Integrated transcriptome study of the tumor microenvironment for treatment response prediction in male predominant hypopharyngeal carcinoma

Yang Zhang (), Gan Liu (), Minzhen Tao, Hui Ning, Wei Guo, Gaofei Yin, Wen Gao, Lifei Feng, Jin Gu, Zhen Xie () and Zhigang Huang ()
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Yang Zhang: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education
Gan Liu: Tsinghua University
Minzhen Tao: Tsinghua University
Hui Ning: Tsinghua University
Wei Guo: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education
Gaofei Yin: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education
Wen Gao: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education
Lifei Feng: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education
Jin Gu: Tsinghua University
Zhen Xie: Tsinghua University
Zhigang Huang: Beijing Tongren Hospital, Capital Medical University, Key Laboratory of Otolaryngology Head and Neck Surgery (Capital Medical University), Ministry of Education

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-37159-8

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