Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma
-
Published:2023-11-08
Issue:1
Volume:14
Page:
-
ISSN:2041-1723
-
Container-title:Nature Communications
-
language:en
-
Short-container-title:Nat Commun
Author:
Dolgalev IgorORCID, Zhou Hua, Murrell Nina, Le HortenseORCID, Sakellaropoulos Theodore, Coudray Nicolas, Zhu Kelsey, Vasudevaraja Varshini, Yeaton Anna, Goparaju Chandra, Li Yonghua, Sulaiman Imran, Tsay Jun-Chieh J., Meyn Peter, Mohamed Hussein, Sydney Iris, Shiomi TomoeORCID, Ramaswami Sitharam, Narula Navneet, Kulicke Ruth, Davis Fred P., Stransky Nicolas, Smolen Gromoslaw A.ORCID, Cheng Wei-Yi, Cai James, Punekar Salman, Velcheti Vamsidhar, Sterman Daniel H., Poirier J. T.ORCID, Neel Ben, Wong Kwok-KinORCID, Chiriboga LuisORCID, Heguy AdrianaORCID, Papagiannakopoulos ThalesORCID, Nadorp BettinaORCID, Snuderl Matija, Segal Leopoldo N., Moreira Andre L., Pass Harvey I., Tsirigos AristotelisORCID
Abstract
AbstractApproximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
Reference63 articles.
1. Wu, C. F. et al. Recurrence risk factors analysis for stage I non-small cell lung cancer. Medicine 94, e1337 (2015). 2. Moreira, A. L. et al. A grading system for invasive pulmonary adenocarcinoma: a proposal from the International Association for the Study of Lung Cancer Pathology Committee. J. Thorac. Oncol. 15, 1599–1610 (2020). 3. Luo, J. et al. Solid predominant histologic subtype and early recurrence predict poor postrecurrence survival in patients with stage I lung adenocarcinoma. Oncotarget 8, 7050–7058 (2017). 4. Wang, X. et al. Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images. Sci. Rep. 7, 13543 (2017). 5. Yu, K.-H. et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat. Commun. 7, 12474 (2016).
|
|