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
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
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
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 [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
SNP Integration Into Clinical Trials
Given this paradigm, SNP analysis has significant implications
for examining both drug efficacy and safety in clinical trials (Figure
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.
1. International Human Genome Sequencing Consortium: Initial
sequencing and analysis of the human genome. Nature 409:860-921, 2001.
2. Venter JC, Adams MD, Myers EW, et al: The sequence of the
human genome. Science 291:1304-1351, 2001.
3. Evans WE, Relling MV: Pharmacogenomics: Translating
functional genomics into rational therapeutics. Science 286:487-491, 1999.
4. Roses AD: Pharmacogenetics and the practice of medicine.
Nature 405:857-865, 2000.
5. Fay JC, Wyckoff GJ, Wu CI: Positive and negative selection on
the human genome. Genetics 158:1227-1234, 2001.
6. Altshuler D, Pollara VJ, Cowles CR, et al: An SNP map of the
human genome generated by reduced representation shotgun sequencing. Nature
7. The International SNP Map Working Group: A map of human
genome sequence variation containing 1.42 million single-nucleotide
polymorphisms. Nature 409:928-933, 2001.
8. Wang DG, Fan JB, Siao CJ, et al: Large-scale identification,
mapping, and genotyping of single-nucleotide polymorphisms in the human genome.
Science 280:1077-1082, 1998.
9. Marshall E: Drug firms to create public database of genetic
mutations. Science 284:406-407, 1999.
10. Collins FS, Guyer MS, Charkravarti A: Variations on a theme:
Cataloging human DNA sequence variation. Science 278:1580-1581, 1997.
11. Cargill M, Altshuler D, Ireland J, et al: Characterization
of single-nucleotide polymorphisms in coding regions of human genes. Nat Genet
12. Brookes AJ: The essence of SNPs. Gene 234:177-186, 1999.
13. Pritchard JK: Are rare variants responsible for
susceptibility to complex diseases? Am J Hum Genet 69:124-137, 2001.
14. Gray IC, Campbell DA, Spurr NK: Single-nucleotide
polymorphisms as tools in human genetics. Hum Mol Genet 9:2403-2408, 2000.
15. Kruglyak L: Prospects for whole-genome linkage
disequilibrium mapping of common disease genes. Nat Genet 22:139-144, 1999.
16. Reich DE, Cargill M, Bolk S, et al: Linkage disequilibrium
in the human genome. Nature 411:199-204, 2001.
17. Riley JH, Allan CJ, Lai E, et al: The use of
single-nucleotide polymorphisms in the isolation of common disease genes.
Pharmacogenomics 1:39-47, 2000.
18. Krynetski EY, Evans WE: Genetic polymorphism of thiopurine
S-methyltransferase: Molecular mechanisms and clinical importance. Pharmacology
19. Caldwell J, Gardner I, Swales N: An introduction to drug
disposition: The basic principles of absorption, distribution, metabolism, and
excretion. Toxicol Pathol 23:102-114, 1995.
20. Kleyn PW, Vesell ES: Genetic variation as a guide to drug
development. Science 281:1820-1821, 1998.
21. Relling MV, Dervieux T: Pharmacogenetics and cancer therapy.
Nature Rev Cancer 1:99-108, 2001.
22. Vesell ES: Advances in pharmacogenetics and pharmacogenomics.
J Clin Pharmacol 40:930-938, 2000.
23. Pfost DR, Boyce-Jacino MT, Grant DM: A SNPshot:
Pharmacogenetics and the future of drug therapy. Trends Biotechnol 18:334-338,
24. McLeod HL, Evans WE: Pharmacogenomics: Unlocking the human
genome for better drug therapy. Annu Rev Pharmacol Toxicol 41:101-121, 2001.
25. Fijal BA, Hall JM, Witte JS: Clinical trials in the genomic
era: Effects of protective genotypes on sample size and duration of trial.
Control Clin Trials 21:7-20, 2000.
26. Elston CW, Sloane JP, Amendoeira I, et al: Causes of
inconsistency in diagnosing and classifying intraductal proliferations of the
breast. European Commission Working Group on Breast Screening Pathology. Eur J
Cancer 36:1769-1772, 2000.
27. Duggan DJ, Bittner M, Chen Y, et al: Expression profiling
using cDNA microarrays. Nat Genet 21:10-14, 1999.
28. Khan J, Saal LH, Bittner ML, et al: Expression profiling in
cancer using cDNA microarrays. Electrophoresis 20:223-229, 1999.
29. Burgess JK: Gene expression studies using microarrays. Clin
Exp Pharmacol Physiol 28:321-328, 2001.
30. Cunningham MJ: Genomics and proteomics: The new millennium
of drug discovery and development. J Pharmacol Toxicol Methods 44:291-300, 2000.
31. Lockhart DJ, Dong H, Byrne MC, et al: Expression monitoring
by hybridization to high-density oligonucleotide arrays. Nat Biotechnol
32. DeRisi J, Penland L, Brown PO, et al: Use of a cDNA
microarray to analyse gene expression patterns in human cancer. Nat Genet
33. Schena M, Shalon D, Davis RW, et al: Quantitative monitoring
of gene expression patterns with a complementary DNA microarray. Science
34. Shalon D, Smith SJ, Brown PO: A DNA microarray system for
analyzing complex DNA samples using two-color fluorescent probe hybridization.
Genome Res 6:639-645, 1996.
35. Bittner M, Meltzer P, Chen Y, et al: Molecular
classification of cutaneous malignant melanoma by gene expression profiling.
Nature 406:536-540, 2000.
36. Chen Y, Dougherty ER, Bittner ML: Ratio-based decisions and
the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364-374,
37. Ermolaeva O, Rastogi M, Pruitt KD, et al: Data management
and analysis for gene expression arrays. Nat Genet 20:19-23, 1998.
38. Kim S, Dougherty ER, Chen Y, et al: Multivariate measurement
of gene expression relationships. Genomics 67:201-209, 2000.
39. Quackenbush J: Computational analysis of microarray data.
Nat Rev Genet 2:418-827, 2001.
40. Alizadeh AA, Eisen MB, Davis RE, et al: Distinct types of
diffuse large B-cell lymphoma identified by gene expression profiling. Nature
41. Golub TR, Slonim DK, Tamayo P, et al: Molecular
classification of cancer: Class discovery and class prediction by gene
expression monitoring. Science 286:531-537, 1999.
42. Ahr A, Holtrich U, Solbach C, et al: Molecular
classification of breast cancer patients by gene expression profiling. J Pathol
43. Hedenfalk I, Duggan D, Chen Y, et al: Gene-expression
profiles in hereditary breast cancer. N Engl J Med 344:539-548, 2001.
44. Perou CM, Jeffrey SS, van de Rijn M, et al: Distinctive gene
expression patterns in human mammary epithelial cells and breast cancers. Proc
Natl Acad Sci U S A 96:9212-9217, 1999.
45. Dhanasekaran SM, Barrette TR, Ghosh D, et al: Delineation of
prognostic biomarkers in prostate cancer. Nature 412:822-826, 2001.
46. Luo J, Duggan DJ, Chen Y, et al: Human prostate cancer and
benign prostatic hyperplasia: Molecular dissection by gene expression profiling.
Cancer Res 61:4683-4688, 2001.
47. Welsh JB, Zarrinkar PP, Sapinoso LM, et al: Analysis of
gene-expression profiles in normal and neoplastic ovarian tissue samples
identifies candidate molecular markers of epithelial ovarian cancer. Proc Natl
Acad Sci U S A 98:1176-1181, 2001.
48. Gruvberger S, Ringner M, Chen Y, et al: Estrogen receptor
status in breast cancer is associated with remarkably distinct gene expression
patterns. Cancer Res 61:5979-5984, 2001.
49. Shipp MA, Ross KN, Tamayo P, et al: Diffuse large B-cell
lymphoma outcome prediction by gene-expression profiling and supervised machine
learning. Nat Med 8:68-74, 2002.
50. Khan J, Wei JS, Ringner M, et al: Classification and
diagnostic prediction of cancers using gene expression profiling and artificial
neural networks. Nat Med 7:673-679, 2001.
51. Triche TJ: Diagnosis of small-, round-cell tumors of
childhood. Bull Cancer 75:297-310, 1988.
52. Chang BD, Swift ME, Shen M, et al: Molecular determinants of
terminal growth arrest induced in tumor cells by a chemotherapeutic agent. Proc
Natl Acad Sci U S A 99:389-394, 2002.
53. Clarke PA, Hostein I, Banerji U, et al: Gene expression
profiling of human colon cancer cells following inhibition of signal
transduction by 17-allylamino-17-demethoxygeldanamycin, an inhibitor of the
hsp90 molecular chaperone. Oncogene 19:4125-4133, 2000.
54. Kudoh K, Ramanna M, Ravatn R, et al: Monitoring the
expression profiles of doxorubicin-induced and doxorubicin-resistant cancer
cells by cDNA microarray. Cancer Res 60:4161-4166, 2000.
55. Scherf U, Ross DT, Waltham M, et al: A gene expression
database for the molecular pharmacology of cancer. Nat Genet 24:236-244, 2000.
56. Kononen J, Bubendorf L, Kallioniemi A, et al: Tissue
microarrays for high-throughput molecular profiling of tumor specimens. Nat Med
57. Bubendorf L, Nocito A, Moch H, et al: Tissue microarray
(TMA) technology: Miniaturized pathology archives for high-throughput in situ
studies. J Pathol 195:72-79, 2001.
58. Bubendorf L, Kolmer M, Kononen J, et al: Hormone therapy
failure in human prostate cancer: Analysis by complementary DNA and tissue
microarrays. J Natl Cancer Inst 91:1758-1764, 1999.