Quantitative real-time PCR for cancer detection: the lymphoma case

Advances in the biologic sciences and technology are providing molecular targets for diagnosis and treatment of cancer. Lymphoma is a group of cancers with diverse clinical courses. Gene profiling opens new possibilities to classify the disease into subtypes and guide a differentiated treatment. Real-time PCR is characterized by high sensitivity, excellent precision and large dynamic range, and has become the method of choice for quantitative gene expression measurements. For accurate gene expression profiling by real-time PCR, several parameters must be considered and carefully validated. These include the use of reference genes and compensation for PCR inhibition in data normalization. Quantification by real-time PCR may be performed as either absolute measurements using an external standard, or as relative measurements, comparing the expression of a reporter gene with that of a presumed constantly expressed reference gene. Sometimes it is possible to compare expression of reporter genes only, which improves the accuracy of prediction. The amount of biologic material required for real-time PCR analysis is much lower than that required for analysis by traditional methods due to the very high sensitivity of PCR. Fine-needle aspirates and even single cells contain enough material for accurate real-time PCR analysis.

[1]  T. Masuda,et al.  Development of consensus fluorogenically labeled probes of the immunoglobulin heavy‐chain gene for detecting minimal residual disease in B‐cell non‐Hodgkin lymphomas , 2003, Cancer science.

[2]  Francisco Vega,et al.  Chromosomal translocations involved in non-Hodgkin lymphomas. , 2003, Archives of pathology & laboratory medicine.

[3]  Alfred Pingoud,et al.  Real‐Time Polymerase Chain Reaction , 2003, Chembiochem : a European journal of chemical biology.

[4]  R. Abramson,et al.  Detection of specific polymerase chain reaction product by utilizing the 5'----3' exonuclease activity of Thermus aquaticus DNA polymerase. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[5]  S. Akilesh,et al.  Customized molecular phenotyping by quantitative gene expression and pattern recognition analysis. , 2003, Genome research.

[6]  Adrian Wiestner,et al.  A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Kazuhiro Nagai,et al.  Identifying progression‐associated genes in adult T‐cell leukemia/lymphoma by using oligonucleotide microarrays , 2004, International journal of cancer.

[8]  M. Pfaffl,et al.  A new mathematical model for relative quantification in real-time RT-PCR. , 2001, Nucleic acids research.

[9]  M. Day,et al.  Real-time RT-PCR: considerations for efficient and sensitive assay design. , 2004, Journal of immunological methods.

[10]  J. Gribben,et al.  Immunoglobulin heavy-chain consensus probes for real-time PCR quantification of residual disease in acute lymphoblastic leukemia. , 2000, Blood.

[11]  B. Liss,et al.  Correlating function and gene expression of individual basal ganglia neurons , 2004, Trends in Neurosciences.

[12]  Dieter Klein,et al.  Quantification using real-time PCR technology : applications and limitations , 2002 .

[13]  B. Liss Improved quantitative real-time RT-PCR for expression profiling of individual cells. , 2002, Nucleic acids research.

[14]  Fred Russell Kramer,et al.  Multicolor molecular beacons for allele discrimination , 1998, Nature Biotechnology.

[15]  N. Brousse,et al.  High Level of Glutathione-S-Transferase π Expression in Mantle Cell Lymphomas , 2004, Clinical Cancer Research.

[16]  Nucleic acid-based technologies: application amplified. , 2004, Pharmacogenomics.

[17]  Frank Vitzthum,et al.  Investigations on DNA intercalation and surface binding by SYBR Green I, its structure determination and methodological implications. , 2004, Nucleic acids research.

[18]  Meland,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[19]  R. Foà,et al.  Monitoring of minimal residual disease after CHOP and rituximab in previously untreated patients with follicular lymphoma. , 2002, Blood.

[20]  Carl T Wittwer,et al.  Real-time PCR technology for cancer diagnostics. , 2002, Clinical chemistry.

[21]  Hans Lehrach,et al.  A comparison of oligonucleotide and cDNA-based microarray systems. , 2004, Physiological genomics.

[22]  P. Paschka,et al.  Early reduction of BCR-ABL mRNA transcript levels predicts cytogenetic response in chronic phase CML patients treated with imatinib after failure of interferon α , 2002, Leukemia.

[23]  F. Watzinger,et al.  Evaluation of candidate control genes for diagnosis and residual disease detection in leukemic patients using ‘real-time’ quantitative reverse-transcriptase polymerase chain reaction (RQ-PCR) – a Europe against cancer program , 2003, Leukemia.

[24]  B. Coiffier,et al.  Diffuse large cell lymphoma. , 2001, Current opinion in oncology.

[25]  Nigel J Walker,et al.  Tech.Sight. A technique whose time has come. , 2002, Science.

[26]  M. Pfaffl,et al.  Standardized determination of real-time PCR efficiency from a single reaction set-up. , 2003, Nucleic acids research.

[27]  T. Godfrey,et al.  Quantitative mRNA expression analysis from formalin-fixed, paraffin-embedded tissues using 5' nuclease quantitative reverse transcription-polymerase chain reaction. , 2000, The Journal of molecular diagnostics : JMD.

[28]  Ash A. Alizadeh,et al.  Prediction of survival in diffuse large-B-cell lymphoma based on the expression of six genes. , 2004, The New England journal of medicine.

[29]  M. Kubista,et al.  Properties of the reverse transcription reaction in mRNA quantification. , 2004, Clinical chemistry.

[30]  B. Liss,et al.  Tuning pacemaker frequency of individual dopaminergic neurons by Kv4.3L and KChip3.1 transcription , 2001, The EMBO journal.

[31]  Petter Mostad,et al.  Quantitative Real-Time PCR Method for Detection of B-Lymphocyte Monoclonality by Comparison of κ and λ Immunoglobulin Light Chain Expression , 2003 .

[32]  P. Ikonomi,et al.  Multiplex quantitative PCR using self-quenched primers labeled with a single fluorophore. , 2002, Nucleic acids research.

[33]  H. Höfler,et al.  Quantitative gene expression analysis in microdissected archival formalin-fixed and paraffin-embedded tumor tissue. , 2001, The American journal of pathology.

[34]  W. Al-Soud,et al.  Identification and Characterization of Immunoglobulin G in Blood as a Major Inhibitor of Diagnostic PCR , 2000, Journal of Clinical Microbiology.

[35]  R. Todd,et al.  Challenges of single-cell diagnostics: analysis of gene expression. , 2002, Trends in molecular medicine.

[36]  Nigel J. Walker,et al.  A Technique Whose Time Has Come , 2002, Science.

[37]  M. Björkholm,et al.  Real-time polymerase chain reaction determination of cytokine mRNA expression profiles in Hodgkin's lymphoma. , 2004, Haematologica.

[38]  D. Arber Molecular diagnostic approach to non-Hodgkin's lymphoma. , 2000, The Journal of molecular diagnostics : JMD.

[39]  M. Mhlanga,et al.  Using molecular beacons to detect single-nucleotide polymorphisms with real-time PCR. , 2001, Methods.

[40]  G. Landes,et al.  Analysis of human transcriptomes , 1999, Nature Genetics.

[41]  E. Schneider,et al.  Validation of sixteen leukemia and lymphoma cell lines as controls for molecular gene rearrangement assays. , 2002, Clinical chemistry.

[42]  Anders Ståhlberg,et al.  Comparison of reverse transcriptases in gene expression analysis. , 2004, Clinical chemistry.

[43]  I. Lossos,et al.  Optimization of quantitative real-time RT-PCR parameters for the study of lymphoid malignancies , 2003, Leukemia.

[44]  B. Smith,et al.  Real-time quantitative reverse transcription-PCR for cyclin D1 mRNA in blood, marrow, and tissue specimens for diagnosis of mantle cell lymphoma. , 2004, Clinical chemistry.

[45]  A. Moorman,et al.  Assumption-free analysis of quantitative real-time polymerase chain reaction (PCR) data , 2003, Neuroscience Letters.

[46]  F. Speleman,et al.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes , 2002, Genome Biology.

[47]  S A Bustin,et al.  Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. , 2002, Journal of molecular endocrinology.

[48]  Sanjay Tyagi,et al.  Molecular Beacons: Probes that Fluoresce upon Hybridization , 1996, Nature Biotechnology.

[49]  John Quackenbush,et al.  Universal RNA reference materials for gene expression. , 2004, Clinical chemistry.

[50]  E. Kjeldsen,et al.  Kinetics of BCR‐ABL fusion transcript levels in chronic myeloid leukemia patients treated with STI571 measured by quantitative real‐time polymerase chain reaction , 2001, European journal of haematology.

[51]  C. Wittwer,et al.  Continuous fluorescence monitoring of rapid cycle DNA amplification. , 1997, BioTechniques.

[52]  Todd,et al.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.

[53]  M. Busslinger,et al.  Independent regulation of the two Pax5 alleles during B-cell development , 1999, Nature Genetics.

[54]  A new minor groove binding asymmetric cyanine reporter dye for real-time PCR. , 2003, Nucleic acids research.

[55]  B. Olgemöller,et al.  Simple technique for internal control of real-time amplification assays. , 2004, Clinical chemistry.

[56]  B. Seed,et al.  A PCR primer bank for quantitative gene expression analysis. , 2003, Nucleic acids research.

[57]  J. Peccoud,et al.  Theoretical uncertainty of measurements using quantitative polymerase chain reaction. , 1996, Biophysical journal.

[58]  J. Lakowicz Principles of fluorescence spectroscopy , 1983 .

[59]  D. Ginzinger Gene quantification using real-time quantitative PCR: an emerging technology hits the mainstream. , 2002, Experimental hematology.

[60]  F. Cabanillas,et al.  Quantitative assessment of disease involvement by follicular lymphoma using real-time polymerase chain reaction measurement of t(14;18)-carrying cells , 2004, International journal of hematology.

[61]  D. Corey,et al.  Locked nucleic acid (LNA): fine-tuning the recognition of DNA and RNA. , 2001, Chemistry & biology.

[62]  F. Kramer,et al.  Thermodynamic basis of the enhanced specificity of structured DNA probes. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[63]  S. Bustin Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. , 2000, Journal of molecular endocrinology.

[64]  J. Gabert,et al.  Detection of minimal residual disease in hematologic malignancies by real-time quantitative PCR: principles, approaches, and laboratory aspects , 2003, Leukemia.

[65]  Kirk M. Ririe,et al.  Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. , 1997, Analytical biochemistry.

[66]  E. Lukhtanov,et al.  3'-minor groove binder-DNA probes increase sequence specificity at PCR extension temperatures. , 2000, Nucleic acids research.

[67]  G. Horgan,et al.  Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR , 2002 .

[68]  R. Warnke,et al.  CyclinD1/CyclinD3 ratio by real-time PCR improves specificity for the diagnosis of mantle cell lymphoma. , 2004, The Journal of molecular diagnostics : JMD.

[69]  Tzachi Bar,et al.  Kinetic Outlier Detection (KOD) in real-time PCR. , 2003, Nucleic acids research.

[70]  D. Melton,et al.  Single-cell transcript analysis of pancreas development. , 2003, Developmental cell.

[71]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.

[72]  Donna Neuberg,et al.  Center B Cells Using Cdna Arrays Gene Expression Profiling of Follicular Lymphoma and Normal Germinal , 2022 .

[73]  L. Dyrskjøt Classification of bladder cancer by microarray expression profiling: towards a general clinical use of microarrays in cancer diagnostics , 2003, Expert review of molecular diagnostics.

[74]  Charles M Perou,et al.  Statistical modeling for selecting housekeeper genes , 2004, Genome Biology.

[75]  M. Kubista,et al.  Detection of PCR products in real time using light-up probes. , 2000, Analytical biochemistry.

[76]  Takeshi Suzuki,et al.  High-Throughput Retroviral Tagging for Identification of Genes Involved in Initiation and Progression of Mouse Splenic Marginal Zone Lymphomas , 2004, Cancer Research.

[77]  I. Nazarenko,et al.  Detection of telomerase activity utilizing energy transfer primers: comparison with gel- and ELISA-based detection. , 1999, BioTechniques.

[78]  R. Rutledge,et al.  Mathematics of quantitative kinetic PCR and the application of standard curves. , 2003, Nucleic acids research.

[79]  D. Whitcombe,et al.  Detection of PCR products using self-probing amplicons and fluorescence , 1999, Nature Biotechnology.

[80]  T. Masuda,et al.  Quantitative assessment of contaminating tumor cells in autologous peripheral blood stem cells of B-cell non-Hodgkin lymphomas using immunoglobulin heavy chain gene allele-specific oligonucleotide real-time quantitative-polymerase chain reaction. , 2003, Leukemia research.

[81]  B. Smith,et al.  Real-time quantitative reverse transcription-PCR for cyclin D1 mRNA in blood, marrow, and tissue specimens for diagnosis of mantle cell lymphoma. , 2004, Clinical chemistry.

[82]  Weihong Liu,et al.  Validation of a quantitative method for real time PCR kinetics. , 2002, Biochemical and biophysical research communications.

[83]  K. Elenitoba-Johnson,et al.  Microarray analysis of B-cell lymphoma cell lines with the t(14;18). , 2002, The Journal of molecular diagnostics : JMD.