P-2: Risk Prediction of platinum-based chemotherapy and Radiotherapy Outcome

该项目的首要目标是确定宿主流行病学和遗传因素 铂为主的化疗或联合胸腔内放疗的疗效和毒性预测 非小细胞肺癌患者。我们将建立一个具有良好特征的1,200名非小细胞肺癌患者队列(III和IV期- 接受以铂为基础的一线化疗(最终的胸部放射治疗)。届时，这批人将成为 对流行病学、临床和大量合理选择的生殖系多态进行研究，以 与临床结果相关联，以使我们能够构建临床疗效和 毒性。我们估计将有大约600名患者仅接受以铂为基础的化疗，其中600人 接受以铂为基础的化疗加明确的胸部放射治疗的III期患者。确实有 三个具体目标：1)我们将识别预测铂类药物疗效和毒性的新的遗传基因座 所有1,200名患者接受化疗和放射治疗。我们将采用基于途径的基因分型和 基因中约8000个SNPs频率的分析方法 铂和辐射反应。我们将研究单个SNP的主效应、单倍型和 SNPs在调节疗效和毒性方面的累积效应。我们的假设是特定的基因类型 改变铂制剂的新陈代谢或作用，或与其遗传毒性效应有关的 放射治疗可能会影响患者对这些疗法的疗效和毒性。2)我们会申请 识别影响非小细胞肺癌的基因-基因和基因-环境相互作用的机器学习工具 结果。我们将开发算法来识别接受不同铂或放射治疗的亚组 效力或毒性。我们的假设是，治疗反应是由常见的、低水平的 外显性多态，以及这些多态相互作用和/或寄主 决定治疗反应的因素。3)我们将构建可预测的生存和风险模型 毒性，通过将临床和流行病学数据与该项目的遗传数据相结合，以及其他 来自致力于这些队列的其他R01研究的信息，例如一系列表型分析。我们 假设将遗传标记添加到标准的临床和流行病学变量 最终将完善预测生存和毒性的风险评估模型。我们会 比较所有患者、单独接受化疗的患者和患者之间的预测准确性 用综合情态疗法治疗。由该项目产生的风险模型可能允许临床医生识别 在治疗开始前，最有可能和最不可能受益或产生毒性的患者 在为个别患者计划化疗和放射治疗方面具有巨大的临床益处。

The overarching goal of this project is to determine host epidemiologic and genetic factors that will be predictive of efficacy and toxicity of platinum-based chemotherapy or combined with thoracic radiotherapy in NSCLC patients. We will construct a well-characterized cohort of 1,200 NSCLC patients (Stages III and IV - receiving first-line platinum-based chemotherapy ¿ definitive thoracic radiotherapy). This cohort will then be studied for epidemiologic, clinical and a large number of rationally selected germline polymorphisms to correlate with the clinical outcome to allow us to construct predictive risk models for clinical efficacy and toxicity. We estimate there will be ~ 600 patients treated by platinum-based chemotherapy alone, and 600 Stage III patients receiving platinum-based chemotherapy plus definitive thoracic radiotherapy. There are three specific aims: 1) we will identify novel genetic loci that predict efficacy and toxicity to platinum-based chemotherapy and radiotherapy in all 1,200 patients. We will adopt a pathway-based genotyping and analyzing approach to evaluate frequencies .of about 8,000 SNPs in genes involved in pathways relevant to platinum and radiation response. We will examine individual SNP main effects, haplotypes, and the cumulative effect of SNPs in modulating efficacy and toxicity. Our hypothesis is that specific genotypes that alter the metabolism or action of platinum agents or relevant to the genotoxic effects of radiotherapy may impact the efficacy and toxicity of patients to these therapies. 2) we will apply machine-learning tools to identify gene-gene and gene-environment interactions influencing NSCLC outcome. We will develop algorithms to identify subgroups with differing platinum or radiotherapy treatment efficacy or toxicity. Our hypothesis is that therapeutic response is modulated by common, low penetrance polymorphisms, and that these polymorphisms interact with each other and/or host factors in determining response to therapy. 3) we will construct predictive risk models for survival and toxicity by integrating clinical and epidemiologic data with the genetic data from this project,and additional information from other R01 studies devoted to these cohorts such as a series of phenotypic assays. We hypothesize that the addition of genetic markers to the standard clinical and epidemiologic variables will improve the prediction of survival and toxicity of the final risk assessment models. We will compare the prediction accuracy among all patients, patients receiving chemotherapy alone, and patients treated by combined modality. The risk models resulting from this project may permit clinicians to identify patients before the start of therapy who are most and least likely to benefit or to develop toxicity and will have immense clinical benefit in terms of planning chemotherapy and radiotherapy for individual patients.