연구성과 홍보

Sci Rep. 2024 Sep 10;14(1):21096.

Title : Dissecting transcriptome signals of anti-PD-1 response in lung adenocarcinoma

Authors : Kyeongmi Lee#1, Honghui Cha#2, Jaewon Kim3, Yeongjun Jang3, Yelin Son3, Cheol Yong Joe2,4, Jaesang Kim5,6, Jhingook Kim7, Se-Hoon Lee8,9*, Sanghyuk Lee10,11,12*

Affiliations :

1Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, South Korea.

2Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, 06351, South Korea.

3Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, 03760, South Korea.

4Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea.

5Department of Life Sciences, Ewha Womans University, Seoul, 03760, South Korea.

6Ewha-JAX Cancer Immunotherapy Research Center, Ewha Womans University, Seoul, 03760, South Korea.

7Department of Lung Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea.

8Department of Health Sciences and Technology, Samsung Advanced Institute of Health Science and Technology, Sungkyunkwan University, Seoul, 06351, South Korea.

9Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea.

10Department of Bio-Information Science, Ewha Womans University, Seoul, 03760, South Korea.

11Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, 03760, South Korea.

12Department of Life Sciences, Ewha Womans University, Seoul, 03760, South Korea.

DOI: 10.1038/s41598-024-72108-5.

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.