Experimental Hematology & Oncology (May 2020)

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma

  • Furong Liu,
  • Lu Qin,
  • Zhibin Liao,
  • Jia Song,
  • Chaoyi Yuan,
  • Yachong Liu,
  • Yu Wang,
  • Heze Xu,
  • Qiaofeng Zhang,
  • Youliang Pei,
  • Hongwei Zhang,
  • Yonglong Pan,
  • Xiaoping Chen,
  • Zhanguo Zhang,
  • Wanguang Zhang,
  • Bixiang Zhang

DOI
https://doi.org/10.1186/s40164-020-00165-3
Journal volume & issue
Vol. 9, no. 1
pp. 1 – 16

Abstract

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Abstract Background Immune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients’ prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated. Methods We performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures. Results We defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient’s immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC. Conclusions Our work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.

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