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Review
.2010 Jul;88(1):34-8.
doi: 10.1038/clpt.2010.96. Epub 2010 Jun 2.

Dissecting variability in responses to cancer chemotherapy through systems pharmacology

Affiliations
Review

Dissecting variability in responses to cancer chemotherapy through systems pharmacology

R Yang et al. Clin Pharmacol Ther.2010 Jul.

Abstract

Variability in patient responses to even the most potent and targeted therapeutics is now the primary challenge facing drug discovery and patient care, particularly in oncology and immune therapy. Variability with respect to mechanisms of induced resistance is observed both in drug-naive patients and among those who are initially responsive. Genomics has developed powerful tools for systematic interrogation of disease genotype and transcriptional states (particularly in cancer) and for correlation of these measures with parameters of disease such as histological diagnosis and outcome. In contrast, mechanistic preclinical studies remain relatively narrowly focused, leading to many apparent contradictions and poor understanding of the determinants of response. We describe the emergence of a systems pharmacology approach that is mechanistic, quantitative, probabilistic, and postgenomic and promises to do for mechanistic pharmacology what genomics is doing for correlative studies. We focus on studies in cell lines (which currently dominate mechanism-oriented analysis), but our arguments are equally valid for real tumors studied in short-term culture as xenografts and, perhaps some time in the future, in humans.

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Conflict of interest statement

CONFLICT OF INTEREST

The authors declared no conflict of interest.

Figures

Figure 1
Figure 1
Different types of variation in drug response affect dose–response curves differently. The panels on the left illustrate a simple drug response pathway in cancer cells. The drug crosses the cell membrane via transporters and inhibits its oncogenic target, leading to alteration of the downstream pathways and ultimately affecting the cell phenotype. A real example of such a pathway is inhibition of the Bcl-Abl kinase by imatinib. The panels on the right illustrate dose–response curves for three measured parameters: the ratio of drug concentrations inside and outside the cell (dotted lines), the fraction of activated targets (red lines), and cell fate response (blue lines). (a) Cancer cell in which the drug is effective. (b) Cancer cell that overexpresses a drug efflux pump for which the drug is a substrate, such as ABCG2. Dose–response for both target activation and phenotypic readouts are shifted to the right, and the cell is drug resistant. This cell could be sensitive to co-drugging with an ABCG2 inhibitor. (c) Cancer cell that overexpresses another oncogenic target, such as theBcl2 oncogene, acting downstream of the original target. The dose–response for target activation is unaffected, whereas the dose–response for phenotype is shifted to the right. This cell could be sensitive to co-drugging with a Bcl2 inhibitor.
Figure 2
Figure 2
Cell-to-cell variability in drug sensitivity. Individual cells in an isogenic population will always have slightly different biochemical properties because of stochastic fluctuation in protein levels. They may also have differences in biological state regulation, e.g., different cell cycle states. These variations at the single-cell as well as population levels will affect the perceived drug responses, measured as EC50. (a) A standard view of drug sensitivity; two cell populations vary in EC50 value, as measured by population-level survival curves at a fixed time point. Green depicts a more drug-sensitive population; red depicts a more drug-resistant one. (b) A less commonly considered possibility, in which two cell populations differ inEmax for survival and not in EC50. Variation in drug penetration, for example, is expected to alter only EC50 because increasing drug concentrations will eventually achieve maximal efficacy. The effects of variation after the drug reaches the target are less predictable. Depending on the kinetic nature of the drug response pathways, they could cause variations in EC50,Emax, or both. (c) Single-cell behavior as a function of time in an isogenic population for whichEmax is ~50% survival. In order for the whole population to show the smooth red curve illustrated ina andb, individual cells in the population must behave nonuniformly, choosing either death (cell B) or survival (cell A). In order to measure this nonuniform behavior directly, single-cell measurements such as high-content imaging and flow cytometry must be used.
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References

    1. Potti A, Nevins JR. Utilization of genomic signatures to direct use of primary chemotherapy. Curr Opin Genet Dev. 2008;18:62–67. - PubMed
    1. Stadler ZK, Come SE. Review of gene-expression profiling and its clinical use in breast cancer. Crit Rev Oncol Hematol. 2009;69:1–11. - PubMed
    1. Zhou SF, et al. Clinical pharmacogenetics and potential application in personalized medicine. Curr Drug Metab. 2008;9:738–784. - PubMed
    1. Edwards SL, et al. Resistance to therapy caused by intragenic deletion in BRCA2. Nature. 2008;451:1111–1115. - PubMed
    1. Pao W, et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA. 2004;101:13306–13311. - PMC - PubMed

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