RT Journal Article
SR Electronic
T1 A machine learning approach yields a multiparameter prognostic marker in liver cancer
JF Cancer Immunology Research
JO Cancer Immunol Res
FD American Association for Cancer Research
SP canimm.0616.2020
DO 10.1158/2326-6066.CIR-20-0616
A1 Liu, Xiaoli
A1 Lu, Jilin
A1 Zhang, Guanxiong
A1 Han, Junyan
A1 Zhou, Wei
A1 Chen, Huan
A1 Zhang, Henghui
A1 Yang, Zhiyun
YR 2021
UL http://cancerimmunolres.aacrjournals.org/content/early/2021/01/11/2326-6066.CIR-20-0616.abstract
AB A number of staging systems have been developed to predict clinical outcomes in hepatocellular carcinoma (HCC). However, no general consensus has been reached regarding the optimal model. New approaches such as machine learning (ML) strategies are powerful tools for incorporating risk factors from multiple platforms. We retrospectively reviewed the baseline information, including clinicopathologic characteristics, laboratory parameters, and peripheral immune features reflecting T-cell function, from three HCC cohorts. A gradient-boosting survival (GBS) classifier was trained with prognosis-related variables in the training dataset and validated in two independent cohorts. We constructed a 20-feature GBS model classifier incorporating 1 clinical feature, 14 laboratory parameters, and 5 T-cell function parameters obtained from peripheral blood mononuclear cells (PBMCs). The GBS model-derived risk scores demonstrated high concordance indexes (C-indexes) - 0.844, 0.827, and 0.806 in the training set and validation sets 1 and 2, respectively. The GBS classifier could separate patients into high-, medium- and low-risk subgroups with respect to death in all datasets (P<0.05 for all comparisons). A higher risk score was positively correlated with a higher clinical stage and the presence of portal vein tumor thrombus (PVTT). Subgroup analyses with respect to Child-Pugh class, Barcelona Clinic Liver Cancer (BCLC) stage, and PVTT status supported the prognostic relevance of the GBS-derived risk algorithm independent of the conventional tumor staging system. In summary, a multiparameter machine learning algorithm incorporating clinical characteristics, laboratory parameters, and peripheral immune signatures offers a different approach to identify patients with the greatest risk of HCC-related death.