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临床试验/NCT01619618
NCT01619618已完成不适用

Evaluation of Polymorphisms and Mutations in Genes Postulated to Alter the Efficacy of Gefitinib in Samples From E1302

Eastern Cooperative Oncology Group0 个研究点目标入组 183 人开始时间: 2012年6月1日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
183
主要终点
Association between biomarkers and clinical endpoints using logistic regression model and Cox proportional hazards

研究概览

简要总结

RATIONALE: Studying samples of blood and tissue from patients with cancer in the laboratory may help doctors learn more about changes that occur in DNA and identify biomarkers related to cancer. It may also help doctors predict how patients will respond to treatment.

PURPOSE: This laboratory study is looking at biomarkers in samples from patients with recurrent or metastatic head and neck cancer treated on ECOG-E1302 trial.

详细描述

OBJECTIVES:

  • To evaluate the frequency of ATP-binding cassette, sub-family G (WHITE), member 2 (ABCG2), met proto-oncogene (hepatocyte growth factor receptor) (c-MET), and v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog (K-ras) polymorphisms or mutations in this study population and the predictiveness of these polymorphisms on survival, time to progression, response rate, and toxicities.

OUTLINE: Archived tumor tissue and peripheral blood mononuclear cells are analyzed for the frequency of ABCG2, c-MET, and K-ras polymorphisms or mutations by polymerase chain reaction (PCR). Results are then correlated with patients' clinical outcomes and toxicity.

研究设计

研究类型
Observational
观察模型
Other
时间视角
Other

入排标准

年龄范围
18 Years 至 120 Years(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • 未提供

排除标准

  • 未提供

结局指标

主要结局

Association between biomarkers and clinical endpoints using logistic regression model and Cox proportional hazards

时间窗: 1 year

The prevalence of c-MET, ABCG2, and K-ras polymorphisms or mutation status summarized by frequency and percentage for all samples

时间窗: 1 year

Association of c-MET, ABCG2, and K-ras polymorphisms or mutation status with toxicity using Fisher's exact test

时间窗: 1 year

Association between biomarkers and time to event distribution estimated by by Kaplan-Meier and estimated by log-rank tests

时间窗: 1 year

次要结局

未报告次要终点

研究者

申办方类型
Network
责任方
Sponsor

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