Fast and efficient high-throughput techniques are essential for the molecular diagnosis of highly heterogeneous hereditary diseases, such as retinitis pigmentosa (RP). We had previously approached RP genetic testing by devising a chip based on co-segregation analysis for the autosomal recessive forms. In this study, we aimed to design a diagnostic tool for all the known genes (40 up to now) responsible for the autosomal dominant and recessive RP and Leber congenital amaurosis (LCA). This new chip analyzes 240 single nucleotide polymorphisms (SNPs) (6 per gene) on a high-throughput genotyping platform (SNPlex, Applied Biosystems), and genetic diagnosis is based on the co-segregation analysis of SNP haplotypes in independent families. In a single genotyping step, the number of RP candidates to be screened for mutations is considerably reduced, and in the most informative families, all the candidates are ruled out at once. In a panel of RP Spanish pedigrees, the disease chip became a crucial tool for selecting those suitable for genome-wide RP gene search, and saved the burdensome direct mutational screening of every known RP gene. In a large adRP family, the chip allowed ruling out of all but the causative gene, and identification of an unreported null mutation (E181X) in PRPF31. Finally, on the basis of the conservation of the SNP haplotype linked to this pathogenic variant, we propose that the E181X mutation spread through a cohort of geographically isolated families by a founder effect.
co-segregation; SNP genotyping; RP; LCA; mutation; founder effect
Genotyping of single-nucleotide polymorphisms (SNPs) is a fundamental technology in modern genetics. The SNPlex™ mid-throughput genotyping system (Applied Biosystems, Foster City, CA, USA) enables the multiplexed genotyping of up to 48 SNPs simultaneously in a single DNA sample. The high level of automation and the large amount of data produced in a high-throughput laboratory require advanced software tools for quality control and workflow management.
We have developed two programs, which address two main aspects of quality control in a SNPlex™ genotyping environment: GMFilter improves the analysis of SNPlex™ plates by removing wells with a low overall signal intensity. It enables scientists to automatically process the raw data in a standardized way before analyzing a plate with the proprietary GeneMapper software from Applied Biosystems. SXTestPlate examines the genotype concordance of a SNPlex™ test plate, which was typed with a control SNP set. This program allows for regular quality control checks of a SNPlex™ genotyping platform. It is compatible to other genotyping methods as well.
GMFilter and SXTestPlate provide a valuable tool set for laboratories engaged in genotyping based on the SNPlex™ system. The programs enhance the analysis of SNPlex™ plates with the GeneMapper software and enable scientists to evaluate the performance of their genotyping platform.
Using the relative expression levels of two SNP alleles of a gene in the same sample is an effective approach for identifying cis-acting regulatory SNPs (rSNPs). In the current study, we established a process for systematic screening for cis-acting rSNPs using experimental detection of AI as an initial approach. We selected 160 expressed candidate genes that are involved in cancer and anticancer drug resistance for analysis of AI in a panel of cell lines that represent different types of cancers and have been well characterized for their response patterns against anticancer drugs. Of these genes, 60 contained heterozygous SNPs in their coding regions, and 41 of the genes displayed imbalanced expression of the two cSNP alleles. Genes that displayed AI were subjected to bioinformatics-assisted identification of rSNPs that alter the strength of transcription factor binding. rSNPs in 15 genes were subjected to electrophoretic mobility shift assay, and in eight of these genes (APC, BCL2, CCND2, MLH1, PARP1, SLIT2, YES1, XRCC1) we identified differential protein binding from a nuclear extract between the SNP alleles. The screening process allowed us to zoom in from 160 candidate genes to eight genes that may contain functional rSNPs in their promoter regions.
We developed the SNPlex Genotyping System to address the need for accurate genotyping data, high sample throughput, study design flexibility, and cost efficiency. The system uses oligonucleotide ligation/polymerase chain reaction and capillary electrophoresis to analyze bi-allelic single nucleotide polymorphism genotypes. It is well suited for single nucleotide polymorphism genotyping efforts in which throughput and cost efficiency are essential. The SNPlex Genotyping System offers a high degree of flexibility and scalability, allowing the selection of custom-defined sets of SNPs for medium- to high-throughput genotyping projects. It is therefore suitable for a broad range of study designs. In this article we describe the principle and applications of the SNPlex Genotyping System, as well as a set of single nucleotide polymorphism selection tools and validated assay resources that accelerate the assay design process. We developed the control pool, an oligonucleotide ligation probe set for training and quality-control purposes, which interrogates 48 SNPs simultaneously. We present performance data from this control pool obtained by testing genomic DNA samples from 44 individuals. in addition, we present data from a study that analyzed 521 SNPs in 92 individuals. Combined, both studies show the SNPlex Genotyping system to have a 99.32% overall call rate, 99.95% precision, and 99.84% concordance with genotypes analyzed by TaqMan probe–based assays. The SNPlex Genotyping System is an efficient and reliable tool for a broad range of genotyping applications, supported by applications for study design, data analysis, and data management.
Single nucleotide polymorphism; genotyping; high throughput; pharmacogenomics
Rapid response to chemotherapy in metastatic colorectal cancer (mCRC) patients (response within 12 weeks of chemotherapy) may increase the chance of complete resection and improved survival. Few molecular markers predict irinotecan-induced rapid response and survival. Single-nucleotide polymorphisms (SNPs) in solute carrier genes are reported to correlate with the variable pharmacokinetics of irinotecan and folate in cancer patients. This study aims to evaluate the predictive role of 3 SNPs in mCRC patients treated with irinotecan and fluoropyrimidine-containing regimens.
Materials and Methods
Three SNPs were selected and genotyped in 137 mCRC patients from a Chinese prospective multicenter trial (NCT01282658). The chi-squared test, univariate and multivariable logistic regression model, and receiver operating characteristic analysis were used to evaluate correlations between the genotypes and rapid response. Kaplan-Meier survival analysis and Cox proportional hazard models were used to evaluate the associations between genotypes and survival outcomes. Benjamini and Hochberg False Discovery Rate correction was used in multiple testing
Genotype GA/AA of SNP rs2306283 of the gene SLCO1B1 and genotype GG of SNP rs1051266 of the gene SLC19A1 were associated with a higher rapid response rate (odds ratio [OR] =3.583 and 3.521, 95%CI =1.301-9.871 and 1.271-9.804, p=0.011 and p=0.013, respectively). The response rate was 70% in patients with both genotypes, compared with only 19.7% in the remaining patients (OR = 9.489, 95%CI = 2.191-41.093, Fisher's exact test p=0.002). Their significances were all maintained even after multiple testing (all pc < 0.05). The rs2306283 GA/AA genotype was also an independent prognostic factor of longer progression-free survival (PFS) (hazard ratio = 0.402, 95%CI = 0.171-0.945, p=0.037). None of the SNPs predicted overall survival.
Polymorphisms of solute carriers’ may be useful to predict rapid response to irinotecan plus fluoropyrimidine and PFS in mCRC patients.
Interactions between genetic variants and risk factors in myelodysplastic syndromes are poorly understood. In this case–control study, we analyzed 1 421 single nucleotide polymorphisms in 408 genes involved in cancer-related pathways in 198 patients and 292 controls.
The Illumina SNP Cancer Panel was used for genotyping of samples. The chi-squared, p-values, odds ratios and upper and lower limits of the 95% confidence interval were calculated for all the SNPs that passed the quality control filtering.
Gene-based analysis showed nine candidate single nucleotide polymorphisms significantly associated with the disease susceptibility (q-value < 0.05). Four of these polymorphisms were located in oxidative damage/DNA repair genes (LIG1, RAD52, MSH3 and GPX3), which may play important roles in the pathobiology of myelodysplastic syndromes. Two of nine candidate polymorphisms were located in transmembrane transporters (ABCB1 and SLC4A2), contributing to individual variability in drug responses and patient prognoses. Moreover, the variations in the ROS1 and STK6 genes were associated with the overall survival of patients.
Our association study identified genetic variants in Czech population that may serve as potential markers for myelodysplastic syndromes.
Myelodysplastic syndromes; SNP; DNA repair; Association study
The recent development of new high-throughput technologies for SNP genotyping has opened the possibility of taking a genome-wide linkage approach to the search for new candidate genes involved in heredity diseases. The two major breast cancer susceptibility genes BRCA1 and BRCA2 are involved in 30% of hereditary breast cancer cases, but the discovery of additional breast cancer predisposition genes for the non-BRCA1/2 breast cancer families has so far been unsuccessful.
In order to evaluate the power improvement provided by using SNP markers in a real situation, we have performed a whole genome screen of 19 non-BRCA1/2 breast cancer families using 4720 genomewide SNPs with Illumina technology (Illumina's Linkage III Panel), with an average distance of 615 Kb/SNP. We identified six regions on chromosomes 2, 3, 4, 7, 11 and 14 as candidates to contain genes involved in breast cancer susceptibility, and additional fine mapping genotyping using microsatellite markers around linkage peaks confirmed five of them, excluding the region on chromosome 3. These results were consistent in analyses that excluded SNPs in high linkage disequilibrium. The results were compared with those obtained previously using a 10 cM microsatellite scan (STR-GWS) and we found lower or not significant linkage signals with STR-GWS data compared to SNP data in all cases.
Our results show the power increase that SNPs can supply in linkage studies.
Single nucleotide polymorphisms (SNPs) have emerged as the genetic marker of choice for mapping disease loci and candidate gene association studies, because of their high density and relatively even distribution in the human genomes. There is a need for systems allowing medium multiplexing (ten to hundreds of SNPs) with high throughput, which can efficiently and cost-effectively generate genotypes for a very large sample set (thousands of individuals). Methods that are flexible, fast, accurate and cost-effective are urgently needed. This is also important for those who work on high throughput genotyping in non-model systems where off-the-shelf assays are not available and a flexible platform is needed.
We demonstrate the use of a nanofluidic Integrated Fluidic Circuit (IFC) - based genotyping system for medium-throughput multiplexing known as the Dynamic Array, by genotyping 994 individual human DNA samples on 47 different SNP assays, using nanoliter volumes of reagents. Call rates of greater than 99.5% and call accuracies of greater than 99.8% were achieved from our study, which demonstrates that this is a formidable genotyping platform. The experimental set up is very simple, with a time-to-result for each sample of about 3 hours.
Our results demonstrate that the Dynamic Array is an excellent genotyping system for medium-throughput multiplexing (30-300 SNPs), which is simple to use and combines rapid throughput with excellent call rates, high concordance and low cost. The exceptional call rates and call accuracy obtained may be of particular interest to those working on validation and replication of genome- wide- association (GWA) studies.
Variations in genes related to anticancer drugs' biologic activity could influence treatment responses and lung cancer prognosis. Genetic variants in four biological pathways, i.e., glutathione metabolism, DNA repair, cell cycle, and EGFR, were systematically investigated to examine their association with survival in advanced-stage NSCLC treated with chemotherapy.
A total of 894 tagging single-nucleotide polymorphisms (tagSNPs) in 70 genes from the four pathways were genotyped and analyzed in a 1076-patient cohort. Association with overall survival was analyzed at single-SNP and whole-gene levels within all patients and major chemotherapy agent combination groups.
A poorer overall survival was observed in patients with genetic variations in GSS (glutathione pathway) and MAP3K1 (EGFR pathway) (HR=1.45, 95% CI=1.20–1.70 and HR=1.25, 95% CI=1.05–1.50, respectively). In stratified analysis on patients receiving platinum plus taxane treatment, we observed a hazardous effect on overall survival by MAP3K1 variant (HR=1.38, 95% CI =1.11–1.72) and a protective effect by RAF1 (HR=0.64, 95% CI=0.5–0.82) in the EGFR pathway. In patients receiving platinum plus gemcitabine treatment, RAF and GPX5 (glutathione pathway) genetic variations showed protective effects on survival (HR=0.54, 95% CI=0.38–0.77; HR=0.67, 95% CI=0.52–0.85, respectively); in contrast, NRAS (EGFR pathway) and GPX7 (glutathione pathway) variations showed hazardous effects on overall survival (HR=1.91, 95% CI=1.30–2.80; HR=1.83, 95% CI=1.27–2.63, respectively). All genes that harbored these significant SNPs remained significant by whole-gene analysis.
Common genetic variations in genes of EGFR and glutathione pathways may be associated with overall survival among patients with advanced-stage NSCLC treated with platinum, taxane, and/or gemicitabine combinations.
non-small cell lung cancer; survival; single-nucleotide polymorphisms; pathway; chemotherapy
Genetic polymorphisms contribute to interindividual variation in drug response. However, a single polymorphism is likely to exhibit a modest effect. Therefore, we applied a pathway-based approach to evaluate the cumulative effect of multiple polymorphisms on clinical outcome of patients with non-small cell lung cancer (NSCLC).
We genotyped 25 functional polymorphisms in 16 key genes involved in cisplatin metabolism and action and evaluated their associations with overall survival in 229 NSCLC patients receiving first-line cisplatin-based chemotherapy.
Several biologically plausible main effects were identified in individual analysis. More importantly, when 6 polymorphisms in nucleotide excision repair genes were analyzed jointly, a significant trend of reduced risk of death with decreasing number of putative unfavorable genotypes was observed (P for trend <0.001 and log-rank p<0.001). Survival tree analysis revealed potential higher-order gene-gene interactions and categorized subgroups with dramatically different survival experiences, based on distinct genotype profiles. The median survival time was 78.5 months for terminal node 1 in the low-risk group, 15.1 months for terminal node 10 in the medium-risk group, and 6.7 months for terminal node 9 in the high-risk group (log rank P<0.001). We also constructed a prediction hazard model. The area under the curve (AUC) increased from 0.71 (using clinical variables only) to 0.84 (using clinical, epidemiological, and genetic variations from survival tree analysis).
Our results highlight the clinical potential of taking a pathway-based approach and using survival tree analytic approach to identify subgroups of patients with distinctly differing outcomes.
High-throughput SNP genotyping platforms use automated genotype calling algorithms to assign genotypes. While these algorithms work efficiently for individual platforms, they are not compatible with other platforms, and have individual biases that result in missed genotype calls. Here we present data on the use of a second complementary SNP genotype clustering algorithm. The algorithm was originally designed for individual fluorescent SNP genotyping assays, and has been optimized to permit the clustering of large datasets generated from custom-designed Affymetrix SNP panels. In an analysis of data from a 3K array genotyped on 1,560 samples, the additional analysis increased the overall number of genotypes by over 45,000, significantly improving the completeness of the experimental data. This analysis suggests that the use of multiple genotype calling algorithms may be advisable in high-throughput SNP genotyping experiments. The software is written in Perl and is available from the corresponding author.
clustering; SNP genotyping; algorithm
To perform a comprehensive evaluation of association of common genetic variants in candidate genes in the dopaminergic pathway with schizophrenia in a sample from Croatian population.
A case-control association study was performed on 104 unrelated patients with schizophrenia recruited from a psychiatric hospital in Zagreb and 131 phenotypically normal Croatian subjects. Forty-nine tagging single nucleotide polymorphisms (tagSNPs) in 8 candidate genes in the dopaminergic pathway were identified from the HapMap database and tested for association. Genotyping was performed using the SNPlex platform. Statistical analysis was conducted to assess allelic and genotypic associations between cases and controls using a goodness of fit χ2 test and trend test, respectively; adjustment for multiple testing was done by permutation based analysis.
Significant allele frequency differences between schizophrenia cases and controls were observed at 4 tagSNPs located in the genes DRD5, HTR1B1, DBH, and TH1 (P < 0.005). A trend test also confirmed the genotypic association (P < 0.001) of these 4 tagSNPs. Additionally, moderate association (P < 0.05) was observed with 8 tagSNPs on SLC6A3, DBH, DRD4, SLC6A4, and COMT.
Common genetic variants in genes involved in the dopaminergic pathway are associated with schizophrenia in the populations of Caucasian descent.
Imputation is a statistical process used to predict genotypes of loci not directly assayed in a sample of individuals. Our goal is to measure the performance of imputation in predicting the genotype of the best known gene polymorphisms involved in drug metabolism using a common SNP array genotyping platform generally exploited in genome wide association studies.
Thirty-nine (39) individuals were genotyped with both Affymetrix Genome Wide Human SNP 6.0 (AFFY) and Affymetrix DMET Plus (DMET) platforms. AFFY and DMET contain nearly 900000 and 1931 markers respectively. We used a 1000 Genomes Pilot + HapMap 3 reference panel. Imputation was performed using the computer program Impute, version 2. SNPs contained in DMET, but not imputed, were analysed studying markers around their chromosome regions. The efficacy of the imputation was measured evaluating the number of successfully imputed SNPs (SSNPs).
The imputation predicted the genotypes of 654 SNPs not present in the AFFY array, but contained in the DMET array. Approximately 1000 SNPs were not annotated in the reference panel and therefore they could not be directly imputed. After testing three different imputed genotype calling threshold (IGCT), we observed that imputation performs at its best for IGCT value equal to 50%, with rate of SSNPs (MAF > 0.05) equal to 85%.
Most of the genes involved in drug metabolism can be imputed with high efficacy using standard genome-wide genotyping platforms and imputing procedures.
Until recently, only a small number of low- and mid-throughput methods have been used for single nucleotide polymorphism (SNP) discovery and genotyping in grapevine (Vitis vinifera L.). However, following completion of the sequence of the highly heterozygous genome of Pinot Noir, it has been possible to identify millions of electronic SNPs (eSNPs) thus providing a valuable source for high-throughput genotyping methods.
Herein we report the first application of the SNPlex™ genotyping system in grapevine aiming at the anchoring of an eukaryotic genome. This approach combines robust SNP detection with automated assay readout and data analysis. 813 candidate eSNPs were developed from non-repetitive contigs of the assembled genome of Pinot Noir and tested in 90 progeny of Syrah × Pinot Noir cross. 563 new SNP-based markers were obtained and mapped. The efficiency rate of 69% was enhanced to 80% when multiple displacement amplification (MDA) methods were used for preparation of genomic DNA for the SNPlex assay.
Unlike other SNP genotyping methods used to investigate thousands of SNPs in a few genotypes, or a few SNPs in around a thousand genotypes, the SNPlex genotyping system represents a good compromise to investigate several hundred SNPs in a hundred or more samples simultaneously. Therefore, the use of the SNPlex assay, coupled with whole genome amplification (WGA), is a good solution for future applications in well-equipped laboratories.
With three available chemotherapy drugs for advanced colorectal cancer (CRC), response rate (RR) and survival outcomes have improved with associated morbidity, accentuating the need for tools to select optimal individualized treatment. Pharmacogenetics identifies the likelihood of adverse events or response based on variants in genes involved in drug transport, metabolism, and cellular targets.
Patients and Methods
Germline DNA was extracted from 520 patients on the North American Gastrointestinal Intergroup N9741 study. Three study arms were evaluated: IFL (fluorouracil [FU] + irinotecan [IRN]), FOLFOX (FU + oxaliplatin), and IROX (IRN + oxaliplatin). Information on adverse events, response, and disease-free survival was available. Thirty-four variants in 15 candidate genes for analysis based on previous associations with adverse events or outcome were assessed. Genotyping was performed using pyrosequencing.
All variants were polymorphic. The homozygous UGT1A1*28 allele observed in 9% of patients was associated with risk of grade 4 neutropenia in patients on IROX (55% v 15%; P = .002). Deletion in GSTM1 was associated with grade 4 neutropenia after FOLFOX (28% v 16%; P = .02). Patients with a homozygous variant genotype for GSTP1 were more likely to discontinue FOLFOX because of neurotoxicity (24% v 10%; P = .01). The presence of a CYP3A5 variant was significantly associated with RR on IFL (29% v 60%; P = .0074). Most previously published genotype-toxicity or -efficacy relationships were not validated in this study.
This study provides a platform to evaluate pharmacogenetic predictors of response or severe adverse events in advanced CRC. Pharmacogenetic studies can be conducted in multicenter trials, and our findings demonstrate that with continued research, clinical application is practical.
Individual genetic variations may have a significant influence on the survival of metastatic prostate cancer (PCa) patients. We aimed to identify target genes and their variations involved in the survival of PCa patients using a single nucleotide polymorphism (SNP) panel. A total of 185 PCa patients with bone metastasis at the initial diagnosis were analyzed. Germline DNA in each patient was genotyped using a cancer SNP panel that contained 1,421 SNPs in 408 cancer-related genes. SNPs associated with survival were screened by a log-rank test. Fourteen SNPs in 6 genes, XRCC4, PMS1, GATA3, IL13, CASP8, and IGF1, were identified to have a statistically significant association with cancer-specific survival. The cancer-specific survival times of patients grouped according to the number of risk genotypes of 6 SNPs selected from the 14 SNPs differed significantly (0-1 v. 2-3 v. 4-6 risk genotypes; P = 7.20 × 10−8). The high-risk group was independently associated with survival in a multivariate analysis that included conventional clinicopathological variables (P = 0.0060). We identified 14 candidate SNPs in 6 cancer-related genes, which were associated with poor survival in patients with metastatic PCa. A panel of SNPs may help predict the survival of those patients.
prostate cancer; bone metastasis; survival; single nucleotide polymorphism
Numerous studies have attempted to relate genetic polymorphisms within the N-acetyltransferase 2 gene (NAT2) to interindividual differences in response to drugs or in disease susceptibility. However, genotyping of individuals single-nucleotide polymorphisms (SNPs) alone may not always provide enough information to reach these goals. It is important to link SNPs in terms of haplotypes which carry more information about the genotype-phenotype relationship. Special analytical techniques have been designed to unequivocally determine the allocation of mutations to either DNA strand. However, molecular haplotyping methods are labour-intensive and expensive and do not appear to be good candidates for routine clinical applications. A cheap and relatively straightforward alternative is the use of computational algorithms. The objective of this study was to assess the performance of the computational approach in NAT2 haplotype reconstruction from phase-unknown genotype data, for population samples of various ethnic origin.
We empirically evaluated the effectiveness of four haplotyping algorithms in predicting haplotype phases at NAT2, by comparing the results with those directly obtained through molecular haplotyping. All computational methods provided remarkably accurate and reliable estimates for NAT2 haplotype frequencies and individual haplotype phases. The Bayesian algorithm implemented in the PHASE program performed the best.
This investigation provides a solid basis for the confident and rational use of computational methods which appear to be a good alternative to infer haplotype phases in the particular case of the NAT2 gene, where there is near complete linkage disequilibrium between polymorphic markers.
Background: The rs2736100 single nucleotide polymorphism (SNP) is located in the intron 2 of human telomerase reverse transcriptase (hTERT) gene. Recent genome-wide association studies (GWAS) have consistently supported the strong association between this SNP and risk for multiple cancers. Given the important role of the hTERT gene and this SNP in cancer biology, we hypothesize that rs2736100 may also confer susceptibility to anti-cancer drug sensitivity. In this study we aim to investigate the correlation between the rs2736100 genotype and the responsiveness to anti-cancer agents in the NCI-60 cancer cell panel.
Methods and Materials: The hTERT rs2736100 was genotyped in the NCI-60 cancer cell lines. The relative telomere length (RTL) of each cell line was quantified using real-time PCR. The genotype was then correlated with publically available drug sensitivity data of two agents with telomerase-inhibition activity: Geldanamycin (HSP90 inhibitor) and RHPS4/BRACO19 (G-quadruplex stabilizer) as well as additional 110 commonly used agents with established mechanism of action. The association between rs2736100 and mutation status of TP53 gene was also tested.
Results: The C allele of the SNP was significantly correlated with increased sensitivity to RHPS4/BRACO19 with an additive effect (r = −0.35, p = 0.009) but not with Geldanamycin. The same allele was also significantly associated with sensitivity to antimitotic agents compared to other agents (p = 0.003). The highest correlation was observed between the SNP and paclitaxel (r = −0.36, p = 0.005). The telomere length was neither associated with rs2736100 nor with sensitivity to anti-cancer agents. The C allele of rs2736100 was significantly associated with increased mutation rate in TP53 gene (p = 0.004).
Conclusion: Our data suggested that the cancer risk allele of hTERT rs2736100 polymorphism may also affect the cancer cell response to both TERT inhibitor and anti-mitotic agents, which might be attributed to the elevated telomerase-independent activity of hTERT, as well as the increased risk for TP53 gene mutagenesis conferred by the polymorphism. Detailed mechanisms need to be further investigated.
TERT; polymorphism; rs2736100; anticancer drug; sensitivity
We have developed and validated a consolidated bead-based genotyping platform, the Bioplex suspension array for simultaneous detection of multiple single nucleotide polymorphisms (SNPs) of the ATP-binding cassette transporters. Genetic polymorphisms have been known to influence therapeutic response and risk of disease pathologies. Genetic screening for therapeutic and diagnostic applications thus holds great promise in clinical management. The allele-specific primer extension (ASPE) reaction was used to assay 22 multiplexed SNPs for eight subjects. Comparison of the microsphere-based ASPE assay results to sequencing results showed complete concordance in genotype assignments. The Bioplex suspension array thus proves to be a reliable, cost-effective and high-throughput technological platform for genotyping. It can be easily adapted to customized SNP panels for specific applications involving large-scale mutation screening of clinically relevant markers.
Genotype; Microspheres; Polymorphism, Genetic
To determine if genetic variation in chemotherapy metabolism are associated with risk of ovarian failure in breast cancer patients after adjuvant chemotherapy.
Prospective cohort study.
Comprehensive cancer center.
Early stage breast cancer patients who were premenopausal at cancer diagnosis and treatment.
Main outcomes measures
Chemotherapy related ovarian failure (CROF)
127 breast cancer subjects who were premenopausal at cancer diagnosis and underwent cyclophosphamide-based chemotherapy were genotyped for 9 single nucleotide polymorphisms (SNPs) in enzymes involved in cyclophosphamide activation (CYP3A4, CYP2B6, CYP3A5) and detoxification (GSTA1, GSTM1, GSTP1, GSTT1). Median age at chemotherapy was 43.2 years. Median years of follow up since chemotherapy were 5.2 years. For the entire cohort, there was no significant association between CROF and SNPs. However, the association between CROF and SNPs was modified by age at chemotherapy. In subjects younger than 45 at chemotherapy, CYP3A4*1B variants had significantly longer time to CROF than CYP3A4*1A homozygotes in an adjusted multivariable Cox model (HR 0.25 [95% CI 0.07–0.9]). Age and tamoxifen use were also independently associated with CROF.
A common SNP in a cyclophosphamide drug metabolizing enzyme appears to be related to ovarian failure after cyclophosphamide-based chemotherapy in young women with breast cancer. Larger prospective studies to validate these results should be directed toward women less than 45 years of age at chemotherapy.
Ovarian failure; chemotherapy metabolism; genetic polymorphisms; breast cancer
Interferon gamma is a major macrophage-activating cytokine during infection with Mycobacterium tuberculosis, the causative pathogen of tuberculosis, and its role has been well established in animal models and in humans. This cytokine is produced by activated T helper 1 cells, which can best deal with intracellular pathogens such as M. tuberculosis. Based on the hypothesis that genes which regulate interferon gamma may influence tuberculosis susceptibility, we investigated polymorphisms in eight candidate genes.
Fifty-four polymorphisms in eight candidate genes were genotyped in over 800 tuberculosis cases and healthy controls in a population-based case-control association study in a South African population. Genotyping methods used included the SNPlex Genotyping System™, capillary electrophoresis of fluorescently labelled PCR products, TaqMan® SNP genotyping assays or the amplification mutation refraction system. Single polymorphisms as well as haplotypes of the variants were tested for association with TB using statistical analyses.
A haplotype in interleukin 12B was nominally associated with tuberculosis (p = 0.02), but after permutation testing, done to assess the significance for the entire analysis, this was not globally significant. In addition a novel allele was found for the interleukin 12B D5S2941 microsatellite.
This study highlights the importance of using larger sample sizes when attempting validation of previously reported genetic associations. Initial studies may be false positives or may propose a stronger genetic effect than subsequently found to be the case.
The large individual variability for anticancer drugs in both outcome and toxicity risk makes the identification of pharmacogenetic markers that can be used to screen patients before therapy selection an attractive prospect.
This work aimed to evaluate the importance of genetic polymorphisms involved in drug detoxification to predict clinical outcomes of anthracycline-based neoadjuvant chemotherapy for breast cancer.
GSTP1 313 AA genotype was associated with a poor clinical response relative to G allele carrier (58.4 vs. 80.8%; p = 0.006), and MDR1 3435 TT genotype had a worse response compared with C allele carrier (33.3 vs. 71.2% p = 0.001). Patients with both the adverse genotypes of GSTP1 313AA and MDR 3435TT showed the worst therapy efficacy in all (14.3%; p = 0.000). Kaplan-Meier survival analysis showed that the patients with no adverse genotype were associated with decreased hazard of relapse (p = 0.002), compared with those with 1 or 2 adverse genotypes. Multivariate analysis demonstrated that clinical response and no adverse genotype was independent predictors of disease-free survival (DFS).
Genotyping was performed by allele-specific oligonucleotide ligation reaction (MnSOD, CAT, GSTP1), multiplex PCR (GSTM1, GSTT1) or PCR-RFLP (MDR1). Based on 153 patients received anthracycline-based neoadjuvant chemotherapy, these genotypes or their combinations in relation to treatment-related response, hematologic toxicity and DFS were investigated.
These results suggest that polymorphisms in GSTP1 and MDR1 may help to predict clinical response and DFS of anthracycline-based chemotherapy, and a polygenic pathway approach should provide more useful information. The findings required independent prospective confirmation.
anthracycline; breast cancer; chemotherapy; clinical response; disease-free survival (DFS); individualizing treatment; polymorphism
Cumulative data has shown that microRNAs (miRNAs) are involved in the etiology and prognosis of colorectal cancer (CRC). Genetic polymorphisms in pre-miRNA genes may influence the biogenesis and functions of their host miRNAs. However, whether these polymorphisms are associated with CRC prognosis remains unknown.
We analyzed the effects of seven single nucleotide polymorphisms (SNPs) in pre-miRNA genes on the prognosis of a Chinese population with 408 CRC patients with surgically-resected adenocarcinoma.
Two SNPs were identified to be significantly associated with recurrence-free survival and overall survival of the patients. The most significant SNP was rs6505162 in pre-miR-423. Compared to the homozygous wild-type genotype, the variant-containing genotypes of this SNP were significantly associated with both the overall survival (HR=2.12, 95% CI1.34–3.34, P=0.001) and the recurrence-free survival (HR=1.59, 95% CI1.08–2.36, P=0.019). Another SNP, rs4919510 in pre-miR-608, was also associated with altered recurrence-free survival (HR=0.61, 95% CI 0.41–0.92, P=0.017). These effects were evident only in patients receiving chemotherapy but not in those without chemotherapy. In addition, the combined analysis of the two SNPs conferred a 2.84-fold (95% CI 1.50–5.37, P=0.001) increased risk of recurrence and/or death. Similarly, this effect was only prominent in those receiving chemotherapy (P<0.001) but not in those without chemotherapy (P=0.999).
Our data suggest that genetic polymorphisms in pre-miRNA genes may impact CRC prognosis especially in patients receiving chemotherapy, a finding that warrants further independent validation.
This is one of the first studies showing a prognostic role of pre-miRNA gene SNPs in CRC.
Polymorphism; microRNA; colorectal cancer
Inherited variability in the prognosis of lung cancer patients treated with platinum-based chemotherapy has been widely investigated. However, the overall contribution of genetic variation to platinum response is not well established. To identify novel candidate SNPs/genes, we performed a genome-wide association study (GWAS) for cisplatin cytotoxicity using lymphoblastoid cell lines (LCLs), followed by an association study of selected SNPs from the GWAS with overall survival (OS) in lung cancer patients.
GWAS for cisplatin were performed with 283 ethnically diverse LCLs. 168 top SNPs were genotyped in 222 small cell and 961 non-small cell lung cancer (SCLC, NSCLC) patients treated with platinum-based therapy. Association of the SNPs with OS was determined using the Cox regression model. Selected candidate genes were functionally validated by siRNA knockdown in human lung cancer cells.
Among 157 successfully genotyped SNPs, 9 and 10 SNPs were top SNPs associated with OS for patients with NSCLC and SCLC, respectively, although they were not significant after adjusting for multiple testing. Fifteen genes, including 7 located within 200 kb up or downstream of the four top SNPs and 8 genes for which expression was correlated with three SNPs in LCLs were selected for siRNA screening. Knockdown of DAPK3 and METTL6, for which expression levels were correlated with the rs11169748 and rs2440915 SNPs, significantly decreased cisplatin sensitivity in lung cancer cells.
This series of clinical and complementary laboratory-based functional studies identified several candidate genes/SNPs that might help predict treatment outcomes for platinum-based therapy of lung cancer.
Lung cancer; cisplatin; pharmacogenomics; lymphoblastoid cell lines; GWAS
Linkage Disequilibrium (LD) bin-tagging algorithms identify a reduced set of tag SNPs that can capture the genetic variation in a population without genotyping every single SNP. However, existing tag SNP selection algorithms for designing custom genotyping panels do not take into account all platform dependent factors affecting the likelihood of a tag SNP to be successfully genotyped and many of the constraints that can be imposed by the user.
SNPPicker optimizes the selection of tag SNPs from common bin-tagging programs to design custom genotyping panels. The application uses a multi-step search strategy in combination with a statistical model to maximize the genotyping success of the selected tag SNPs. User preference toward functional SNPs can also be taken into account as secondary criteria. SNPPicker can also optimize tag SNP selection for a panel tagging multiple populations. SNPPicker can optimize custom genotyping panels including all the assay-specific constraints of Illumina's GoldenGate and Infinium assays.
A new application has been developed to maximize the success of custom multi-population genotyping panels. SNPPicker also takes into account user constraints including options for controlling runtime. Perl Scripts, Java source code and executables are available under an open source license for download at http://mayoresearch.mayo.edu/mayo/research/biostat/software.cfm