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Genomic Approaches To Cancer

Genomic Approaches To Cancer. Introduction to the course Tatjana Crnogorac-Jurcevic 20 th September 2017. Aims and Objectives. THE AIMS OF THE MODULE To provide detailed teaching on the principles, interpretation and applications of large scale ‘ omics ’ approaches to study cancer

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Genomic Approaches To Cancer

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  1. Genomic Approaches To Cancer Introduction to the course Tatjana Crnogorac-Jurcevic 20th September 2017

  2. Aims and Objectives THE AIMS OF THE MODULE To provide detailed teaching on the principles, interpretation and applications of large scale ‘omics’ approaches to study cancer LEARNING OBJECTIVES By the end of this module students will be able to demonstrate a knowledge of: • The working principles of ‘omics’ platforms • The advantages and limitations of using ‘omics’ in studying cancer • The application of ‘omics’ technologies in personalised medicine • 10 sessions: 9 taught, last reserved for poster presentation

  3. Genome and Genomics “A genome is an organism's complete set of DNA, including all of its genes. Each contains all of the information needed to build and maintain that organism. In humans, a copy of the entire genome-more than 3 billion DNA base pairs is contained in all cells that have a nucleus.”* London Canary islands 3,000km = length of your genome 19th -20th c: A central role of genome in cancer (David von Hanssemann and Theodore Boveri: incorrect combination of chromosomes generate cancer cell with the ability of unlimited growth). *http://ghr.nlm.nih.gov/handbook/hgp/genome

  4. Content Metabolites Protein DNA RNA

  5. Cytogenetics ‘toolbox’ FISH Conventional aCGH SKY

  6. Chromosomal aberrations Structural Numerical CN changes t(14;18) UPD Ring chromosome

  7. Content Metabolites Protein DNA RNA

  8. What are SNPs? SNP arrays genetic variation

  9. Why do we care about genetic variations? 1. Genetic variations underlie phenotypic differences among different individuals 2. Genetic variations determine our predisposition to complex diseases and responses to drugs and environmental factors 3. Genetic variations reveal clues of ancestral human migration history

  10. (0.005386 Mbp)‏ Genome sequencing First individual's sequences 2003

  11. Cancer genome landscape and evolution Stratton et al, Nature, 2009 Yachida et al, Nature, 2010

  12. Epigenetics Genetics alone cannot explain the diversity of phenotypes within cells (Greek: epi – over, outside of) Stable alterations in genome function that do not entail a change in the DNA sequence (chromatin modification, methylation) - a change in phenotype without the change in genotype

  13. Transcriptomics (RNASeq and arrays) Metabolites Cell Protein DNA RNA

  14. Is it benign? Which class of cancer? What are my chances? Which treatment? Gene expressionprofiling and applications Therapeutic Choice Diagnosis Classification Prognosis

  15. MicroRNAs

  16. Proteomics Metabolites Cell Protein DNA RNA

  17. Proteomics “Meat on the bone of Genome” • ~20,000 genes - proteome space more complex (splicing, PTMs, protein complexes) • poor correlation of mRNA and protein levels • health and diseases are protein-driven processes • two drafts of human proteomes (ProteomicsDB; www.humanproteomemap.org)

  18. Separate proteins in 2nd dimension by mass Tissues/body fluids Run 1st dimension Database search to identify proteins A/Cy3 A+B/Cy2 Excise spots of interest LC-MS/MS B/Cy5 Introduction to proteomics and applications Basic principles of separation and identification of peptides/proteins using gel and non-gel based techniques as well as the principles of MS

  19. Phosphoproteomics

  20. Biomarkers What are they and their role in personalised medicine Liotta&Petricoin, J Clin Invest, 2006. Rong Fanet al, Nature Biotechnology 2008

  21. Metabolomics Metabolome Metabolites Cell Protein DNA RNA

  22. 5 4 3 2 1 0 ppm NAA tCr tCho mI Metabolomics • MRI scanners can be adapted to measure brain metabolites while the patient lies in the magnet. • Nothing is injected or biopsied. • The peaks in the spectrum correspond to the different metabolites. MR spectrum of normal brain

  23. Assessment of the Unit In-course assessment: Poster presentations (40%) Topics randomly assigned: 1. What have we learned from Next Generation Sequencing of cancer genomes 2. Epigenetics and cancer 3. Roles of microRNAs in cancer biology 4. The application of microarrays in cancer gene profiling 5. Proteomics in cancer research 6. The discovery and use of cancer biomarkers Double marked for: 1. Content (50%) 2. Presentation (20%) 3. Response to questions (30%)

  24. Assessment of the Unit Examination Paper (60%): 18 Multiple Choice Questions (MCQ) & 3 Short Answer Questions (SAQ); time = 1hr 15min Part A: MCQ weighting 35% Part B: SAQ weighting 65% MCQ: expl.What is …? a) to e); select the best single answer SAQ: expl. The success of a microarray experiment depends upon several factors. Several sub-questions; answer with couple of short sentences

  25. Housekeeping rules DO attend the lectures DO ask questions DO study ENJOY THE COURSE! t.c.jurcevic@qmul.ac.uk

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