Pharmacogenomic Characterization of US FDA-approved Cytotoxic Drugs

Peters, Eric; Motsinger‐Reif, Alison A.; Havener, Tammy M.; Everitt, Lorraine; Hardison, Nicholas E.; Watson, Venita Gresham; Wagner, Michael J.; Richards, Kristy L.; Province, Mike; McLeod, Howard L.

doi:10.2217/pgs.11.92

Cited by 43 publications

(64 citation statements)

References 13 publications

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“…Several noninherited factors may mask the pharmacogenetic effects (38). However, Peters and colleagues described the response to paclitaxel treatment as having a high-heritability when assessing heritable drug-induced cell-killing on 125 lymphoblastoid cell lines derived from 14 families (39). If paclitaxel-induced toxicity and treatment outcome are more heritable than pharmacokinetics, remains to be elucidated.…”

Section: Discussionmentioning

confidence: 99%

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

Graan

Elens

Smid

et al. 2013

Clinical Cancer Research

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Purpose: Paclitaxel is used in the treatment of solid tumors and displays high interindividual variation in exposure. Low paclitaxel clearance could lead to increased toxicity during treatment. We present a genetic prediction model identifying patients with low paclitaxel clearance, based on the drug-metabolizing enzyme and transporter (DMET)-platform, capable of detecting 1,936 genetic variants in 225 metabolizing enzyme and drug transporter genes.Experimental Design: In 270 paclitaxel-treated patients, unbound plasma concentrations were determined and pharmacokinetic parameters were estimated from a previously developed population pharmacokinetic model (NONMEM). Patients were divided into a training-and validation set. Genetic variants determined by the DMET platform were selected from the training set to be included in the prediction model when they were associated with low paclitaxel clearance (1 SD below mean clearance) and subsequently tested in the validation set.Results: A genetic prediction model including 14 single-nucleotide polymorphisms (SNP) was developed on the training set. In the validation set, this model yielded a sensitivity of 95%, identifying most patients with low paclitaxel clearance correctly. The positive predictive value of the model was only 22%. The model remained associated with low clearance after multivariate analysis, correcting for age, gender, and hemoglobin levels at baseline (P ¼ 0.02).Conclusions: In this first large-sized application of the DMET-platform for paclitaxel, we identified a 14 SNP model with high sensitivity to identify patients with low paclitaxel clearance. However, due to the low positive predictive value we conclude that genetic variability encoded in the DMET-chip alone does not sufficiently explain paclitaxel clearance.

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Section: Discussionmentioning

confidence: 99%

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

Graan

Elens

Smid

et al. 2013

Clinical Cancer Research

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“…The lack of effective treatments for the neuropathy creates an urgent need to identify markers that can help to personalize treatment and avoid severe neuropathy events. The patient genetic background has been proposed to play a relevant role in the susceptibility for suffering neuropathy (5). In this regard, paclitaxel pharmacokinetic (6, 7) and pharmacodynamic (8,9) pathways have been included in studies of candidate genes and, more recently genome-wide association studies (GWAS) have been performed (10,11).…”

Section: Introductionmentioning

confidence: 99%

Targeted Sequencing Reveals Low-Frequency Variants in EPHA Genes as Markers of Paclitaxel-Induced Peripheral Neuropathy

Apellániz-Ruiz

Tejero

Inglada‐Pérez

et al. 2017

Clinical Cancer Research

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Translational RelevancePaclitaxel treatment frequently cause peripheral neuropathy, an adverse event that can limit treatment course and lead to permanent symptoms drastically decreasing quality of life. Our group has contributed to the identification and validation of common polymorphisms in EPHA genes associated with paclitaxel neuropathy, but a large part of the inter-individual variation in neuropathy remains unexplained. We hypothesized that low-frequency variants with strong effects may contribute to the neuropathy variability in patients. By performing targeted exon sequencing of candidate genes we found for the first time that patients carrying low-frequency non-synonymous coding variants in EPHA5/6/8 contribute to paclitaxelinduced neuropathy susceptibility. Furthermore, these genes might also be relevant neuropathy markers for other neurotoxic drugs due to the involvement of Eph receptors in neuronal functions. 4ABSTRACT Purpose: Neuropathy is the dose limiting toxicity of paclitaxel and a major cause for decreased quality of life. Genetic factors have been shown to contribute to paclitaxel neuropathy susceptibility; however, the major causes for inter-individual differences remain unexplained. In this study we identified genetic markers associated with paclitaxel-induced neuropathy through massive sequencing of candidate genes. were present exclusively in patients with high neuropathy and all affected the ligand binding domain. Accumulated dose analysis in the discovery series showed a significantly higher neuropathy risk for EPHA5/6/8 low-frequency non-synonymous variant carriers (HR=14.60, P=0.0042) and an independent cohort confirmed an increased neuropathy risk (HR=2.07, 95%CI=1.14-3.77, P=0.017). Combining the series gave an estimated 2.50-fold higher risk of neuropathy (95%CI=1.46-4.31; P=9.1x10 -4 ). Conclusion:This first study sequencing EPHA genes revealed that low frequency variants in EPHA6, EPHA5 and EPHA8 contribute to the susceptibility to paclitaxel-induced neuropathy.Furthermore, EPHAs neuronal injury repair function suggests that these genes might constitute important neuropathy markers for many neurotoxic drugs.

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“…For example, it has been used to assess the heritability of a number of dose-response outcomes [4, 12], and to successfully detect genetic associations of clinical relevance, with notable instances being in head and neck cancer [13] and temozolomide response [10]. Additionally, similarity in dose response has been seen across drugs within a single drug family [14], reinforcing the potential of the model to categorize responses as a function of mechanism of action.…”

Section: Introductionmentioning

confidence: 99%

Genome-Wide Association and Pharmacological Profiling of 29 Anticancer Agents Using Lymphoblastoid Cell Lines

Brown

Havener

Medina³

et al. 2014

Pharmacogenomics

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Aims Association mapping with lymphoblastoid cell lines (LCLs) is a promising approach in pharmacogenomics research, and in the current study we utilize this model to perform association mapping for 29 chemotherapy drugs. Materials and Methods Currently, we use LCLs to perform genome-wide association mapping of the cytotoxic response of 520 European Americans to 29 different anticancer drugs, the largest LCL study to date. A novel association approach using a multivariate analysis of covariance design was employed with the software program MAGWAS, testing for differences in the dose-response profiles between genotypes without making assumptions about the response curve or the biological mode of association. Additionally, by classifying 25 of the 29 drugs into 8 families according to structural and mechanistic relationships, MAGWAS was used to test for associations that were shared across each drug family. Finally, a unique algorithm using multivariate responses and multiple linear regressions across pairs of response curves was used for unsupervised clustering of drugs. Results Among the single drug studies, suggestive associations were obtained for 18 loci, 12 within/near genes. Three of these, MED12L, CHN2 and MGMT, have been previously implicated in cancer pharmacogenomics. The drug family associations resulted in 4 additional suggestive loci (3 contained within/near genes). One of these genes, HDAC4, associated with the DNA alkylating agents, shows possible clinical interactions with temozolomide. For the drug clustering analysis, 18 of 25 drugs clustered into the appropriate family. Conclusions This study demonstrates the utility of LCLs for identifying genes having clinical importance in drug response, for assigning unclassified agents to specific drug families, and proposes new candidate genes for follow-up in a large number of chemotherapy drugs.

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Pharmacogenomic Characterization of US FDA-approved Cytotoxic Drugs

Cited by 43 publications

References 13 publications

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

A Pharmacogenetic Predictive Model for Paclitaxel Clearance Based on the DMET Platform

Targeted Sequencing Reveals Low-Frequency Variants in EPHA Genes as Markers of Paclitaxel-Induced Peripheral Neuropathy

Genome-Wide Association and Pharmacological Profiling of 29 Anticancer Agents Using Lymphoblastoid Cell Lines

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