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. 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."
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.
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/). 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. This valuable resource continues to expand as more SNPs are added to the SNP database.
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. 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. 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. 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(Drug information on 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.
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).
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. 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. Moreover, lead molecules targeted to clinically nonresponsive patients could be more rapidly developed. A focused clinical trial approach is important for individualsboth in terms of response to treatment and in sparing the patient unnecessary adverse treatment effects.