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EPD2 Present and Future

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Information about EPD2 Present and Future
Education

Published on February 28, 2008

Author: Susann

Source: authorstream.com

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Tumour Markers: Present and Future:  Tumour Markers: Present and Future Eleftherios P. Diamandis, M.D., Ph.D., FRCP(C) Dept. of Pathology & Laboratory Medicine, Mount Sinai Hospital Dept. of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Canada The New Cancer Diagnostics :  The New Cancer Diagnostics We Need:  We Need better (more objective) and more biologically-relevant tumor classification schemes for prognosis, selection of therapy better tumor markers for population screening and early diagnosis Paradigm Shift (2000 and Beyond):  Paradigm Shift (2000 and Beyond) Traditional Method: Study one molecule at a time. New Method: Multiparametric analysis (thousands of molecules at a time). Cancer: Does every cancer have a unique fingerprint? (genomic/proteomic?) Changes are Coming:  Changes are Coming Changes seen are driven by recent biological / technological advances: Human Genome Project Bioinformatics Array Analysis (DNA; proteins; tissues) Mass Spectrometry Automated DNA Sequencing /PCR Laser Capture Microdissection SNPs Comparative Genomic Hybridization Technological Advances:  Technological Advances Microarrays:  Microarrays What is a microarray? A microarray is a compact device that contains a large number of well-defined immobilized capture molecules (e.g. synthetic oligos, PCR products, proteins, antibodies) assembled in an addressable format. You can expose an unknown (test) substance on it and then examine where the molecule was captured. You can then derive information on identity and amount of captured molecule. Slide8:  Science 2004; 306: 630-631 Slide9:  Microscope slide DNA microarray Slide10:  Cy3-dUTP green fluorescent reverse transcriptase, T7 RNA polymerase Cy5-dUTP red fluorescent cRNA cRNA RNA extraction and labeling to determine expression level sample of interest compared to standard reference Microarray Milestone: June 2003 :  Microarray Milestone: June 2003 Following their papers in Nature and NEJM Nature 2002; 415: 530-536 NEJM 2002; 347: 1999-2009 Van’t Veer and colleagues (Netherlands Cancer Institute) will use microarray profiling as a routine tool for breast cancer management (administration of adjuvant chemotherapy after surgery). prospective trials under way; EORTC; 2005 onwards Applications of Microarrays:  Applications of Microarrays Simultaneous study of gene expression patterns of genes Single nucleotide polymorphism (SNP) detection Sequences by hybridization / genotyping / mutation detection Study protein expression (multianalyte assay) Protein-protein interactions Provides: Massive parallel information Slide14:  Microarrays, such as Affymetrix’s GeneChip, now include all 50,000 known human genes. Science, 302: 211, 10 October, 2003 Comparative Genomic Hybridization:  Comparative Genomic Hybridization A method of comparing differences in DNA copy number between tests (e.g. tumor) and reference samples Can use paraffin-embedded tissues Good method for identifying gene amplifications or deletions by scanning the whole genome. Comparative Genomic Hybridization:  Comparative Genomic Hybridization Label with Cy-3 Label with Cy-5 Cot1DNA blocks repeats) Nature Reviews Cancer 2001;1:151-157 Arrayed CGH:  Arrayed CGH Same as previous slide but use arrays of BAC clones instead of chromosomes Laser Capture Microdissection:  Laser Capture Microdissection An inverted microscope with a low intensity laser that allows the precise capture of single or defined cell groups from frozen or paraffin-embedded histological sections Allows working with well-defined clinical material. Tumor Heterogeneity (Prostate Cancer):  Tumor Heterogeneity (Prostate Cancer) Tumor Cells: Red Benign Glands: Blue Rubin MA J Pathol 2001;195;80-86 Laser Capture Microdissection:  Laser Capture Microdissection LCM uses a laser beam and a special thermoplastic polymer transfer cup (A).The cap is set on the surface of the tissue and a laser pulse is sent through the transparent cap,expanding the thermoplastic polymer. The selected cells are now adherent to the transfer cap and can be lifted off the tissue and placed directly onto an eppendorf tube for extraction (B). Rubin MA, J Pathol 2001;195:80-86 Tissue Microarray:  Tissue Microarray Printing on a slide tiny amounts of tissue Array many patients in one slide (e.g. 500) Process all at once (e.g. immunohistochemistry) Works with archival tissue (paraffin blocks) Gene Expression Analysis of Tumors:  Gene Expression Analysis of Tumors cDNA Microarray Lakhani and Ashworth Nature Reviews Cancer 2001;1:151-157 Tissue Microarray:  Tissue Microarray From Jacquemier1 et al Cancer Res 2005;65:767-779 Molecular Profiling of Prostate Cancer:  Molecular Profiling of Prostate Cancer Rubin MA, J Pathol 2001;195:80-86 Single Nucleotide Polymorphisms (SNPs):  Single Nucleotide Polymorphisms (SNPs) DNA variation at one base pair level; found at a frequency of 1 SNP per 1,000 - 2,000 bases A map of 9 x 106 SNPs have been described in humans by the International SNP map working group (HapMap) 60,000 SNPs fall within exons; the rest are in introns Why Are SNPs Useful?:  Why Are SNPs Useful? Human genetic diversity depends on SNPs between individuals (these are our genetic differences!) Specific combinations of alleles (called “The Haplotype”) seem to play a major role in our genetic diversity How does this genotype affect the phenotype Disease Disposition? Haplotype Patterns:  Haplotype Patterns Person A A T T G A T C G G A T. . . C C A T C G G A . . . C T A A Person B A T T G A T A G G A T. . . C C A G C G G A . . . C T C A Person C A T T G A T C G G A T. . . C C A T C G G A . . . C T A A Person E A T T G A T C G G A T. . . C C A T C G G A . . . C T A A Person D A T T G A T A G G A T. . . C C A G C G G A . . . C T C A Persons B and D share a haplotype unlike the other three, characterized by three different SNPs. Science, 2002; 296: 1391-1393. Why Are SNPs Useful?:  Why Are SNPs Useful? Diagnostic Application Determine somebody’s haplotype (sets of SNPs) and assess disease risk. Be careful: These disease-related haplotypes are not as yet known! SNP Analysis by Microarray:  SNP Analysis by Microarray GeneChip® HuSNPTM Mapping Assay (Affymetrix) More than 100,000 single nucleotide polymorphisms (SNPs) covering all 22 autosomes and the X chromosome in a single experiment Coverage: 1 SNP per 20 kb of DNA Needs: 250 ng of genomic DNA-1 PCR reaction Commercial Microarray for Clinical Use (Pharmacogenomics):  Commercial Microarray for Clinical Use (Pharmacogenomics) Roche Product CYP 450 Genotyping (drug metabolizing system) First FDA approved microarray-based diagnostic test; 2004 Proteomics & Protein Microarrays:  Proteomics & Protein Microarrays Slide32:  High-throughput proteomic analysis Haab et al. Genome Biology 2000;1:1-22 Protein array now commercially available from BD Biosciences (2002) Applications of Protein Microarrays :  Applications of Protein Microarrays Screening for: Small molecule targets Post-translational modifications Protein-protein interactions Protein-DNA interactions Enzyme assays Epitope mapping Cytokine Specific Microarray ELISA:  BIOTINYLATED MAB CAPTURE MAB ANTIGEN Detection system Cytokine Specific Microarray ELISA Recently Published Examples:  Recently Published Examples Rationale For Improved Subclassification of Cancer by Microarray Analysis:  Rationale For Improved Subclassification of Cancer by Microarray Analysis Classically classified tumors are clinically very heterogeneous – some respond very well to chemotherapy; some do not. Hypothesis:  Hypothesis The phenotypic diversity of cancer might be accompanied by a corresponding diversity in gene expression patterns that can be captured by using cDNA microarray Then Systematic investigation of gene expression patterns in human tumors might provide the basis of an improved taxonomy of cancer  Molecular portraits of cancer Molecular signatures Molecular Portraits of Cancer:  Molecular Portraits of Cancer Breast Cancer Perou et al. Nature 2000;406:747-752 Green: Underexpression Black: Equal expression Red: Overexpression Left Panel: Cell Lines Right Panel: Breast Tumors Figure Represents 1753 Genes Differential Diagnosis of Childhood Malignancies:  Differential Diagnosis of Childhood Malignancies Ewing Sarcoma: Yellow Rhabdomyosarcoma: Red Burkitt Lymphoma: Blue Neuroblastoma: Green Khan et al. Nature Medicine 2001;7:673-679 Applications (continued) Vant’t Veer L. et al. Nature 2002:415-586:  Applications (continued) Vant’t Veer L. et al. Nature 2002:415-586 Examine lymph node negative breast cancer patients and identified specific signatures for: Poor prognosis BRCA carriers The “poor prognosis” signature consisted of genes regulating cell cycle invasion, metastasis and angiogenesis. Conclusion This gene expression profile will outperform all currently-used clinical parameters in predicting disease outcome This may be a good strategy to select node-negative patients who would benefit from adjuvant therapy. Validation of prognosis signature:  Validation of prognosis signature performance on unselected consecutive series at 10 years (n=295) Lymph node negative patients (n=151) Lymph node positive patients (n=144) <53 yrs, tumor <5cm, no prior malignancy predictive value compared to classical clinical parameters relevance for treatment tailoring Van’t Veer et al New Engl J Med 2002;347:1999-2009 Slide42:  Cohort of 295 tumors patients < 53 yrs, lymph node negative or positive Unselected consecutive patient series, mean follow-up ~ 7 yrs 295 tumors 70 prognosis genes Kaplan-Meier survival curves for all 295 patients:  Kaplan-Meier survival curves for all 295 patients Slide44:  premenopausal, lymph node negative Treatment tailoring by profiling Therapeutic implications:  Therapeutic implications Who to treat: Prognosis profile as diagnostic tool improvement of accurate selection for adjuvant therapy (less under- and overtreatment) Prognosis profile implemented in clinical trials reduction in number of patients & costs (select only patients that are at metastases risk) How to treat: Predictive profile for drug response selection of patients who benefit Commercial Products:  Commercial Products Oncotype DX by “Genomic Health Inc”, Redwood City, CA A prognostic test for breast cancer metastasis based on profiling 250 genes; 16 genes as a group have predictive value; $3,400 per test 215,000 breast cancer cases per year (potential market value > $500 million!) Test has no value for predicting response to treatment Am J Pathol 2004;164:35-42 Commercial Products:  Commercial Products Mammaprint marketed by Agendia, Amsterdam, The Netherlands Based on L.Van’t Veer publications Test costs Euro 1650; based on 70 gene signature Prospective trials underway Celera and Arcturus developing similar tests (prognosis/prediction of therapy) Science 2004;303:1754-5 Mass Spectrometry for Proteomic Pattern Generation:  Mass Spectrometry for Proteomic Pattern Generation Serum analysis by SELDI-TOF mass spectrometry after extraction of lower molecular weight proteins Data analyzed by a “pattern recognition” algorithm ProteinChip® Arrays: SELDI affinity chip surfaces (Ciphergen):  ProteinChip® Arrays: SELDI affinity chip surfaces (Ciphergen) Reverse Phase Anionic Cationic IMAC Normal Phase Slide50:  Proteins are captured, retained and purified directly on the chip (affinity capture ) Laser The SELDI Process and ProteinChip® Arrays Sample goes directly onto the ProteinChip® Array Surface is “read” by Surface-Enhanced Laser Desorption/Ionization (SELDI) ProteinChip® Array Slide51:  Detector Laser Flight Tube Mass Spectrometry-Based Proteomics and Bioinformatics The Future of Biomarkers m/z Relative Intensity Target Results: Ovarian Cancer:  Results: Ovarian Cancer Petricoin III EF, et al. Lancet 2002;359:572-577 Slide53:  Diamandis, EP J Natl Cancer Inst 2004; 96: 353-356 Reviews / Opinions / Commentaries Diamandis, EP Clin Chem 2003; 49: 1272-1275 Diamandis, EP Mol Cell Proteomics 2004; 3:367-78 Microarray discrepancies (185 genes):  Microarray discrepancies (185 genes) Science 2004; 306: 630-631 Prediction of cancer outcome with microarrays: a multiple random validation strategy:  Michiels et al. Lancet, 2005; 365: 488-492. Prediction of cancer outcome with microarrays: a multiple random validation strategy Description of eligible studies :  Description of eligible studies Microarrays & molecular research: noise discovery?:  Microarrays & molecular research: noise discovery? In 5 of the 7 largest studies on cancer prognosis, this technology performs no better than flipping a coin. The other two studies barely beat horoscopes… J.P. Ioannides Lancet 2005; 365: 454-455 Prediction of cancer outcome with microarrays: a multiple random validation strategy:  Prediction of cancer outcome with microarrays: a multiple random validation strategy Findings: The list of genes identified as predictors of prognosis was highly unstable; molecular signatures strongly depended on the selection of patients in the training sets Michiels et al. Lancet, 2005; 365: 488-492. Prediction of cancer outcome with microarrays: a multiple random validation strategy:  Findings: Because of inadequate validation, our chosen studies published overoptimistic results compared with those from our own analyses. Michiels et al. Lancet, 2005; 365: 488-492. Prediction of cancer outcome with microarrays: a multiple random validation strategy The Future??:  Imaging Multiparametric/miniature testing of serum on a protein array Mass spectrometric serum/urine proteomic pattern generation The Future?? The Future??:  Asymptomatic individuals Predisposition to certain disease Prevention (drugs; lifestyle) Surveillance Whole genome SNP analysis The Future?? The Future??:  The Future?? Cancer patient Cancerous tissue Tumour fingerprint Individualized treatment Surgery / Biopsy Array analysis

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