Tumor Microenvironment-based Gene Signatures Divides Novel Immune and Stromal Subgroup Classification of Lung Adenocarcinoma

TL;DR

This study identifies novel lung adenocarcinoma subgroups based on tumor microenvironment signatures, revealing distinct molecular and immune characteristics, and develops a prognostic model to predict survival and immunotherapy response.

Contribution

It introduces a new classification of lung adenocarcinoma based on integrated immune and stromal signatures and develops the PMBT model for prognosis and therapy response prediction.

Findings

Four tumor subgroups with distinct molecular and immune profiles

High-immune, low-stromal group linked to better survival

PMBT model predicts overall survival and immunotherapy response

Abstract

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.