Related Experiment Video
Updated: Oct 10, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Multi-omics analysis of an immune-based prognostic predictor in non-small cell lung cancer
Yang Zheng1, Lili Tang2, Ziling Liu3
1Jilin University First Hospital, Changchun, Jilin, People's Republic of China.
Background:
Inhibitors targeting immune checkpoints, such as PD-1/PD-L1 and CTLA-4, have prolonged survival in small groups of non-small cell lung cancer (NSCLC) patients, but biomarkers predictive of the response to the immune checkpoint inhibitors (ICIs) remain rare.
Methods:
The nonnegative matrix factorization (NMF) was performed for TCGA-NSCLC tumor samples based on the LM22 immune signature to construct subgroups. Characterization of NMF subgroups involved the single sample gene set variation analysis (ssGSVA), and mutation/copy number alteration and methylation analyses. Construction of RNA interaction network was based on the identification of differentially expressed RNAs (DERs). The prognostic predictor was constructed by a LASSO-Cox regression model. Four GEO datasets were used for the validation analysis.
Results:
Four immune based NMF subgroups among NSCLC patients were identified. Genetic and epigenetic analyses between subgroups revealed an important role of somatic copy number alterations in determining the immune checkpoint expression on specific immune cells. Seven hub genes were recognized in the regulatory network closely related to the immune phenotype, and a three-gene prognosis predictor was constructed.
Conclusions:
Our study established an immune-based prognosis predictor, which might have the potential to select subgroups benefiting from the ICI treatment, for NSCLC patients using publicly available databases.
Insights
This study identified immune-based subgroups in non-small cell lung cancer (NSCLC) and developed a three-gene predictor to identify patients likely to benefit from immune checkpoint inhibitors (ICIs). This may improve ICI treatment selection for NSCLC.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Immune checkpoint inhibitors (ICIs) like PD-1/PD-L1 and CTLA-4 improve survival in some non-small cell lung cancer (NSCLC) patients.
- Biomarkers for predicting response to ICIs in NSCLC are limited.
Purpose of the Study:
- To identify immune-based subgroups within NSCLC.
- To develop a predictive model for ICI treatment response in NSCLC patients.
Main Methods:
- Nonnegative matrix factorization (NMF) on TCGA-NSCLC data using the LM22 immune signature.
- Gene set variation analysis (GSVA), mutation, copy number alteration, and methylation analyses for subgroup characterization.
- LASSO-Cox regression to build a prognostic predictor using RNA expression data.
Main Results:
- Four distinct immune-based NMF subgroups were identified in NSCLC.
- Somatic copy number alterations influence immune checkpoint expression on immune cells.
- A three-gene prognostic predictor was constructed based on seven hub genes from an RNA interaction network.
Conclusions:
- An immune-based prognostic predictor was developed for NSCLC.
- This predictor may help identify NSCLC patient subgroups that benefit from immune checkpoint inhibitor therapy.
