Association analysis of a chemo-response signature identified within The Cancer Genome Atlas aimed at predicting genetic risk for chemo-response in (serous) ovarian cancer Ovarian Cancer and Us OVARIAN CANCER and US Ovarian Cancer and Us

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Thursday, May 19, 2016

Association analysis of a chemo-response signature identified within The Cancer Genome Atlas aimed at predicting genetic risk for chemo-response in (serous) ovarian cancer



open access
 Published online 2016 Mar 23

 

Abstract

Background: A gene signature associated with chemo-response in ovarian cancer was created through integration of biological data in The Cancer Genome Atlas (TCGA) and validated in five independent microarray experiments. Our study aimed to determine if single nucleotide polymorphisms (SNPs) within the 422-gene signature were associated with a genetic predisposition to platinum-based chemotherapy response in serous ovarian cancer
Methods: An association analysis between SNPs within the 422-gene signature and chemo-response in serous ovarian cancer was performed under the log-additive genetic model using the ‘SNPassoc’ package within the R environment (p<0.0001). Subsequent validation of statistically significant SNPs was done in the Ovarian Cancer Association Consortium (OCAC) database. 
Results: 19 SNPs were found to be associated with chemo-response with statistical significance. None of the SNPs found significant in TCGA were validated within OCAC for the outcome of interest, chemo-response. 
Conclusions: SNPs associated with chemo-response in ovarian cancer within TGCA database were not validated in a larger database of patients and controls from OCAC. New strategies integrating somatic and germline information may help to characterize genetic predictors for treatment response in ovarian cancer.


 Validation of the association between chemo-response and these 19 SNPs was performed using the OCAC database. Within the OCAC database, over 2,500 patients with treatment for serous ovarian cancer were identified, but none of the mentioned SNPs were significant for the outcome of interest. This indicates that while individual SNPs may not be significantly associated with the outcome, further analysis of downstream gene expression profiling and pathways are nevertheless potentially useful in a validated predictive algorithm.


 

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