ArticleFrontiers in immunology2021
Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer.
Article in Frontiers in immunology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Antibody-based regimens targeting PD-1/PD-L1 and VEGF/VEGFR in advanced or metastatic NSCLC: a meta-analysis of RCTs.Frontiers in immunology · 2026Pooled it
- Development and validation of a PD-L1 and tumor mutational burden-based predictive model for pathological complete response after neoadjuvant immunotherapy in resectable lung cancer.International journal of clinical pharmacy · 2026Article
- Fusing data from CT deep learning, CT radiomics and peripheral blood immune profiles to diagnose lung cancer in a cohort of patients experiencing symptoms.EBioMedicine · 2026Article
- The role of lnc‑MAPKAPK5‑AS1 in immune cell infiltration in hepatocellular carcinoma: Bioinformatics analysis and validation.Oncology letters · 2025Article
- Glyco-signatures in patients with advanced lung cancer during anti-PD-1/PD-L1 immunotherapy.Acta biochimica et biophysica Sinica · 2024Article
- Immunomodulatory Precision: A Narrative Review Exploring the Critical Role of Immune Checkpoint Inhibitors in Cancer Treatment.International journal of molecular sciences · 2024Review
- Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients.PNAS nexus · 2023Article
- Construction of immune cell infiltration score model to assess prognostic ability of tumor immune environment in lung adenocarcinoma.American journal of translational research · 2023Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Construction of a predictive model for immunotherapy efficacy in lung squamous cell carcinoma based on the degree of tumor-infiltrating immune cells and molecular typing.Journal of translational medicine · 2022Article
- Systematic pan-cancer analysis showed that RAD51AP1 was associated with immune microenvironment, tumor stemness, and prognosis.Frontiers in genetics · 2022Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value. Methods: Two published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response. Results: We firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80. Conclusion: We developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.
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