MSP1Y2CAN016
[16] Computational cancer biology
Lecturer: Dr Sylvia Nagl
Aim
To provide an introduction to gene expression profiles for tumour classification and diagnosis.
Objectives
By the end of the session students should be able to
 define the concept of gene expression profiles
 explain their uses in tumour classification using an example
 discuss key ideas regarding the relationship between histological and molecular classification
Further reading
Ramaswamy and Golub (2002), DNA microarrays in clinical oncology. Journal of Clinical Oncology 20:19321941.
Brown et al. (2003). Essentials of Medical Genomics. Wiley-LISS, pp. 163-184.
Campbell and Heyer (2003) Discovering genomics, proteomics and bioinformatics. Cold Spring Harbor
Laboratory Press and Benjamin Cummings, pp. 138-146.
Session content
DNA microarrays in clinical oncology
Cancer is a genetic disease, mostly resulting from acquired mutations and epigenetic changes that influence gene
expression (e.g., chromosomal translocations). Therefore, a major focus in cancer research is the identification of
genetic markers that can be used for precise diagnosis or therapy.
For example, the characterisation of the bcr-abl gene translocation product enabled the molecular classification
of chronic myelogenous leukaemia and subsequently led to the design of the molecularly targeted tyrosine kinase
inhibitor STI571 ('Gleevec', Novartis) for the treatment of this type of cancer. This example illustrates how, in
certain types of cancer, the identification of a single marker may lead to better classification and may at times
also pinpoint a molecular target for therapy.
However, roughly 90% of human cancers are epithelial in origin and exhibit extensive aneuploidy (chromosomal
amplification), multiple gene amplifications and deletions, and genetic instability. This results in a large number
of changes in the structure and function of the genome, and abnormal expression levels can be observed for a
range of genes. This complexity might be the reason for the clinical diversity of histologically similar tumours,
and a comprehensive analysis of the multiple genetic alterations present is required for more informative and
reliable classification.
The sequencing of the human genome, and technologic advances in the new field of functional genomics, now
make it possible to study the genetic complexity of human cancers in a global manner. The ultimate aim is the
characterisation of all changes that occur in cancer, at the DNA, RNA and protein levels. The use of DNA
microarrays enables the large-scale analysis of RNA expression (expression profiling) in tumour samples.
Expression profiling can be used to simultaneously assess the expression of thousands of genes and allows the
large-scale identification of genetic tumour markers.
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Example study
We will explore data obtained from a microarray experiment that was carried out by Charles Perou, Therese
Sorlie and colleagues (2000) to classify breast cancer samples by expression profiling. The primary aim of the
study was to determine which genes might prove informative for the identification of subclasses of breast
tumours. If successful, this might lead to the development of a clinically useful expression profiling procedure.
1. Histology
We will look at tissue biopsies and observe the histological appearance of sample for each grade level (1 is the
least and 3 is the most progressed). The biopsies are also classified on the basis of whether the oestrogen
receptor gene (ER) and the ERBB2 gene is expressed (1) or not (0).
The team collected 65 surgical specimens from 42 women with breast tumours. Twenty of the tumours were
sampled twice, before and after standard chemotherapy with doxorubicin. The expression of 1,753 genes was
analysed by DNA microarrays. The microarray chips were used to quantify the amount of mRNA transcribed
from a given gene in a tumour sample compared to the amount present in control cell lines.
2. Gene expression levels
The gene expression matrix is arranged in such a way that each row represents a gene and each column
represents a tissue sample. The samples were clustered to arrange them according to similar expression profiles.
Red indicates overexpression of the gene in the biopsy sample compared to the control (more mRNA was
present in the biopsy sample) and green stands for underexpression.
Discussion of microarray data from ER-expressing tumours during the lecture (see also lecture slides).
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