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Role of Genomics in Identifying New Targets for Cancer Therapy

Role of Genomics in Identifying New Targets for Cancer Therapy

ABSTRACT: The detailed map of the human genome can potentially transform future cancer therapy by merging genomics with pharmacology, thereby identifying which patients will benefit from specific therapeutic agents. Single-nucleotide polymorphisms (SNPs) provide a valuable tool for this pharmacogenetic approach to cancer therapy. The discovery of SNPs as disease markers may facilitate identification of populations at increased risk for certain cancers. In addition, SNP genetic screening may facilitate administration of appropriate treatment modalities or reveal specific genetic profiles that have importance in drug efficacy and toxicity. In addition to SNP analysis, DNA and tissue microarray analyses have the potential to transform the future of cancer therapy. For example, DNA microarrays may improve tumor classification systems as well as provide a molecular level dissection of global gene expression changes that occur in carcinogenesis. Tissue microarrays would allow one to verify candidate genes, identified from DNA microarrays, against archival tumor specimens with known clinical outcome. In addition, both microarray technologies may be combined to rapidly validate gene targets. We will review and discuss these state-of-the-art technologies including data suggesting that the combined use of these high throughput technologies will facilitate our understanding of the genetic complexities inherent in cancer and will revolutionize cancer therapy. [ONCOLOGY 16 (Suppl 4):7-13, 2002]

The recently announced completion of the
"working draft" of the human genome sequence is an important milestone
in medical science.[1,2] Identifying all the genes and their regulatory regions
provides the essential framework for the genetic blueprint of humankind and will
facilitate an understanding of the molecular basis of disease. In turn, the
postsequence era has brought forth a new foundation for a broad range of genomic
tools that can be applied to medical science, which will ultimately change the
practice of modern medicine.

Numerous lines of evidence have demonstrated that the risk of
developing certain disorders and the metabolism of drugs used to treat these
conditions are influenced by one’s genetic makeup.[3] With a detailed
structure of the human genome at hand, the future of cancer therapy involves a
merging of genomics with pharmacology, in which underlying genomic variations
can be used to predict both the efficacy and toxicity of therapeutic agents.
This pharmacogenetic approach will likely result in vastly improved patient
care.

In this article, we describe how the most abundant class of
genetic variants in the human genome, termed single-nucleotide polymorphisms (SNPs),
provides a valuable tool for pharmacogenetics in cancer therapy. We also
highlight two high-throughput technologies, DNA and tissue microarrays, which
have the potential to significantly augment the field of pharmacogenomics,
defined by Roses as "the determination and analysis of the genome (DNA) and
its products (RNA and proteins) as they relate to drug response."[4]

The application of technologies such as SNP analysis, DNA
microarray, and tissue microarray analysis will undoubtedly revolutionize cancer
therapy. It will soon be possible to identify patients who respond or fail to
respond to treatment early in the clinical drug development process. This
information would provide a significant step towards "individualizing"
cancer therapy and maximizing the benefits of treatment by tailoring patient
therapy.

The human genome, composed of approximately 3 billion base pairs
of DNA, is commonly referred to as the "book of life." Chapters of
this book represent individual chromosomes, the sentences represent genes, and
the words are codons made up of the DNA bases, adenine, cytosine, thymine, and
guanine. It is estimated that approximately 99.9% of the genetic makeup of all
individuals is identical, leaving genomic sequence variance to less than a
fraction of 1% (0.01% or about 3 million bases). Though seemingly negligible,
this 0.01% difference is significant indeed, because a single base
change/mutation can cause clinical disease. An individual is estimated to carry
approximately 300 to 1,200 deleterious mutations.[5]

In addition to deleterious mutations, silent base pair changes
(ie, changes that result in no apparent effect in an individual) seem to occur
throughout the genome, with an average frequency of 1 per 1,000 to 2,000
bases.[6-8] Single base pair differences that occur when the DNA sequences of
individuals are compared are SNPs (Figure 1). Intuitively, a high-density,
genome-wide map of all these SNPs would help to create a fingerprint of the
polymorphic variants in each individual and would have significant implications
for disease gene discovery, diagnosis, and treatment.

Identifying and cataloguing these sequence variations to create
a high-density SNP map of the entire human genome are the primary goals of The
SNP Consortium and the Human Genome Project (see http://www.ncbi.nlm.nih.gov/SNP/).[9]
Recently, a map was published of 1.42 million SNPs distributed throughout the
human genome (an average density of 1 SNP per 1.9 kilobases), providing one of
the first highly detailed marker maps of the sequence variability in human
genomes.[7] This valuable resource continues to expand as more SNPs are added to
the SNP database.

Single-Nucleotide Polymorphism Analysis

Because of their mean density, stability, and high-throughput
genotyping capabilities, SNPs have recently emerged as genetic markers of choice
for disease gene discovery and mapping.[10] Use of SNPs facilitates disease gene
mapping in two ways, genome-wide association studies and linkage disequilibrium
analysis. Single-nucleotide polymorphisms may be directly associated with a
disease trait by effecting the expression or function of the gene where they are
located. These "functional" SNPs may exist in a regulatory region, may
result in an amino acid change in a gene product, or may alter the exon-intron
splicing pattern. Functional SNPs may be enriched in particular disease
populations compared with controls. It has been estimated that individuals are
heterozygous for 24,000 to 40,000 polymorphisms that have been found to alter
amino acid composition.[11] However, it is thought that single disease-related
SNP alleles can increase or modify risk for disease, but are not sufficient to
cause disease.[12,13]

Alternatively, SNPs may be used as markers for linkage
disequilibrium.[14-17] Linkage disequilibrium is the measure of the degree of
association between two or more genetic markers that lie near each other on a
chromosome. Studies using linkage disequilibrium can identify regions of the
genome associated with a disease in a population. Single-nucleotide
polymorphisms that alter the risk of disease outcome will be the most predictive
of a possible clinical phenotype.

Genetic Screening for Treatment of Disease

Single-nucleotide polymorphism analysis also provides a useful
tool in genetic screening for the treatment of disease. There are a number of
clinically relevant SNPs that have been shown to be associated with drug
response and toxicity.[3] Polymorphisms in genes that encode drug metabolizing
enzymes for example, are observed at varying frequencies throughout the human
population. Among the most commonly prescribed of all anticancer drugs, the
thiopurines (eg, mercaptopurine [Purinethol] and thioguanine) must be converted
to thioguanine nucleotides by various enzymes in the body. These nucleotides are
then incorporated into the patient’s DNA. Polymorphisms in the
drug-metabolizing enzyme thiopurine methyltransferase have been linked to the
therapeutic efficacy of mercaptopurine, as well as to its toxicity. Patients
with two mutant thiopurine methyltransferase alleles have very low thiopurine
methyltransferase activity and, therefore, have an impaired capacity to
eliminate mercaptopurine and thioguanine from the body. This results in serious,
often life-threatening, toxicity.[18]

Gene-specific polymorphisms have been observed in a number of
other drug-metabolizing enzymes, including dihydropyrimidine dehydrogenase,
glucuronosyl transferase, Cyp17, glutathione transferase, cytochrome P-450, and
5,10-methylenetetra-hydrofolate reductase. Furthermore, polymorphisms have also
been identified in genes that encode proteins involved in drug absorption,
distribution, and elimination.[19-22] Thus, we are currently able to identify
inherited differences between individuals that may affect patient outcomes with
anticancer drug therapies.

Apart from the known gene-specific polymorphisms that have
relevance to cancer treatment, how can nondisease/nongene specific SNP analysis
be used to predict patient response to medicine? An attractive, evolving model
is to obtain genome-wide SNP profiles from large numbers of cancer patients
receiving anticancer drugs.[4,23] If a specific SNP pattern from patients who
responded to therapy is compared with that of patients who failed to respond, a
common set of genetic variants between the two groups might be revealed.
Additionally, SNP profiles from patients who experience adverse events during
treatment can be compared with those patients who did not suffer adverse events
to identify DNA regions associated with drug toxicity. Taken together, these SNP
signatures, or "medicine response profiles," provide a potentially
powerful tool to predict whether an individual is likely to respond to a drug (Figure
2
).[4]

SNP Integration Into Clinical Trials

Given this paradigm, SNP analysis has significant implications
for examining both drug efficacy and safety in clinical trials (Figure
3
).[4,24]
Single-nucleotide polymorphism analysis can be implemented into clinical trials
in two ways.[4] First, patients should be selected for phase III trials based on
the response profiles obtained from high-density SNP scoring of responders and
nonresponders in phase II trials. Second, SNP profiles should be identified that
characterize patients who suffer serious or common drug adverse events compared
with those patients who respond to therapy with no drug adverse events. The goal
is to combine these two SNP profiles and generate a comprehensive medical
response to drug efficacy.

This SNP signature, or fingerprint, would result in the
development of phase III trials that are faster to complete, require fewer
patients, and cost less to conduct.[25] Moreover, lead molecules targeted to
clinically nonresponsive patients could be more rapidly developed. A focused
clinical trial approach is important for individuals—both in terms of response
to treatment and in sparing the patient unnecessary adverse treatment effects.

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