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Updated: Oct 5, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Biomarkers for predicting the efficacy of immune checkpoint inhibitors
Chengji Wang1, He-Nan Wang2, Liang Wang2,3
1Beijing Tongren Hospital, Capital Medical University, Beijing 100730, China.
Abstract:
Immune checkpoint blockade has vastly changed the landscape of cancer treatment and showed a promising prognosis for cancer patients. However, there is still a large portion of patients who have no response to this therapy. Therefore, it's essential to investigate biomarkers to predict the efficacy of immune checkpoint inhibitors. This article summarizes the predictive value of established biomarkers, including programmed cell death ligand 1(PD-L1) expression level, tumor mutational burden, tumor-infiltrating lymphocytes, and mismatch repair deficiency. It also addresses the predictive value of tumorous mutations, circulation factors, immune-related factors, and gut microbiome with immunotherapy treatment. Furthermore, some of the emerging novel biomarkers, and potential markers for hyper progressive disease are discussed, which should be validated in clinical trials in the future.
Insights
Predicting cancer immunotherapy response is crucial. This review covers biomarkers like PD-L1, tumor mutational burden, and the gut microbiome to guide immune checkpoint inhibitor efficacy.
Area of Science:
- Oncology
- Immunology
- Biomarker Discovery
Background:
- Immune checkpoint blockade revolutionized cancer therapy, offering improved prognoses.
- A significant patient subset does not respond to these treatments, necessitating predictive biomarkers.
Purpose of the Study:
- To review established and emerging biomarkers for predicting immune checkpoint inhibitor (ICI) efficacy.
- To discuss biomarkers associated with hyperprogressive disease during immunotherapy.
Main Methods:
- Literature review of established biomarkers: PD-L1 expression, tumor mutational burden (TMB), tumor-infiltrating lymphocytes (TILs), and mismatch repair deficiency (dMMR).
- Exploration of novel predictive factors including tumor mutations, circulating factors, immune-related factors, and gut microbiome composition.
- Discussion of emerging biomarkers and potential markers for hyperprogressive disease (HPD).
Main Results:
- Established biomarkers (PD-L1, TMB, TILs, dMMR) show varying predictive value for ICI response.
- Tumor mutations, circulating factors, immune profiles, and gut microbiome represent promising areas for predictive biomarker development.
- Novel markers for HPD require further investigation and clinical validation.
Conclusions:
- Accurate prediction of ICI response remains a challenge, highlighting the need for comprehensive biomarker strategies.
- Integrating multiple biomarkers may improve patient selection for immunotherapy.
- Future clinical trials are essential to validate novel biomarkers for predicting immunotherapy outcomes and HPD.
