# Immune Clustering Reveals Molecularly Distinct Subtypes of Lung Adenocarcinoma

## TL;DR

This study uses immune profiles to identify distinct subtypes of lung adenocarcinoma, revealing differences in immune checkpoint expression and tumor microenvironment.

## Contribution

A novel approach using unsupervised machine learning to classify lung adenocarcinoma subtypes based on immune profiles.

## Key findings

- Three distinct immune-based subgroups were identified in lung adenocarcinoma patients.
- One subgroup was predominantly composed of EGFR-mutated tumors.
- Subgroups showed significant differences in immune checkpoint expression.

## Abstract

Background/objectives: Lung adenocarcinoma, the most prevalent type of non-small cell lung cancer, consists of two driver mutations in KRAS or EGFR. These mutations are generally mutually exclusive and biologically and clinically different. In this study, we aimed to test if lung adenocarcinoma tumors could be separated by their immune profiles using an unsupervised machine learning method. The underlying assumption was that differences in the immune response to tumors are characteristic of tumor subtypes. Methods: RNA-seq data were projected into inferred immune profiles. Unsupervised learning was used to divide the lung adenocarcinoma population based on their projected immune profiles. Results: The patient population was divided into three subgroups, one of which appeared to contain mostly EGFR patients. The tumors in the different clusters significantly differed in their expression of some of their known immune checkpoints (TIGIT, PD-1/PD-L1, and CTLA4). Discussion: We argue that EGFR mutations in each subgroup are immunologically different, which implies a distinct tumor microenvironment and might relate to the relatively high resistance of EGFR-positive tumors to immune checkpoint inhibitors. However, we cannot make the same claim about KRAS mutations.