Prognostic models for immunotherapy in non-small cell lung cancer: A comprehensive review
Siqi Ni,
Qi Liang,
Xingyu Jiang,
Yinping Ge,
Yali Jiang,
Lingxiang Liu
Affiliations
Siqi Ni
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
Qi Liang
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
Xingyu Jiang
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
Yinping Ge
The Friendship Hospital of Ili Kazakh Autonomous Prefecture Ili & Jiangsu Joint Institute of Health, Yining 835000, Xinjiang Uygur Autonomous Regio, China
Yali Jiang
The Friendship Hospital of Ili Kazakh Autonomous Prefecture Ili & Jiangsu Joint Institute of Health, Yining 835000, Xinjiang Uygur Autonomous Regio, China; Corresponding author.
Lingxiang Liu
Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China; Corresponding author. Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing, Jiangsu, 210029, China.
The introduction of immune checkpoint inhibitors (ICIs) has revolutionized the treatment of lung cancer. Given the limited clinical benefits of immunotherapy in patients with non-small cell lung cancer (NSCLC), various predictors have been shown to significantly influence prognosis. However, no single predictor is adequate to forecast patients' survival benefit. Therefore, it's imperative to develop a prognostic model that integrates multiple predictors. This model would be instrumental in identifying patients who might benefit from ICIs. Retrospective analysis and small case series have demonstrated the potential role of these models in prognostic prediction, though further prospective investigation is required to evaluate more rigorously their application in these contexts. This article presents and summarizes the latest research advancements on immunotherapy prognostic models for NSCLC from multiple omics perspectives and discuss emerging strategies being developed to enhance the domain.
