Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation.
Qiheng Gou, Zijian Liu, Yuxin Xie, Yulan Deng, Ji Ma, Jiangping Li, Hong Zheng
Author Information
Qiheng Gou: Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Zijian Liu: Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Yuxin Xie: Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Yulan Deng: Department of Medical Oncology, West China Hospital, Sichuan University, Chengdu, China.
Ji Ma: Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Jiangping Li: Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China.
Hong Zheng: Department of Medical Oncology of Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
The role of the tumor microenvironment (TME) in predicting prognosis and therapeutic efficacy has been demonstrated. Nonetheless, no systematic studies have focused on TME patterns or their function in the effectiveness of immunotherapy in triple-negative breast cancer. We comprehensively estimated the TME infiltration patterns of 491 TNBC patients from four independent cohorts, and three cohorts that received immunotherapy were used for validation. The TME subtypes were comprehensively evaluated based on immune cell infiltration levels in TNBC, and the TRG score was identified and systematically correlated with representative tumor characteristics. We sequenced 80 TNBC samples as an external validation cohort to make our conclusions more convincing. Two TME subtypes were identified and were highly correlated with immune cell infiltration levels and immune-related pathways. More representative TME-related gene (TRG) scores calculated by machine learning could reflect the fundamental characteristics of TME subtypes and predict the efficacy of immunotherapy and the prognosis of TNBC patients. A low TRG score, characterized by activation of immunity and ferroptosis, indicated an activated TME phenotype and better prognosis. A low TRG score showed a better response to immunotherapy in TNBC by TIDE (Tumor Immune Dysfunction and Exclusion) analysis and sensitivity to multiple drugs in GDSC (Genomics of Drug Sensitivity in Cancer) analysis and a significant therapeutic advantage in patients in the three immunotherapy cohorts. TME subtypes played an essential role in assessing the diversity and complexity of the TME in TNBC. The TRG score could be used to evaluate the TME of an individual tumor to enhance our understanding of the TME and guide more effective immunotherapy strategies.
