Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy

Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy
Title:
Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy
Other Titles:
BMC Cancer
DOI:
10.1186/s12885-018-4471-x
Publication Date:
11 May 2018
Citation:
Limviphuvadh, V., Tan, C.S., Konishi, F. et al. Discovering novel SNPs that are correlated with patient outcome in a Singaporean cancer patient cohort treated with gemcitabine-based chemotherapy. BMC Cancer 18, 555 (2018). https://doi.org/10.1186/s12885-018-4471-x
Abstract:
BACKGROUND: Single Nucleotide Polymorphisms (SNPs) can influence patient outcome such as drug response and toxicity after drug intervention. The purpose of this study is to develop a systematic pathway approach to accurately and efficiently predict novel non-synonymous SNPs (nsSNPs) that could be causative to gemcitabine-based chemotherapy treatment outcome in Singaporean non-small cell lung cancer (NSCLC) patients. METHODS: Using a pathway approach that incorporates comprehensive protein-protein interaction data to systematically extend the gemcitabine pharmacologic pathway, we identified 77 related nsSNPs, common in the Singaporean population. After that, we used five computational criteria to prioritize the SNPs based on their importance for protein function. We specifically selected and screened six candidate SNPs in a patient cohort with NSCLC treated with gemcitabine-based chemotherapy. RESULT: We performed survival analysis followed by hematologic toxicity analyses and found that three of six candidate SNPs are significantly correlated with the patient outcome (P < 0.05) i.e. ABCG2 Q141K (rs2231142), SLC29A3 S158F (rs780668) and POLR2A N764K (rs2228130). CONCLUSIONS: Our computational SNP candidate enrichment workflow approach was able to identify several high confidence biomarkers predictive for personalized drug treatment outcome while providing a rationale for a molecular mechanism of the SNP effect.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
This work was supported by the Agency for Science, Technology and Research (A*STAR) Joint Council Office (JCO) Grant JCOAG04_FG03_2009. The clinical trial was supported by the BMRC grant 06/1/26/19/482. RAS is supported by the National Research Foundation, Singapore and the Singapore Ministry of Education under its Research Centers of Excellence initiative.
Description:
ISSN:
1471-2407
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