Research Paper Volume 12, Issue 16 pp 16514—16538

Coupled immune stratification and identification of therapeutic candidates in patients with lung adenocarcinoma

Weilei Hu1,2, *, , Guosheng Wang3, *, , Yundi Chen3, , Lonny B. Yarmus4, , Biao Liu5, , Yuan Wan3, ,

  • 1 Institute of Translational Medicine, Zhejiang University, Hangzhou 310029, China
  • 2 Center for Disease Prevention Research and Department of Pharmacology and Toxicology, Medical College of Wisconsin, Milwaukee, WI 53226, United States
  • 3 The Pq Laboratory of Micro/Nano BiomeDx, Department of Biomedical Engineering, Binghamton University—SUNY, Binghamton, NY 13902, United States
  • 4 Division of Pulmonary and Critical Care, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21218, United States
  • 5 Department of Pathology, Nanjing Medical University Affiliated Suzhou Hospital, Suzhou 215006, Jiangsu, China
* Equal contribution

Received: February 8, 2020       Accepted: July 14, 2020       Published: August 27, 2020
How to Cite

Copyright © 2020 Hu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


In recent years, personalized cancer immunotherapy, especially stratification-driven precision treatments have gained significant traction. However, due to the heterogeneity in clinical cohorts, the uncombined analysis of stratification/therapeutics may lead to confusion in determining ideal therapeutic options. We report that the coupled immune stratification and drug repurposing could facilitate identification of therapeutic candidates in patients with lung adenocarcinoma (LUAD). First, we categorized the patients into four groups based on immune gene profiling, associated with distinct molecular characteristics and clinical outcomes. Then, the weighted gene co-expression network analysis (WGCNA) algorithm was used to identify co-expression modules of each groups. We focused on C3 group which is characterized by low immune infiltration (cold tumor) and wild-type EGFR, posing a significant challenge for treatment of LUAD. Five drug candidates against the C3 status were identified which have potential dual functions to correct aberrant immune microenvironment and also halt tumorigenesis. Furthermore, their steady binding affinity against the targets was verified through molecular docking analysis. In sum, our findings suggest that such coupled analysis could be a promising methodology for identification and exploration of therapeutic candidates in the practice of personalized immunotherapy.


EGFR: Epidermal growth factor receptor; TKI: Tyrosine kinase inhibitors; LUAD: lung adenocarcinoma; TME: complex tumor microenvironment; CMap: The Connectivity Map; PCA: Principal component analysis; CPs: compounds; KD/OE: genes knockdown/overexpress; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; WGCNA: Weighted correlation network analysis; ALK: Anaplastic lymphoma kinase; FPKM: The fragments per kilobase of gene per million fragments; TPM: Transcripts Per Kilobase of exon model per Million mapped reads; ImmPort: Immunology Database and Analysis Portal; CDF: Consensus cumulative distribution function; FDR: False discovery rate; ANOVA: Analysis of Variance; TOM: Topological overlap matrix; ME: Module eigengenes; TNM: Tumor, Node, Metastasis; TOM: Topological matrix; FMT: Fecal microbiota transplantation.