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The Predictive Value of PAK7 Mutation for Immune Checkpoint Inhibitors Therapy in Non-Small Cell Cancer
Hao Zeng1, Fan Tong1, Yawen Bin1
1Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background:
To date, immunotherapy has improved the 5-year survival rate of patients with advanced non-small cell lung cancer (NSCLC) from 4% to 15%. However, only 30%-50% of the NSCLC patients respond to immune checkpoint inhibitors (ICIs) immunotherapy. Therefore, screening patients for potential benefit with precise biomarkers may be of great value.
Methods:
First, an immunotherapy NSCLC cohort was analyzed to identify the gene mutations associated with the prognosis of ICI treatment. Further analyses were conducted using NSCLC cohort in The Cancer Genome Atlas (TCGA) project to validate the correlations between the specific gene mutations and tumor immunogenicity, antitumor immunity, and alterations in the tumor-related pathways using Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Gene set enrichment analysis (GSEA).
Results:
In the immunotherapy NSCLC cohort (n = 266), significantly longer overall survival (OS) rates were observed in the PAK7-mutant type (PAK7-MT) group (n = 13) than the PAK7-wild type (PAK7-WT) group (n = 253) (P = 0.049, HR = 0.43, 95%CI = 0.23-0.79). In the TCGA cohort, PAK7 mutations were correlated with the higher tumor mutation burden (TMB) (14.18 vs. 7.13, P <0.001), increased neoantigen load (NAL) (7.52 vs. 4.30, P <0.001), lower copy number variation (CNV), and higher mutation rate in the DNA damage response (DDR)-related pathways. In addition, PAK7 mutations were also positively correlated with immune-related genes expressions and infiltrating CD8+ T cells (0.079 vs. 0.054, P = 0.005). GSEA results showed that several tumor-related pathways varied in the PAK7-MT group, suggesting the potential mechanisms that regulate the tumor immune-microenvironment.
Conclusions:
This study suggested that the PAK7 mutations might be a potential biomarker to predict the efficacy of immunotherapy for NSCLC patients. Considering the heterogeneity among the patients and other confounding factors, a prospective clinical trial is proposed to further validate the impact of PAK7 mutation on the immunotherapy outcomes in NSCLC.
Insights
PAK7 gene mutations may predict immunotherapy success in non-small cell lung cancer (NSCLC). Patients with PAK7 mutations showed improved survival, indicating its potential as a biomarker for immune checkpoint inhibitor (ICI) treatment efficacy.
Area of Science:
- Oncology
- Immunotherapy
- Genomics
Background:
- Immunotherapy has improved survival for advanced non-small cell lung cancer (NSCLC) patients, but response rates to immune checkpoint inhibitors (ICIs) remain limited (30%-50%).
- Precise biomarkers are needed to identify NSCLC patients likely to benefit from ICI therapy.
Purpose of the Study:
- To identify gene mutations associated with prognosis in NSCLC patients undergoing immunotherapy.
- To validate the correlation between specific gene mutations, tumor immunogenicity, antitumor immunity, and pathway alterations.
Main Methods:
- Analysis of an immunotherapy NSCLC cohort (n=266) to identify prognostic gene mutations.
- Utilized The Cancer Genome Atlas (TCGA) NSCLC cohort for validation.
- Employed Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Gene Set Enrichment Analysis (GSEA).
Main Results:
- PAK7-mutant NSCLC patients exhibited significantly longer overall survival (OS) compared to wild-type (P=0.049).
- PAK7 mutations correlated with higher tumor mutation burden (TMB), neoantigen load (NAL), and increased CD8+ T cell infiltration.
- Mutations were linked to lower copy number variation (CNV) and altered DNA damage response (DDR) pathways.
Conclusions:
- PAK7 mutations show potential as a predictive biomarker for immunotherapy efficacy in NSCLC.
- Further validation in prospective clinical trials is recommended to confirm the impact of PAK7 mutations on treatment outcomes.
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