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Updated: Jul 23, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
A tumor mutational burden-derived immune computational framework selects sensitive immunotherapy/chemotherapy for
Wenlong Zhang1, Chuzhong Wei1, Fengyu Huang1
1Huizhou First Hospital, Guangdong Medical University, Huizhou, China.
Background:
Lung adenocarcinoma (LUAD) kills millions of people every year. Recently, FDA and researchers proved the significance of high tumor mutational burden (TMB) in treating solid tumors. But no scholar has constructed a TMB-derived computing framework to select sensitive immunotherapy/chemotherapy for the LUAD population with different prognoses.
Methods:
The datasets were collected from TCGA, GTEx, and GEO. We constructed the TMB-derived immune lncRNA prognostic index (TILPI) computing framework based on TMB-related genes identified by weighted gene co-expression network analysis (WGCNA), oncogenes, and immune-related genes. Furthermore, we mapped the immune landscape based on eight algorithms. We explored the immunotherapy sensitivity of different prognostic populations based on immunotherapy response, tumor immune dysfunction and exclusion (TIDE), and tumor inflammation signature (TIS) model. Furthermore, the molecular docking models were constructed for sensitive drugs identified by the pRRophetic package, oncopredict package, and connectivity map (CMap).
Results:
The TILPI computing framework was based on the expression of TMB-derived immune lncRNA signature (TILncSig), which consisted of AC091057.1, AC112721.1, AC114763.1, AC129492.1, LINC00592, and TARID. TILPI divided all LUAD patients into two populations with different prognoses. The random grouping verification, survival analysis, 3D PCA, and ROC curve (AUC=0.74) firmly proved the reliability of TILPI. TILPI was associated with clinical characteristics, including smoking and pathological stage. Furthermore, we estimated three types of immune cells threatening the survival of patients based on multiple algorithms. They were macrophage M0, T cell CD4 Th2, and T cell CD4 memory activated. Nevertheless, five immune cells, including B cell, endothelial cell, eosinophil, mast cell, and T cell CD4 memory resting, prolonged the survival. In addition, the immunotherapy response and TIDE model proved the sensitivity of the low-TILPI population to immunotherapy. We also identified seven intersected drugs for the LUAD population with poor prognosis, which included docetaxel, gemcitabine, paclitaxel, palbociclib, pyrimethamine, thapsigargin, and vinorelbine. Their molecular docking models and best binding energy were also constructed and calculated.
Conclusions:
We divided all LUAD patients into two populations with different prognoses. The good prognosis population was sensitive to immunotherapy, while the people with poor prognosis benefitted from 7 drugs.
Insights
A new TMB-derived immune lncRNA prognostic index (TILPI) framework stratifies lung adenocarcinoma (LUAD) patients. The low-TILPI group shows immunotherapy sensitivity, while the high-TILPI group benefits from specific chemotherapy drugs.
Area of Science:
- Oncology
- Genomics
- Immunotherapy
Background:
- Lung adenocarcinoma (LUAD) remains a major cause of cancer mortality worldwide.
- High tumor mutational burden (TMB) is recognized for its therapeutic significance in solid tumors.
- A TMB-derived computational framework for predicting LUAD prognosis and treatment sensitivity is lacking.
Purpose of the Study:
- To develop a TMB-derived computing framework for predicting prognosis and guiding immunotherapy/chemotherapy selection in LUAD.
- To identify key immune-related genes and their association with TMB in LUAD.
- To evaluate the efficacy of the developed framework in stratifying patients for targeted treatments.
Main Methods:
- Construction of the TMB-derived immune lncRNA prognostic index (TILPI) using TMB-related genes, oncogenes, and immune genes via WGCNA.
- Immune landscape analysis using eight algorithms and assessment of immunotherapy sensitivity via TIDE and TIS models.
- Molecular docking for identifying and validating potential therapeutic drugs for different prognostic groups.
Main Results:
- The TILPI framework, based on six immune lncRNAs (TILncSig), reliably stratified LUAD patients into distinct prognostic groups (AUC=0.74).
- TILPI correlated with clinical factors like smoking and pathological stage, and identified prognostic immune cell types.
- The low-TILPI group demonstrated sensitivity to immunotherapy, while the high-TILPI group was associated with seven specific chemotherapy drugs identified through drug-sensitivity prediction and molecular docking.
Conclusions:
- The TILPI framework effectively categorizes LUAD patients into good and poor prognosis groups.
- Patients with good prognosis (low TILPI) are predicted to respond well to immunotherapy.
- Patients with poor prognosis (high TILPI) may benefit from a selection of seven identified chemotherapy drugs, offering personalized treatment strategies.
