Mutational variant allele frequency profile as a biomarker of response to immune checkpoint blockade in non-small cell lung Cancer

Identifying new biomarkers for predicting immune checkpoint inhibitors (ICIs) response in non-small cell lung cancer (NSCLC) is crucial. We aimed to assess the variant allele frequency (VAF)-related profile as a novel biomarker for NSCLC personalized therapy. We utilized genomic data of 915 NSCLC pa...

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Bibliographic Details
Published in:Journal of translational medicine 2024-06, Vol.22 (1), p.576-15, Article 576
Main Authors: Gao, Ruyun, Lou, Ning, Li, Lin, Xie, Tongji, Xing, Puyuan, Tang, Le, Yao, Jiarui, Han, Xiaohong, Shi, Yuankai
Format: Article
Language:English
Subjects:
Aged
Analysis
Biological markers
Biomarker
Biomarkers, Tumor - genetics
Carcinoma, Non-Small-Cell Lung - drug therapy
Carcinoma, Non-Small-Cell Lung - genetics
Carcinoma, Non-Small-Cell Lung - immunology
Care and treatment
Cohort Studies
Diagnosis
Female
Gene Frequency - genetics
Gene mutations
Genetic aspects
Health aspects
Humans
Immune checkpoint inhibitor
Immune Checkpoint Inhibitors - therapeutic use
Immunotherapy
Lung cancer, Non-small cell
Lung Neoplasms - drug therapy
Lung Neoplasms - genetics
Lung Neoplasms - immunology
Male
Microsatellite instability
Middle Aged
Mutation
Mutation - genetics
Non-small cell lung cancer
Treatment Outcome
Variant allele frequency
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Summary:Identifying new biomarkers for predicting immune checkpoint inhibitors (ICIs) response in non-small cell lung cancer (NSCLC) is crucial. We aimed to assess the variant allele frequency (VAF)-related profile as a novel biomarker for NSCLC personalized therapy. We utilized genomic data of 915 NSCLC patients via cBioPortal and a local cohort of 23 patients for model construction and mutational analysis. Genomic, transcriptomic data from 952 TCGA NSCLC patients, and immunofluorescence (IF) assessment with the local cohort supported mechanism analysis. Utilizing the random forest algorithm, a 15-gene VAF-related model was established, differentiating patients with durable clinical benefit (DCB) from no durable benefit (NDB). The model demonstrated robust performance, with ROC-AUC values of 0.905, 0.737, and 0.711 across training (n = 313), internal validation (n = 133), and external validation (n = 157) cohorts. Stratification by the model into high- and low-score groups correlated significantly with both progression-free survival (PFS) (training: P 
ISSN:1479-5876
1479-5876
DOI:10.1186/s12967-024-05400-7