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Updated: Nov 7, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer
Jiehan Jiang1, Zheng Jin2, Yiqun Zhang2
1Department of Pulmonary and Critical Care Medicine, University of South China Affiliated Changsha Central Hospital, Changsha, China.
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Two published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent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.
Insights
A new pathway-model improves prediction of immune checkpoint inhibitor (ICI) efficacy in non-small cell lung cancer (NSCLC) patients. This model integrates tumor mutational burden (TMB) and PD-L1 expression, outperforming existing biomarkers for immunotherapy response.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Immune checkpoint inhibitors (ICIs) revolutionized cancer treatment, but low response rates (25% in NSCLC) necessitate better predictive biomarkers.
- Current biomarkers like tumor mutational burden (TMB) and PD-L1 expression have limitations in accuracy and validated cutoffs.
- Predicting immunotherapy response in non-small cell lung cancer (NSCLC) remains a challenge.
Purpose of the Study:
- To develop and validate a novel predictive model for ICI treatment efficacy in NSCLC patients.
- To overcome the limitations of single biomarkers in predicting immunotherapy response.
- To identify immune-related pathways associated with overall survival in NSCLC patients treated with ICIs.
Main Methods:
- Collected data from 129 NSCLC patients across training, validation, and independent test cohorts.
- Identified six immune-related pathways significantly associated with overall survival after ICI treatment.
- Developed a Bayesian-regularization neural networks (BRNN) model integrating pathway mutational status, TMB, PD-L1, and intratumor heterogeneity (MATH).
Main Results:
- TMB, PD-L1, and MATH were confirmed as independent predictive biomarkers.
- Mutational status of identified immune-related pathways distinguished overall survival.
- The pathway-model achieved an AUC of 0.85 in the validation cohort and 0.74/0.80 in test cohorts, outperforming previous predictors.
Conclusions:
- A novel pathway-model effectively predicts ICI efficacy in NSCLC patients, addressing the low accuracy of single biomarkers.
- This approach offers a significant advancement in predicting immunotherapy response.
- Further research with larger datasets is recommended to refine the model's predictive performance.
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