Malignancies are characterized by somatic mutations (often in combination with inherited genetic pre-dispositions) that result in harmful survival and over-proliferation of certain cells or cell types. Historically, cancer genetics has largely concerned itself with the characterization of such somatic mutations to identify underlying genetic causes of specific carcinomas. Gene copy number amplifications and deletions are common characteristics of many cancers and identifying these genetic gains and losses provides useful information about the genesis of specific diseases. It is only in the last decade, however, that high-throughput systems have been developed to screen cancerous cells for genome-wide analysis of copy number gains or losses (1, 2, 3).
Comparative Genomic Hybridization
The development of Comparative Genomic Hybridization (CGH) has proved to be an invaluable
tool to study the genetic mutations responsibly for cancer development. The assay was first
developed and described in Science by Kallioniemi et al. in 1992 (4). The basic strategy of
the technique is to use genomic DNA from cancer cells labeled with one fluorochrome and genomic
DNA from normal reference cells labeled with a second fluorochrome and co-hybridize the labeled
samples to a metaphase spread from a normal reference cell (4). An outline of the basic
protocol is diagramed below.
The ratio of the two fluorochrome intensities is then calculated and regions where the
cancer DNA are amplified or deleted are readily detected on the metaphase spread. This
technique not only gives you information about copy number gains and losses in the cancer
genomic DNA but also allows one to identify the specific chromosomes and the regions of the
chromosomes where these changes have occurred (4). CGH provides superior resolution to
traditional karyotype analysis which only allows detection of gross chromosome amplifications
and deletions by microscope. However, traditional CGH is still limited to 3,000-10,000 Kb
resolution. For the modern oncogenomics era, higher resolution analysis was needed to be
able to detect single copy gene number changes and to identify much smaller target areas for
these changes so that single causative genes could ultimately be identified.
Array CGH
A major development in CGH technology was heralded in 1997 by Solinas-Toldo et al. with a
technique they described as matrix-CGH (1). Solinas-Toldo et al. developed a technique by
which target DNA was immobilized onto glass slides and hybridized with biotinylated tumor DNA
and digoxygenin-labeled reference DNA (1). These slides were then labeled with streptavidin-FITC
and anti-digoxygenin-TRITC and ratios of the two fluorochromes on the slides were analyzed by
confocal microscopy (1). Importantly, the matrix-CGH results were compared to traditional CGH
experiments performed on the same samples and found to correlate nicely. In fact, the matrix-CGH
analysis was able tot detect genomic gains and losses not detected by traditional CGH. While
these experiments seem very laborious and primitive in retrospect, they provided the framework
from which high-throughput array CGH would develop.
Array CGH would undergo constant development over the next several years with several important
papers refining the technique to demonstrate its sensitivity to detect single gene copy number
gains and losses (2, 3). The next major development to fully bring CGH to the realm of
high-throughput science was the development of CGH using true microarrays as probes for the
assay (5). Pollack et al. were the first to use a cDNA microarray to provide full genome-wide
analysis of a number of cancer cell lines (5). The most exciting aspect of the development
of cDNA microarray CGH was the prospect of obtaining information about genomic changes as well
as performing expression analysis with the very same array. The advent of array platforms as probes for CGH had important implications for clinical
investigation and provided a host of possibilities for what is actually used as the probe
material. A list of some of these probe options is illustrated in the diagram below.
The use of YAC/BAC clones and cDNA as probes were important for obtaining higher resolution
CGH analysis. However, genomic representation arrays, oligonucleotide arrays, and specific
target arrays provide even greater resolution power to the array CGH platform.
Higher Resolution Array CGH
Genomic Representation Arrays: Lucito et al. (Genome Res., 2000) demonstrated
that if total DNA is simplified in the probe samples than even higher resolution CGH is possible (6).
In this paper, they created a "genomic representation" by using a restriction enzyme (Bgl II)
digest of total DNA to simplify the probe information (6). By performing this representation,
they were able to show that enzyme-digested DNA provided a higher signal-to-noise ratio than
total genomic DNA and thus provided a more sensitive assay. The theoretical resolution limit of
such an assay is less than 1Kb. It is important to note that this assay is sensitive to single
nucleotide polymorphisms at restriction sites so it requires normal tissue reference from each
patient when looking at clinical cancer cells (6, 7).
Oligonucleotide Arrays: Lucito et al. (Genome Res, 2003) showed that even higher
resolution could be obtained from CGH analysis if olignucleotide probes were constructed based on
genomic representations of genomic DNA (6, 7). In this paper, 15-20mer oligonucleotide probes
were designed off of a restriction enzyme representational digest and it was demonstrated that
this assay is even more sensitive than the original representational digest developed by this
group.
Another recent paper by Barret et al. compares an in situ synthesized oligonucleotide array
to a normal array of cDNA probes and demonstrates increased sensitivity of the oligonucleotide array (8).
Using cell lines with serial increases of X-chromosomes, Barret et al. were able to show the
sensitivity of the oligonucleotide arrays to detect sequential copy number increases as
demonstrated in
Figure 3 (8). The ability of the oligonucleotide array to detect known
homozygous deletions in the A2BP1 and FRA16D genes in cancer cell lines is further demonstrated
in
Figure 5 of this paper (8). The power of oligonucleotide arrays lies in the ability to create
specific probes targeting regions of interest be it specific chromosomal regions or specific
genes themselves.
Specific Target Arrays: A number of papers in recent years have used the power of
array CGH to examine specific chromosomal regions of interest and specific gene sets of interest
(9-12). Takeo et al. examined 20 different hepatocellular carcinomas by using a probe set for
57 different known oncogenes (9). This kind of approach allows researches to look at how a
defined gene set is characterized in sample population of a specific disease. Similarly,
Massion et al. and Fritz et al. performed array CGH using both gene-specific probe sets and chromosome
specific probe sets (10, 11). Thus the power of array CGH can be further amplified when narrowed
down to specific target areas of interest. A very interesting paper by Clark et al. used 172
chromosome-17 specific expression probes to analyze the BT474 breast carcinoma cell line (12).
They performed CGH analysis using their probe set and also performed expression analysis in
parallel with the same array (12). Interestingly they found that genes that were duplicated
were sometimes characterized by an even greater fold-increase in expression levels (12).
The above represents just some of the ways that array CGH is currently being refined and
utilized by the cancer research community.
Application: Using Array CGH to Identify a Novel Gene Deletion in Mantle Cell Lymphoma
Background: Mantle Cell Lymphoma (MCL) is a disease representing approximately 6% of all
lymphomas and primarily affects males over 65 years of age. It is characterized by generalized
lymphadenopathy, often splenomagaly, and diffuse polyploidy lesions in the gut. The median
survival of MCL patients is three years after diagnosis. The disease is highly associated with
a translocation involving the Cyclin D1 locus in chromosome-11 and the IgH locus in chromosome-14,
leading to Cyclin D1 over-expression in about 75% of MCL cases. This Cyclin D1 over-expression
promotes cell cyle progression from the G1 to S phase and directly contributes to B-cell
over-proliferation.
Study: Tagawa et al. performed a genome-wide CGH analysis of 29 MCL patients and 7 MCL cell
lines using 2348 BAC/PAC clones (13). In the study, they characterized a number of previously
identified chromosome alterations in MCL as well as identifying some unique recurrent chromosomal
losses. Ultimately, they focused on a copy number loss at 2q13 and identified BIM as candidate
tumor suppressor gene affected by this chromosomal loss (13). In
Figure 1, Tagawa et al. show the CGH analysis profile of a representative patient (a) and cell line (b) (13).
Figure 2 of
the paper illustrates the frequency of the copy number gains and losses identified by the genome
wide analysis.
Table 1of the paper summarizes the results of Figure 2 by listing the recurrent
and most frequent regions of gains and loss identified in the study (13).
In
Table 2, Tagawa et al. narrow down their results to list the BAC probes showing homozygous
loss either in the patients or cell lines in the study (13). At this point in the paper,
Tagawa et al. focus on the genetic region of loss at the 2q13 locus where 5 of 29 patient samples
showed heterozygous loss and 5 of 7 of the cell lines showed at least heterozygous loss (3 of the
cell lines showed homozygous loss) (13).
Figure 3 shows
the CGH analysis of a representative patient (heterozygous loss) and 3 cell lines (homozygous
loss) at the 2q13 locus with the BIM gene position noted at the top of the figure (13).
Next, they wanted to identify the minimum region of loss at the 2q13 locus by doing Southern
Blot analysis of the BAC438K19 probe which represent this region of chromosome-2 (13). In
Figure 4, they
show a schematic of the probe designs for their Southern Blot (probes 7 and 8 are located in the
gene upstream of the BIM locus not shown in the figure) (13). Figure 4 shows that minimum region
of loss is represented in the region between probes 2 and 4 that is shown in SP53, Z138, and
Jeko-1 cell lines (13). While Southern Blot analysis suggests that the BIM gene is the gene
affected by DNA loss at 2q13 in MCL cell lines, this is demonstrated more substantially by Northern
Blot Analysis of the same cell lines using a BIM-specific probe in the final panel of
Figure 4(13).
In Figure 5a,
Tagawa et al. show homozygous deletion of the BIM locus in the Jeko-1 cell line and a heterozygous
deletion of the BIM in representative patient G468 (13). These results are further confirmed by
FISH analysis of adjacent BAC clones demonstrated in
Figure 5b(13).
While perhaps the biggest weakness of the paper is that they did not identify any homozygous
deletions of the BIM gene in any of the 29 MCL patient samples, there is good support for the
role of BIM in B-cell apoptosis and how heterozygous loss of BIM might lead to cancer
development (13, 14, 15).
A paper by Enders et al. shows that B-cell receptor (BCR) ligation promotes the association
of BIM and BCL-2 and leads to B-cell apoptosis (14).
Figure 1 of this paper demonstrates
how the loss of BIM prevents deletion of self-reactive B-cells and thus leads to
self-reactive B cell accumulation (14). Enders et al. conclude from their study that BIM
plays a critical role in BCR-mediated apoptosis and B lymphocyte deletion (14). A study that
complements these findings was performed by Egle et al. and shows that BIM is a
haploinsufficient tumor suppressor gene in B lymphocytes (15).
Figure 1 of Egle et al. shows that Myc transgene-expressing mice with either heterozygous
or homozygous deletions in the BIM gene have a higher cumulative incidence of B-cell lymphomas
as compared to wild-type BIM mice (with the Myc transgene) (15). Also, this figure correlates
increased B-cell lymphoma incidence with dramatic white blood cell increase in the peripheral
blood of these mice (15). These finding support the results of Tagawa et al. that BIM may
function as a tumor suppressor gene whose loss contributes to the proliferatioin of B lymphocytes and
the genesis of MCL in human patients (13, 15).
Summary
The papers discussed in the above web presentation speak to the power of array CGH in
studying various carcinomas at the genetic level and how this technique can be applied to
identify individual oncogenes or tumor suppressor genes that contribute to the development of
specific cancers. Array CGH is a flexible platform that allows researchers to manipulate their
genomic analysis with probe design and to perform high-throughput genetic analysis of tumor
samples leading to a better understanding of the genetic causes of these cancers.
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