Elsevier

Gene

Volume 506, Issue 1, 10 September 2012, Pages 62-68
Gene

Identification of candidate SNPs for drug induced toxicity from differentially expressed genes in associated tissues

https://doi.org/10.1016/j.gene.2012.06.053Get rights and content

Abstract

The growing collection of publicly available high-throughput data provides an invaluable resource for generating preliminary in silico data in support of novel hypotheses. In this study we used a cross-dataset meta-analysis strategy to identify novel candidate genes and genetic variations relevant to paclitaxel/carboplatin-induced myelosuppression and neuropathy. We identified genes affected by drug exposure and present in tissues associated with toxicity. From ten top-ranked genes 42 non-synonymous single nucleotide polymorphisms (SNPs) were identified in silico and genotyped in 94 cancer patients treated with carboplatin/paclitaxel. We observed variations in 11 SNPs, of which seven were present in a sufficient frequency for statistical evaluation. Of these seven SNPs, three were present in ABCA1 and ATM, and showed significant or borderline significant association with either myelosuppression or neuropathy. The strikingly high number of associations between genotype and clinically observed toxicity provides support for our data-driven computations strategy to identify biomarkers for drug toxicity.

Highlights

► We present a bioinformatic approach to identify genes associated with drug toxicity. ► Twist — to identify genes affected by drugs and expressed in toxicity related tissue. ► Genetic variants in these genes were genotyped in 94 cancer patients. ► Variants in ABCA1 and ATM were associated with paclitaxel/carboplatin toxicity.

Introduction

Biomarkers are used as a key tool in predicting individual's risk of disease, therapeutic response or drug induced toxicity. Genome-wide association studies (GWAS) represent one high-throughput strategy for discovering genetic biomarkers associated with a phenotype of interest. However, the complex experimental design and the requirements for large sample sizes make GWAS an impractical standard experimental approach for biomarker identification.

Large volumes of high-throughput data available in public repositories, such as GEO (Gene Expressio Omnibus (Edgar et al., 2002)), ArrayExpress (Brazma et al., 2003), SMD (Stanford Microarray Database (Sherlock et al., 2001)) and TCGA (The Cancer Genome Atlas (Anon., 2008)), provide a great source of experimental data for generating novel hypotheses in silico and for identifying potential candidate biomarkers linked to a phenotype of interest. A large body of work already exists in the field of cancer chemotherapy, for example. Many public datasets that link compound effects to gene expression changes are publicly available to the research community. Given the right strategy, these data can be mined for additional genes and genetic variants associated with clinical responses to anti-cancer therapies.

Paclitaxel and carboplatin are highly active anticancer drugs that are used in combination to treat many forms of cancer, such as cancer of the breast, lung, and ovary. In the example of ovarian cancer, although paclitaxel/carboplatin combinatorial therapy improves the survival rates compared with earlier regimens, the patients are at risk for considerable sometimes life-threatening toxicity, especially neuropathy (sometimes immobilizing the patient) and myelosuppression (McGuire et al., 1996). Beyond the impact on quality of life, severe toxicity may necessitate dose reduction, delay, or even cessation of treatment. Several studies have attempted to establish a link between patient genotypes and toxicities associated with paclitaxel/carboplatin chemotherapy (Green et al., 2006, Green et al., 2008, Green et al., 2009, Leskela et al., 2011, Marsh et al., 2007, Sissung et al., 2006).

It has been suggested that the pharmacodynamics and pharmacokinetics of paclitaxel and carboplatin are influenced by the activity of several proteins, such as metabolic enzymes and drug transporters (Marsh et al., 2007). Systemic elimination of paclitaxel occurs by hepatic metabolism involving the cytochrome P450 (CYP) enzymes, CYP3A4 and CYP2C8 (Walle et al., 1995). Paclitaxel is also a substrate for the multidrug resistance P-glycoprotein, encoded by the ABCB1 gene, which is believed to affect both the tumor resistance (Marsh et al., 2007) towards paclitaxel as well as the elimination of the drug via the liver (Sparreboom et al., 1997). Genetic variations in CYP2C8 have been associated with altered clearance of the drug as well as hematological toxicity (Green et al., 2009). In some studies, polymorphisms in ABCB1 have been associated with development of neuropathy (Sissung et al., 2006) and progression-free survival (Green et al., 2006, Green et al., 2008). However these associations have not been seen by others (Marsh et al., 2007).

The discrepancies in these studies imply a problem in the selection of accurate and relevant markers for different traits. In this study, we developed a novel approach for identifying new candidate biomarkers of toxicity starting with the body of publicly available microarray gene expression data. We applied our strategy to the study of carboplatin and paclitaxel-induced toxicity in lung and ovarian cancer patients. More than 100 high quality public domain gene expression data sets from studies on paclitaxel and carboplatin were used to identify candidate genes which were subsequently evaluated by targeted genotyping in chemotherapy patient samples.

Section snippets

Overall approach for candidate biomarker analysis from gene expression signatures

The overall approach to our strategy is represented in Fig. 1. For each drug (carboplatin and paclitaxel) two meta-signatures were computed — one the combined meta-signature representing genes regulated by the drug and the other the genes with tissue-specific expression either in tissues associated with toxicity or tissues responsible for drug elimination and metabolism (kidney and liver). The four meta-signatures for carboplatin and paclitaxel were combined to create a candidate list of genes

Candidate toxicity gene sets for SNP selection

We sought to identify a single combined drug-tissue toxicity signature that prioritized genes that were affected by the drugs and were expressed in tissues associated with toxicity or pharmacokinetics. These required successive rounds of meta-analysis applied to gene expression data from the relevant tissues and from experimental studies that measured the effects of drug treatment (see Table S1 for summary of source data materials). First, we computed carboplatin and paclitaxel meta-signatures

Discussion

In this study we present a new strategy for using meta-analysis of publicly available gene expression data to generate new gene candidates for pharmacogenetic traits, such as drug-induced toxicity. Our study demonstrates that by using large collections of gene expression data as a starting point, we can identify sequence variants associated with adverse responses to chemotherapy in ovarian cancer patients. To make our approach even more robust, we expanded our consideration of tissues beyond

Conclusions

In conclusion, the approach described in this paper represents a novel methodology for using gene expression data to inform the selection of candidate toxicity genes and to subsequently identify genetic variants linked to patient responses to chemotherapy. The large quantities of data accumulating in public databases present a new and exciting opportunity for the research community — to use in silico research approaches and to come up with better designed and more cost-effective experiments.

The

Competing interests

Some of the authors (Ilya Kupershmidt, Qiaojuan Jane Su) are employed by a commercial company, NextBio. There are also a number of patents filed with respect to the technology and algorithms described in the article. NextBio also provides a commercial software platform in both free and paid versions. These competing interests do not alter the authors' adherence to all the journal policies on sharing data and materials.

Acknowledgments

The authors thank Kicki Holmberg for technical help and assistance during the genotyping analysis. We also thank Bahram Amini for his help with the Sanger sequencing. The authors also thank Professor Dimcho Bachvarov, Département de médecine, Université Laval for sharing his microarray data. This work was financially supported by grants from the European Commission (CHEMORES LSHC-CT-2007-037665), the Swedish Cancer Society, Swedish Research Council, Swedish Cancer Foundation and the County

References (30)

  • I.A. Eaves

    Combining mouse congenic strains and microarray gene expression analyses to study a complex trait: the NOD model of type 1 diabetes

    Genome Res.

    (2002)
  • R. Edgar et al.

    Gene Expression Omnibus: NCBI gene expression and hybridization array data repository

    Nucleic Acids Res.

    (2002)
  • M. Gardiner-Garden et al.

    A comparison of microarray databases

    Brief Bioinform.

    (2001)
  • H. Green et al.

    mdr-1 single nucleotide polymorphisms in ovarian cancer tissue: G2677T/A correlates with response to paclitaxel chemotherapy

    Clin. Cancer Res.

    (2006)
  • H. Green

    Pharmacogenetic studies of Paclitaxel in the treatment of ovarian cancer

    Basic Clin. Pharmacol. Toxicol.

    (2009)
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