| Literature DB >> 23208557 |
Shun-Ichi Suzuki1, Mariko Komori, Mitsuharu Hirai, Norio Ureshino, Shinya Kimura.
Abstract
Genetic testing prior to treatment, pharmacogenetic analysis, is key to realizing personalized medicine which is a topic that has attracted much attention recently. Through the optimization of therapy selection and dosage, a reduction in side effects is expected. Genetic testing has been conducted as a type of pharmacogenetic analysis in recent years, but it faces challenges in terms of cost effectiveness and its complicated procedures. Here we report on the development of a novel platform for genetic testing, the i-densy™, with the use of quenching probe system (QP-system) as principle of mutant detection. The i-densy™ automatically performs pre-treatment, PCR and detection to provide the test result from whole blood and extracted DNA within approximately 90 and 60 min, respectively. Integration of all steps into a single platform greatly reduces test time and complicated procedures. An even higher-precision genetic analysis has been achieved through the development of novel and highly-specific detection methods. The applications of items measured using the i-densy™ are diverse, from single nucleotide polymorphism (SNP), such as CYP2C19 and UGT1A1, to somatic mutations associated with cancer, such as EGFR, KRAS and JAK2. The i-densy™ is a useful tool for optimization of anticancer drug therapy and can contribute to personalized medicine.Entities:
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Year: 2012 PMID: 23208557 PMCID: PMC3571800 DOI: 10.3390/s121216614
Source DB: PubMed Journal: Sensors (Basel) ISSN: 1424-8220 Impact factor: 3.576
Figure 1.Measurement principle—Quenching Probe (QProbe). (a) QProbe quenches when hybridizing to the target nucleic acid. By detecting the change of fluorescence, it is possible to detect the existence of the target sequence. (b) A typical detection result using QProbe.
Figure 2.Measurement principle—Mutation biased PCR (MBP). (a) The primers for wild and mutant types are mixed with plasmid DNA, which leads to high specificity because each primer can be competitively hybridized to wild type and mutant type sequences. In addition, the length of primer for mutant type is longer than that for wild type, and annealing temperature was optimized to the mutant primer, resulting in higher amplification efficiency for the mutant type compared to the wild type. (b) Measurement results of the detection of T790M mutation in EGFR exon 20 using MBP-QP system.
Figure 3.Measurement principle—Wild Inhibition PCR (WIP). (a) DNA fragments from the wild type as well as primers for the wild and mutant types are mixed with plasmid DNA. Amplification of wild type sequence is inhibited by this DNA fragment. (b) Measurement results of the detection of in-frame deletion in EGFR exon 19 using WIP-QP system.
Figure 4.i-densy™—Fully integrated and automatic gene typing system. (a) i-densy™ has a compact design: 410 mm (width) by 450 mm (depth) by 415 mm (height). (b) Before measurement, the necessary number of tips, reaction tubes, and reagent packs are put in place. The instrument contains 4 independently programmable reaction sites. (c) When measurement is complete, measurement results are automatically printed out.
Gene items measured with the i-densy™.
| Cancer | EGFR | exon18 G719X | Drug efficacy prediction of gefitinib and erlotinib |
| exon19 deletion | |||
| exon20 T790M | |||
| exon21 L858R | |||
| KRAS | codon12, 13 | Drug efficacy prediction of cetuximab | |
| BRAF | V600E | ||
| SULT1A1 | *2 | Drug efficacy prediction of tamoxifen | |
| CYP2D6 | *10 | ||
| abl | T315I | Diagnosis of chronic myelogenous leukemia (CML), drug efficacy prediction of imatinib | |
| 15 activate mutations | |||
| ABCG2 | 421C>A | ||
| ABCB1 | 1236C>T | Drug efficacy prediction of erlotinib | |
| 2677G>T/A | |||
| EML4-ALK | Fusion gene | Drug efficacy prediction of crizotinib | |
| ALK | L1196M | ||
| C1156Y | |||
| JAK2 | V617F | Diagnosis of myeloproliferative neoplasma | |
| Exon12 deletion | |||
| MPL | W515L/K | ||
| Coagulation | PON1 | Q192R | Drug efficacy prediction of clopidogrel |
| CYP2C19 | *2/*3 | Drug efficacy prediction of many drugs such as clopidogrel | |
| CYP2C9 | *3 | Drug efficacy prediction of many drugs such as warfarin | |
| VKORC1 | C1173T | ||
| −1639G>A | |||
| HCV | IL28B | rs8099917(T/G) | Drug efficacy prediction of pegylated interferon |
| ITPA | rs1127354 | Prediction of adverse effect of ribavirin | |
| Rheumatoid arthritis | IL-10 | −819C>T | Related to antigen production |
| MTHFR | C677T | Drug efficacy prediction of methotrexate | |
| A1298C | |||
| NAT2 | *5/*6/*7 | Drug efficacy prediction of antitubercular agent sulfasalazine and isoniazid | |
| TPMT | *3C | Drug efficacy prediction of mercaptopurine and azathioprine | |
| Transplant | CYP3A4 | *16 | Drug efficacy prediction of many drugs such as tacrolimus |
| CYP3A5 | *3 | ||
| ABCB1 | 3435C>T | Drug efficacy prediction of tacrolimus and cyclosporin | |
| ABCC2 | −24C>T | ||
| ITPA | C94A | Drug efficacy prediction of azathioprine | |
| SLC28A1 | G565A | Drug efficacy prediction of mizoribine | |
| SLC28A2 | C65T | ||
| SLC28A3 | A338G | ||
| Resk prediction | ALDH2 | *2 | Prediction of alcohol sensitivity |
| ADH2(ADH1B) | *2 | ||
| β2AR | R16G | Prediction of basal metabolism | |
| β3AR | W64R | ||
| UCP1 | A-3826G | ||
| FTO | rs9939609 | ||
| ADRB3 | rs4994 | ||
| UCP1 | rs1800592 | ||
| Prognostic factor | c-kit | D816-N822 | Prognosis prediction of acute myelogenous leukemia (AML) |
| D816H(CAC) | |||
| D816V(GTG) | |||
| N822K(AAG) |