癌症基因组图谱中确定的化学反应特征的关联分析,旨在预测卵巢癌化学反应的遗传风险。

International journal of molecular epidemiology and genetics Pub Date : 2016-03-23 eCollection Date: 2016-01-01
Erin A Salinas, Andreea M Newtson, Kimberly K Leslie, Jesus Gonzalez-Bosquet
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引用次数: 0

摘要

背景:通过整合癌症基因组图谱(TCGA)中的生物学数据,创建了与卵巢癌化疗反应相关的基因标记,并在五个独立的微阵列实验中得到验证。我们的研究旨在确定422个基因标记中的单核苷酸多态性(SNPs)是否与浆液性卵巢癌中铂基化疗反应的遗传易感性相关。方法:使用R环境中的“SNPassoc”软件包,在对数加性遗传模型下,分析浆液性卵巢癌422个基因标记中的snp与化疗反应之间的关联(结果:发现19个snp与化疗反应相关,具有统计学意义)。在TCGA中发现的显著snp均未在OCAC中得到验证,以获得感兴趣的结果,即化学反应。结论:TGCA数据库中与卵巢癌化疗反应相关的snp未在OCAC患者和对照组的更大数据库中得到验证。整合体细胞和种系信息的新策略可能有助于表征卵巢癌治疗反应的遗传预测因子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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.

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