Construction of possible integrated predictive index based on EGFR and ANXA3 polymorphisms for chemotherapy response in fluoropyrimidine-treated Japanese gastric cancer patients using a bioinformatic method

Abstract Background Variability in drug response between individual patients is a serious concern in medicine. To identify single-nucleotide polymorphisms (SNPs) related to drug response variability, many genome-wide association studies have been conducted. Methods We previously applied a knowledge-based bioinformatic approach to a pharmacogenomics study in which 119 fluoropyrimidine-treated gastric cancer patients were genotyped at 109,365 SNPs using the Illumina Human-1 BeadChip. We identified the SNP rs2293347 in the human epidermal growth factor receptor (EGFR) gene as a novel genetic factor related to chemotherapeutic response. In the present study, we reanalyzed these hypothesis-free genomic data using extended knowledge. Results We identified rs2867461 in annexin A3 (ANXA3) gene as another candidate. Using logistic regression, we confirmed that the performance of the rs2867461 + rs2293347 model was superior to those of the single factor models. Furthermore, we propose a novel integrated predictive index (iEA) based on these two polymorphisms in EGFR and ANXA3. The p value for iEA was 1.47 × 10−8 by Fisher’s exact test. Recent studies showed that the mutations in EGFR is associated with high expression of dihydropyrimidine dehydrogenase, which is an inactivating and rate-limiting enzyme for fluoropyrimidine, and suggested that the combination of chemotherapy with fluoropyrimidine and EGFR-targeting agents is effective against EGFR-overexpressing gastric tumors, while ANXA3 overexpression confers resistance to tyrosine kinase inhibitors targeting the EGFR pathway. Conclusions These results suggest that the iEA index or a combination of polymorphisms in EGFR and ANXA3 may serve as predictive factors of drug response, and therefore could be useful for optimal selection of chemotherapy regimens.

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PID https://www.doi.org/10.6084/m9.figshare.c.3607607
PID https://www.doi.org/10.6084/m9.figshare.c.3607607.v1
URL http://dx.doi.org/10.6084/m9.figshare.c.3607607
URL http://dx.doi.org/10.6084/m9.figshare.c.3607607.v1
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Author Ohtsu, Atsushi
Author Takahashi, Hiro
Author Kaniwa, Nahoko
Author Saito, Yoshiro
Author Sai, Kimie
Author Hamaguchi, Tetsuya
Author Shirao, Kuniaki
Author Shimada, Yasuhiro
Author Matsumura, Yasuhiro
Author Yoshino, Takayuki
Author Doi, Toshihiko
Author Takahashi, Anna
Author Odaka, Yoko
Author Okuyama, Misuzu
Author Jun-Ichi Sawada
Author Sakamoto, Hiromi
Author Teruhiko Yoshida
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Publication Date 2015-01-01
Publisher Figshare
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Language UNKNOWN
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keyword FOS: Biological sciences
keyword FOS: Mathematics
keyword FOS: Health sciences
system:type other
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Source https://science-innovation-policy.openaire.eu/search/other?orpId=dedup_wf_001::4f766f5188b6e82684d5bc9c0759f4da
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Last Updated 19 December 2020, 09:07 (CET)
Created 19 December 2020, 09:07 (CET)