  Tumor microenvironment has complex effects on tumorigenesis and metastasis.
However, there is still a lack of comprehensive understanding of the
relationship among molecular and cellular characteristics in tumor
microenvironment, clinical prognosis and immunotherpy response. In this study,
the immune and stromal (non-immune) signatures of tumor microenvironment were
integrated to identify novel subgroups of lung adenocarcinoma by
eigendecomposition and extraction algorithms of bioinformatics and machine
learning, such as non-negative matrix factorization and multitask learning.
Tumors were classified into 4 groups according to the activation of immunity
and stroma by novel signatures. The 4 groups had different mutation landscape,
molecular, cellular characteristics and prognosis, which have been validation
in 6 independent data sets containing 1551 patients. High-immune and
low-stromal activation group links to high immunocyte infiltration, high
immunocompetence, low fibroblasts, endothelial cells, collagen, laminin, tumor
mutation burden, and better overall survival. We developed a novel model based
on tumor microenvironment by integrating immune and stromal activation, namely
PMBT (prognostic model based on tumor microenvironment). The PMBT showed the
value to predict overall survival and immunotherapy responses.
