Predictive mutation signature of immunotherapy benefits in NSCLC based on machine learning algorithms - Liu, Zhichao; Lin, Guo; Yan, Zeping; Li, Linduo; Wu, Xingchen; Shi, Jingrong; He, Jianxing; Zhao, Lei; Liang, Hengrui; Wang, Wei (Frontiers in Immunology, 2022)
Background: The development of prediction tools for immunotherapy approaches is a clinically important and rapidly emerging field. The routinely used prediction biomarker is not accurate enough and may not adequately utilize the large amounts of medical data. Machine learning is a promising way to predict benefit of immunotherapy from individual data by individuating the most important features from genomic data and clinical characteristics. Methods: Machine learning was applied to identify a list of candidate genes that may predict for benefits from immunotherapy by the use of data from published cohort of 853 patients with NSCLC. We used XGBoost to capture nonlinear relations among many mutation genes and ICI benefits. The value of the derived machine learning based-mutation signature (ML-signature) on immunotherapy efficacy was evaluated and compared with the tumor mutational burden (TMB) and other clinical characteristics. The predictive power of ML-signature was also evaluated in independent cohorts of patients with NSCLC treated with ICI. Results: We constructed the ML-signature based on 429 (training/validation = 8/2) patients received immunotherapy, and extracted 88 eligible predictive genes. Additionally, we conducted internal and external validation with the utility of OAK+POPLAR dataset and independent cohorts, respectively. This ML-signature shown the enrichment in immune-related signaling pathways; and compared to TMB, ML-signature equipped with favorable predictive value and stratification. Conclusion: Artificial intelligence is a promising way of examining data for hidden patterns that would be useful for improving prediction biomarker of immunotherapy. Future studies could focus more on this aspect, for example by using machine learning to accelerate biomarker identification and in-depth translational studies uncovering the molecular mechanisms underlying the effects.