A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients.
A novel protein-based prognostic signature improves risk stratification to guide clinical management in early-stage lung adenocarcinoma patients.
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DOI：
10.1002/path.5096
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发表时间：
2018-08
期刊：
影响因子：
--
通讯作者：
Pajares MJ
中科院分区：
文献类型：
--
作者：
Martínez-Terroba E;Behrens C;de Miguel FJ;Agorreta J;Monsó E;Millares L;Sainz C;Mesa-Guzman M;Pérez-Gracia JL;Lozano MD;Zulueta JJ;Pio R;Wistuba II;Montuenga LM;Pajares MJ
手术切除的非小细胞肺癌患者被分类的每一个病理阶段(I-IIIa)都隐藏着隐藏的生物学异质性，在每个阶段表现为不同的结果。因此，寻找强大而精确的分子分类器来评估个体患者的风险是一个未得到满足的医疗需求。在这里，我们确定并验证了基于三种蛋白(BRCA1、QKI和SLC2A1)的新的预后标志物的临床实用性，以根据复发或死亡的风险对早期肺腺癌患者进行分层。患者按照新的国际肺癌研究协会(IASLC)分期标准(2018年第8版)进行分期。使用测试队列(n=239)来评估基于这三种蛋白的这一新的预后指数(PI)的价值。预后标志是根据严格的统计学标准(Tripod：针对个体预后或诊断的多变量预测模型的透明报告)通过Cox回归得出的。该模型对无病生存期(P<；0.001)和总生存期(P<；0.001)的五年结果有非常显著的预测作用。该模型的预后能力在一个独立的多机构患者队列中得到了外部验证(n=114，P=0.021)。我们还证明，这种分子分类器为基于TNM的金标准病理分期增加了相关信息，似然比有非常显著的改善。我们随后开发了一个包括分子和病理数据的综合预后指数，该指数改善了两个队列中的风险分层(P≤0.001)。此外，签名可能有助于选择可能受益于辅助化疗的I-IIA期患者。总之，这种基于蛋白质的特征准确地识别了那些复发和死亡风险较高的患者，并为基于TNM的临床分期增加了进一步的预后信息，甚至应用了新的IASLC第8版分期标准。更重要的是，它可能是选择患者进行辅助治疗的有价值的工具。
Each of the pathological stages (I-IIIa) in which surgically resected non-small cell lung cancer patients are classified conceals hidden biological heterogeneity, manifested in heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers to assess individual patient risk is an unmet medical need. Here we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI and SLC2A1) to stratify early lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged following the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8th edition, 2018). A test cohort (n=239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression following stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of five-year outcome for disease-free survival (P<0.001) and overall survival (P<0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n=114, P=0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging with a highly significant improvement of likelihood ratio. We subsequently developed a combined prognostic index (CPI) including both the molecular and the pathological data which improved the risk stratification in both cohorts (P≤0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even applying the new IASLC 8th edition staging criteria. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy.
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预测切除非鳞状非小细胞肺癌生存率的实用分子测定：开发和国际验证研究
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