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
A central role of genome in cancer development emerged in the late 19th (David von Hanssemann- mitotic figures and aberrant mitoses) and early 20th century (Theodore Boveri – incorrect combination of chromosomes generate cancer cell with the ability of unlimited growth). Von Waldeyer ( ) gave the chromosomes the name (chroma, body – coloured bodies)
David von Hansemann and Theodor Boveri – examining dividing cells under the microscope, they observed the presence of bizarre chromosomal aberrations, which led to the proposal that cancers are abnormal chromosomes. That is why we will start our journey by looking at a chromosome
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). *

4. Content Metabolites Protein DNA RNA
David von Hansemann and Theodor Boveri – examining dividing cells under the microscope, they observed the presence of bizarre chromosomal aberrations, which led to the proposal that cancers are abnormal chromosomes. That is why we will start our journey by looking at a chromosome
DNA
RNA

5. Cytogenetics ‘toolbox’
FISH
Conventional
aCGH
Conventional karyotyping to much higher resolution analyses
SKY

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

7. Content
Metabolites
Protein
DNA
RNA

8. What are SNPs? SNP arrays genetic variation
Even higher resolution down to single base pair change

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. Genome sequencing 2003 (0.005386 Mbp)‏ First individual's sequences
How has NGS changed our understanding of cancer disease, through establishment of cancer landscapes and deciphering of caner evolution through detection of underlying changes in development and progression of cancer

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

15. MicroRNAs
We will then change gear somewhat, and teach you about the relatively recently discovered class of RNA, which is non-coding - miRNA.

16. Metabolites Cell Protein DNA RNA
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;

18. 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
Database search
to identify proteins
Separate proteins in 2nd dimension by mass
Excise spots of interest
LC-MS/MS
A/Cy3
B/Cy5
A+B/Cy2
Run 1st dimension
Tissues/body fluids
You will be introduced to 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. Metabolomics MR spectrum of normal brain
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. 5 4 3 2 1
ppm
NAA
tCr
tCho mI
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%)
Of course, you will also have a chance to show us what you have learned. And you will be assessed

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
Of course, you will also have a chance to show us what you have learned. And you will be assessed

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