Cadherin 13, H-cadherin (heart) | ++ | ++ | GPI anchor |
Gene symbol . | Cytoband . | Description . | 0.0125 μmol/L . | 0.025 μmol/L . | 0.05 μmol/L . | 0.1 μmol/L . | 0.2 μmol/L . | 1 μmol/L . | IC50 . | Functional pathway or categories or evidence* . |
---|---|---|---|---|---|---|---|---|---|---|
CACNA1S | 1q32 | Calcium channel, voltage-dependent, L type, α 1S subunit | ++ | Cardiovascular disease | ||||||
SKIP† | 2q36 | Sphingosine kinase type 1 interacting protein | ++ | + | ++ | Regulation of cell proliferation | ||||
LOC646794 | 2q36.3 | Similar to EAP30 subunit of ELL complex | ++ | Unknown | ||||||
TM4SF20† | 2q36.3 | Transmembrane 4 L six family member 20 | + | ++ | + | ++ | Integral to membrane | |||
NGEF† | 2q37 | Neuronal guanine nucleotide exchange factor | ++ | + | Axon guidance | |||||
SP110† | 2q37.1 | SP110 nuclear body protein | ++ | + | Pulmonary tuberculosis | |||||
ILKAP† | 2q37.3 | Integrin-linked kinase-associated serine/threonine phosphatase 2C | ++ | Cell cycle | ||||||
KIF1A† | 2q37.3 | Kinesin family member 1A | ++ | Membrane transport | ||||||
TAGLN3† | 3q13.2 | Transgelin 3 | ++ | Central nervous system development | ||||||
ZBTB20† | 3q13.2 | Zinc finger and BTB domain containing 20 | ++ | DNA binding | ||||||
LSAMP† | 3q13.2–q21 | Limbic system–associated membrane protein | ++ | GPI anchor | ||||||
LOC653584 | 3q13.31 | Similar to zinc finger and BTB domain containing 20 | ++ | Unknown | ||||||
LOC391698 | 4q28.2 | Similar to elongation factor 1-γ (EF-1γ; eEF-1Bγ) | ++ | Unknown | ||||||
INPP4B† | 4q31.21 | Inositol polyphosphate-4-phosphatase, type II, 105 kDa | ++ | + | + | Phosphatidylinositol signaling system | ||||
LOC338095 | 4q32 | Proteasome activator subunit 2 pseudogene | ++ | Unknown | ||||||
FLJ25371 | 4q32.1 | Hypothetical protein FLJ25371 | ++ | Unknown | ||||||
FLJ33641 | 5q11.2 | Hypothetical protein FLJ33641 | + | ++ | Unknown | |||||
LOC441074 | 5q11.2 | Hypothetical LOC441074 | ++ | Unknown | ||||||
SDCCAG10† | 5q12.3 | Serologically defined colon cancer antigen 10 | + | ++ | + | ++ | Protein folding | |||
MAP1B† | 5q13 | Microtubule-associated protein 1B | ++ | Neuronal survival | ||||||
PIK3R1† | 5q13.1 | PI3K, regulatory subunit 1 (p85 α) | ++ | Phosphatidylinositol signaling system | ||||||
RANBP17† | 5q34 | RAN binding protein 17 | ++ | Nuclear export | ||||||
SLIT3 | 5q35 | Slit homologue 3 (Drosophila) | ++ | + | ++ | Axon guidance | ||||
TMEM16C | 11p14.2 | Transmembrane protein 16C | ++ | ++ | Integral to membrane | |||||
LUZP2† | 11p14.3 | Leucine zipper protein 2 | ++ | Unknown | ||||||
CLYBL† | 13q32 | Citrate lyase β like | ++ | Up-regulation of Clybl in murine metastatic tumor cells | ||||||
GPC5 | 13q32 | Glypican 5 | ++ | + | GPI anchor | |||||
CMIP† | 16q23 | c-Maf–inducing protein | ++ | ++ | Cellular growth and proliferation | |||||
DC13† | 16q23.2 | Chromosome 16 open reading frame 61 | ++ | Unknown | ||||||
CDH13† | 16q24.2–q24.3 | Cadherin 13, H-cadherin (heart) | ++ | ++ | GPI anchor |
NOTE: ++, FDR ≤ 0.1 and P ≤ 2 × 10−4; +, FDR > 0.1 and P ≤ 2 × 10−4.
Pathway, category, and evidence information gathered from KEGG knowledge database and National Center for Biotechnology Information dbGENE. Bold text represents significantly overrepresented pathway.
Gene is expressed in human cardiac tissue as measured by HG-U95A array (42).
Pathway analysis. Using 1,278 genes in 1 LOD confidence interval of 11 linkage regions as the background, the 30 genes that were significantly associated with daunorubicin cytotoxic phenotypes were imported for gene ontology and KEGG pathway analysis. The results showed that phosphatidylinositol signaling system (P = 0.034; PIK3R1 and INPP4B), GPI-anchored proteins (P = 0.027; CDH13, GPC5, and LSAMP), and axon guidance pathway (P = 0.06; NGEF and SLIT3) were significantly or marginally overrepresented in the candidate gene list (Table 3).
Discussion
Identification of genetic variants that predict chemotherapeutic outcome is critical for the design of individualized therapy. With enriched publicly available resources of marker genotypes9,14
CEPH database; http://www.cephb.fr/cephdb/.
Perlegen; http://www.perlegen.com.
Focus array transcriptional levels; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE1485.
The present study strongly suggests that sensitivity to daunorubicin-induced cytotoxicity is a polygenic trait with different genes contributing at different concentrations of drug. The fluctuation of estimated heritability values at differing drug concentrations suggests that genetic components contributing to human variation in daunorubicin-induced cytotoxicity are dose dependent. Linkage peaks differing at low versus high drug concentrations further imply that some genes are likely turned on at lower concentrations of drug, whereas others contribute to variation in susceptibility to cytotoxicity at higher dosages. This is in agreement with previous work demonstrating that cell-cycle arrest and cell death follow distinct pathways depending on the daunorubicin concentration (19) and other investigators showing higher daunorubicin doses correlating to more rapid caspase-3 induction (29). Our hierarchical cluster view of the seven daunorubicin phenotypes further support this concept with midrange drug concentration treatment effects clustering together, whereas the percentage cell survival at the highest and lowest concentration exhibit distinguishable patterns.
To date, candidate gene approaches have focused on genes that most likely play a role in the pharmacokinetic and pharmacodynamics of daunorubicin. However, evaluation of a genetic polymorphism within the multidrug resistance 1 gene, whose expression correlated to daunorubicin resistance (30) in acute myeloid leukemia cell lines showed a negative correlation between this polymorphism and response to doxorubicin, an analogue of daunorubicin (31). In addition, association studies conducted for genetic variants located in topoisomersase II, c-raf, bcl-2, and p53, whose expression correlates with resistance to anthracyclines (32), also yielded negative results (33, 34). There are several reasons for these discrepancies, including the multigenic nature of sensitivity of cells to drugs and the cell-specific nature of these candidate genes. Our whole-genome approach, which makes no a priori assumptions and gives equal weight to all genes, would more likely identify genetic polymorphism signatures that are important to daunorubicin-induced cytotoxicity. These signatures include all SNPs within the 30 genes identified using our linkage-directed association studies.
Dolan et al. (21) and Watters et al. (27) have shown that sensitivity to cytotoxicity induced by cisplatin, 5-fluorouracil, and docetaxel are heritable traits, which might be influenced by many low penetrance genes. The present study differs from these previously published studies in several ways: (a) the present analysis reports heritability, linkage, and association studies of daunorubicin; (b) the power of the linkage scan is enhanced by a significant increase in the sample size (24 pedigrees) and the marker density (7,209 markers), compared with 10 pedigrees and 1,784 markers in our previous study (21); (c) association studies were not done in previous studies, whereas the present analysis includes linkage-directed association studies using trios that are part of the HapMap CEU cell lines, thereby providing dense SNP coverage; (d) pathway and gene ontology analysis was done, showing that genes associated with daunorubicin cytotoxicity were overrepresented in phosphatidylinositol signaling system consistent with literature evidence (35), axon guidance pathway, and GPI-anchored proteins family.
Our linkage-directed association analyses identified 30 genes showing significant association with cellular susceptibility to daunorubicin and located under the linkage peaks. Although all 30 genes are considered equally important, the SNPs located within PIK3R1 and INPP4B and corresponding phosphatidylinositol signaling pathway is of considerable interest. The phosphatidylinositol signaling pathway involves the metabolism of inositol lipids. The lipid products, such as phosphatidylinositol-3,4-biphosphate and phosphatidylinositol-3,4,5-triphosphate, have been shown to interact with a large variety of downstream effectors, including serine-threonine kinase Akt (36). It was observed that daunorubicin could stimulate the phosphoinositide-3 kinase (PI3K)/Akt-mediated survival pathway in human acute myeloid leukemia cell lines (37); and PI3K has been shown to protect cells from another anthracycline, doxorubicin-induced apoptosis (38). PIK3R1 encodes the 85 kDa regulatory subunit of PI3K, which was reported to be involved in generating the antiapoptotic and chemoresistant phenotype associated with accelerated local tumor recurrence (39). INPP4B encoding inositol polyphosphate 4-phosphatase type II is also involved in phosphatidylinositol signaling pathways. Our genetic analysis identifying the phosphatidylinositol signaling pathway, particularly PI3K, is consistent with literature evidence demonstrating that the pathway contributes to protection from daunorubicin-induced cytotoxicity (38).
The utility of daunorubicin is limited by a dose-dependent cardiotoxicity (10) that can lead to long-term side effects and severe morbidity (8). In a study on childhood leukemia, nearly 60% of 115 survivors had echocardiographic abnormalities in heart function (40). Attempts to reduce anthracycline cardiotoxicity have been directed toward dose and schedule modification, developing less cardiotoxic analogues and concurrently administering cardioprotective agents to attenuate the effects of anthracyclines on the heart (41); however, the genetic basis of anthracycline-induced cardiotoxicity is largely unknown. Although our unbiased genetic model uses lymphoblastoid, not cardiac, cells, the ultimate goal is to identify variants that predispose an individual to the toxicities associated with daunorubicin. Of the 30 genes we identified in LCLs, 19 were also expressed in human cardiac tissue as shown in a gene expression study17
GeneCards; http://www.genecards.org.
Although the full implications and biological significance of other genes and networks identified through our approach are not yet completely understood, they may serve as a platform to further explore relevant mechanisms and improve the understanding of the molecular basis of daunorubicin-induced cytotoxicity. Moreover, this study also highlights similarities and differences among seven daunorubicin cytotoxic phenotypes at the molecular level. Because family studies cannot be done in unaffected individuals, human LCLs represent our best in vitro model with extensive genotypic information in the public domain. We recognize limitations, such as differences in expression and posttranslational modification of genes in various tissues.
In summary, using heritability analysis and whole-genome linkage scan with linkage-directed association studies, we provide a balanced approach to decipher the genetic factors contributing to chemotherapy-induced cytotoxicity. Our data suggests that genetic factors contribute to cytotoxicity to a greater degree at lower concentrations of daunorubicin indicating the relative contribution of genetic factors and environment may vary depending on the dosage of daunorubicin. Three overrepresented pathways and 30 genes are associated with the daunorubicin-induced cytotoxicity in the linkage-directed association studies. Although the relatively small sample size in the association studies produce results that require confirmation, the findings obtained may be important in relation to the ongoing search for genes responsible for the mechanism of daunorubicin-associated toxicity. Furthermore, this model can be applied to any phenotype that can be evaluated in LCLs.
Note: Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).
A Pharmacogenetics of Anticancer Agents Research Group study (http://pharmacogenetics.org).
Acknowledgments
Grant support: NIH/National Institute of General Medical Sciences (NIGMS) grant GM61393 and NIH/NIGMS Pharmacogenetics Research Network and Database, U01GM61374 (Russ Altman, PI; http://pharmgkb.org).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.