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| Journal of the New Zealand Medical Association,
11-October-2002, Vol 115 No 1163
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Cancer genomics: the chips are on the table
Kieran Holland and Michael Sullivan
Despite tremendous progress in our
understanding of the genetic origins of cancer, this diverse group of diseases
remains enigmatic. Patients with apparently similar tumours may experience very
different outcomes. Existing methods of classifying tumour samples often tell
clinicians little about how an individual patient will respond to treatment.
This review discusses the role that recently developed DNA microarray
(‘gene chip’) technology may play in the development of powerful new
molecular classification systems for cancer.
Diagnosis: a clinical
classification
Diagnosis is a form of classification that enables
clinicians to make informed judgements about a patient’s prognosis and
most suitable treatment options. In the broadest sense, classification is the
process of grouping entities according to similarities in selected properties so
that we can compare, contrast, and make predictions. The power of a
classification system is limited primarily by our ability to measure properties
of interest and our ability to effectively subdivide things according to those
properties.
Until recently, the properties that we have used to classify
cancers have been those most readily accessible: macroscopic and microscopic
morphological features. These features include the organ of origin, tumour size,
tissue type, cellular appearance, lymph node involvement, and evidence of
distant spread. The widely used TNM (Tumour-Node-Metastasis) system, originally
developed by Pierre Denoix over 50 years ago, incorporates many of these
features into a standardised format that has been favoured for its utility,
simplicity, and general
applicability.1
However, morphological cancer classification systems are far
from ideal. The subjective nature of morphological typing means that the
interpretation of histological sections frequently differs between
specialists.2 Within existing diagnostic
groupings there are often large variations in patient outcome and, as the
groupings have been refined through the addition of extra parameters, the
systems are becoming increasingly complex.
Molecular ‘markers’ are now used in clinical
practice to improve cancer diagnoses. In breast cancer, for example, oestrogen
receptor and ERBB2 (HER-2/neu)
expression is used to predict responses to tamoxifen and trastuzumab
(Herceptin), respectively.3 But measuring a few
isolated molecular properties of cancer cells may leave many important
distinguishing features undetected, and as the number of markers increases it is
becoming difficult to integrate them with standard classification systems.
Recent advances may lead to significantly more powerful molecular classification
systems for cancer.
The molecular view of
cancer
What sets cancer cells apart from normal cells is a series
of progressively acquired genetic changes that corrupt the molecular pathways
designed to constrain cell growth. These genetic changes interfere with the
expression of the genome, leading to changes in the protein content of the cell
(proteome). For a gene to be expressed, its DNA sequence is first transcribed to
create an RNA copy and then processed to produce ‘messenger’ RNA
(mRNA). The ribosomal machinery of the cell translates the mRNA sequence into a
protein product. The draft sequence of the human genome, published in
2001,4,5 estimated the total number of human
genes to be around 32 000. However, this is a preliminary result and competing
estimates have ranged between 30 000 and 120 000
genes.6
The subset of genes implicated in the development of cancer
fall into two major classes: oncogenes and tumour suppressor genes. Oncogenes
are normal genes that have undergone ‘gain-of-function’ mutations,
resulting in pathological stimulation of cell growth. Tumour suppressor genes
normally act to constrain the proliferation of abnormal cells, but in cancers
may be disabled by ‘loss-of-function’ mutations. Most cancers result
from multiple genetic changes involving both oncogenes and tumour suppressor
genes. Genetic changes that provide the cell with a growth or survival advantage
are positively selected during tumour development. Underlying these genetic
events is thought to be an acquired or inherited instability in the cancer
genome – the so-called mutator phenotype – that predisposes the
cancer cell to the acquisition of additional mutations through errors in genome
replication or repair.7
Hanahan and Weinburg have drawn together the last 25 years
of cancer research to produce a framework of six functional changes necessary
for a cell to become cancerous.7 These are: 1)
growth independent of normal growth-stimulating signals; 2) invulnerability to
the mechanism of regulated cell death (apoptosis); 3) induction of a blood
supply (angiogenesis); 4) unlimited potential for replication; 5) capacity to
invade other tissues; and 6) ability to grow at metastatic sites. Each cancer
may acquire these functions in a slightly different way. Although mutations in
oncogenes and tumour suppressor genes may be the initial trigger, the expression
patterns of many more genes will be altered as a consequence, providing a unique
fingerprint for each tumour. By studying global alterations of gene expression
in cancer cells, it may be possible to identify the functional differences that
underlie much of the variability in the behaviour of morphologically similar
tumours.
DNA
microarrays
While it remains difficult to study global gene expression
patterns at the protein level,8 inferences
about gene expression can be made from the mRNA content of the cell
(transcriptome). Until recently, it was possible to study the mRNA levels of
only a few genes at a time, but the advent of DNA microarray technology has made
it possible to study the expression of thousands of genes at
once.9
An expression microarray consists of a grid of DNA spots,
each containing a sequence from a particular gene. These DNA sequences are
printed onto a glass slide by robot or synthesised directly on a silicon wafer
using methods similar to those for making computer chips. Figure 1 illustrates
how microarrays can be used to compare the mRNA levels in a tumour tissue sample
with the levels in a standardised reference sample to form an
expression profile for that
tumour.
Figure 1. Expression profiling at a glance
In a typical cancer expression profiling experiment,
the mRNA levels in a tumour tissue sample are compared to levels in a
standardised reference sample. (a) First, mRNA is extracted from the tissue
samples. (b) The RNA is reverse transcribed into cDNA. The tumour cDNA is
labelled with a red fluorescent dye (Cy5), whereas the reference cDNA is
labelled with a green fluorescent dye (Cy3). (c) The labelled cDNA samples,
containing a mix of all the expressed gene sequences, are simultaneously
hybridised to a microarray slide, where they bind to the complimentary gene
sequences. The amount of cDNA binding to each arrayed gene sequence is
proportional to the level of expression of that gene in the tumour and reference
tissues. (d) The hybridised slides are washed and then imaged by a laser
scanner, which detects fluorescence in the red and green spectrums. (e) Image
processing software is used to determine the ratio of the fluorescent intensity
in the red and green images for each spot on the microarray. This ratio provides
a measure of the relative quantity of RNA in the tumour sample compared to the
reference sample for each gene sequence probed. (f) A computer-generated
dendrogram clusters tumours with similar gene expression profiles
together.
 Expression profiles reveal so many properties of cancers
that researchers have been left wondering how to make the most of this
information. Can expression profiles be used to create better cancer
classifications? Which genes are important? And how might this improve the
management of individual patients? The first step in answering these questions
has been the development of automated computational methods for identifying
patterns within expression profiles.
Bioinformatics: dissecting
the data
Bioinformatics is the use of mathematical and computational
approaches to solving biological problems. This rapidly expanding field arose
out of the need for systems to manage and analyse the large volumes of data
being produced by genome sequencing and protein structure research. There are
many different methods being used by bioinformaticians to identify the molecular
signatures that characterise different types of cancer, but the majority fall
into two groups: clustering algorithms and class prediction
algorithms.10
Clustering algorithms (unsupervised learning) identify
groups of tumours with similar gene expression profiles. Hierarchical clustering
has been particularly popular for microarray experiments because the results can
be displayed in the form of a dendrogram
– a tree-like diagram where the most similar examples are grouped
together on nearby branches (Figure 1, (f)). Clustering algorithms help to
identify previously unrecognised similarities between tumours, but the results
can vary greatly depending on which genes are included and which clustering
algorithm is used.
Class prediction algorithms
(supervised learning) use a representative ‘training set’ of
tumour expression profiles, grouped according to some existing criteria such as
diagnosis or response to treatment, to generate a tumour class predictor. The
class predictor uses the gene expression pattern of a new tumour to predict
which group it belongs to. The classifier is judged according to its success in
classifying examples in an independent ‘testing set’. Prediction
algorithms are powerful tools for identifying patterns of gene expression that
correlate with known tumour properties but are less likely to reveal previously
unrecognised similarities between tumours.
A new way of classifying
cancer
In 1999, Golub et al showed that a class prediction
algorithm could accurately distinguish between the expression profiles of acute
myeloid leukaemia (AML) and acute lymphocytic leukaemia
(ALL).11 Although this distinction was already
possible with existing techniques, the finding validated the notion that cancers
might be classified using expression profiles. Ramaswamy et al (2001) added
support for this idea by showing that tissue samples from 14 different tumour
types could be distinguished by their expression profiles
alone.12 Now the focus of research is rapidly
shifting towards clinical applications of this method.
Exposing a disease within a
disease
Expression profiling has recently demonstrated what
oncologists have long suspected: patients with clinically and histologically
identical tumours may actually have distinct disease subtypes that were not
previously detectable. Alizadeh et al (2000)13
and Shipp et al (2002)14 profiled the tumours
of patients with large diffuse B-cell lymphoma and were able to identify
sub-types that were associated with markedly different survival. The prognostic
power of these new classifications was independent of the standard clinical
staging criteria, the International Prognostic
Index.15 In a similar analysis of paediatric
ALL, Yeoh et al (2002) identified distinct gene expression patterns associated
with known molecular subtypes and also identified a cluster of samples that may
represent a new subtype of
leukaemia.16
Individualising
treatment
A particularly difficult problem in oncology is determining
the intensity with which to treat an individual patient. One example comes from
patients with low-stage breast cancer, where adjuvant chemo- and hormonal
therapies are used to reduce the risk of metastases. Currently, patients are
selected for treatment on the basis of clinical and histological assessment, but
the selection procedure is imprecise and many patients who receive adjuvant
therapy may have survived without it. Using expression profiling, van ’t
Veer et al (2002) studied 117 primary breast cancers in women under the age of
55 years.17 By applying a class prediction
algorithm, they were able to accurately identify the subgroup of patients most
likely to benefit from adjuvant therapy and conversely the group to which it
conferred no advantage. This approach provides a strategy for targeting
high-risk cancer patients with more intense therapy and reducing the unnecessary
treatment of low-risk patients leading to a reduction in treatment failures,
side effects, and unnecessary use of costly therapies.
Assisting drug
discovery
Expression profiling will also provide new targets for drug
development by identifying regulatory pathways that are activated specifically
in malignant cells, hopefully resulting in more powerful treatments with fewer
side effects. The value of targeting cancer treatments at specific molecular
abnormalities has recently been demonstrated by the success of imatinib mesylate
(Glivec), a signalling pathway inhibitor that has proved highly effective in the
treatment of chronic myeloid
leukaemia.18
Microarrays for
medicine?
Only a fraction of the cancer markers reported in the
literature are ever used in the clinic. A new classification tool like
expression profiling will only be adopted if it is both proven and practical for
use in day-to-day clinical practice. The clinical benefits of expression
profiling must first be validated in large clinical trials. Whether microarrays
then become practical as a clinical tool will depend on factors such as cost,
speed, and simplicity. With increasing demand for this technology, competition
amongst commercial suppliers is rapidly driving the costs down. Conceivably,
expression analysis could be performed within a hospital laboratory and the
result reported as a simple diagnostic grouping with a prognostic index and list
of predicted drug sensitivities. While it is unlikely that expression profiling
will replace morphological analysis, it may provide an efficient alternative to
performing multiple molecular, cytogenetic, and immunohistochemical
tests.
But there are still important issues to be resolved before
expression profiling is ready for clinical use. Standard protocols for tumour
tissue collection will need to be adapted to cater for molecular techniques. For
example, the formalin preservatives used for histology are damaging to
RNA19 so a portion of fresh tumour tissue must
be allocated for expression analysis, taking care not to interfere with
important morphological features such as tumour margins.
The management of gene expression data remains one of the
greatest challenges. The genome sequencing projects have paved the way for
large-scale biological data collection but, whereas DNA sequences can naturally
be represented by strings of letters, there is no equally intuitive format for
gene expression profiles. Furthermore, expression profiles can vary with
different array technologies and laboratory methods. In an effort to address
these problems, the Microarray Gene Expression Database (MGED) group is
developing standards for annotation, storage, normalisation and exchange of gene
expression data.20 The widespread adoption of
such standards is vital to progress in this field because at the moment it is
impossible to systematically compare the results of different
laboratories.
Conclusions
Microarray gene expression profiling promises to help define
new molecular classification systems for cancer that transcend the limitations
of morphological classification. Preliminary studies suggest that expression
profiling will result in more reliable prognoses for cancer patients and permit
individualised disease management plans. Nevertheless, early results must be
confirmed in large clinical trials and technical issues remain to be solved
before expression profiling is ready for clinical use.
The challenges posed by this new technology are diverse, and
translation of research into better health care will require close collaboration
between a wide range of specialists including clinicians, biologists,
statisticians and bioinformaticians. Coupled with other developments in
molecular biology, it is likely that expression profiling will have a
significant impact on the practice of clinical oncology in the coming
decade.
Author information:
Kieran Holland, Medical Student; Michael Sullivan, Senior Lecturer in Paediatric
Oncology, Department of Paediatrics, Christchurch School of Medical and Health
Sciences, University of Otago, Christchurch
Acknowledgements: We
thank Lana Ellis, Christine Morris, Bridget Robinson, and Logan Walker for their
valuable advice and comments. Kieran is grateful for the financial aid provided
by the Tassell Scholarship for Cancer Research during the course of his Bachelor
of Medical Science degree.
Correspondence: Dr
Mike Sullivan, Department of
Paediatrics, Christchurch School of Medical and Health Sciences, P O Box 4345,
Christchurch. Fax: (03) 364 0747; email: mike.sullivan@otago.ac.nz
References:
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