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Updated: Jun 13, 2025
Orthotopic Transplantation of Syngeneic Lung Adenocarcinoma Cells to Study PD-L1 Expression
Published on: January 19, 2019
Dissecting transcriptome signals of anti-PD-1 response in lung adenocarcinoma
Kyeongmi Lee1, Honghui Cha2, Jaewon Kim3
1Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, South Korea.
Abstract:
Immune checkpoint blockades are actively adopted in diverse cancer types including metastatic melanoma and lung cancer. Despite of durable response in 20-30% of patients, we still lack molecular markers that could predict the patient responses reliably before treatment. Here we present a composite model for predicting anti-PD-1 response based on tumor mutation burden (TMB) and transcriptome sequencing data of 85 lung adenocarcinoma (LUAD) patients who received anti-PD-(L)1 treatment. We found that TMB was a good predictor (AUC = 0.81) for PD-L1 negative patients (n = 20). For PD-L1 positive patients (n = 65), we built an ensemble model of 100 XGBoost learning machines where gene expression, gene set activities and cell type composition were used as input features. The transcriptome-based models showed excellent accuracy (AUC > 0.9) and highlighted the contribution of T cell activities. Importantly, nonresponder patients with high prediction score turned out to have high CTLA4 expression, which suggested that neoadjuvant CTLA4 combination therapy might be effective for these patients. Our data and analysis results provide valuable insights into developing biomarkers and strategies for treating LUAD patients using immune checkpoint inhibitors.
Insights
Predicting anti-PD-1 response in lung adenocarcinoma is crucial. A new model combining tumor mutation burden and transcriptome data accurately identifies patients likely to benefit from immunotherapy, guiding treatment strategies.
Area of Science:
- Oncology
- Immunology
- Genomics
Background:
- Immune checkpoint inhibitors (ICIs) like anti-PD-1 offer durable responses in some cancers, but reliable predictive biomarkers are lacking.
- Predicting patient response to anti-PD-1 therapy in lung adenocarcinoma (LUAD) remains a significant clinical challenge.
Purpose of the Study:
- To develop and validate a composite model for predicting anti-PD-1/PD-L1 response in LUAD patients.
- To identify molecular features associated with treatment response using tumor mutation burden (TMB) and transcriptome data.
Main Methods:
- Analysis of TMB and transcriptome sequencing data from 85 LUAD patients treated with anti-PD-(L)1 therapy.
- Development of a TMB-based predictor for PD-L1 negative patients and an ensemble XGBoost model for PD-L1 positive patients incorporating gene expression, gene set activities, and cell type composition.
Main Results:
- TMB demonstrated good predictive performance (AUC=0.81) for PD-L1 negative patients.
- Transcriptome-based ensemble models achieved high accuracy (AUC>0.9) for PD-L1 positive patients, emphasizing the role of T cell activities.
- High CTLA4 expression was observed in non-responders, suggesting potential therapeutic targets.
Conclusions:
- A composite model integrating TMB and transcriptome data can effectively predict anti-PD-1 response in LUAD.
- T cell activity is a key factor in predicting immunotherapy response.
- CTLA4 expression may indicate a subset of patients who could benefit from combination therapies.
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