Integration of single sample and population analysis for understanding immune evasion mechanisms of lung cancer

Abstract A deep understanding of the complex interaction mechanism between the various cellular components in tumor microenvironment (TME) of lung adenocarcinoma (LUAD) is a prerequisite for understanding its drug resistance, recurrence, and metastasis. In this study, we proposed two complementary computational frameworks for integrating multi-source and multi-omics data, namely ImmuCycReg framework (single sample level) and L0Reg framework (population or subtype level), to carry out difference analysis between the normal population and different LUAD subtypes. Then, we aimed to identify the possible immune escape pathways adopted by patients with different LUAD subtypes, resulting in immune deficiency which may occur at different stages of the immune cycle. More importantly, combining the research results of the single sample level and population level can improve the credibility of the regulatory network analysis results. In addition, we also established a prognostic scoring model based on the risk factors identified by Lasso-Cox method to predict survival of LUAD patients. The experimental results showed that our frameworks could reliably identify transcription factor (TF) regulating immune-related genes and could analyze the dominant immune escape pathways adopted by each LUAD subtype or even a single sample. Note that the proposed computational framework may be also applicable to the immune escape mechanism analysis of pan-cancer.