Division of Oncology and Pathology, Department of Clinical Sciences Lund, Lund University, Lund, Sweden. CREATE Health Strategic Center for Translational Cancer Research, Lund University, Lund, Sweden.
Clin Cancer Res. 2016 Jan 1;22(1):218-29. doi: 10.1158/1078-0432.CCR-15-0529. Epub 2015 Aug 11.
Primary lung adenocarcinoma remains a deadly disease. Gene-expression phenotypes (GEPs) in adenocarcinoma have potential to provide clinically relevant disease stratification for improved prognosis and treatment prediction, given appropriate clinical and methodologic validation.
2,395 transcriptional adenocarcinoma profiles were assembled from 17 public cohorts and classified by a nearest centroid GEP classifier into three subtypes: terminal respiratory unit (TRU), proximal-proliferative, and proximal-inflammatory, and additionally scored by five transcriptional metagenes representing different biologic processes, including proliferation. Prognostic- and chemotherapy-predictive associations of the subtypes were analyzed by univariate and multivariate analysis using overall survival or distant metastasis-free survival as endpoints.
Overall, GEPs were associated with patient outcome in both univariate and multivariate analyses, although not in all individual cohorts. The prognostically relevant division was between TRU- and non-TRU-classified cases, with expression of proliferation-associated genes as a key prognostic component. In contrast, GEP classification was not predictive of adjuvant chemotherapy response. GEP classification showed stability to random perturbations of genes or samples and alterations to classification procedures (typically <10% of cases/cohort switching subtype). High classification variability (>20% of cases switching subtype) was observed when removing larger or entire fractions of a single subtype, due to gene-centering shifts not addressable by the classifier.
In a large-scale evaluation, we show that GEPs add prognostic value to standard clinicopathologic variables in lung adenocarcinoma. Subject to classifier refinement and confirmation in prospective cohorts, GEPs have potential to affect the prognostication of adenocarcinoma patients through a molecularly driven disease stratification.
原发性肺腺癌仍然是一种致命的疾病。腺癌的基因表达表型（GEPs）有可能通过适当的临床和方法学验证，为改善预后和治疗预测提供具有临床相关性的疾病分层。
从 17 个公共队列中收集了 2395 个转录本腺癌谱，并通过最近质心 GEP 分类器将其分为三个亚型：终末呼吸单位（TRU）、近端增殖型和近端炎症型，并进一步通过代表不同生物学过程的五个转录元进行评分，包括增殖。通过单变量和多变量分析，使用总生存期或无远处转移生存期作为终点，分析亚组与预后和化疗预测的相关性。
总体而言，GEPs 在单变量和多变量分析中与患者的生存结局相关，尽管并非在所有个体队列中都相关。具有预后相关性的分类是在 TRU 和非 TRU 分类病例之间，增殖相关基因的表达是关键的预后成分。相比之下，GEP 分类不能预测辅助化疗反应。GEP 分类对基因或样本的随机扰动以及分类过程的改变具有稳定性（通常只有 <10%的病例/队列改变亚组）。当去除单个亚组的较大或整个部分时，分类的变异性很高（>20%的病例改变亚组），这是由于分类器无法解决的基因中心偏移。
在大规模评估中，我们表明 GEPs 在肺腺癌中为标准临床病理变量增加了预后价值。在前瞻性队列中经过分类器的改进和验证后，GEPs 有可能通过分子驱动的疾病分层来影响腺癌患者的预后。
