Multiplexing of Singleplex Real-Time-PCR Assays into Duplexes and Triplexes
- Art: Bachelorarbeit
- Autor: Patrick Maaß
- Abgabedatum: Mai 2009
- Umfang: 54 Seiten
- Dateigröße: 798,8 KB
- Note: 1,0
- Institution / Hochschule: Johannes Gutenberg-Universität Mainz Deutschland
- Bibliografie: ca. 24
- ISBN (eBook): 978-3-8428-1437-0
- Sprache: Englisch
- Arbeit zitieren: Maaß, Patrick Mai 2009: Multiplexing of Singleplex Real-Time-PCR Assays into Duplexes and Triplexes, Hamburg: Diplomica Verlag
- Schlagworte: PCR, Multiplexing, Brustkrebs, real-time PCR, kPCR
Bachelorarbeit von Patrick Maaß
‘Cancer’ describes a group of various diseases, where cells start changing their molecular structure and begin to grow and to supersede normal cells. Cancer is induced by numerous different elicitors, which finally all lead to an interference of the genetically regulated balance between cell cycle and apoptosis. Although every organ in the human body can be afflicted with cancer, there are significant differences in frequency relating amongst others to age, sex, geographic region and personal habits.
In the industrialized countries, breast cancer is the leading cause of death for women at the age between 30 and 60 years. With estimated 636.000 incident cases in the developed countries and 514.000 in the developing countries, breast cancer is the most prevalent cancer type among woman worldwide.
Once detected, the cancer is classified based upon pathological characterizations of the tumor or a biopsy and the lymph nodes. A clinical way of characterizing the tumor is the TNM-classification, which describes the size of the tumor (T), the number of affected lymph nodes (N) and the existence of distant metastases (M). The histological classification characterizes the carcinoma according to its structural and cellular appearance and the amitosis rate leading to a grading from 1 to 3. An immuno-histological examination provides information about the estrogen- and the progesterone-receptor- and about the Her-2/neu-status.
Breast cancer is a very heterogeneous disease. There are basic classifications that are unquestioned, even today. Recent studies confirmed the need to determine well known markers (i.e. estrogen (ER) and progesterone (PR) receptor status or HER2 status), but the large variety of subtypes and the corresponding different molecular pattern impede a uniform treatment.
Although already today other factors than anatomical classifications are being taken into consideration, there exists a need for further biological markers to assist the physician in charge with his evaluation. Beside the diagnostic recognition, the choice of appropriate therapy and the prediction of prognosis are goals that should be reached in order to prevent early stage cancer patients from therapies that provide minimal benefit but reduce their quality of life by intense adverse reactions.
RNA Expression Profiling and Prediction:
Already today there are numerous genes associated with breast cancer occurrence, therapy selection and prognosis. These profiles can be generated by different techniques, like DNA-microarrays or kinetic polymerase chain reaction (kPCR). The kPCR displays the handier platform for research laboratories in terms of choices of genes. It was the method of choice during this work.
Besides choosing the appropriate genes for analysis, there are several other elementary requirements that may lead to solid results like a statistical relevant number of tissue samples and a dependable and accurate follow-up documentation of the patient’s disease history. Within this follow-up, clinical parameters and important events like the recurrence of tumors, discovery of metastasis or demise must be documented. These parameters can then be correlated to gene expression profiles by extensive statistical analysis.
Targeting patients with lymph-node-negative breast cancer, Siemens Healthcare Diagnostics designed a diagnostic assay to predict the probability of developing distant metastasis after surgery within 5 to 10 years. This kPCR-based quantification method draws upon several previously determined and selected genes, and uses formalin-fixed, paraffin-embedded (FFPE) tissue samples of 310 patients from the Department of Obstetrics and Gynecology, Johannes-Gutenberg-University Mainz, Germany. After quantifying these genes of interest (GOI) and normalizing on housekeeping genes (HKG), the determined amounts were correlated with comprehensive statistical data of the patients. Finally an algorithm was developed to predict the forecast of patients and to determine their need for chemotherapy.
This diagnostic assay consists of 9 GOIs, 2 HKGs and 1 DNA control and is performed within a 96-well polypropylene plate.
To verify suitable algorithms, they have to be confirmed within control groups. 7 different algorithms were developed by in-house statisticians, who applied different mathematical approaches. These 7 algorithms were then applied to control groups to determine the most reliable and reproducible method.
The goal of this work was to reduce the amounts of wells per patient and assay by transforming the existing 12 singleplex assays into duplex- and triplex-formats, in order to increase the number of samples per plate or to allow more reference-control genes within a multiplex assay.
The primary goals of this project are:
Reduction of cost of approximately 50% for mastermixes per patient.
Higher throughput due to larger sample number per plate.
Larger number and higher variety of GOIs per patient and plate.
Retrenchment of very valuable RNA material.
Generation of resources to run additional quality assurance (housekeeping-genes).
The platform the kPCRs were performed on was Stratagene’s MX3005p – a multichannel kPCR machine which possesses a tungsten halogen lamp and 5 different filter sets for parallel analysis of the corresponding amount of reporter dyes. It is able to excite and detect fluorophores with an excitation and emission wavelength between 400 and 700nm. The identification of suitable reporter dyes for the filter sets by performance-analysis was the initial requirement during this work, following the evaluation of compatibility of dyes among each other within multiplex assays. Finally the most promising and reliable multiplex assays had to be identified amongst the numerous different possible combinations.
In the context of this evaluation process great attention was put on consistent parameter during kPCR (i.e. buffer conditions, reaction volume, usage of identical lots, cycle conditions, etc.) and to strictly exclude any crosstalk between the individual channel.
The results emerging from this work will deliver a substantiated basis upon which further comparative studies with more comprehensive numbers of patients can be measured. Then, in-house bioinformaticians will test the existing algorithm on data gained from multiplex assays of a statistical larger universe of patients.
Table of Contents:
|1.2||RNA Expression Profiling and Prediction||2|
|1.3||Conceptional Formulation and Objectives||3|
|2||Materials and Methods||5|
|2.1.1||Instruments and Consumables||5|
|2.1.2||Buffers and Chemicals for Nucleic Acid Purification||5|
|2.1.3||Nucleic Acids, Cell Lines and Tissue Samples||6|
|2.1.5||Chemicals and Kits for PCR||6|
|2.2.2||Purification of Nucleic Acids||7|
|2.2.3||Creation of a Standard Reference RNA Pool||8|
|2.2.4||Real-Time Kinetic RT-PCR||9|
|2.2.5||Designing Dually Labeled Primer-Probe Sets||10|
|2.2.6||Dilution of Primer-Probe Sets||11|
|2.2.7||Setup of Singleplex kPCR Assays||11|
|2.2.8||Evaluation of Different Reporter Dyes||13|
|2.2.9||Controlling of Primer-Probe-Set Performance||14|
|2.2.10||Testing Combinations of Two Sets (Duplexing)||15|
|2.2.11||Testing Combinations of Three Sets (Triplexing)||17|
|3.1||Identification of Suitable Reporter Dyes||18|
|3.2||Evaluation of Primer-Probe-Performance||23|
|3.3||Identification of Suitable Duplex Combinations||24|
|3.4||Identification of Suitable Triplex Combinations||27|
|4.1||Statistical Evaluation of Primer Performances||32|
|4.2||Suitable Reporter Dyes and Quencher||36|
|4.3||Optimization of Assays and kinetic RT-PCR Parameter||37|
|7.5||Origins of Breast Cancer Samples for MAVPOOL080623a|
Chapter 2.2.4, Real-Time Kinetic RT-PCR:
The real-time quantitative RT-PCR method used to quantify RNA in breast cancer tissue combines two successive steps.
First, RNA is transcribed into cDNA by the enzyme reverse transcriptase, an RNA-dependent DNA-polymerase, which was first discovered in retroviruses and which is able to synthesize a RNA-DNA-hybrid-strand from a single-stranded RNA, degrade the residual RNA and complete the molecule into a double-stranded cDNA.
In the second step, the cDNA serves as a template for the following quantitative polymerase chain reaction (PCR). The PCR uses two sequence-specific oligonucleotides and a DNA-dependent polymerase to amplify a definite DNA segment.
An improvement of the PCR is the real-time quantitative PCR, where a third oligonucleotide, a hybridization probe labeled with two different fluorescent dyes and located between the forward- and the reverse-primer, is used. Since one dye works as the reporter dye (i.e. FAM, Cy5, Yakima Yellow, etc.) and the other one as the corresponding quencher (like TAMRA, BHQ1, BHQ2, etc.), the quencher absorbs the emission of the reporter dye by fluorescent resonance energy transfer (FRET).
When this dual-labeled probe hybridizes with the template DNA, the 5’-3’ nucleolytic activity of the polymerase degrades this probe resulting in a loss of quenching activity. Thus, a continuous increase of occurs during PCR. The amount of fluorescence at a given time point during PCR corresponds to the amount of PCR product. Since fluorescence is measured following each PCR cycle, it is possible to observe the amplification process ‘real-time’ and to count back to the initial amount of cDNA.