The objective was to include genes likely to be involved in therapy outcome. For many of these main candidate genes, genetic studies have already suggested or confirmed functional polymorphisms, but we also include other potential candidate genes/polymorphisms. The main focus of the current study was to include known functional polymorphisms candidate genes based on available literature. The genotyping panels have not been geared primarily to cover all main haplotypes for each gene, but rather to focus on functional SNPs as much as they are known. The purpose therefore is not primarily the discovery of new functional polymorphisms, but rather the assessment of the clinical impact of known ones. We anticipate that in the future we will be able to focus the genotyping panels even more on known functional SNPs, in an effort to develop clinically relevant biomarker panels. The approach takes into consideration that new candidate genes and polymorphisms continue to emerge [20] that need to be flexibly included in the genotyping panels.

We chose candidate SNPs that for the most part have been implicated in cancer biology and chemotherapy in more than one study. For the selected genes, we first surveyed recent reviews for known polymorphisms reported to be related to cancer risk or drug metabolism [12-14], and a lung cancer risk study targeting 250 SNPs in 101 genes [11]. We further searched PubMed for additional polymorphisms associated with cancer risk, revealing genes that are also likely to affect treatment outcome [21]. Lastly, we searched the NCBI dbSNP database for SNPs in the transcribed regions with > 5% minor allele frequency to capture the main haplotypes in genes where only 1–2 SNPs had been selected by the other methods. SNPs from dbSNP were frequent and fully validated by different research projects, such as HapMap project [22] and the NCI SNP500 Cancer project [23]. SNPs in high LD (D'>0.7) with another SNP already in the panel were generally excluded, although in some case we added such SNPs for the assays design, to assure that either one was represented in the panel design. For cytochrome P450 genes, we included the known functional polymorphisms from human allele nomenclature database for cytochrome P450 enzymes [24]. We also searched the UDP-glucuronosyltransferase (UGT) alleles nomenclature database [25] and NAT nomenclature database [26]. We also consulted various drug transporter databases, including the human membrane transporter database [27] and PharmGKB [28].

For the selected SNPs [see Additional file 1], either NCBI SNP reference cluster IDs (rs numbers) or SNP sequences were submitted to Applied Biosystems for the design of SNPlex panels following their proprietary selection algorithms. We separated the genes into different groups: drug metabolism and transport, DNA repair/apoptosis and cell cycle/cell growth/drug targets. DNA sequence surrounding a specific polymorphism must meet specific requirements for probe design, including but not limited to: A. genomic screening; the DNA sequence flanking the target SNP must be unique and not have more than 1 genomic alignment hit with 21 or more contiguous bases to ensure annealing specificity, and there is no second SNP nearby. B. The target sequences should have appropriate features for annealing efficiency. C. Pooling rules: stringent pooling rules are used to determine optimal multiplex composition. SNPlex panels and reagents were synthesized by Applied Biosystems.

Thirty nine blood DNA samples from chronic lymphocytic leukemia patients were collected by Dr. John Byrd following the institutional review board (IRB) protocol at the Ohio State University for a flavopiridol phase I clinical trials at The Ohio State University Comprehensive Cancer Center. In addition, 90 colorectal cancer samples were chosen from a series of 1262 consecutively accrued patients with colorectal carcinoma diagnosed in the main hospitals of Metropolitan Columbus, whose tumors did not show microsatellite instability, as described previously [29]. Control groups were obtained from previously genotyped cohorts where the same SNPs are accessible (HapMap and other datasets as indicated). The research protocol and consent form were approved by the institutional review board at each participating hospital, and all patients provided written informed consent.

SNPlex genotyping was carried out according to the manufacturer's suggested protocol with slight modifications to accommodate a manual procedure using 96-well plate (90 testing DNAs plus positive and no DNA template controls, and 4 wells for allelic ladders). The multi-step procedure has been previously described (Figure 1) [18,19]. Step 1, for DNA fragmentation, 40 nanogram genomic DNA (2 μl) was fragmented at 99°C for 10 min. Step 2 involved phosphorylation and ligation of allele-specific oligonucleotide (ASO, 2 for each SNP) ligation probes, locus-specific oligonucleotide (LSO, one for each SNP) ligation probes and linkers. SNPlex ASO and LSO ligation probes, universal linkers, dATP and oligonucleotide ligation assay (OLA) master mix containing DNA kinase and ligase were mixed and 3 μl were added to the fragmented DNA. The ligation probes and universal linkers were phosphorylated and then ligated based on the sequence specificity complementary to the genomic DNA template. Step 3, exonuclease digestion served to purify the ligation product. Lambda Exonuclease and Exonuclease I were used to remove unligated probes, universal linkers and genomic DNA. In Step 4, the purified ligation product was PCR amplified using one pair of universal primers, with one primer labeled by biotin. Step 5, the biotinylated PCR products are bound to 96-well streptavidin-coated plate (Sigma). The PCR products were then denatured and the unbounded strands were washed off. Step 6 consisted of hybridization with fluorescence labeled universal ZipChute probe. For 48 SNPs, 96 unique ZipChute probes were hybridized to the complimentary sequence within each allele-specific oligonucleotide (ASO) probes. Each ZipChute probe migrates differently due to a mobility modifier. Step 7, the bound ZipChute probes were eluted and analyzed on Applied Biosystems 3730 DNA Analyzer. Step 8, the elution profiles were analyzed by GeneMapper software (Applied Biosystems) to determine genotypes. With 48 SNPs per run, 48 capillaries in the Applied Biosystems 3730 DNA Analyzer, and up to three injections per hour, the throughput is 5–10,000 SNPs per hour.

SNaPshot was performed following a previously published procedure based on single nucleotide primer extension that has been successfully adapted to the Applied Biosystems 3730 DNA Analyzer [30]. A stretch of genomic DNA (50 to 150 base pairs) was amplified by PCR, and the genotype was measured by primer extension using fluorescently labeled terminator nucleotides. Three single nucleotide polymorphisms, rs42427 in the APC gene, rs1800392 in WRN, and rs2228000 in XPC, were multiplexed for the study. Three pairs of PCR primers were amplified simultaneously in 15 μl reactions using 2× ReadyMix™ Taq PCR Reaction Mix with MgCl₂ (Sigma, St. Louis, MO). For each SNP, 0.15 μl PCR forward and reverse primers (10 μM) were added to the PCR reactions. The amplification was carried out for 30 cycles starting with denaturation at 95°C for 30 s, and then primer annealing at 60°C for 1 min, followed by extension at 72°C for 1 min. The forward and reverse primers were as follows: for rs42427 in APC, 5'-CCCTCCAAATGAGTTAGCTGCT-3' and 5'-GCCTTCTGTAGGAATGGTATCTCG-3'; for rs222800 in XPC, 5'-GGAGCCATCGTAAGGACCCA-3' and 5'-TGCCTCTTTTACTGCTTGAAGAGC-3'; for SNP rs1800392 in WRN, 5'-GGTCCAACAATCATCTACTGTCCTT and 5'-TGATGAATGTCTTTCCTTGTGCTAAA-3'. After PCR amplification, the reactions were treated with Exonuclease I and Bacterial Antarctic Alkaline Phosphatase (New England Biolabs, Beverly, MA). For the primer extension, a gene-specific primer was designed with its 3'-end one base from the SNP position. The forward extension primers were as follows: for rs42427 in APC, 5'-TTTTTTTTTTTTTTTTTTCTGGAGAAGGAGTTAGAGGAGG (40 mer); for rs222800 in XPC, 5'-TAAGGACCCAAGCTTGCCAG-3' (20 mer), for SNP rs1800392 in WRN, 5'-TTTTTTTTCAAGTTACAGGTGAACTTAGGAAACT-3' (34 mer). SNaPshot reagent from Applied Biosystems was used to incorporate a single fluorescently labeled dideoxynucleotide into the 3' end of the primer directed by the DNA template. The primer extension reactions were analyzed using an Applied Biosystems 3730 capillary electrophoresis DNA instrument, and analyzed with GeneMapper 3.0 software (Applied Biosystems), with a throughput of 150 to 750 per hour (if multiplexed to 5 reactions). For CYP2D6, the multiplexed SNaPshot was carried out following a previously published protocol with slight modifications [31]. The forward PCR primer (5'-ATGGCAGCTGCCATACAATCCACCTG-3') was redesigned to analyze the promoter SNP rs1080985. The SNaPshot extension primer for rs1080985 was 5'-(T)₅₈CCTGGACAACTTGGAAGAACC-3'. A total of 12 polymorphims were analyzed in parallel, by designing extension primers that are separable by capillary electrophoresis.

The UGT1A1 dinucleotide repeat was genotyped according to previously designed PCR sequences and PCR conditions [32]. The forward primer sequence was 5'-GTCACGTGACACAGTCAAAC-3'. The reverse primer sequence was 5'-TTTGCTCCTGCCAGAGGTT-3' and FAM-labeled. The PCR products were analyzed using an Applied Biosystems 3730 DNA Analyzer.

Hardy-Weinberg equilibrium for each SNP was analyzed using HelixTree according to the manufacture's manual (Golden Helix, Inc. Bozeman, MT, USA).

We have designed cancer genotyping SNPlex panels, selecting genes involved in drug metabolism and transport, DNA repair and apoptosis, cell cycle/cell growth/drug targets. We have selected polymorphisms for genotyping along the following criteria: polymorphisms known to affect enzyme/transporter functions, and SNPs in transcribed genic regions and htSNPs with high abundance obtained from HapMap and other databases. We have selected 560 SNPs for 160 genes, ordered into different categories:

Transporters: ABCA1, ABCA2, ABCA3, ABCA9, ABCA10, MDR1/ABCB1, ABCB4, ABCB11, ABCC1, ABCC2, ABCC3, ABCC4, ABCC5, ABCC6, ABCG2/BCRP, ABCG5, ABCG8, SLC19A1 (RFC) and SLC21A6.

Phase I metabolism enzymes: CYP1A1, 1A2, 1B1, 2A6, 2B6, 2C8, 2C9, 2C18, 2C19, 2D6, CYP2E1, 3A4, 3A5, 17A1, DIA4/NQO1, EPHX1/EH, MPO and SOD2.

Phase II metabolism enzymes: GSTA1 GSTA2, GSTA4, GSTM1, GSTM3, GSTP1, GSTT1, GSTT2, NAT1, NAT2, SULT1A1, SULT1A2, TPMT, COMT, UGT1A1, UGT1A6, UGT1A7, UGT1A9 and UGT2B7.

DNA repair genes: ADPRT/PARP, ADPRTL1, APEX1/APE1, ATM, ATR, BARD1, BLM, BRCA1, BRCA2, CHEK2, ERCC2/XPD, ERCC4/XPF, ERCC5/XPG, FANCD2, LIG1, LIG3, LIG4, MGMT/AGT, MLH1, MPG, MSH2, MSH3, MSH6, MYH/MUTYH, NBS1, NT5E, OGG1, PCNA, PMS2, POLB, RAD23A, RAD51, RAD52, RAD54B, RAD9A, RECQL, WRN, XPA, XPC, XRCC1, XRCC2, XRCC3, XRCC4, XRCC5 and XRCC9/FANCG.

Drug targets, cell signaling, cell cycle and apoptosis related genes: DHFR, DPYD, TYMS, VKORC1, EGFR, ERBB2, FLT1 (VEGFR1), KDR (VEGFR2), FLT4 (VEGFR3), PDGFRA, PDGFRB, KIT, RET, CDA, BAX, CASP3, CASP8, CASP9, CASP10, CCND1, CCNH, CDK7, CDKN1A/p21, CDKN1B/p27, CDKN2A/p16, CDKN2B/p15, GADD45A, IRS2, MDM2, RB1, TERC/hTR, TERT, TP53, TP53BP1, TP53BP2, TP73, APC, NF1, NF2, HPC1, VHL, ECRG1, WT1, MEN1, SMAD2, SMAD4, TNFRSF10A, PTCH and CDH1.

Among the 560 SNPs, 432 SNPs (77%) were successfully designed to be included in the SNPlex panels [see Additional file 1]. The SNPs were divided into several groups so that a subset of the SNPlex panels might be sufficient for a specific research project.

• Drug metabolism and transports: 4 panels, 189 SNPs.

• DNA repair: 3 panels, 148 SNPs.

• Cell cycle/growth/apoptosis: 2 panels, 95 SNPs.

The selection of polymorphisms for this study included some redundancy to account for limitation of the SNPlex approach. Any polymorphisms that could not be included with the SNPlex panels were omitted, or if thought to be critical, targeted by alternative methods. For example, a majority of the SNPs that are not suitable for SNPlex genotyping can be genotyped by multiplexed SNaPshot assay (see multiplexed SNaPshot for CYP2D6 in this manuscript as an example). Similarly, small insertions/deletions and repeats can be amplified by PCR and the variants determined by PCR product size difference based on gel electrophoresis or capillary electrophoresis using fluorescent-labeled primers (see UGT1A1 promoter dinucleotide repeat polymorphism in this manuscript). Based on the sequence information and literature search, possible alternative methods for detection of these genetic variants are listed in Additional file 1.

We selected SNaPshot (Applied Biosystems), based on single base-pair extension, as a reliable reference genotyping method [33,34]. Three SNPs, rs1800392 in WRN gene, rs2228000 in XPC, and rs42427 in APC, were genotyped using multiplexed SNaPshot. For 74 colorectal cancer samples, the two methods produced identical results for all three SNPs. We monitored the quality of SNPlex genotyping for the three SNPs (Figure 2) to identify the output features indicative of obtaining robust results. The homozygous and heterozygous alleles were clearly separated into different clusters on the Cartesian plots (Figure 2). Similar precision of genotype calls was observed in other projects performed in our laboratory comparing the genotyping results generated from SNPlex, SNaPshot, or TaqMan real-time PCR genotyping (unpublished data). This is consistent with the precision rate that was reported by Applied Biosystems [18].

Examples of the most extensively studied polymorphisms with clinical relevance, such as cancer risk and cancer therapeutic response are summarized in Table 1, which includes 66 SNPs for 24 genes that can be genotyped by our SNPlex panels. We selected 15 SNPs based on their potential significance to cancer treatment response and cancer risk, to illustrate the SNPlex genotyping results (Figure 3). In each case, homozygous and heterozygous genotypes were clearly separated, and therefore, readily assigned.

SNPs excluded from SNPlex panels for genomic sequence and other types of polymorphisms, such as insertions/deletions and variable number tandem repeat (VNTR), can be genotyped by different approaches, including multiplexed SNaPshot, TaqMan PCR, sequencing and SYBR Green melting curve assays [35], to detect all genetic variants of interest for a specific project. Mutiplexed SNaPshot is a primer extension assay with fluorescent terminator dyes, followed by capillary electrophoresis (Applied Biosystems 3730 DNA Aanalyzer). For faster throughput, we multiplex SNaPshot (up to 12 SNPs per reaction) for CYP2D6, by using extension primers of varying length (Fig. 4), yielding a throughput of between 150 (single SNP) to 1,800 (multiplex) per hour. Moreover, base pair deletions or insertions can be detected (Fig 4, 1707 del T, 2549 del A, 2615 del AGA).

The UGT1A1 promoter dinucleotide repeat polymorphism, (TA)nTAA, was analyzed by capillary electrophoresis following PCR amplification. The repeats were amplified with unique flanking sequences on each side. The variation of dinucleotide repeat number leads to PCR products of different sizes that can be distinguished by capillary electrophoresis (Applied Biosystems 3730 DNA Analyzer, Figure 5).

Cytochrome P450's are Phase I drug metabolizing enzymes harboring numerous mutations. For example, the two most important allele variants of CYP2C9, CYP2C9*2 and CYP2C9*3, cause a poor metabolizer phenotype associated with adverse warfarin effects [12]. Figure 3A shows the genotyping results with rs1057910 (Fig. 3A) in CYP2C9*3.

CYP2D6 metabolizes many commonly used drugs and is one of the best studied cytochrome P450 enzymes, with numerous variant alleles designated *1 to *61. The incidence of CYP2D6 poor metabolizers, carrying two null alleles, is 5–10% of Caucasians, imparting increased risk of adverse reactions from drugs requiring 2D6 metabolism for elimination. Nearly 99% of poor metabolizers have any two of the following alleles: *3, *4, *5, *6, *7 *8 or *11 [12,46]. Our SNPlex panels include key polymorphisms for alleles *4, *7, *8 and *11. For example, SNP rs3892097 (1847G>A, Fig. 3B), a common SNP in CYP2D6*4A to *4N, causes splicing defect [47], and it accounts for more than 75% of poor metabolizers in Caucasian [46]. CYP2D6*5 is a deletion of the entire functional CYP2D6 gene. In alleles *3 and *6, single nucleotide deletions causing CYP2D6 protein reading frame shift are undetectable by SNPlex genotyping. Multiplexed SNaPshot is complementary to SNPlex and can detect alleles *3 and *6 [31]. In addition, 4 SNPs, rs1065852, rs28371706, rs3892097 and rs16947, overlap in SNPlex panels and multiplexed SNaPshot, serving as a quality control. CYP2D6 catalyzes the conversion of tamoxifen to more potent metabolites, and poor CYP2D6 enzymatic activity has been associated with tamoxifen treatment outcome [12].

SNP rs776746 in CYP3A5 (6986A>G, CYP3A5*3, Fig. 3D) causes aberrantly spliced mRNA that is unstable, resulting in severely decreases protein level in the liver. The CYP3A5*3 allele frequency is approximately 90% in Caucasians [12].

Dihydropyrimidine dehydrogenase (DPYD) is a rate-limiting phase I metabolizing enzyme for 5-FU inactivation in the liver. SNP rs3918290 (Fig. 3E) is located at an RNA splicing donor site, causing DPYD exon 14 skipping (deletion) and leading to inactive enzyme. DPYD deficiency conveys risk for severe, life-threatening 5-FU toxicity [13].

For GSTP1 I105V (rs947894, Fig. 3F), Val/Val homozygotes express lower enzyme activity and decreased clearance rate of chemotherapeutic compounds, which leads to an increased survival following 5FU/oxaliplatin treatment of colorectal cancer patients. A better survival was also observed for breast cancer patients following treatment [12].

Polymorphisms affecting acetylator phenotype are common genetic variants for the biotransformation of drugs and carcinogens. N-acetyltransferase 2 (NAT2) polymorphisms are among the best studied examples in pharmacogenetics (Table 1 and Fig. 3G, rs1801280). These polymorphisms affect enzyme activity and are associated with drug toxicity and increased risk to develop certain cancers [48].

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) mediates glucocuronidation of bilirubin and anticancer drugs, such as SN38 (active irinotecan metabolite with antitumor activity). The UGT1A1*28 (promoter (TA)₆TAA to (TA)₇TAA) is a common genetic variant reducing UGT1A1 activity associated with irinotecan toxicity and hyperbilirubinaemia. Since this is a dinucleotide repeat variation, it is not suitable for detection with SNPlex. Fluorescently labeled PCR was designed to amplify the repeat and flanking DNA region. The repeat number was determined by the PCR product length (Figure 5). The SNPs in UGT1A1*6, *7, *27 and *62 are in the SNPlex panels (Table 1 and Fig. 3H).

ABCB1/Multidrug resistance (MDR1) transporter is an efflux pump. High expression of MDR1 conveys resistance to a number of chemotherapeutic agents, including paclitaxel, doxorubicin and irinotecan [49]. C3435T (rs1045642, Fig. 3I) is a synonymous SNP without amino acid change. However, the T allele has been reported to affect RNA stability [33] and possibly translation [50] and lead to decreased protein expression. Nevertheless, varying results have been reported about the effects of MDR1 polymorphisms on pharmacokinetics and pharmacodynamics [12,13]; possibly, the functional polymorphism(s) behave differently in different tissues.

ABCG2 is another extrusion transporter that renders chemoresistance to a variety of anticancer drugs, such as mitoxantrone, methotrexate, doxorubicin and camptothecin-based anticancer drugs [49]. The minor allele of rs2231142 (Q141K, Fig. 3J) is associated with decreased protein expression and results in hypersensitivity to anticancer drugs in caner cell lines [12].

BRCA2 N372H (rs144848, Fig. 3K), XRCC1 R399Q (rs25487, Fig. 3L), and OGG1 S326C (rs1052133, Fig. 3M) are three SNPs in DNA repair genes consistently associated with cancer risk, supported by thirty studies [51]. In addition, the ERCC2/XPD variant Lys751Gln (rs13181, Fig. 3N) was associated with the response to treatment with 5-fluorouracil and oxaliplatin in colorectal cancer patients. Lys/Lys homozygotes responded better and had longer survival time [52]. However, contradictory results were observed for cisplatin treatment of non-small cell lung cancer patients [13].

5,10-methylenetrtrahydrofolate reductase (MTHFR), a key enzyme in folate metabolism, catalyzes the conversion of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, which is involved in DNA and protein synthesis as a methyl donor [1]. SNP rs1801133 (C677T, A222V, Fig. 3O) in MTHFR is a functional variant associated with reduced MTHFR enzyme activity in TT homozygotes compared with heterozygots. As a result, the polymorphism increases toxicity to methotrexate [53].

In summary, the selected SNPs have broad applications for cancer research. Furthermore, the SNP panels are not limited to genes involved in cancer treatment outcomes with current drugs in clinical use. Hence, the developed SNPlex panels are not only applicable to studying pharmacogenomics/genetics of novel anticancer compounds under development, but also any drugs for the treatment of other diseases that are metabolized and/or transported by these gene products.

SNPlex has the advantage of being flexible and expandable for different studies, critical for translational research applications, including clinical drug trials. With the implementation of this platform, we have established a pharmacogenomics core with specific application to cancer chemotherapy. We hypothesize that genotyping on a large scale, both with respect to number of polymorphisms and subject populations, will yield valuable information on treatment outcomes. This concept will be applied to Phase I and II clinical trials with novel drugs or drug combinations, in comparison to pharmacokinetic analyses. Availability of population data across all subjects, collected over several years, will support multiple studies and has the potential to reveal novel mechanisms affecting drug response.