Cancer Pharmacogenomics and Pharmacoepidemiology: Setting a Research Agenda to Accelerate Translation

National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-7393, USA.
CancerSpectrum Knowledge Environment (Impact Factor: 15.16). 10/2010; 102(22):1698-705. DOI: 10.1093/jnci/djq390
Source: PubMed

ABSTRACT Recent advances in genomic research have demonstrated a substantial role for genomic factors in predicting response to cancer therapies. Researchers in the fields of cancer pharmacogenomics and pharmacoepidemiology seek to understand why individuals respond differently to drug therapy, in terms of both adverse effects and treatment efficacy. To identify research priorities as well as the resources and infrastructure needed to advance these fields, the National Cancer Institute (NCI) sponsored a workshop titled "Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation" on July 21, 2009, in Bethesda, MD. In this commentary, we summarize and discuss five science-based recommendations and four infrastructure-based recommendations that were identified as a result of discussions held during this workshop. Key recommendations include 1) supporting the routine collection of germline and tumor biospecimens in NCI-sponsored clinical trials and in some observational and population-based studies; 2) incorporating pharmacogenomic markers into clinical trials; 3) addressing the ethical, legal, social, and biospecimen- and data-sharing implications of pharmacogenomic and pharmacoepidemiologic research; and 4) establishing partnerships across NCI, with other federal agencies, and with industry. Together, these recommendations will facilitate the discovery and validation of clinical, sociodemographic, lifestyle, and genomic markers related to cancer treatment response and adverse events, and they will improve both the speed and efficiency by which new pharmacogenomic and pharmacoepidemiologic information is translated into clinical practice.

Download full-text


Available from: Muin J Khoury, Jul 31, 2015
  • Source
    • "Yan and Beckman, 2005). Recently, several clinically relevant examples of the utility of pharmacogenomics that associate specific genetic polymorphisms in drug metabolizing enzymes, drug transporters, and drug target enzymes with clinical outcomes in patients treated with commonly prescribed chemotherapy drugs have been established (Lee et al., 2005, Freedman et al., 2010). The ultimate goal for genetic and pharmacogenomic studies is the development of personalized medicine, facilitating prescription of drugs based on a patient's individual genetic profile (Yan and Beckman, 2005). "
    [Show abstract] [Hide abstract]
    ABSTRACT: The impact of genomics and pharmacogenomics in the current arena of clinical oncology is well-established. In breast cancer, mutations in the BRCA1 and BRCA2 genes have been well-characterized to carry a high risk of the disease during a woman's lifespan. However, these high risk genes contribute to only a small proportion of the familial cases of breast cancer. Hence, further efforts aimed to study the contribution of genetic mutations in other genes, including the estrogen receptor gene, TP53, CYP19, and mismatch repair genes to further investigate the genetic component of breast cancer. Multiple pharmacogenomic studies have previously linked genetic variants in known pathways with treatment response in cancer patients. Currently, polymorphisms in drug metabolizing enzymes, efflux transporters, as well as, drug targets have shown correlations to variations in response and toxicity to commonly prescribed chemotherapeutic treatments of breast cancer. CYP2D6 variants have been correlated with tamoxifen response and interindividual variability seen. An emerging application of cancer genetics and pharmacogenetics involves the use of inherited or acquired genetic abnormalities to predict treatment toxicity or outcomes. Recently, methods that involve the scanning of entire genomes for common variants have begun to influence studies of cancer causation. Currently, treatment individualization for breast cancer can take place on the basis of few molecular targets including the estrogen receptor and the overexpression of the HER2 receptor. Overall, the current review summarizes the recent findings in the genetic and pharmacogenetic research of breast cancer and the advances made in personalization of treatment.
    Asian Pacific journal of cancer prevention: APJCP 01/2011; 12(5):1127-40. · 2.51 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: "Gene-drug interactions are still poorly understood and there is a need for prospective highly powered studies in phenotyped populations to evaluate the cost-effectiveness of pre-emptive drug selection and dosage according to the genotypic and phenotypic data."
    Personalized Medicine 05/2011; 8(3):289-292. DOI:10.2217/pme.11.13 · 1.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Unlike traditional chemotherapy, targeted cancer therapies are expected to work in only a subset of people with a particular cancer. However, biomarkers of response are not always known before clinical trial initiation. We present MATCH (Merging genomic and pharmacologic Analyses for Therapy CHoice), an algorithm for using genome-wide gene expression data to identify and validate a genomic biomarker of sensitivity (see Figure 1). Our proof-of-principle example is valproic acid (VPA), but we also show that an estrogen blocking drug currently used for breast cancer and a B-RAF inhibitor in trials for melanoma give predictions that correspond to their clinical uses. We use genome-wide gene expression data from treated and untreated samples from the Connectivity Map to generate a VPA response signature. We validate that the VPA signature can identify treated and untreated cells in an independent data set of normal cells and in independent samples from the Connectivity Map. The AUC for the ROC curve is 0.86. We then apply the VPA signature to publically available data sets from a panel of cancer cell lines and from primary tumor and normal tissue samples. These data suggest that there is a subset of women with breast cancer who will be sensitive to VPA. Finally, we validate that our predictions correlate with sensitivity to VPA in breast cancer cell lines grown in two-dimensional culture, primary breast tumor samples grown in three-dimensional culture, and in vivo mouse breast cancer xenografts. Together, these studies show that MATCH can identify cancer patients most likely to respond to a specific drug treatment.
    Molecular Systems Biology 07/2011; 7:513. DOI:10.1038/msb.2011.47 · 14.10 Impact Factor
Show more