Kou Fan, Wu Lei, Guo Yan, Zhang Bailu, Li Baihui, Huang Ziqi, Ren Xiubao, Yang Lili
Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Cancer Immunology and Biotherapy, Tianjin 300060, China.
Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
Cancer Biol Med. 2021 Aug 27;19(5):685-95. doi: 10.20892/j.issn.2095-3941.2020.0728.
Lung cancer is the most common cause of cancer-related deaths worldwide. Somatic copy number alterations (SCNAs) have been used to predict responses to therapies in many cancers, including lung cancer. However, little is known about whether they are predictive of radiotherapy outcomes. We aimed to understand the prognostic value and biological functions of SCNAs.
We analyzed the correlation between SCNAs and clinical outcomes in The Cancer Genome Atlas data for 486 patients with non-small cell lung cancer who received radiotherapy. Gene set enrichment analyses were performed to investigate the potential mechanisms underlying the roles of SCNAs in the radiotherapy response. Our results were validated in 20 patients with lung adenocarcinoma (LUAD) receiving radiotherapy.
SCNAs were a better predictor of progression-free survival (PFS) in LUAD ( = 0.024) than in lung squamous carcinoma ( = 0.18) in patients treated with radiotherapy. Univariate and multivariate regression analyses revealed the superiority of SCNAs in predicting PFS in patients with LUAD. Patients with stage I cancer and low SCNA levels had longer PFS than those with high SCNA levels ( = 0.022). Our prognostic nomogram also showed that combining SCNAs and tumor/node/metastasis provided a better model for predicting long-term PFS. Additionally, high SCNA may activate the cell cycle pathway and induce tumorigenesis.
SCNAs may be used to predict PFS in patients with early-stage LUAD with radiotherapy, in combination with TNM, with the aim of predicting long-term PFS. Therefore, SCNAs are a novel predictive biomarker for radiotherapy in patients with LUAD.
肺癌是全球癌症相关死亡的最常见原因。体细胞拷贝数改变（SCNAs）已被用于预测包括肺癌在内的多种癌症对治疗的反应。然而，关于它们是否能预测放疗结果，人们知之甚少。我们旨在了解SCNAs的预后价值和生物学功能。
我们分析了486例接受放疗的非小细胞肺癌患者的癌症基因组图谱数据中SCNAs与临床结果之间的相关性。进行基因集富集分析以研究SCNAs在放疗反应中作用的潜在机制。我们的结果在20例接受放疗的肺腺癌（LUAD）患者中得到验证。
在接受放疗的患者中，SCNAs对LUAD患者无进展生存期（PFS）的预测效果（P = 0.024）优于肺鳞癌患者（P = 0.18）。单因素和多因素回归分析显示SCNAs在预测LUAD患者PFS方面具有优越性。I期癌症且SCNAs水平低的患者比SCNAs水平高的患者PFS更长（P = 0.022）。我们的预后列线图还显示，将SCNAs与肿瘤/淋巴结/转移情况相结合能更好地预测长期PFS。此外，高SCNAs可能激活细胞周期通路并诱导肿瘤发生。
SCNAs可与TNM相结合，用于预测早期LUAD患者放疗后的PFS，以预测长期PFS。因此，SCNAs是LUAD患者放疗的一种新型预测生物标志物。
