Lung adenocarcinoma (LUAD) is a devastating disease with poor patient survival. Cancer immunotherapy has revolutionized the treatment of LUAD, but only a limited number of patients effectively respond to this treatment. Thus, the work to elucidate the LUAD immune heterogeneity could be crucial in developing new immunotherapeutic strategies with better efficacy. Non-negative matrix factorization-based deconvolution was performed to identify robust clusters of 489 LUAD patients in The Cancer Genome Atlas (TCGA) and verify their reproducibility and stability in an independent LUAD cohort of 439 patients from the Gene Expression Omnibus (GEO). We used the graph learning-based dimensionality reduction to visualize the distribution of individual patients. In this study, four reproducible immune subtypes, Clusters 1–4 (C1–C4) associated with distinct gene module signatures, clinicopathological features, molecular and cellular characteristics were identified and validated. The immune-cold subtype, C3, was associated with the Dead event, the most advanced T stage, N stage, TNM stage and the worst prognosis for LUAD patients. Moreover, C3 exhibited the lowest infiltrating levels of B cells, T cell receptor (TCR) repertoire diversity and the highest level of neoantigen and mutation rate among C1–C4. On the other hand, the immune-hot subtype (C4) exhibited the highest infiltration of six types of infiltrating immune cells as well as the greatest leukocyte fraction, TCR and B cell receptor (BCR) repertoire diversity. C1 and C2 subtypes showed diverse clinicopathological and immunological features. Finally, our investigations discovered a complex immune landscape with a scattered immune subtype profile. This work may help inform immunotherapeutic decision-making and design advanced immunotherapy strategies for the treatment of lung cancer.

肺腺癌（LUAD）是一种破坏性疾病，患者生存期较差。癌症免疫疗法彻底改变了LUAD的治疗方法，但是只有少数患者对这种治疗方法有效。因此，阐明LUAD免疫异质性的工作对于开发具有更好疗效的新免疫治疗策略至关重要。进行了基于非负矩阵分解的反卷积，以识别癌症基因组图谱（TCGA）中489位LUAD患者的强壮簇，并在来自基因表达综合（GEO）的439位患者的独立LUAD队列中验证了其可重复性和稳定性。我们使用基于图学习的降维来可视化各个患者的分布。在这项研究中，有四种可重现的免疫亚型，鉴定并验证了与独特的基因模块特征，临床病理特征，分子和细胞特征相关的聚类1-4（C1-C4）。免疫性冷亚型C3与LUAD患者的死亡事件，最晚期的T期，N期，TNM期和最差的预后有关。此外，在C1-C4中，C3的B细胞浸润​​水平最低，T细胞受体（TCR）的库多样性较高，新抗原和突变率最高。另一方面，免疫热亚型（C4）表现出六种浸润性免疫细胞的最高浸润性，以及最大的白细胞级分，TCR和B细胞受体（BCR）谱系多样性。C1和C2亚型表现出多种临床病理和免疫学特征。最后，我们的研究发现了一个复杂的免疫环境，具有分散的免疫亚型特征。这项工作可能有助于指导免疫治疗决策，并设计先进的免疫治疗策略来治疗肺癌。