In the past half-century, pharmacogenetics and pharmacogenomics have rapidly incorporated advances in modern biology, genetics, and genomics. The field has evolved from focusing on monogenic traits to emphasizing polygenic pathways. These pathways encompass genes in all relevant drug-metabolizing enzymes, drug transporters, and drug targets, as well as signaling cascades that are upstream or downstream from the targets.

Hypothesis-driven approaches have resulted in some striking examples of pharmacogenetics that have elicited strong interest and raised expectations for the clinical application of pharmacogenetics and personalized therapy. The SNP-based genome-wide scan, although a hot topic in disease association studies, has yet to be implemented in a pharmacogenomic study, but will certainly be applied in the near future with major collective efforts. However, other whole-genome platforms, such as array-CGH, gene expression profiling and proteomics, have produced potentially informative predictive profiles for drug response, underlining the importance of taking a global approach for pharmacogenomic research. Each approach we described in this chapter has its pros and cons (Table 1) and we will see the application of each in pharmacogenetic studies for a long time.

Despite the progress of exploratory research, the translation of pharmacogenomics from research to bedside has not been as rapid as we hoped. The major hurdle seems to be a lack of reproducibility and validation. The inconsistent results that currently exist in the literature are astonishing. These inconsistent results need to be confirmed by larger retrospective studies. Then, results from retrospective studies need to be verified in prospective studies.

The path to individualized therapy is complex and will ultimately require a combination of genomic and proteomic analyses, which will incorporate tumor characteristics and somatic events (mutation, deletion and amplification) in order to provide a complete

Table 1

Comparisons Between the Three Approaches in Pharmacogenomic Studies


Hypothesis-driven study and the results are easy to interpret; small sample size requirement; simple analysis; no multiple comparison issue; low cost; easy to validate

Candidate gene approach

Pathway-based approach

Genome-wide scanning

Higher predictive power than candidate gene approach; taking consideration of gene-gene interactions in polygenic traits

Hypothesis-generating study which is not dependent on current knowledge and may identify completely novel markers; non-biased global assessment of genes and polymorphisms which may uncover gene-gene interactions


Limited by our current knowledge of gene and polymorphism function and drug pathways which may miss the true candidate genes and polymorphisms; low predictive power for polygenic traits; no consideration of gene-gene interaction Limited by our current knowledge; requiring larger sample size; assuming equal contribution for each gene and allele; the resulting gene-gene interactions are difficult to test biologically; validation is more difficult Huge number of false-positives; very large sample size requirement; sophisticated statistical analysis; high cost picture of treatment response. For adverse reactions, germline genetic polymorphisms play a decisive role. However, for treatment efficacy, somatic aberrations may play a more significant role. Clinical validation of the polygenic models requires a large homogeneous patient population, a uniform treatment regimen, and sophisticated statistical analyses.

It should be noted that the current drugs are designed to downplay the importance of individual variation in drug response, in contrast to the purpose of pharmacogenomics. Future pharmacogenomic studies should be incorporated into the early-phase clinical trials during the development of drugs. Such pharmacogenomic information may help to reduce the size and cost of phase III trials and improve the chance of developing effective novel targeted drugs. The prospect of personalized chemotherapy based on a pharmacogenomic testing in the next half-century is indeed bright.

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