Understanding the effect of aging on the lung adenocarcinoma tumor microenvironment

PROJECT SUMMARY Advances in immune checkpoint inhibition (ICI) therapies have shown great promise in activating lung adenocarcinoma (LUAD) patients’ own anti-tumor immunity and prolonging survival. However, such immunotherapies have shown variability in response among LUAD patients of different ages. With the increasing age of lung cancer patients at diagnosis (35% are >75), there is a critical need for better understanding of the relationship between age and the LUAD tumor microenvironment (TME). New insight into the age-related cellular and molecular mechanisms and pathways in LUAD have the potential to uncover new therapeutic opportunities to modulate the TME, synergize with existing therapies, and improve disease outcome in elderly patients. We propose to use highly-parallelized single-cell RNA-sequencing (scRNA-seq) and emerging single-cell resolution spatial technologies to characterize the effect of age on the LUAD TME. We will integrate the scRNA-seq data collected as part of the Human Tumor Atlas Network (HTAN) with publicly available data to increase our cohort size to ≥178 LUAD patients. The expanded scRNA-seq data set will provide us with the necessary statistical power to decipher the cell type expression, cell composition, and regulatory differences between LUAD patients of different ages. We will also use machine learning to model the effect of cell type specific gene and pathway expression on TME cell compostion and search for therapeutic targets across the integrated cohort in order to better understand the underlying immunomodulatory mechanisms in the LUAD TME and how they associate with age. Finally, we will validate our computational modeling by measuring the spatial organization of the TME using tissue samples from LUAD patients of different ages. Our study promises to improve our understanding of the effect of age on cell-cell interactions, gene expression programs, and multicellular communities in the TME, identify new treatment strategies, and uncover new biomarkers that can improve therapy decisions for elderly LUAD patients. Finally, our general methodology can be extended to study the relationship between age and the TME in other tumor types in order to discover new age-specific therapeutic targets and improve patient stratification for treatment in different disease contexts.