Abstract

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Highlights

  • Non–small cell lung cancer (NSCLC) is the most common variety of lung cancer, which is the leading cause of cancer-related death worldwide (Bray et al, 2018; Duma et al, 2019)

  • Patients in cluster 2 tend to be more likely to benefit from immunotherapy, which may be due to the Mutant-allele tumor heterogeneity (MATH)-based classification

  • Intratumor heterogeneity (ITH) is a common phenomenon existing in all kinds of tumors (Andor et al, 2016)

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Summary

INTRODUCTION

Non–small cell lung cancer (NSCLC) is the most common variety of lung cancer, which is the leading cause of cancer-related death worldwide (Bray et al, 2018; Duma et al, 2019). Lung adenocarcinoma (LUAD) is the major histological type of NSCLC. Despite that great advance in the treatment of LUAD has been made in the past few decades, the 5-years survival is still not satisfactory

A Novel Strategy for LUAD
MATERIALS AND METHODS
RESULT
DISCUSSION
Findings
DATA AVAILABILITY STATEMENT
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