Rationale: Platinum-based chemotherapy is one of treatment mainstay for patients with advanced lung squamous cell carcinoma (LUSC) but it is still a "one-size fits all" approach. Here, we aimed to investigate the predictive and monitoring role of circulating cell-free DNA (cfDNA) profiling for the outcome of first-line chemotherapy in patients with advanced LUSC. Methods: Peripheral blood samples of 155 patients from a phase IV trial and 42 cases from an external real-world cohort were prospectively collected. We generated a copy number variations-based classifier via machine learning algorithm to integrate molecular profiling of cfDNA, named RESPONSE SCORE (RS) to predict the treatment outcome. To monitor the treatment efficacy, cfDNA samples collected at different time points were subjected to an ultra-deep sequencing platform. Results: The results showed that patients with high RS showed substantially higher objective response rate than those with low RS in training set (P < 0.001), validation set (P < 0.001) and real-world cohort (P = 0.019). Furthermore, a significant difference was observed in both progression-free survival (training set, P < 0.001; validation set: P < 0.001; real-world cohort: P = 0.019) and overall survival (training set, P < 0.001; validation set: P = 0.037) between high and low RS group. Notably, variant allele frequency (VAF) calculated from an ultra-deep sequencing platform significantly reduced in patients experienced a complete or partial response after 2 cycles of chemotherapy (P < 0.001), while it significantly increased in these of non-responder (P < 0.001). Moreover, VAF undetectable after 2 cycles of chemotherapy was correlated with markedly better objective response rate (P < 0.001) and progression-free survival (P < 0.001) than those with detectable VAF. Conclusions: These findings indicated that the RS, a circulating cfDNA sequencing-based stratification index, could help to guide first-line chemotherapy in advanced LUSC. The change of VAF is valuable to monitor the treatment response.
Jiang Tao, Jiang Liyan, Dong Xiaorong, Gu Kangsheng, Pan Yueyin, Shi Qin, Zhang Guojun, Wang Huijuan, Zhang Xiaochun, Yang Nong, Li Yuping, Xiong Jianping, Yi Tienan, Peng Min, Song Yong, Fan Yun, Cui Jiuwei, Chen Gongyan, Tan Wei, Zang Aimin, Guo Qisen, Zhao Guangqiang, Wang Ziping, He Jianxing, Yao Wenxiu, Wu Xiaohong, Chen Kai, Hu Xiaohua, Hu Chunhong, Yue Lu, Jiang Da, Wang Guangfa, Liu Junfeng, Yu Guohua, Li Junling, Zhang Henghui, Wu Lihong, Fang Lu, Liang Dandan, Zhao Yi, Zhao Weihong, Xie Wenmin, Ren Shengxiang, Zhou Caicun
Non-small-cell lung cancer, cell-free DNA, chemotherapy, machine learning