Literature DB >> 19412175

Common variations in BARD1 influence susceptibility to high-risk neuroblastoma.

Mario Capasso1, Marcella Devoto, Cuiping Hou, Shahab Asgharzadeh, Joseph T Glessner, Edward F Attiyeh, Yael P Mosse, Cecilia Kim, Sharon J Diskin, Kristina A Cole, Kristopher Bosse, Maura Diamond, Marci Laudenslager, Cynthia Winter, Jonathan P Bradfield, Richard H Scott, Jayanti Jagannathan, Maria Garris, Carmel McConville, Wendy B London, Robert C Seeger, Struan F A Grant, Hongzhe Li, Nazneen Rahman, Eric Rappaport, Hakon Hakonarson, John M Maris.   

Abstract

We conducted a SNP-based genome-wide association study (GWAS) focused on the high-risk subset of neuroblastoma. As our previous unbiased GWAS showed strong association of common 6p22 SNP alleles with aggressive neuroblastoma, we restricted our analysis here to 397 high-risk cases compared to 2,043 controls. We detected new significant association of six SNPs at 2q35 within the BARD1 locus (P(allelic) = 2.35 x 10(-9)-2.25 x 10(-8)). We confirmed each SNP association in a second series of 189 high-risk cases and 1,178 controls (P(allelic) = 7.90 x 10(-7)-2.77 x 10(-4)). We also tested the two most significant SNPs (rs6435862, rs3768716) in two additional independent high-risk neuroblastoma case series, yielding combined allelic odds ratios of 1.68 each (P = 8.65 x 10(-18) and 2.74 x 10(-16), respectively). We also found significant association with known BARD1 nonsynonymous SNPs. These data show that common variation in BARD1 contributes to the etiology of the aggressive and most clinically relevant subset of human neuroblastoma.

Entities:  

Mesh:

Substances:

Year:  2009        PMID: 19412175      PMCID: PMC2753610          DOI: 10.1038/ng.374

Source DB:  PubMed          Journal:  Nat Genet        ISSN: 1061-4036            Impact factor:   38.330


Neuroblastoma, one of the most common solid tumors in childhood, is characterized by diverse clinical phenotypes1. While a substantial proportion of patients may show a favorable outcome and may even have spontaneous regression of a localized, or even disseminated, tumor3,4, approximately 50% of cases show an aggressive clinical course with widespread metastases to bone and bone marrow at diagnosis1. These latter children have survival rates of less than 35% despite aggressive therapy with dose-intensive induction chemotherapy and surgery, followed by myeloablative therapy with stem cell rescue, local radiation therapy and biological response modification using retinoids and/or immunotherapy1,5,6. Our recent GWAS demonstrated that three common SNP alleles within the predicted genes FLJ22536 and FLJ44180 at chromosome band 6p22 were associated with neuroblastoma2. No other region of the genome contained SNPs that reached genome-wide significance and survived our replication effort. Of particular interest was the finding that not only were the three 6p22 SNPs associated with the likelihood of developing neuroblastoma, but patients who carried the 6p22 risk alleles were more likely to develop the clinically aggressive form of the disease and suffer tumor recurrence (dbGaP: http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000124.v1.p1)2. These data support the hypothesis that the benign and malignant forms of neuroblastoma may represent distinct entities in terms of the genetic events that initiate tumorigenesis. We therefore performed a second genome-wide analysis, this time limiting the cases to those patients with high-risk neuroblastoma as defined by the Children’s Oncology Group (COG)1. We identified 397 high-risk cases from the 1032 neuroblastoma patients included in the discovery set from our original study, and analyzed them against the same 2043 unaffected children from the discovery set control group. Quality control filters were applied to SNP genotype data as previously described2, resulting in a total of 462,866 autosomal SNPs available for analysis (see Supplementary Information). This analysis confirmed the three previously identified SNPs at chromosome band 6p22 (rs6939340, rs4712653, rs9295536) being significantly associated to high-risk neuroblastoma (Fig. 1; Pallelic = 3×10−11, 8×10−11, 6×10−10). These results in just 397 cases showed more highly significant P-values than those observed in the analysis of all 1032 cases with the identical control group2. In addition, we were able to identify a new association with common intronic SNPs at the BARD1 (BRCA1-associated RING domain-1) gene locus at chromosome band 2q35 (Fig. 1), with a total of six SNPs showing allelic test P-values less than 1×10−7 (Table 1, Fig. 2 and Supplementary Table 1). All BARD1 SNP P-values were minimally affected by correction for population stratification based on principal component analysis (Supplementary Table 1 and Supplementary Figure 1).
Figure 1

Summary of the neuroblastoma GWAS results in the discovery set restricted to the high-risk group of patients

The y-axis represents the level of significance for each SNP (log transformed P-values) at the relative genomic position (Build 35) on each chromosome along the x-axis from short arm terminus (left) to long arm terminus (right). The horizontal line indicates our threshold for follow-up analysis (P-value < 1×10−7). The previously reported 6p22 signal is more significant in this discovery case series enriched for the subset of 397 high-risk patients compared to the 1032 unselected neuroblastoma cases previously studied,2 and a new chromosome 2q35 signal emerged.

Table 1

Summary of discovery and replication results for six BARD1 SNPs with allelic P<10−7 in discovery case series.

SNP(bp position)Case-controlgroupAlleles*CasegenotypesControlgenotypesCaseMAFControlMAFPallelicPtrendPCMHORallelic(95% CI)ORhet(95% CI)OR hom(95% CI)ORCMH(95% CI)
rs6435862AT/G149/177/711015/850/1760.400.292.35×10−93.29×10−91.61 (1.38–1.88)1.42 1.12–1.80)2.75 (1.99–3.80)
(215,498,052)B77/85/27642/458/780.370.261.51×10−51.58×10−51.65 (1.31–2.07)1.55 (1.11–2.15)2.89 (1.75–4.75)
C28/39/19397/329/480.450.272.16×10−61.41×−10−62.14 (1.55–2.95)1.68 (1.01–2.79)5.61 (2.91–10.80)
D40/39/1475/74/100.360.301.33×10−11.27×10−11.34 (0.91–1.97)0.99 (0.57–1.70)2.62 (1.07–6.44)
Combined8.65×10−181.68 (1.491.90)
rs3768716AA/G181/163/511192/738/1120.340.243.19×10−94.44×10−91.64 (1.39–1.93)1.45 (1.15–1.83)2.99 (2.08–4.32)
(215,461,300)B85/81/21712/424/420.330.221.39×10−68.28×10−71.78 (1.41–2.26)1.60 (1.15–2.22)4.19 (2.37–7.41)
C35/42/7452/302/280.330.232.56×10−31.55×10−31.68 (1.20–2.37)1.79 (1.12–2.88)3.23 (1.32–7.91)
D35/31/2888/62/90.460.251.05×10−66.31×10−62.56 (1.75–3.76)1.26 (0.70–2.25)7.82 (3.35–18.25)
Combined2.74×10−161.68 (1.481.91)
rs17487792AC/T181/163/501189/737/1110.330.245.66×10−97.48×10−91.63 (1.38–1.92)1.45 (1.15–1.83)2.96 (2.05–4.28)
(215,469,006)B85/82/21715/421/420.330.217.90×10−74.74×10−71.80 (1.42–2.28)1.64 (1.18–2.27)4.21 (2.38–7.44)
Combined3.11×10−141.68 (1.471.92)
rs6712055AT/C131/189/69960/859/2050.420.316.63×10−99.13×10−91.59 (1.36–1.86)1.61 (1.27–2.05)2.47 (1.77–3.42)
(215,492,410)B71/88/29574/510/920.390.302.77×10−42.25×10−41.52 (1.21–1.90)1.39 (0.99–1.95)2.55 (1.57–4.14)
Combined8.04×10−121.56 (1.371.78)
rs7587476AC/T168/169/571106/795/1290.360.269.66×10−91.03×10−81.60 (1.36–1.88)1.40 (1.11–1.76)2.91 (2.05–4.13)
(215,479,393)B82/81/25659/458/610.350.252.73×10−52.16×10−51.64 (1.30–2.06)1.42 (1.02–1.98)3.29 (1.96–5.53)
Combined1.22×10−121.61 (1.411.84)
rs6715570AC/T141/180/76948/899/1960.420.322.25×10−82.39×10−81.56 (1.33–1.82)1.35 (1.06–1.71)2.61 (1.89–3.58)
(215,498,946)B73/85/31609/476/930.390.281.99×10−52.29×10−51.63 (1.30–2.04)1.49 (1.07–2.08)2.78 (1.73–4.46)
Combined2.19×10−121.58 (1.391.79)

A total of 23 SNPs are located at the BARD1 locus on the HH550 SNP chip (Supplemental Table 1), and shown here are the six SNPs showing allelic P<10−7 in the discovery series, ranked according to their P-values. A. Discovery case series; B. CHOP replication case series; C. UK replication case series D. CCG replication case series. Breslow-Day tests for heterogeneity of the allelic odds-ratios among the case-control groups were not significant for all SNPs. All P-values for deviation from Hardy Weinberg equilibrium > 0.01 except SNP rs3768716 in control series C (P=0.01) and case series D (P=0.001).

Major/minor alleles. Pallelic: Fisher’s exact test on allele frequencies; Ptrend: Armitage-Cochran trend test on genotype frequencies; PCMH: Cochran-Mantel-Haenszel test for stratified tables on allele frequencies.

Figure 2

Regional plot of the BARD1 locus associated with high-risk neuroblastoma

Single marker results (−log10P-values) for association testing of all SNPs at the BARD1 locus with six SNPs showing P-values <1×10−7 indicated in relation to the BARD1 genomic structure (exons indicated by vertical boxes) and in relation to the r linkage disequilibrium structure at all BARD1 SNPs with MAF>0.05 from HapMap CEU data, generated with the use of Haploview software. The position of five additional nsSNPs subsequently genotyped is also indicated (rs7585356 located 3′ downstream of BARD1 was also genotyped but is not shown).

Associations for all SNPs with P-values < 1×10−7 in the discovery set from both the 2q35 and 6p22 regions showed replication in a second independent group of 189 high-risk cases and 1178 unaffected controls genotyped genome-wide (Table 1 for BARD1 SNPs and Pallelic = 0.010, 0.008, 0.005 for chromosome 6p22 SNPs rs6939340, rs4712653, rs9295536, respectively). In contrast, association with the BARD1 SNPs was not significant in 575 low- and intermediate-risk patients from the discovery series (P-values > 0.05), and just three of them reached nominal significance in the total 756 low- and intermediate-risk patients from the discovery and replication sets combined, the smallest allelic P-value being 0.003 for rs3768716 (Supplementary Table 2). We then tested the two most significantly associated BARD1 SNPs (rs6435862 and rs3768716) for association with high-risk neuroblastoma, utilizing the cases within our previous two separate independent case series from the United Kingdom (UK) and the US-based legacy Children’s Cancer Group (CCG) with a high-risk phenotype.2 Both BARD1 SNPs were found significantly associated to high-risk neuroblastoma in the UK case series, whereas only rs3768716 showed a significant allelic P-value in the smaller CCG case series (Table 1). Combining data from all four groups of cases and controls showed allelic odds-ratios for these two SNPs of 1.68 each, with P-values of 9×10−18 for rs6435862 and of 3×10−16 for rs3768716 (Table 1). The six genome-wide significant SNPs in the discovery phase are located in introns 1, 3 and 4 of BARD1 (Fig. 2). Pairwise linkage disequilibrium (LD) analysis showed that these SNPs are in relatively strong LD with each other (r2=0.47–0.96), but are not in LD with the non-significant SNPs elsewhere within BARD1 (Supplementary Fig. 2). Haplotype analysis on the six most significant SNPs in cases (586) and controls (3221) from CHOP discovery and replication sets combined revealed only seven haplotypes with frequency greater than 1% in both cases and controls, and only four with frequency greater than 2% (Supplementary Table 3). The most frequent haplotype was composed of all major alleles, and the second most frequent of all minor alleles, in both cases and controls. These were the only haplotypes with different frequencies in cases and controls (0.50 in cases and 0.60 in controls the first; 0.31 in cases and 0.21 in controls the second). These results are consistent with the high correlation observed among all SNPs in this region. Logistic regression analysis performed in the same cases and controls showed that a model including only rs3768716 explained the observed association as well as a model including any additional SNPs. It is therefore possible that a single variant in high LD with these SNPs explains all of the observed association, but regional resequencing will likely be required to address this definitively. BARD1 contains several known coding variants (http://www.ncbi.nlm.nih.gov/SNP/snp_ref.cgi?locusId=580)7–10, and it was possible that one or more of these may be disease causal. We therefore selected the three common non-synonymous SNPs (minor allele frequency [MAF] > 0.10) for genotyping; rs1048108 (P24S) in exon 1, rs2229571 (R378S) in exon 4, and rs2070094 (V507M) in exon 6 (Fig. 2). According to HapMap CEU data, these three coding SNPs are in strong LD with the neuroblastoma-associated intronic SNPs rs6744811 (r2=0.86), rs7557557 (r2=0.76), and rs7584646 (r2=0.75). In addition, we also selected two rare non-synonymous SNPs for additional genotyping because of previously reported associations with breast cancer;7,9,10 rs28997576 (C557S) in exon 7 and rs3738888 (R658C) in exon 10 (Fig. 2). Finally, we also genotyped the common SNP rs7585356 located 3′ downstream of BARD1 because of it being in LD with a SNP rs16852600 (r2=0.89) that was of borderline significance, and because of it being located in a highly conserved region of the genome (SNPseek; http://snp.wustl.edu/cgi-bin/SNPseek/index.cgi). These six SNPs were assayed by TaqMan®-based genotyping in a case series consisting of 540 high-risk neuroblastoma cases from the discovery and replication set, and 1142 controls from the replication set. Four of these SNPs showed statistically significant association with high-risk neuroblastoma and that these were in strong LD as there were only 6 observed haplotypes with an observed frequency > 2% (Pallelic ranging from 1×10−8 for rs2070094 to 3×10−6 for rs2229571; Table 2; Supplementary Table 4). However, none of the nsSNPs was more strongly associated with high-risk neuroblastoma than the GWAS intronic SNPs rs3768716 and rs6435862, which yielded allelic ORs of 1.82 (rs3768716: 95% CI: 1.56–2.13, P = 5×10−14; rs6435862: 95% CI: 1.57–2.11, P = 2×10−15), when analyzed in a comparable group of 586 cases (genome-wide discovery and replication cases combined) and 1178 controls (genome-wide replication controls). SNP rs28997576 was not polymorphic in our dataset, and association with rs3738888, which had a very low minor allele frequency, was not significant.
Table 2

Results for BARD1 regulatory and coding SNPs genotyped in CHOP cases and controls

SNP (bp position)RoleAA changeAlleles*Case genotypesControl genotypesCase MAFControl MAFPallelicPtrendORallelic (95% CI)ORhet (95% CI)ORhom (95% CI)
rs7585356 (215,417,812)3′ regulatory region-G/A333/188/22527/423/980.210.307.95×10−71.02×10−60.65 (0.55–0.77)0.70 (0.57–0.88)0.36 (0.22–0.58)
rs3738888 (215,420,670)Coding exon 10R658CC/T547/12/11059/20/00.010.013.86×10−14.00×10−11.35 (0.68–2.69)1.16 (0.56–2.39)5.81 (0.24–142.75)
rs28997576 (215,442,684)Coding exon 7C557SG/C567/−/−1109/−/−0.00.0-----
rs2070094 (215,457,761)Coding exon 6V507MG/A277/161/10 1398/326/28 20.340.441.21×10−89.03×10−70.64 (0.55–0.75)0.71 (0.56–0.91)0.52 (0.39–0.68)
rs2229571 (215,470,970)Coding exon 4R378SC/G139/278/14 5363/552/18 80.510.423.41×10−63.13×10−61.41 (1.22–1.63)1.32 (1.03–1.68)2.01 (1.50–2.70)
rs1048108 (215,499,730)Coding exon 1P24SC/T298/218/45448/488/15 50.270.371.43×10−72.50×10−70.66 (0.56–0.77)0.67 (0.54–0.83)0.44 (0.30–0.63)

Major/minor alleles. Pallelic: Fisher’s exact test on allele frequencies; Ptrend: Armitage-Cochran trend test on genotype frequencies

To assess the joint impact on disease risk of the genetic variants at chromosome regions 2q35 and 6p22, we estimated the two-locus genotype odds-ratios for the two most significant SNPs from each region (rs6435862 and rs9295536) in the CHOP discovery and replication sets combined (Table 3). Each locus independently contributed to disease risk with odds-ratios for carriers of 1 or 2 risk alleles relatively to non-carriers of 1.52 (95% CI: 1.03–2.24, P = 0.037) for rs6435862, of 1.75 (95% CI: 1.26–2.45, P = 0.001) for rs9295536, and of 2.99 (95% CI: 2.17–4.11, P = 3×10−13) for the two SNPs together. No significant interaction between the two SNPs was detected (P = 0.6).
Table 3

Estimates of high-risk neuroblastoma risk (odds-ratios) by genotype (number of risk alleles) at BARD1 (rs6435862) and 6p22 (rs9295536)

rs9295536
01 or 2
CasesControlsOR (95% CI)CasesControlsOR (95% CI)
rs6435862049541117711141.75 (1.26, 2.45)
1 or 2664811.52 (1.03, 2.24)29210802.99 (2.17, 4.11)

No significant interaction was detected between the two loci (P-value = 0.6).

Taken together, these data strongly support BARD1 as the second identified susceptibility locus to sporadically occurring neuroblastoma. Coupled with the recent discovery of highly penetrant mutations in the ALK oncogene as the major cause of hereditary neuroblastoma,11 the genetic basis of human neuroblastoma is now coming into focus. This report further confirms that common genomic variants are highly associated with this childhood cancer, and unlike our 6p22 discovery within putative transcripts of unknown function,2 these data clearly implicate a known and well characterized gene. BARD1 heterodimerizes with the familial breast cancer gene product BRCA1,12 and is considered to be essential for the latter genes known tumor suppressive function. Since pathogenic BRCA1 mutations interfere with heterodimerization to BARD1, it has been postulated that BARD1 may also function as a breast cancer susceptibility gene. However, while many studies have investigated the potential role of BARD1 in breast cancer susceptibility, there is currently no compelling evidence that DNA sequence alterations influence breast cancer pathogenesis and the locus has not emerged in breast cancer GWAS efforts.13–15 We now report the first definitive evidence that this gene is involved in cancer susceptibility. Ongoing studies are now focused on understanding the biological consequences of these SNP variations at the BARD1 locus in the developing sympathetic neuroblast, and how these influence malignant transformation. It is also important to emphasize that this work clearly demonstrates the power of having robust phenotypic information available in GWAS approaches to human disease. Because the 6p22 association was enriched in the more aggressive subset of neuroblastoma cases, we were able to focus a new discovery case series on this clinically important group of patients. It is clear that at a somatic level, a single cancer histology may represent multiple different genomic subsets. Our data suggest that genetic initiating events may predispose not only to cancer, but to a particular subphenotype of the disease, and thus to patient outcome. This may have implications for both screening and identifying critical pathways for targeted therapeutics.

METHODS

Subjects and neuroblastoma risk grouping

For genome-wide genotyping, cases were defined as a child diagnosed with neuroblastoma or ganglioneuroblastoma and registered through the Children’s Oncology Group (COG). The blood samples from the neuroblastoma cases were identified through the COG Neuroblastoma bio-repository for specimen collection at the time of diagnosis. The majority of specimens were annotated with clinical and genomic information that included: age at diagnosis, site of origin, disease stage by the International Neuroblastoma Staging System, INPC International Neuroblastoma Pathology Classification, MYCN oncogene copy number, and DNA index, and assignment to the low-, intermediate- or high-risk subsets based on these data as described.1 The COG definition of high-risk disease included patients greater than 12 months of age at diagnosis with metastatic (Stage 4) disease or Stage 3 disease and unfavorable histologic features and any patient with a tumor showing MYCN amplification EXCEPT for a completely resected (Stage 1) tumor, All cases were determined to be of European ancestry as determined by self-report or parental report. Of the 1032 cases used in our previously reported discovery case series,2 397 were identified as high-risk (575 low- or intermediate-risk, 60 unknown) and were used for the discovery case series in this report. Of the 409 cases in our previously reported initial replication effort,2 189 were identified as high-risk (182 low- or intermediate-risk, 30 unknown) and used for the initial replication effort here. Control subjects were recruited from the Philadelphia region through the Children’s Hospital of Philadelphia (CHOP) Health Care Network, including four primary care clinics and several group practices and outpatient practices that included well child visits. Eligibility criteria for control subjects were European ancestry as determined by self-report or parental report and no serious underlying medical disorder including cancer. We utilized the same 3414 control subjects as our prior report,2 with the same 2236 controls used in the discovery phase, and 1178 in the initial replication effort. Two additional case series were used for the purpose of replication, both subsets from our prior GWAS replication efforts.2 Of the 252 Caucasian neuroblastoma cases from the United Kingdom (UK), 86 met criteria for high-risk disease and were compared to 782 controls. Likewise, of the 96 unrelated Caucasian neuroblastoma studied previously from the US-based Children’s Cancer Group (CCG), all were high-risk and compared to 159 controls. Written informed consent was obtained for all participants and this study was approved by each participating center’s Institutional Review Board as well as the COG Scientific Council, COG Neuroblastoma Disease Committee and Cancer Therapy Evaluation Program (CTEP) at the NCI.

Genotyping and statistical methods

Since this analysis was a subset of a previously reported GWAS, we utilized identical methods for genome-wide genotyping, controlling for population substructure, and data analysis.2 Description of these methods are included in the Supplementary Information materials. The initial replication effort was with samples genotyped genome-wide on the same platform as the discovery case series. The subsequent two replication efforts focused on the two most significantly associated SNPs with genotyping by conventional TaqMan®assays. Finally, we performed additional PCR-based allelic discrimination assays of all nsSNPs and potential regulatory SNPs in LD with significant SNPs from the discovery series. To maximize power, we utilized all of the high-risk cases available at CHOP (N=586) by combining the discovery and initial replication sets, but utilized only the replication controls (N=1178) to contain costs while providing sufficient statistical power. The primary statistical tests for association in the discovery case series were carried out using the software package PLINK.16 We set 1×10−7 as the threshold for follow-up analysis, and single marker analyses for the genome-wide data were carried out using the χ2 test based on allele count differences and the Cochran-Armitage test for trends on genotype frequencies. Allelic odds ratios and the corresponding 95% confidence intervals were calculated for the association analyses. Association analyses were also performed after correction for substructure based on principal components analysis as implemented in Eigenstrat and as previously described2,17,18. Combined odds-ratios and p-values over the different datasets were calculated using the Cochran-Mantel-Haenszel test, and heterogeneity of the allelic odds-ratio was tested using the Breslow-Day test. Haplotypes were estimated from the unphased genotypes by means of the EM algorithm implemented in Haploview19. Interactions between variants at the 6p and 2q loci were tested by logistic regression analysis.
  19 in total

Review 1.  Is there more to BARD1 than BRCA1?

Authors:  Irmgard Irminger-Finger; Charles Edward Jefford
Journal:  Nat Rev Cancer       Date:  2006-05       Impact factor: 60.716

2.  Haploview: analysis and visualization of LD and haplotype maps.

Authors:  J C Barrett; B Fry; J Maller; M J Daly
Journal:  Bioinformatics       Date:  2004-08-05       Impact factor: 6.937

3.  A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.

Authors:  David J Hunter; Peter Kraft; Kevin B Jacobs; David G Cox; Meredith Yeager; Susan E Hankinson; Sholom Wacholder; Zhaoming Wang; Robert Welch; Amy Hutchinson; Junwen Wang; Kai Yu; Nilanjan Chatterjee; Nick Orr; Walter C Willett; Graham A Colditz; Regina G Ziegler; Christine D Berg; Saundra S Buys; Catherine A McCarty; Heather Spencer Feigelson; Eugenia E Calle; Michael J Thun; Richard B Hayes; Margaret Tucker; Daniela S Gerhard; Joseph F Fraumeni; Robert N Hoover; Gilles Thomas; Stephen J Chanock
Journal:  Nat Genet       Date:  2007-05-27       Impact factor: 38.330

4.  BARD1 variants Cys557Ser and Val507Met in breast cancer predisposition.

Authors:  Pia Vahteristo; Kirsi Syrjäkoski; Tuomas Heikkinen; Hannaleena Eerola; Kristiina Aittomäki; Karl von Smitten; Kaija Holli; Carl Blomqvist; Olli-Pekka Kallioniemi; Heli Nevanlinna
Journal:  Eur J Hum Genet       Date:  2006-02       Impact factor: 4.246

5.  Myeloablative megatherapy with autologous stem-cell rescue versus oral maintenance chemotherapy as consolidation treatment in patients with high-risk neuroblastoma: a randomised controlled trial.

Authors:  Frank Berthold; Joachim Boos; Stefan Burdach; Rudolf Erttmann; Günter Henze; Johann Hermann; Thomas Klingebiel; Bernhard Kremens; Freimut H Schilling; Martin Schrappe; Thorsten Simon; Barbara Hero
Journal:  Lancet Oncol       Date:  2005-09       Impact factor: 41.316

6.  Treatment of high-risk neuroblastoma with intensive chemotherapy, radiotherapy, autologous bone marrow transplantation, and 13-cis-retinoic acid. Children's Cancer Group.

Authors:  K K Matthay; J G Villablanca; R C Seeger; D O Stram; R E Harris; N K Ramsay; P Swift; H Shimada; C T Black; G M Brodeur; R B Gerbing; C P Reynolds
Journal:  N Engl J Med       Date:  1999-10-14       Impact factor: 91.245

7.  The BARD1 Cys557Ser polymorphism and breast cancer risk: an Australian case-control and family analysis.

Authors:  Sharon E Johnatty; Jonathan Beesley; Xiaoqing Chen; John L Hopper; Melissa C Southey; Graham G Giles; David E Goldgar; Georgia Chenevix-Trench; Amanda B Spurdle
Journal:  Breast Cancer Res Treat       Date:  2008-05-15       Impact factor: 4.872

Review 8.  Neuroblastoma.

Authors:  John M Maris; Michael D Hogarty; Rochelle Bagatell; Susan L Cohn
Journal:  Lancet       Date:  2007-06-23       Impact factor: 79.321

9.  The BARD1 Cys557Ser variant and breast cancer risk in Iceland.

Authors:  Simon N Stacey; Patrick Sulem; Oskar T Johannsson; Agnar Helgason; Julius Gudmundsson; Jelena P Kostic; Kristleifur Kristjansson; Thora Jonsdottir; Helgi Sigurdsson; Jon Hrafnkelsson; Jakob Johannsson; Thorarinn Sveinsson; Gardar Myrdal; Hlynur Niels Grimsson; Jon T Bergthorsson; Laufey T Amundadottir; Jeffrey R Gulcher; Unnur Thorsteinsdottir; Augustine Kong; Kari Stefansson
Journal:  PLoS Med       Date:  2006-07       Impact factor: 11.069

10.  SNP-SNP interactions in breast cancer susceptibility.

Authors:  Venüs Ummiye Onay; Laurent Briollais; Julia A Knight; Ellen Shi; Yuanyuan Wang; Sean Wells; Hong Li; Isaac Rajendram; Irene L Andrulis; Hilmi Ozcelik
Journal:  BMC Cancer       Date:  2006-05-03       Impact factor: 4.430

View more
  147 in total

Review 1.  Pediatric oncology.

Authors:  Andrew M Davidoff
Journal:  Semin Pediatr Surg       Date:  2010-08       Impact factor: 2.754

Review 2.  Genetically engineered murine models--contribution to our understanding of the genetics, molecular pathology and therapeutic targeting of neuroblastoma.

Authors:  Louis Chesler; William A Weiss
Journal:  Semin Cancer Biol       Date:  2011-09-21       Impact factor: 15.707

Review 3.  A compendium of genome-wide associations for cancer: critical synopsis and reappraisal.

Authors:  John P A Ioannidis; Peter Castaldi; Evangelos Evangelou
Journal:  J Natl Cancer Inst       Date:  2010-05-26       Impact factor: 13.506

4.  RNAi screen of the protein kinome identifies checkpoint kinase 1 (CHK1) as a therapeutic target in neuroblastoma.

Authors:  Kristina A Cole; Jonathan Huggins; Michael Laquaglia; Chase E Hulderman; Mike R Russell; Kristopher Bosse; Sharon J Diskin; Edward F Attiyeh; Rachel Sennett; Geoffrey Norris; Marci Laudenslager; Andrew C Wood; Patrick A Mayes; Jayanti Jagannathan; Cynthia Winter; Yael P Mosse; John M Maris
Journal:  Proc Natl Acad Sci U S A       Date:  2011-02-02       Impact factor: 11.205

5.  Racial and ethnic disparities in risk and survival in children with neuroblastoma: a Children's Oncology Group study.

Authors:  Tara O Henderson; Smita Bhatia; Navin Pinto; Wendy B London; Patrick McGrady; Catherine Crotty; Can-Lan Sun; Susan L Cohn
Journal:  J Clin Oncol       Date:  2010-11-22       Impact factor: 44.544

Review 6.  Family-based designs for genome-wide association studies.

Authors:  Jurg Ott; Yoichiro Kamatani; Mark Lathrop
Journal:  Nat Rev Genet       Date:  2011-06-01       Impact factor: 53.242

7.  Nervous system: Embryonal tumors: Neuroblastoma.

Authors:  Caileigh Pudela; Skye Balyasny; Mark A Applebaum
Journal:  Atlas Genet Cytogenet Oncol Haematol       Date:  2020-07

8.  Genome-wide association studies in cancer--current and future directions.

Authors:  Charles C Chung; Wagner C S Magalhaes; Jesus Gonzalez-Bosquet; Stephen J Chanock
Journal:  Carcinogenesis       Date:  2009-11-11       Impact factor: 4.944

9.  Mitochondrial DNA Haplogroups and Susceptibility to Neuroblastoma.

Authors:  Xiao Chang; Marina Bakay; Yichuan Liu; Joseph Glessner; Komal S Rathi; Cuiping Hou; Huiqi Qu; Zalman Vaksman; Kenny Nguyen; Patrick M A Sleiman; Sharon J Diskin; John M Maris; Hakon Hakonarson
Journal:  J Natl Cancer Inst       Date:  2020-12-14       Impact factor: 13.506

10.  Trans-population analysis of genetic mechanisms of ethnic disparities in neuroblastoma survival.

Authors:  Eric R Gamazon; Navin Pinto; Anuar Konkashbaev; Hae Kyung Im; Sharon J Diskin; Wendy B London; John M Maris; M Eileen Dolan; Nancy J Cox; Susan L Cohn
Journal:  J Natl Cancer Inst       Date:  2012-12-14       Impact factor: 13.506

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.