Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer
Integrating Tumor and Stromal Gene Expression Signatures With Clinical Indices for Survival Stratification of Early-Stage Non-Small Cell Lung Cancer
复制标题
DOI：
10.1093/jnci/djv211
复制
发表时间：
2015-10-01
影响因子：
10.3
通讯作者：
Diehn, Maximilian
中科院分区：
文献类型：
--
作者：
Gentles, Andrew J.;Bratman, Scott V.;Diehn, Maximilian
背景：早期非小细胞肺癌（NSCLC）的准确生存分层可以为辅助治疗的使用提供信息。我们开发了一种临床上可实施的死亡风险评分，结合了不同的肿瘤微环境基因表达特征和临床变量。方法：使用 1106 个非鳞状 NSCLC 的基因表达谱来生成九基因分子预后指数 (MPI) 并进行内部验证。在福尔马林固定石蜡包埋 (FFPE) 组织 (n = 98) 的独立队列中开发并验证了定量聚合酶链反应 (qPCR) 测定法。使用监测、流行病学和最终结果数据并结合 MPI 生成使用临床变量的预后评分。所有生存统计检验均为双边。 结果：MPI 将 I 期患者分为三个微阵列和一个 FFPE qPCR 验证队列的预后类别（HR = 2.99，95% CI = 1.55 至 5.76，在最大的微阵列验证队列的 IA 期患者中 P < .001；HR = 3.95，95% CI = 1.24 至 12.64，P = .01 在 qPCR 队列的 IA 期）。预后基因在不同的肿瘤细胞亚群中表达，与增殖和干细胞相关的基因预示着不良结果，而与正常肺分化和免疫浸润相关的基因则与较高的生存率相关。将 MPI 与临床变量相结合可提供最大的预后能力（在最大的微阵列队列的 I 期患者中，HR = 3.43，95% CI = 2.18 至 5.39，P < .001；在 qPCR 队列的 I 期患者中，HR = 3.99，95% CI = 1.67 至 9.56，P < .001）。最后，无论 EGFR、KRAS、TP53 和 ALK 的体细胞改变如何，MPI 都具有预后作用。结论：MPI 整合了肿瘤及其微环境中表达的基因，可以在 FFPE 组织上使用 qPCR 检测在临床上实施。将 MPI 与临床变量相结合的复合模型可提供最准确的风险分层。
Background: Accurate survival stratification in early-stage non-small cell lung cancer (NSCLC) could inform the use of adjuvant therapy. We developed a clinically implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables.Methods: Gene expression profiles from 1106 nonsquamous NSCLCs were used for generation and internal validation of a nine-gene molecular prognostic index (MPI). A quantitative polymerase chain reaction (qPCR) assay was developed and validated on an independent cohort of formalin-fixed paraffin-embedded (FFPE) tissues (n = 98). A prognostic score using clinical variables was generated using Surveillance, Epidemiology, and End Results data and combined with the MPI. All statistical tests for survival were two-sided.Results: The MPI stratified stage I patients into prognostic categories in three microarray and one FFPE qPCR validation cohorts (HR = 2.99, 95% CI = 1.55 to 5.76, P < .001 in stage IA patients of the largest microarray validation cohort; HR = 3.95, 95% CI = 1.24 to 12.64, P = .01 in stage IA of the qPCR cohort). Prognostic genes were expressed in distinct tumor cell subpopulations, and genes implicated in proliferation and stem cells portended poor outcomes, while genes involved in normal lung differentiation and immune infiltration were associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR = 3.43, 95% CI = 2.18 to 5.39, P < .001 in stage I patients of the largest microarray cohort; HR = 3.99, 95% CI = 1.67 to 9.56, P < .001 in stage I patients of the qPCR cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK.Conclusion: The MPI incorporates genes expressed in the tumor and its microenvironment and can be implemented clinically using qPCR assays on FFPE tissues. A composite model integrating the MPI with clinical variables provides the most accurate risk stratification.