We conducted a large-scale association study to identify low-penetrance susceptibility alleles for chronic lymphocytic leukemia (CLL), analyzing 992 patients and 2707 healthy controls. To increase the likelihood of identifying disease-causing alleles we genotyped 1467 coding nonsynonymous single nucleotide polymorphisms (nsSNPs) in 865 candidate cancer genes, biasing nsSNP selection toward those predicted to be deleterious. Preeminent associations were identified in SNPs mapping to genes pivotal in the DNA damage-response and cell-cycle pathways, including ATM F858L (odds ratio [OR] = 2.28, P < .0001) and P1054R (OR = 1.68, P = .0006), CHEK2 I157T (OR = 14.83, P = .0008), BRCA2 N372H (OR = 1.45, P = .0032), and BUB1B Q349R (OR = 1.42, P = .0038). Our findings implicate variants in the ATM-BRCA2-CHEK2 DNA damage-response axis with risk of CLL.

Introduction

Chronic lymphocytic leukemia (CLL) is the most common form of leukemia and is 1 of a number of B-cell lymphoproliferative disorders (B-cell LPDs) that include Hodgkin lymphoma (HL) and non-Hodgkin lymphoma (NHL). Inherited predisposition to CLL and other B-cell LPDs is well documented, with epidemiologic studies showing that the risk of CLL in first-degree relatives of patients with CLL is elevated 7-fold.1,2  Furthermore, studies have demonstrated that familial associations exist between different types of B-cell LPDs, with risks of HL and NHL increased 2-fold in relatives of patients with CLL.2  Whereas part of the familial risk could be due to high-penetrance mutations in as-yet-unidentified genes, a polygenic model based on low-penetrance alleles provides an alternative explanation. Such a hypothesis is supported by the recent observation that monoclonal B-cell lymphocytosis with an identical phenotype to indolent CLL can be detected in a high proportion of healthy members of CLL families.3,4 

Alleles conferring small relative risks are difficult, if not impossible, to identify through classic genome-wide linkage scans.5  The search for low-penetrance disease alleles has therefore centered on association studies based on comparing the frequency of polymorphic genotypes in patients and control subjects. The spectrum of mutations in Mendelian disease genes, coupled with issues of statistical power, provides a compelling rationale for the application of a sequence-based approach targeting nonsynonymous single nucleotide polymorphisms (nsSNPs) rather than reliance on a map of anonymous haplotypes.6  We sought to identify novel low-penetrance susceptibility alleles for CLL by genotyping nsSNPs across 865 genes with relevance to cancer biology, biasing selection of nsSNPs toward those likely to have deleterious consequences. Genotyping 992 patients with CLL and 2707 healthy controls from the United Kingdom population across 1467 nsSNPs provided strong evidence for association between genes in the DNA damage-response and cell-cycle pathways and risk of CLL.

Patients, materials, and methods

Patients and control subjects

Patients with adult CLL (992 total; 688 men, 304 women; mean age at diagnosis, 61 years; SD ± 11.4) ascertained through the Royal Marsden Hospital National Health Service Trust (RMHNHST) Haemato-Oncology Unit were included in the study. The RMHNHST serves as a tertiary referral center and patient selection was not significantly biased to any specific geographic region within the United Kingdom. The diagnosis of B-cell CLL in patients was established using standard clinico-pathologic and immunologic criteria in accordance with current World Health Organization classification guidelines.7  A total of 2707 healthy individuals were recruited as part of the National Cancer Research Network Trial (1999-2002), the Royal Marsden Hospital Trust/Institute of Cancer Research Family History and DNA Registry (1999-2004), or the National Study of Colorectal Cancer Genetics Trial (2004), all established within the United Kingdom. Control subjects (836 men, 1871 women; mean age, 59 years; SD ± 10.9) were the spouses of patients with nonhematologic malignancies. None of the controls had a personal history of malignancy. All patients and control subjects were white and British, and there were no obvious differences in the demography of patients and control subjects in terms of place of residence within the United Kingdom. Blood samples were obtained with informed consent and ethics review board approval in accordance with the tenets of the Declaration of Helsinki. DNA was extracted from samples using conventional methodologies and quantified using PicoGreen (Invitrogen, Carlsbad, CA).

Selection of candidate genes and SNPs

We have previously established a publicly accessible PICS (Predicted Impact of Coding SNPs) database of potentially functional nsSNPs in genes with relevance to cancer biology.8  Briefly, candidate genes were identified by interrogating the Gene Ontology Consortium database,9  Kyoto Encyclopedia of Genes and Genomes database,10  Iobion's Interaction Explorer PathwayAssist Program, National Center for Biotechnology Information (NCBI) Entrez Gene database,11  and the CancerGene database. Both keyword and gene-pathway specific queries were performed using the following categories: catalytic activity; cellular processes, growth, and death; development; enzyme regulator activity; folding, sorting, and degradation; ligand-receptor interaction; nucleotide metabolism; physiologic processes; regulation of biologic processes; replication and repair; signal transduction and signal transducer activity; transcription and transcription regulator activity; translation and translation regulator activity; and transporter activity. A total of 9537 validated nsSNPs with minor allele frequency (MAF) data were identified within 21 506 LocusLink annotated genes in NCBI dbSNP Build 123. Filtering this list and linking it to 7080 candidate cancer genes yielded 3666 validated nsSNPs with MAF of 0.01 or more in white populations. The functional impact of each nsSNP was predicted using the in silico computational tools PolyPhen12  and SIFT (version 2.1).13  Using the PICS database and published work on resequencing of DNA repair genes,14-18  we prioritized a set of 1467 nsSNPs for the current study (Figure S1, available on the Blood website; see the Supplemental Figures link at the top of the online article). Annotated flanking sequence information for each SNP was derived from the University of California Santa Cruz (UCSC) Human Genome Browser (Assembly hg17).

SNP genotyping and data manipulation

Genotyping of samples was performed using customized Illumina Sentrix Bead Arrays (Illumina, San Diego, CA) according to the manufacturer's protocols. DNA samples with GenCall (Illumina) scores lower than 0.25 at any locus were considered “no calls.” A DNA sample was deemed to have failed if it generated genotypes at fewer than 95% of loci. A SNP was deemed to have failed if fewer than 95% of DNA samples generated a genotype at the locus. Conversion of genotype data into formats suitable for processing was performed using in-house Perl scripts (available upon request). Conventional statistical manipulations were undertaken in STATA (version 8; http://www.stata.com), S-Plus (version 7; http://www.insightful.com) or R (version 2.0.0; http://www.r-project.org).

Population stratification

Genotypic frequencies in control subjects for each SNP were tested for departure from Hardy-Weinberg equilibrium (HWE) using a χ2 test or Fisher exact test where an expected cell count was less than 5. SNPs that violate the HWE in the control population can indicate selection bias or genotyping errors, and were thus removed from further analyses. To detect and control for possible population stratification, we employed the genomic control approach19  using all SNPs to estimate the stratification parameter

\({\hat{{\lambda}}}\)
and its associated 95% confidence interval (CI). The possibility of sex differences as a source of population substructure in our controls was evaluated by χ2 test or Fisher exact test.

Risk of CLL associated with nsSNPs

The most efficient test of association depends on the true mode of inheritance of alleles. Since this is not known, we based our analyses on the difference between allelic frequencies in patients and control subjects using the χ2 test with 1 degree of freedom or Fisher exact test if an expected cell count was less than 5. We denoted this test statistic TA with the corresponding P value PA. We also investigated 2 further tests based on 2 × 2 tables combining the heterozygotes with either the common or rare homozygotes to derive the statistics TR and TD with corresponding P values PR and PD, which are most powerful under recessive or dominant models, respectively. The risks associated with each SNP were estimated by allelic, dominant, and recessive odds ratios (ORs) using unconditional logistic regression, and associated 95% CIs were calculated in each patient. Where it was not possible to calculate ORs and their CIs by asymptotic methods, an exact approach was implemented using LogXact software (Cytel, Cambridge, MA).

Multiple testing

Correction for multiple testing in association studies using simple adjustment approaches such as the Bonferroni correction are known to be conservative due to the assumption of independence between tests, which can lead to type II errors. To control the type II error rate, we adopted an empirical Monte Carlo simulation approach20  based on 10 000 permutations, which takes into account the fact that tests may be correlated due to the presence of linkage disequilibrium (LD) throughout the genome. At each iteration patient and control subject labels are permuted at random and maximum test statistics TAmax, TDmax, and TRmax are determined. For each of these statistics (allelic, dominant, or recessive models), significance levels of the observed statistics from the original data are then estimated by the proportion of permutation samples with TAmax, TDmax, and T maxR larger than that in the observed data. Although this approach adjusts for multiple testing for each of the 3 statistics separately, the consequent increase in false-positive rate is expected to be small due to the strong dependence between tests.

Assessment of linkage disequilibrium between SNPs

To assess the level of LD between SNPs, we calculated the pairwise LD measure D′ between consecutive pairs of markers throughout the genome using the expectation-maximization algorithm to estimate 2-locus haplotype frequencies. We chose to use the measure D′ as it is less sensitive to small minor allele frequencies than other measures such as r2. This information was used to investigate the relationship between haplotypes and disease status. Specifically, haplotypes were reconstructed using a Markov chain Monte Carlo method, and their frequencies in patient and control samples compared by permutation testing, using the PHASE program (http://www.stat.washington.edu/stephens/software.html).21,22 

Covariates and interactions

Information on a number of covariates was available for the patients, including sex, family history of CLL, and age at diagnosis. The test statistic TA was computed for all subgroups, together with ORs and their associated 95% CIs. Under certain conditions, a 2-stage process incorporating estimates of pairwise interactions between significant SNPs can yield greater power to detect association.23  To investigate epistatic interactions, each pair of SNPs that displayed a significant allelic association at the 5% level was evaluated by fitting a saturated logistic regression model and the log likelihood ratio statistic for comparison with the main effects model computed. This was compared against a χ2 distribution with 1 degree of freedom (d.f.). Statistics were then adjusted for multiple testing using a Bonferroni correction.

Results

Data quality and genotyping success

Of the 3699 DNA samples submitted for genotyping, a total of 3657 samples were successfully processed, generating in excess of 4 million genotypes. Genotypes were obtained for 962 (97.0%) of 992 patients and 2695 (99.6%) of 2707 control subjects. The likelihood of a DNA sample failing to genotype correlated with sample DNA concentration. SNP call rates per sample for each of the 3657 DNA samples were greater than 99.6% in patients and control subjects. Of the 1467 SNPs submitted for analysis, 1218 SNPs were satisfactorily genotyped (83%), with mean individual sample call rates of 99.7% and 99.8% in patients and control subjects, respectively. Of the 1218 SNP loci satisfactorily genotyped, 188 were fixed in all samples, leaving 1030 SNPs for which genotype data were informative.

Population stratification

Of the 1030 polymorphic nsSNPs, 55 were found to violate HWE in controls at the 5% significance level (expected number of failures, 52). After Bonferroni correction, 6 SNPs still violated HWE and were removed, leaving a total of 1024 for further analysis. Each of the 6 SNPs removed had low genotyping reliability scores. Table S1 details all MAF data in 2695 controls for each of the 1024 nsSNPs. Of the remaining SNPs that violated HWE at the nominal 5% level, none was associated (P < .05) with risk of CLL. Implementing the genomic control method indicated no evidence of population stratification in our data as a cause of false-positive results, as the 95% confidence interval for the stratification parameter

\({\hat{{\lambda}}}\)
(0.92-1.31) encompassed unity. Furthermore, no evidence was found for differences in allele frequencies of SNPs between male and female control subjects as a source of potential confounding in subsequent analyses.

SNPs and risk of CLL

Statistically significant associations were identified for 49 of 1024 SNPs at the 5% level by means of the TA statistic, 3 of which were significant at the 0.1% level (Table 1). The test statistics TD and TR and ORs under dominant and recessive models were computed for 1024 and 886 SNPs with sufficient MAF, respectively.

Table 1.

SNPs showing significant allelic association with chronic lymphocytic leukemia






Allelic statistic

Dominant/recessive statistics
SNP
Gene*
Substitution
MAF
OR (95% CI)
PA
ORD/R (95% CI)
PD/R
rs1800056  ATM  F858L   0.014   2.14 (1.45, 3.16)   .0001   2.28 (1.53, 3.40)D§  < .0001D§ 
rs1800057  ATM  P1054R   0.028   1.70 (1.27, 2.27)   .0003   1.68 (1.25, 2.28)D  .0006D 
rs1801376  BUB1B  Q349R   0.308   1.21 (1.08, 1.35)   .0008   1.42 (1.12, 1.81)R  .0038R 
rs17879961  CHEK2  I157T   0.001   14.81 (1.85, ∞)   .0048   14.83 (1.85, ∞)D  .0008R 
rs10927851  FBLP-1  F191S   0.272   1.18 (1.05, 1.32)   .0058   1.44 (1.11, 1.86)R  .0055R 
rs9975588  MCM3AP  S102L   0.395   0.86 (0.77, 0.96)   .0059   0.78 (0.63, 0.96)R  .0189R 
rs1048201  NUDT6  R209Q   0.177   1.20 (1.05, 1.37)   .0065   1.26 (1.08, 1.48)D  .0029D 
rs2250889  MMP9  R574P   0.038   1.41 (1.09, 1.82)   .0085   1.44 (1.11, 1.87)D  .0064D 
rs1801265  DPYD  C29R   0.215   0.84 (0.74, 0.96)   .0097   0.82 (0.70, 0.96)D  .0113D 
rs2293925  TOP1MT  R525W   0.442   0.87 (0.78, 0.97)   .0097   0.80 (0.66, 0.96)R  .0190R 
rs4874147  ZC3HDC3  G452S   0.265   0.85 (0.76, 0.96)   .0098   0.81 (0.70, 0.94)D  .0052D 
rs7288201  GGTLA1  I475V   0.023   0.60 (0.40, 0.89)   .0106   0.58 (0.38, 0.87)D  .0072D 
rs10067  OVGP1  H604Q   0.080   1.26 (1.05, 1.51)   .0144   1.26 (1.03, 1.53)D  .0238D 
rs17409304  CLCA2  Q306E   0.345   0.87 (0.78, 0.97)   .0149   0.84 (0.73, 0.98)D  .0244D 
rs11569017  EGF  D784V   0.050   0.73 (0.56, 0.94)   .0149   0.73 (0.56, 0.96)D  .0216D 
rs3206824  DKK3  G335R   0.239   1.16 (1.03, 1.31)   .0152   1.37 (1.01, 1.84)R  .0394R 
rs144848 BRCA2  N372H   0.288   1.15 (1.03, 1.29)   .0158   1.45 (1.13, 1.86)R  .0032R 
rs4619  IGFBP1  I253M   0.355   1.14 (1.02, 1.27)   .0166   1.25 (1.01, 1.56)R  .0414R 
rs16978899  ZNF233  S247P   0.001   10.72 (1.15, ∞)   .0182   10.79 (1.16, ∞)D  .0037D 
rs712665 U2AF1L1  S155G   0.351   1.14 (1.02, 1.27)   .0185   1.21 (1.04, 1.41)D  .0117D 
rs5167  APOC4  L96R   0.339   1.14 (1.02, 1.28)   .0188   1.22 (1.04, 1.41)D  .0115D 
rs2235006  ATM  F582L   0.001   11.23 (1.25, 100)   .0189   8.43 (0.88, 81.12)D  .0269D 
rs328  LPL  S474X   0.104   0.81 (0.68, 0.97)   .0192   0.81 (0.67, 0.98)D  .0305D 
rs4150521  ERCC3  S704L   0.003   0.13 (0.02, 0.99)   .0202   0.13 (0.02, 0.99)R  .0200R 
rs10494745  CFHL4  G306E   0.102   0.81 (0.68, 0.97)   .0202   0.81 (0.67, 0.99)R  .0347D 
rs2269429  TNXB  G2555S   0.080   0.79 (0.64, 0.97)   .0211   0.79 (0.63, 0.97)D  .0268D 
rs17050550  OGG1  A85S   0.002   3.16 (1.22, 8.21)   .0228   3.17 (1.22, 8.25)R  .0124R 
rs17293607  MMP10  G65R   0.146   0.84 (0.72, 0.98)   .0248   0.83 (0.70, 0.98)D  .0293D 
rs619203 ROS1  S2229C   0.255   0.87 (0.77, 0.98)   .0261   0.85 (0.73, 0.98)D  .0284D 
rs459552 APC  V1822D   0.228   1.15 (1.02, 1.29)   .0283   1.18 (1.02, 1.37)D  .0278D 
rs508405 CAPN13  T280A   0.329   1.13 (1.01, 1.26)   .0293   1.27 (1.01, 1.60)R  .0437R 
rs3136797  POLB  P242R   0.019   1.47 (1.03, 2.10)   .0307   1.50 (1.05, 2.15)D  .0250D 
rs11102001  EPS8L3  P356S   0.061   1.26 (1.02, 1.55)   .0317   1.27 (1.02, 1.58)D  .0301D 
rs1738023  AKR7A3  D215N   0.170   1.16 (1.01, 1.33)   .0327   1.17 (1.00, 1.37)D  .0475D 
rs3821979  ShrmL  L146H   0.133   1.17 (1.01, 1.36)   .0359   1.57 (0.96, 2.54)R  .0678R 
rs1805087  MTR  D919G   0.192   1.15 (1.01, 1.31)   .0363   1.41 (0.97, 2.04)R  .0718R 
rs2452600  LIM  S136F   0.320   0.89 (0.79, 0.99)   .0369   0.80 (0.62, 1.02)R  .0756R 
rs2278106  EPHA7  P278S   0.032   1.34 (1.02, 1.78)   .0382   1.36 (1.02, 1.81)D  .0368D 
rs204900 TNXB  S873A   0.081   0.81 (0.67, 0.99)   .0395   0.80 (0.65, 0.99)D  .0397D 
rs16900023  MSH5  P786S   0.017   0.62 (0.39, 0.99)   .0410   0.62 (0.39, 0.99)D  .0440D 
rs1800100  CFTR  R668C   0.010   0.53 (0.29, 0.99)   .0415   0.53 (0.28, 0.98)R  .0404R 
rs1211554  HUS1B  Y268D   0.085   0.82 (0.67, 0.99)   .0434   0.79 (0.64, 0.97)D  .0261D 
rs2032729  ZNF24  S220N   0.080   1.21 (1.01, 1.46)   .0435   1.21 (0.99, 1.48)D  .0577D 
rs17738527  SEC14L4  E211K   0.243   0.88 (0.78, 1.00)   .0460   0.67 (0.48, 0.93)R  .0172R 
rs1799954  BRCA2  R2034C   0.006   0.43 (0.18, 1.02)   .0473   0.43 (0.18, 1.01)R  .0466R 
rs17337252  RB1CC1  M234T   0.498   1.11 (1.00, 1.23)   .0480   1.19 (1.00, 1.42)D  .0448D 
rs8207  PPIG  N699D   0.261   0.89 (0.79, 1.00)   .0481   0.65 (0.48, 0.89)R  .0063R 
rs17704912  MYO18B  W1037S   0.072   1.22 (1.00, 1.48)   .0481   1.24 (1.01, 1.53)D  .0362D 
rs3130618
 
BAT4
 
R41L
 
0.196
 
1.14 (1.00, 1.30)
 
.0482
 
1.14 (0.98, 1.33)D
 
.0936D
 





Allelic statistic

Dominant/recessive statistics
SNP
Gene*
Substitution
MAF
OR (95% CI)
PA
ORD/R (95% CI)
PD/R
rs1800056  ATM  F858L   0.014   2.14 (1.45, 3.16)   .0001   2.28 (1.53, 3.40)D§  < .0001D§ 
rs1800057  ATM  P1054R   0.028   1.70 (1.27, 2.27)   .0003   1.68 (1.25, 2.28)D  .0006D 
rs1801376  BUB1B  Q349R   0.308   1.21 (1.08, 1.35)   .0008   1.42 (1.12, 1.81)R  .0038R 
rs17879961  CHEK2  I157T   0.001   14.81 (1.85, ∞)   .0048   14.83 (1.85, ∞)D  .0008R 
rs10927851  FBLP-1  F191S   0.272   1.18 (1.05, 1.32)   .0058   1.44 (1.11, 1.86)R  .0055R 
rs9975588  MCM3AP  S102L   0.395   0.86 (0.77, 0.96)   .0059   0.78 (0.63, 0.96)R  .0189R 
rs1048201  NUDT6  R209Q   0.177   1.20 (1.05, 1.37)   .0065   1.26 (1.08, 1.48)D  .0029D 
rs2250889  MMP9  R574P   0.038   1.41 (1.09, 1.82)   .0085   1.44 (1.11, 1.87)D  .0064D 
rs1801265  DPYD  C29R   0.215   0.84 (0.74, 0.96)   .0097   0.82 (0.70, 0.96)D  .0113D 
rs2293925  TOP1MT  R525W   0.442   0.87 (0.78, 0.97)   .0097   0.80 (0.66, 0.96)R  .0190R 
rs4874147  ZC3HDC3  G452S   0.265   0.85 (0.76, 0.96)   .0098   0.81 (0.70, 0.94)D  .0052D 
rs7288201  GGTLA1  I475V   0.023   0.60 (0.40, 0.89)   .0106   0.58 (0.38, 0.87)D  .0072D 
rs10067  OVGP1  H604Q   0.080   1.26 (1.05, 1.51)   .0144   1.26 (1.03, 1.53)D  .0238D 
rs17409304  CLCA2  Q306E   0.345   0.87 (0.78, 0.97)   .0149   0.84 (0.73, 0.98)D  .0244D 
rs11569017  EGF  D784V   0.050   0.73 (0.56, 0.94)   .0149   0.73 (0.56, 0.96)D  .0216D 
rs3206824  DKK3  G335R   0.239   1.16 (1.03, 1.31)   .0152   1.37 (1.01, 1.84)R  .0394R 
rs144848 BRCA2  N372H   0.288   1.15 (1.03, 1.29)   .0158   1.45 (1.13, 1.86)R  .0032R 
rs4619  IGFBP1  I253M   0.355   1.14 (1.02, 1.27)   .0166   1.25 (1.01, 1.56)R  .0414R 
rs16978899  ZNF233  S247P   0.001   10.72 (1.15, ∞)   .0182   10.79 (1.16, ∞)D  .0037D 
rs712665 U2AF1L1  S155G   0.351   1.14 (1.02, 1.27)   .0185   1.21 (1.04, 1.41)D  .0117D 
rs5167  APOC4  L96R   0.339   1.14 (1.02, 1.28)   .0188   1.22 (1.04, 1.41)D  .0115D 
rs2235006  ATM  F582L   0.001   11.23 (1.25, 100)   .0189   8.43 (0.88, 81.12)D  .0269D 
rs328  LPL  S474X   0.104   0.81 (0.68, 0.97)   .0192   0.81 (0.67, 0.98)D  .0305D 
rs4150521  ERCC3  S704L   0.003   0.13 (0.02, 0.99)   .0202   0.13 (0.02, 0.99)R  .0200R 
rs10494745  CFHL4  G306E   0.102   0.81 (0.68, 0.97)   .0202   0.81 (0.67, 0.99)R  .0347D 
rs2269429  TNXB  G2555S   0.080   0.79 (0.64, 0.97)   .0211   0.79 (0.63, 0.97)D  .0268D 
rs17050550  OGG1  A85S   0.002   3.16 (1.22, 8.21)   .0228   3.17 (1.22, 8.25)R  .0124R 
rs17293607  MMP10  G65R   0.146   0.84 (0.72, 0.98)   .0248   0.83 (0.70, 0.98)D  .0293D 
rs619203 ROS1  S2229C   0.255   0.87 (0.77, 0.98)   .0261   0.85 (0.73, 0.98)D  .0284D 
rs459552 APC  V1822D   0.228   1.15 (1.02, 1.29)   .0283   1.18 (1.02, 1.37)D  .0278D 
rs508405 CAPN13  T280A   0.329   1.13 (1.01, 1.26)   .0293   1.27 (1.01, 1.60)R  .0437R 
rs3136797  POLB  P242R   0.019   1.47 (1.03, 2.10)   .0307   1.50 (1.05, 2.15)D  .0250D 
rs11102001  EPS8L3  P356S   0.061   1.26 (1.02, 1.55)   .0317   1.27 (1.02, 1.58)D  .0301D 
rs1738023  AKR7A3  D215N   0.170   1.16 (1.01, 1.33)   .0327   1.17 (1.00, 1.37)D  .0475D 
rs3821979  ShrmL  L146H   0.133   1.17 (1.01, 1.36)   .0359   1.57 (0.96, 2.54)R  .0678R 
rs1805087  MTR  D919G   0.192   1.15 (1.01, 1.31)   .0363   1.41 (0.97, 2.04)R  .0718R 
rs2452600  LIM  S136F   0.320   0.89 (0.79, 0.99)   .0369   0.80 (0.62, 1.02)R  .0756R 
rs2278106  EPHA7  P278S   0.032   1.34 (1.02, 1.78)   .0382   1.36 (1.02, 1.81)D  .0368D 
rs204900 TNXB  S873A   0.081   0.81 (0.67, 0.99)   .0395   0.80 (0.65, 0.99)D  .0397D 
rs16900023  MSH5  P786S   0.017   0.62 (0.39, 0.99)   .0410   0.62 (0.39, 0.99)D  .0440D 
rs1800100  CFTR  R668C   0.010   0.53 (0.29, 0.99)   .0415   0.53 (0.28, 0.98)R  .0404R 
rs1211554  HUS1B  Y268D   0.085   0.82 (0.67, 0.99)   .0434   0.79 (0.64, 0.97)D  .0261D 
rs2032729  ZNF24  S220N   0.080   1.21 (1.01, 1.46)   .0435   1.21 (0.99, 1.48)D  .0577D 
rs17738527  SEC14L4  E211K   0.243   0.88 (0.78, 1.00)   .0460   0.67 (0.48, 0.93)R  .0172R 
rs1799954  BRCA2  R2034C   0.006   0.43 (0.18, 1.02)   .0473   0.43 (0.18, 1.01)R  .0466R 
rs17337252  RB1CC1  M234T   0.498   1.11 (1.00, 1.23)   .0480   1.19 (1.00, 1.42)D  .0448D 
rs8207  PPIG  N699D   0.261   0.89 (0.79, 1.00)   .0481   0.65 (0.48, 0.89)R  .0063R 
rs17704912  MYO18B  W1037S   0.072   1.22 (1.00, 1.48)   .0481   1.24 (1.01, 1.53)D  .0362D 
rs3130618
 
BAT4
 
R41L
 
0.196
 
1.14 (1.00, 1.30)
 
.0482
 
1.14 (0.98, 1.33)D
 
.0936D
 
*

According to NCBI Entrez Gene (http://www.ncbi.nih.gov/entrez/query.fcgi?db=gene).

MAF in patients.

Most significant association under a dominant (D) or recessive (R) model.

§

Globally significant after permutation testing.

Of the 49 SNPs showing significant association (PA ≤ .05), 2 SNPs have previously been documented to be functional: I157T in CHK2 checkpoint yeast homolog (CHEK2 [MIM 604373]), a cell-cycle checkpoint regulator, and P1054R in ataxia telangiectasia mutated (ATM [MIM 607585]), a cell-cycle checkpoint kinase required for cellular response to DNA damage. In addition, 1 SNP encodes a termination codon; S474X in lipoprotein lipase (LPL [MIM 238600]), and a further 31 SNPs are predicted by at least 1 in silico algorithm to be deleterious (Table 2).

Table 2.

Description and predicted functionality of nsSNPs showing significant association with chronic lymphocytic leukemia risk


SNP

Substitution

Predicted functionality*

Gene

Gene description

Gene ontology

MIM§
rs1800056   F858L   Possibly damaging/potentially intolerant  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell-cycle control  607585 
rs1800057   P1054R   Probably damaging/intolerant  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell-cycle control   607585  
rs1801376   Q349R   Potentially damaging  BUB1B  BUB1 budding uninhibited by benzimidazoles 1 homolog beta   Cell cycling and proliferation   602860  
rs17879961   I157T   —  CHEK2  CHK2 checkpoint homolog   DNA damage checkpoint   604373  
rs10927851   F191S   —  FBLIM1 (FBLP-1)  Filamin-binding LIM protein-1   Cell adhesion   607747  
rs9975588   S102L   Potentially damaging  MCM3AP  MCM3 minichromosome maintenance—deficient 3 associated protein   DNA replication   603294  
rs1048201   R209Q   Potentially damaging/potentially intolerant  NUDT6  Nudix (nucleoside diphosphate linked moiety X) motif 6   Growth factor   606261  
rs2250889   R574P   Possibly damaging  MMP9  Matrix metalloproteinase 9   Collagen catabolism   120361  
rs1801265   C29R   —  DPYD  Dihydropyrimidine dehydrogenase   Thymidine/uracil catabolism   274270  
rs2293925   R525W   Possibly damaging/intolerant  TOP1MT  DNA topoisomerase I   DNA unwinding   606387  
rs4874147   G452S   Potentially damaging  ZC3H3 (ZC3HDC 3)  Zinc finger CCCH-type domain containing 3   Nucleic acid binding   NA  
rs7288201   I475V   Intolerant  GGTLA1  Gamma-glutamyltransferase-like activity 1   Amino acid metabolism   137168  
rs10067   H604Q   Probably damaging/intolerant  OVGP1  Oviductal glycoprotein 1   Chitin catabolism   603578  
rs17409304   Q306E   Intolerant  CLCA2  Chloride channel, calcium activated, member 2   Chloride transport   604003  
rs11569017   D784V   Possibly damaging/intolerant  EGF  Epidermal growth factor   DNA replication, receptor signaling, regulation of cell proliferation   131530  
rs3206824   G335R   Intolerant  DKK3  Dickkopf homolog 3   Receptor signaling   605416  
rs144848   N372H   —  BRCA2  Breast cancer 2, early onset   DNA repair, regulation of transcription   600185  
rs4619   I253M   —  IGFBP1  Insulin-like growth factor-binding protein 1   Regulation of cell growth, signal transduction   146730  
rs16978899   S247P   Possibly damaging/intolerant  ZNF233  Zinc finger protein 233   Regulation of transcription   NA  
rs712665   S155G   Possibly damaging  U2AF1L1  U2 small nuclear RNA auxillary factor 1-like 1   Nucleotide binding   601079  
rs5167   L96R   Probably damaging  APOC4  Apolipoprotein C-IV   Lipid metabolism   600745  
rs2235006   F582L   Possibly damaging  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell cycle control   607585  
rs328   S474X   Stop codon  LPL  Lipoprotein lipase   Fatty acid metabolism   238600  
rs4150521   S704L   Possibly damaging  ERCC3  Excision repair cross-complementing rodent repair deficiency, complementation group 3   Induction of apoptosis, regulation of transcription   133510  
rs10494745   G306E   Possibly damaging  CFHL4  Complement factor H-related 4   Lipid transporter activity   605337  
rs2269429   G2555S   Possibly damaging  TNXB  Tenascin XB   Cell-matrix adhesion   600985  
rs17050550   A85S   —  OGG1  8-Oxoguanine DNA glycosylase   Base-excision repair   601982  
rs17293607   G65R   Intolerant  MMP10  Matrix metalloproteinase 10   Collagen catabolism   185260  
rs619203   S2229C   Probably damaging  ROS1  v-ros UR2 sarcoma virus oncogene homolog 1   Signal transduction   165020  
rs459552   V1822D   —  APC  Adenomatosis polyposis coli   Receptor signaling, cell-cycle control   175100  
rs508405   T280A   Potentially intolerant  CAPN13  Calpain 13   Proteolysis and peptidolysis   NA  
rs3136797   P242R   Possibly damaging/intolerant  POLB  Polymerase (DNA directed), beta   DNA repair, DNA replication   174760  
rs11102001   P356S   Possibly damaging  EPS8L3  EPS8-like 3   Receptor activity   NA  
rs1738023   D215N   Possibly damaging  AKR7A3  Aldo-keto reductase family 7, member A3   Aldehyde metabolism   608477  
rs3821979   L146H   Possibly damaging/intolerant  SHRM (Shrml)  Shroom   Death receptor adaptor protein activity   604570  
rs1805087   D919G   —  MTR  5-Methyltetrahydrofolate-homocysteine methyltransferase   Folic acid and derivative biosynthesis, transferase activity   156570  
rs2452600   S136F   Potentially damaging/intolerant  PDLIM5 (LIM)  PDZ and LIM domain 5   Metal ion binding   605904  
rs2278106   P278S   Intolerant  EPHA7  EPH receptor A7   Receptor signalling   602190  
rs204900   S873A   Intolerant  TNXB  Tenascin XB   Cell-matrix adhesion   600985  
rs16900023   P786S   Potentially damaging  MSH5  mutS homolog 5   DNA repair, DNA metabolism   603382  
rs1800100   R668C   Probably damaging/intolerant  CFTR  Cystic fibrosis transmembrane conductance regulator   Ion transport   602421  
rs1211554   Y268D   Probably damaging/potentially intolerant  HUS1B  HUS1 checkpoint homolog b   Cell-cycle control   NA  
rs2032729   S220N   Potentially damaging  ZNF24  Zinc finger protein 24   Regulation of transcription   194534  
rs17738527   E211K   Possibly damaging/intolerant  SEC14L4  SEC14-like 4   Intracellular protein transport   NA  
rs1799954   R2034C   Possibly damaging/intolerant  BRCA2  Breast cancer 2, early onset   DNA repair, regulation of transcription   600185  
rs17337252   M234T   Possibly damaging  RB1CC1  RB1-inducible coiled-coil 1   Kinase activity   606837  
rs8207   N699D   Potentially damaging/potentially intolerant  PPIG  Peptidyl-prolyl isomerase G   RNA splicing, protein folding   606093  
rs17704912   W1037S   Probably damaging  MYO18B  Myosin XVIIIB   Nucleotide binding   607295  
rs3130618
 
R41L
 
Possibly damaging
 
BAT4
 
HLA-B—associated transcript 4
 
Nucleic acid binding
 
142610
 

SNP

Substitution

Predicted functionality*

Gene

Gene description

Gene ontology

MIM§
rs1800056   F858L   Possibly damaging/potentially intolerant  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell-cycle control  607585 
rs1800057   P1054R   Probably damaging/intolerant  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell-cycle control   607585  
rs1801376   Q349R   Potentially damaging  BUB1B  BUB1 budding uninhibited by benzimidazoles 1 homolog beta   Cell cycling and proliferation   602860  
rs17879961   I157T   —  CHEK2  CHK2 checkpoint homolog   DNA damage checkpoint   604373  
rs10927851   F191S   —  FBLIM1 (FBLP-1)  Filamin-binding LIM protein-1   Cell adhesion   607747  
rs9975588   S102L   Potentially damaging  MCM3AP  MCM3 minichromosome maintenance—deficient 3 associated protein   DNA replication   603294  
rs1048201   R209Q   Potentially damaging/potentially intolerant  NUDT6  Nudix (nucleoside diphosphate linked moiety X) motif 6   Growth factor   606261  
rs2250889   R574P   Possibly damaging  MMP9  Matrix metalloproteinase 9   Collagen catabolism   120361  
rs1801265   C29R   —  DPYD  Dihydropyrimidine dehydrogenase   Thymidine/uracil catabolism   274270  
rs2293925   R525W   Possibly damaging/intolerant  TOP1MT  DNA topoisomerase I   DNA unwinding   606387  
rs4874147   G452S   Potentially damaging  ZC3H3 (ZC3HDC 3)  Zinc finger CCCH-type domain containing 3   Nucleic acid binding   NA  
rs7288201   I475V   Intolerant  GGTLA1  Gamma-glutamyltransferase-like activity 1   Amino acid metabolism   137168  
rs10067   H604Q   Probably damaging/intolerant  OVGP1  Oviductal glycoprotein 1   Chitin catabolism   603578  
rs17409304   Q306E   Intolerant  CLCA2  Chloride channel, calcium activated, member 2   Chloride transport   604003  
rs11569017   D784V   Possibly damaging/intolerant  EGF  Epidermal growth factor   DNA replication, receptor signaling, regulation of cell proliferation   131530  
rs3206824   G335R   Intolerant  DKK3  Dickkopf homolog 3   Receptor signaling   605416  
rs144848   N372H   —  BRCA2  Breast cancer 2, early onset   DNA repair, regulation of transcription   600185  
rs4619   I253M   —  IGFBP1  Insulin-like growth factor-binding protein 1   Regulation of cell growth, signal transduction   146730  
rs16978899   S247P   Possibly damaging/intolerant  ZNF233  Zinc finger protein 233   Regulation of transcription   NA  
rs712665   S155G   Possibly damaging  U2AF1L1  U2 small nuclear RNA auxillary factor 1-like 1   Nucleotide binding   601079  
rs5167   L96R   Probably damaging  APOC4  Apolipoprotein C-IV   Lipid metabolism   600745  
rs2235006   F582L   Possibly damaging  ATM  Ataxia telangiectasia mutated   DNA repair, transcription regulation, cell cycle control   607585  
rs328   S474X   Stop codon  LPL  Lipoprotein lipase   Fatty acid metabolism   238600  
rs4150521   S704L   Possibly damaging  ERCC3  Excision repair cross-complementing rodent repair deficiency, complementation group 3   Induction of apoptosis, regulation of transcription   133510  
rs10494745   G306E   Possibly damaging  CFHL4  Complement factor H-related 4   Lipid transporter activity   605337  
rs2269429   G2555S   Possibly damaging  TNXB  Tenascin XB   Cell-matrix adhesion   600985  
rs17050550   A85S   —  OGG1  8-Oxoguanine DNA glycosylase   Base-excision repair   601982  
rs17293607   G65R   Intolerant  MMP10  Matrix metalloproteinase 10   Collagen catabolism   185260  
rs619203   S2229C   Probably damaging  ROS1  v-ros UR2 sarcoma virus oncogene homolog 1   Signal transduction   165020  
rs459552   V1822D   —  APC  Adenomatosis polyposis coli   Receptor signaling, cell-cycle control   175100  
rs508405   T280A   Potentially intolerant  CAPN13  Calpain 13   Proteolysis and peptidolysis   NA  
rs3136797   P242R   Possibly damaging/intolerant  POLB  Polymerase (DNA directed), beta   DNA repair, DNA replication   174760  
rs11102001   P356S   Possibly damaging  EPS8L3  EPS8-like 3   Receptor activity   NA  
rs1738023   D215N   Possibly damaging  AKR7A3  Aldo-keto reductase family 7, member A3   Aldehyde metabolism   608477  
rs3821979   L146H   Possibly damaging/intolerant  SHRM (Shrml)  Shroom   Death receptor adaptor protein activity   604570  
rs1805087   D919G   —  MTR  5-Methyltetrahydrofolate-homocysteine methyltransferase   Folic acid and derivative biosynthesis, transferase activity   156570  
rs2452600   S136F   Potentially damaging/intolerant  PDLIM5 (LIM)  PDZ and LIM domain 5   Metal ion binding   605904  
rs2278106   P278S   Intolerant  EPHA7  EPH receptor A7   Receptor signalling   602190  
rs204900   S873A   Intolerant  TNXB  Tenascin XB   Cell-matrix adhesion   600985  
rs16900023   P786S   Potentially damaging  MSH5  mutS homolog 5   DNA repair, DNA metabolism   603382  
rs1800100   R668C   Probably damaging/intolerant  CFTR  Cystic fibrosis transmembrane conductance regulator   Ion transport   602421  
rs1211554   Y268D   Probably damaging/potentially intolerant  HUS1B  HUS1 checkpoint homolog b   Cell-cycle control   NA  
rs2032729   S220N   Potentially damaging  ZNF24  Zinc finger protein 24   Regulation of transcription   194534  
rs17738527   E211K   Possibly damaging/intolerant  SEC14L4  SEC14-like 4   Intracellular protein transport   NA  
rs1799954   R2034C   Possibly damaging/intolerant  BRCA2  Breast cancer 2, early onset   DNA repair, regulation of transcription   600185  
rs17337252   M234T   Possibly damaging  RB1CC1  RB1-inducible coiled-coil 1   Kinase activity   606837  
rs8207   N699D   Potentially damaging/potentially intolerant  PPIG  Peptidyl-prolyl isomerase G   RNA splicing, protein folding   606093  
rs17704912   W1037S   Probably damaging  MYO18B  Myosin XVIIIB   Nucleotide binding   607295  
rs3130618
 
R41L
 
Possibly damaging
 
BAT4
 
HLA-B—associated transcript 4
 
Nucleic acid binding
 
142610
 

— indicates prediction not possible; NA, not available.

*

Functional predictions based on SIFT (intolerant) and PolyPhen (probably damaging, possibly damaging).

According to NCBI Entrez Gene (see Table 1 footnote for URL); genes in brackets have been recently revised.

Gene Ontology Consortium annotation (http://www.geneontology.org).

§

Online Mendelian Inheritance in Man (http://www.ncbi.nih.gov/entrez/query.fcgi?db=OMIM) accession number.

ATM SNPs F858L (rs1800056) and P1054R (rs1800057), which are in strong LD, showed the most significant allelic association with CLL, with strongest association under a dominant model (ORD = 2.28; 95% CI, 1.53-3.40; PD < .0001; ORD = 1.68; 95% CI, 1.25-2.28; PD = .0006), respectively. After permutation analysis to adjust for multiple testing, ATM F858L (rs1800056) was found to still be significantly associated with CLL risk, with adjusted P = .03 at the genome-wide level. Additionally, the haplotype formed by the minor alleles of ATM F858L and P1054R was significantly overrepresented in patients compared with control subjects (ORD = 2.32; 95% CI, 1.56-3.45; PD < .0001, P = .01 after permutation testing).

Nine additional SNPs located within the DNA damage-response axis also showed significant association (Figure 1; Table 1). Pre-eminent SNPs on the basis of biologic relevance were I157T (rs17879961) in CHEK2 (ORD = 14.83; 95% CI, 1.85-8; PD = .0008), N372H (rs144848) in breast cancer 2 early onset (BRCA2 [MIM 600185]), a tumor suppressor involved in DNA double-strand break repair (ORR = 1.45; 95% CI, 1.13-1.86; PR = .0032), and Q349R (rs1801376) in BUB1 budding uninhibited by benzimidazoles yeast homolog 1 (BUB1B [MIM 602860]), encoding a kinase involved in spindle checkpoint function (ORR = 1.42; 95% CI, 1.12-1.81; PR = .0038).

Stratification of patients by sex, family history of the disease, and age at diagnosis (≤ 60 years, > 60 years) did not significantly affect study findings. We examined for interactive effects between the 49 SNPs significantly associated with risk of CLL (PA < .05) by fitting full logistic regression models for each pair, generating 1176 models, and comparing these with the main effects model. The strongest interaction was between BRCA2 N372H and EPH receptor A7 (EPHA7 [MIM 602190]) P278S (P = .0007), albeit nonsignificant after correction for multiple testing.

Discussion

We evaluated nsSNPs on the basis that each has the capacity to directly affect the function of expressed proteins, implying a higher probability of being directly causally related to susceptibility. Allelic loss in cells used in genetic analyses is a potential source of bias, because an apparent increase in homozygosity may be due to loss of heterozygosity in tumor leukocytes. There was no evidence of such confounding in our study as a source of spurious results, since the number of SNPs showing deviation from Hardy-Weinberg equilibrium followed the expected distribution, and associations were primarily based upon an overrepresentation of heterozygotes.

For 2 of the nsSNPs identified, CHEK2 I157T and ATM P1054R, there is evidence they are likely to directly affect the risk of malignancy. Furthermore, for an additional 32 of the SNPs significantly associated with CLL risk, the substitution either resulted in a termination codon or was predicted to be functionally deleterious using the in silico algorithms PolyPhen and/or SIFT. Although predictions about the functional consequences of amino acid changes are not definitive, these algorithms have been demonstrated in benchmarking studies to successfully categorize 80% of amino-acid substitutions.24 

Through interrogation of the Pathway Assist program (Stratagene, La Jolla, CA), 11 of the 49 associated SNPs were found within genes encoding pivotal components of the ATM-BRCA2-CHEK2 DNA damage-response and cell-signaling pathways.

The 3 SNPs in ATM associated with increased risk of CLL, F582L, F858L, and P1054R, are each predicted to be deleterious. Heterozygosity for P1054R has been reported to be associated with decreased ATM expression in CLL;25  furthermore, cell lines from breast cancer patients harboring the linked heterozygous F858L and P1054R variants exhibited increased radiosensitivity.26 ATM 1054R has previously been associated with an increased risk of breast27,28  and prostate cancer.28  While the functional significance of F582L is unknown, this SNP has previously been reported to confer an elevated risk of acute lymphocytic leukemia.29 

We have recently conducted a genome-wide linkage search of 115 families segregating CLL and other related B-cell LPDs but did not demonstrate significant linkage to ATM (P = .08).30  This observation is not contradictory to our current findings of an overrepresentation of the minor alleles of ATM F858L and P1045L in patients with CLL as the impact of ATM on the familial risk of CLL generated by both variants is approximately 1.03, insufficient to generate a significant departure in expected allele-sharing probabilities between affected individuals in the 115 families.

ATM is critical for regulation of cell-cycle checkpoints, and activation of ATM by DNA damage leads to ATM-dependent phosphorylation of CHEK2.31 CHEK2 I157T is localized in a functionally important domain of CHEK2, and the variant protein has been shown to be defective in its ability to bind TP5332  and BRCA1.33  Previously, CHEK2 I157T has been associated with increased risk of breast, colon, kidney, and prostate cancers.34  Furthermore, possession of 157T has been shown to confer a 2-fold increase in risk of NHL,34  supporting the role of inherited dysregulation of CHEK2 in the development of B-cell LPDs.

BRCA2 is involved in the monitoring and repair of DNA double-strand breaks.35  The minor allele of N372H has been documented to confer an elevated risk of breast36  and ovarian cancers.37  N372H is located between residues 290 and 453 of BRCA2, a region shown to interact with the transcriptional coactivator P/CAF,38  and hence has the potential to directly modify BRCA2-mediated regulation of transcription.

An additional 6 nsSNPs were identified in genes that interact either directly or indirectly with the ATM-BRCA2-CHEK2 DNA damage-response axis. These include SNPs D784V in EGF, I253M in insulin-like growth factor–binding protein 1 (IGFBP1 [MIM 146730]), and R574P in matrix metallopeptidase 9 (MMP9 [MIM 120361]), which are involved in Sp1-mediated down-regulation of ATM transcription by EGF.39  Despite the low minor-allele frequency of the SNPs individually associated with risk of CLL in our study, there was some evidence for an interaction between BRCA2 N372H and EPHA7 P278S (P = .0007), albeit nonsignificant after correction for multiple testing.

Figure 1.

Interrelationship between genes involved in the DNA damage-response axis for SNPs associated with risk of CLL. Relationships between genes were established using Pathway Assist software; supporting publications are indicated with their corresponding NCBI Entrez PubMed ID number in square brackets. (1) Regulation [15020226]; (2) Expression [8625897]; (3) Regulation [12511596]; (4) Expression [9096655]; (5) Expression [11751435]; (6) Binding [10973490]; (7) Binding [11438675]; (8) Binding [10866324]; (9) Binding [9774970]; (10) Binding [11034101]; (11) Binding [11034101]; and (12) Binding [12815053].

Figure 1.

Interrelationship between genes involved in the DNA damage-response axis for SNPs associated with risk of CLL. Relationships between genes were established using Pathway Assist software; supporting publications are indicated with their corresponding NCBI Entrez PubMed ID number in square brackets. (1) Regulation [15020226]; (2) Expression [8625897]; (3) Regulation [12511596]; (4) Expression [9096655]; (5) Expression [11751435]; (6) Binding [10973490]; (7) Binding [11438675]; (8) Binding [10866324]; (9) Binding [9774970]; (10) Binding [11034101]; (11) Binding [11034101]; and (12) Binding [12815053].

The prior probability of identifying a significant association with CLL risk for a series of SNPs mapping to a single gene pathway is intuitively small. Genotyping a total of 81 SNPs across 50 genes (including ATM, BRCA2, and CHEK2) implicated in the cell-cycle pathway via Gene Ontology Consortium annotations identified 8 SNPs displaying statistical association with risk of CLL, a significantly greater number than expected a priori (P < .05). By contrast, no significant associations were observed for SNPs mapping to genes encoding components of the cell-cell signaling (21 SNPs, 18 genes) and cell differentiation (20 SNPs, 17 genes) pathways.

Several lines of evidence support a role for inherited dysfunction in the ATM-CHEK2-BRCA2 axis as a cause of predisposition to CLL. Recessive ataxia telangiectasia (A-T), caused by mutations in ATM, is well established to confer a substantive increase in risk of LPD,40  and an overrepresentation of LPD has been documented in relatives of patients with A-T.41  Mutations in ATM, CHEK2, and BRCA2 are documented to confer an increased risk of breast cancer. This fact, coupled with the elevated risk of LPD reported in relatives of patients with breast cancer,1  suggests that a subset of breast cancers and LPDs have a common biology.

Our study provides evidence that inherited predisposition to CLL is in part mediated through low-penetrance alleles, specifically variants in the ATM-BRCA2-CHEK2 DNA damage-response axis. Clearly it is, however, desirable to validate our study findings through analysis of additional large datasets.

Prepublished online as Blood First Edition Paper, March 30, 2006; DOI 10.1182/blood-2005-12-5022.

Supported by Leukaemia Research, Cancer Research UK, the Arbib Foundation, National Cancer Research Network, and the European Union (CCPRB).

M.F.R. and G.S.S. contributed equally to this study.

An Inside Blood analysis of this article appears at the front of this issue.

The publication costs of this article were defrayed in part by page charge payment. Therefore, and solely to indicate this fact, this article is hereby marked “advertisement” in accordance with 18 U.S.C. section 1734.

We gratefully acknowledge the participation of all patients with CLL and control individuals. The authors are indebted to Ruth Allinson, Richard Coleman, Christina Fleischmann, Nicholas Hearle, Athena Matakidiou, Mobshra Qureshi, Hayley Spendlove, and Remben Talaban for sample ascertainment.

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