Immune Clustering Reveals Molecularly Distinct Subtypes of Lung Adenocarcinoma.

ABSTRACT: Background/objectives: Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. These mutations are generally mutually exclusive and biologically and clinically different. In this study, we aimed to test if lung adenocarcinoma tumors could be separated by their immune profiles using an unsupervised machine learning method. The underlying assumption was that differences in the immune response to tumors are characteristic of tumor subtypes. Methods: RNA-seq data were projected into inferred immune profiles. Unsupervised learning was used to divide the lung adenocarcinoma population based on their projected immune profiles. Results: The patient population was divided into three subgroups, one of which ap

SUBMITTER: Lender Y 

PROVIDER: S-EPMC12024753 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature