Myeloid signature reveals immune contexture and predicts the prognosis of hepatocellular carcinoma

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Abstract

BACKGROUND Despite an increasing appreciation of the roles that myeloid cells play in tumor progression and therapy, challenges remain in interpreting the tumor-associated myeloid response balance and its translational value. We aimed to construct a simple and reliable myeloid signature for hepatocellular carcinoma (HCC).METHODS Using in situ immunohistochemistry, we assessed the distribution of major myeloid subtypes in both peri- and intratumoral regions of HCC. A 2-feature-based, myeloid-specific prognostic signature, named the myeloid response score (MRS), was constructed using an L1-penalized Cox regression model based on data from a training subset (n = 244), a test subset (n = 244), and an independent internal (n = 341) and 2 external (n = 94; n = 254) cohorts.RESULTS The MRS and the MRS-based nomograms displayed remarkable discriminatory power, accuracy, and clinical usefulness for predicting recurrence and patient survival, superior to current staging algorithms. Moreover, an increase in MRS was associated with a shift in the myeloid response balance from antitumor to protumor activities, accompanied by enhanced CD8+ T cell exhaustion patterns. Additionally, we provide evidence that the MRS was associated with the efficacy of sorafenib treatment for recurrent HCC.CONCLUSION We identified and validated a simple myeloid signature for HCC that showed remarkable prognostic potential and may serve as a basis for the stratification of HCC immune subtypes.FUNDING This work was supported by the National Science and Technology Major Project of China, the National Natural Science Foundation of China, the Science and Information Technology of Guangzhou, the Fundamental Research Funds for the Central Universities, the Guangdong Basic and Applied Basic Research Foundation, and the China Postdoctoral Science Foundation.

Authors

Chong Wu, Jie Lin, Yulan Weng, Dan-Ni Zeng, Jing Xu, Shufeng Luo, Li Xu, Mingyu Liu, Qiaomin Hua, Chao-Qun Liu, Jin-Qing Li, Jing Liao, Cheng Sun, Jian Zhou, Min-Shan Chen, Chao Liu, Zhenhong Guo, Shi-Mei Zhuang, Jin-Hua Huang, Limin Zheng