Dataset Information

Genetic variations associated with gemcitabine treatment outcome in pancreatic cancer.

ABSTRACT:

Background

Pancreatic cancer is a rapidly fatal disease with gemcitabine remaining the first-line therapy. We performed a genotype-phenotype association study to identify biomarkers for predicting gemcitabine treatment outcome.

Materials and methods

We selected the top 200 single nucleotide polymorphisms (SNPs) identified from our previous genome-wide association study to associate with overall survival using 400 patients treated with/or without gemcitabine, followed by imputation analysis for regions around the identified SNPs and a replication study using an additional 537 patients by the TaqMan genotyping assay. Functional validation was performed using quantitative reverse transcription-PCR for gemcitabine-induced expression in genotyped lymphoblastoid cell lines and siRNA knockdown for candidate genes in pancreatic cancer cell lines.

Results

Four SNPs in chromosome 1, 3, 9, and 20 showed an interaction with gemcitabine from the discovery cohort of 400 patients (P<0.01). Subsequently, we selected those four genotyped plus four imputed SNPs for SNP×gemcitabine interaction analysis using the secondary validation cohort. Two imputed SNPs in CDH4 and KRT8P35 showed a trend in interaction with gemcitabine treatment. The lymphoblastoid cell lines with the variant sequences showed increased CDH4 expression compared with the wild-type cells after gemcitabine exposure. Knockdown of CDH4 significantly desensitized pancreatic cancer cells to gemcitabine cytotoxicity. The CDH4 SNPs that interacted with treatment are more predictive than prognostic.

Conclusion

We identified SNPs with gemcitabine-dependent effects on overall survival. CDH4 might contribute to variations in gemcitabine response. These results might help us to better predict gemcitabine response in pancreatic cancer.

SUBMITTER: Li L

PROVIDER: S-EPMC5083195 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Although gemcitabine monotherapy is the standard treatment for advanced pancreatic cancer, patient outcome varies significantly, and a considerable number do not benefit adequately. We therefore searched for new biomarkers predictive of overall patient survival. Using LC-MS, we compared the base-line plasma proteome between 29 representative patients with advanced pancreatic cancer who died within 100 days and 31 patients who survived for more than 400 days after receiving at least two cycles of the same gemcitabine monotherapy. Identified biomarker candidates were then challenged in a larger cohort of 304 patients treated with the same protocol using reverse-phase protein microarray. Among a total of 45,277 peptide peaks, we identified 637 peaks whose intensities differed significantly between the two groups (p < 0.001, Welch's t test). Two MS peaks with the highest statistical significance (p = 2.6 x 10(-4) and p = 5.0 x 10(-4)) were revealed to be derived from alpha(1)-antitrypsin and alpha(1)-antichymotrypsin, respectively. The levels of alpha(1)-antitrypsin (p = 8.9 x 10(-8)) and alpha(1)-antichymotrypsin (p = 0.001) were significantly correlated with the overall survival of the 304 patients. We selected alpha(1)-antitrypsin (p = 0.0001), leukocyte count (p = 0.066), alkaline phosphatase (p = 8.3 x 10(-8)), and performance status (p = 0.003) using multivariate Cox regression analysis and constructed a scoring system (nomogram) that was able to identify a group of high risk patients having a short median survival time of 150 days (95% confidence interval, 123-187 days; p = 2.0 x 10(-15), log rank test). The accuracy of this model for prognostication was internally validated and showed good calibration and discrimination with a bootstrap-corrected concordance index of 0.672. In conclusion, an increased level of alpha(1)-antitrypsin is a biomarker that predicts short overall survival of patients with advanced pancreatic cancer receiving gemcitabine monotherapy. Although an external validation study will be necessary, the current model may be useful for identifying patients unsuitable for the standardized therapy.