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- Title
Identification of mutational signature for lung adenocarcinoma prognosis and immunotherapy prediction.
- Authors
Zhang, Sainan; Li, Mengyue; Tan, Yilong; Zhang, Juxuan; Liu, Yixin; Jiang, Wenbin; Li, Xin; Qi, Haitao; Tang, Lefan; Ji, Ran; Zhao, Wenyuan; Gu, Yunyan; Qi, Lishuang
- Abstract
There is no robust genomic signature to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). It was known that clonal heterogeneity was closely associated to tumour progression and prognosis prediction. Herein, using stage I patients from The Cancer Genome Atlas, we identified the clonal/subclonal events of each gene and preselected a set of genes with prognosis-specific mutation patterns based on a robust published transcriptomic prognostic signature. Subsequently, we constructed a mutational prognostic signature (MPS), whose prognostic performance was independently validated in two datasets of stage I samples. The predicted high-risk patients had significantly higher immune cell infiltration, along with higher expression of cytotoxic and immune checkpoint genes, and an integrated dataset with 88 samples confirmed that high-risk patients could benefit from immunotherapy. The developed MPS can identify the high-risk patients with stage I LUAD and improve individualised treatment planning of high-risk patients who might benefit from immunotherapy. Key messages: We creatively developed a prognostic signature (57-MPS) based on clonal diversity. The high-risk samples displayed an underlying immunosuppressive mechanism. 57-MPS improved the predictive performance of PD-L1 for immunotherapy.
- Subjects
IMMUNOTHERAPY; IMMUNE checkpoint proteins; ADENOCARCINOMA; PROGNOSIS; LUNGS
- Publication
Journal of Molecular Medicine, 2022, Vol 100, Issue 12, p1755
- ISSN
0946-2716
- Publication type
Article
- DOI
10.1007/s00109-022-02266-4