Department of Otolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education, 100730, Beijing, China.
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Center for Synthetic and System Biology, Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China.
Nat Commun. 2023 Mar 16;14(1):1466. doi: 10.1038/s41467-023-37159-8.
The efficacy of the first-line treatment for hypopharyngeal carcinoma (HPC), a predominantly male cancer, at advanced stage is only about 50% without reliable molecular indicators for its prognosis. In this study, HPC biopsy samples collected before and after the first-line treatment are classified into different groups according to treatment responses. We analyze the changes of HPC tumor microenvironment (TME) at the single-cell level in response to the treatment and identify three gene modules associated with advanced HPC prognosis. We estimate cell constitutions based on bulk RNA-seq of our HPC samples and build a binary classifier model based on non-malignant cell subtype abundance in TME, which can be used to accurately identify treatment-resistant advanced HPC patients in time and enlarge the possibility to preserve their laryngeal function. In summary, we provide a useful approach to identify gene modules and a classifier model as reliable indicators to predict treatment responses in HPC.
头颈部鳞状细胞癌（HNSCC）是一种主要发生于男性的癌症，对于晚期患者的一线治疗疗效仅约为 50%，且缺乏可靠的分子预后指标。在本研究中，我们根据治疗反应将头颈部鳞状细胞癌患者一线治疗前后的活检样本分为不同组。我们分析了头颈部鳞状细胞癌肿瘤微环境（TME）在治疗反应下的单细胞水平变化，并鉴定出与晚期头颈部鳞状细胞癌预后相关的三个基因模块。我们根据头颈部鳞状细胞癌样本的 bulk RNA-seq 估计细胞组成，并基于 TME 中非恶性细胞亚型丰度构建了一个二分类器模型，该模型可用于准确识别治疗抵抗的晚期头颈部鳞状细胞癌患者，并提高保留其喉部功能的可能性。总之，我们提供了一种有用的方法来鉴定基因模块和分类器模型，作为预测头颈部鳞状细胞癌治疗反应的可靠指标。
