UGT2B17 deletion polymorphism and exemestane-induced toxicity.

Packard, Elizabeth; Campbell-Baird, Cynthia; Leitzel, Kim; Luo, Shaman; Chen, Gang; Zhu, Junjia; Cream, Leah; Lazarus, Philip; Truica, Cristina I.

doi:10.1200/jco.2018.36.15_suppl.e12534

Cited by 1 publication

(1 citation statement)

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“…Deletion of UGT2B17 was significantly associated with overall exemestane metabolism. Packard et al (2018) examined and confirmed the correlation between deletion of UGT2B17 and joint pain in treatment with exemestane for breast cancer. Rumiato et al (2016) found that UGT2A1 rs4148304 was significantly associated with response to AI therapy in elderly breast cancer patients.…”

Section: Metabolism Of and Response To Aismentioning

confidence: 69%

Genetic Underpinnings of Musculoskeletal Pain During Treatment With Aromatase Inhibitors for Breast Cancer: A Biological Pathway Analysis

Zhu

Koleck

Bender

et al. 2019

Biological Research For Nursing

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Background: Musculoskeletal pain (MSKP) is the most reported symptom during treatment with aromatase inhibitors (AIs) for breast cancer. The mechanisms underlying MSKP are multidimensional and not well understood. The goals of this biological pathway analysis were to (1) gain an understanding of the genetic variation and biological mechanisms underlying MSKP with AI therapy and (2) identify plausible biological pathways and candidate genes for future investigation. Method: Genes associated with MSKP during AI therapy or genes involved in drug metabolism of and response to AIs were identified from the literature. Studies published through February 2019 were queried in PubMed®. The genes identified from the literature were entered into QIAGEN’s Ingenuity® Pathway Analysis (IPA) software to generate canonical pathways, upstream regulators, and networks through a core analysis. Results: The 17 genes identified were ABCB1, ABCG1, CYP17A1, CYP19A1, CYP27B1, CYP2A6, CYP3A4, CYP3A5, ESR1, OATP1B1, OPG, RANKL, SLCO3A1, TCL1A, UGT2A1, UGT2B17, and VDR. These genes are involved in encoding bone-remodeling regulators, drug-metabolizing enzymes (cytochrome P450 family, UDP-glucuronosyltransferases family), or drug transporters (ATP-binding cassette transporters, organic anion transporters). Multiple plausible biological pathways (e.g., nicotine degradation, melatonin degradation) and candidate genes (e.g., NFKB, HSP90, AKT, ERK1/2, FOXA2) are proposed for future investigation based on the IPA results. Conclusion: Multiple genes and molecular-level etiologies may contribute to MSKP with AI therapy in women with breast cancer. Our innovative combination of gene identification from the literature plus biological pathway analysis allowed for the emergence of novel candidate genes and biological pathways for future investigations.

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Section: Metabolism Of and Response To Aismentioning

confidence: 69%