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- W4366201243 abstract "Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. In general, these mutations are mutually exclusive, and biologically and clinically different. In this study, we attempted to find if we could separate lung adenocarci-noma tumors by their immune profile using an unsupervised machine learning method. By projecting RNA-seq data into inferred immune profiles and using unsupervised learning, we were able to divide the lung adenocarcinoma population into three subgroups, one of which appeared to contain mostly EGFR patients. We argue that EGFR mutations in each subgroup are different immunologically which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we could not make the same claim about KRAS mutations." @default.
- W4366201243 created "2023-04-19" @default.
- W4366201243 creator A5040745258 @default.
- W4366201243 date "2023-04-17" @default.
- W4366201243 modified "2023-10-16" @default.
- W4366201243 title "Immune clustering reveals molecularly distinct subtypes of lung adenocarcinoma" @default.
- W4366201243 doi "https://doi.org/10.1101/2023.04.17.537140" @default.
- W4366201243 hasPublicationYear "2023" @default.
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