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 classifier model as reliable indicators to predict treatment responses in HPC.

Dr. Zhen Xie is an associate professor in Bioinformatics Division, Department of Automation, Beijing National Research Center for Information Science and Technology at Tsinghua University. He has been serving as Assistant Editor-in-Chief for Quantitative Biology journal since 2013. He is also the committee member for the 5th Security Committee of Genetic Modified Organisms at China Department of Agriculture, and serve as the director for Synthetic Biology Branch of China Medicinal Biotech Association